From 9158d75082b91c8613c44dafefa5b31f1a2dedcb Mon Sep 17 00:00:00 2001 From: ccsarapas Date: Mon, 2 Feb 2026 15:01:38 -0600 Subject: [PATCH 01/13] vignettes WIP --- .Rbuildignore | 1 + .gitignore | 1 + DESCRIPTION | 4 + README.Rmd | 100 +++++++++++++++++ README.md | 75 ++++++++++++- man/figures/README-categorical.png | Bin 0 -> 137237 bytes man/figures/README-numeric 1.png | Bin 0 -> 279633 bytes man/figures/README-numeric.png | Bin 0 -> 276547 bytes man/figures/README-overview 1.png | Bin 0 -> 69663 bytes man/figures/README-overview 2.png | Bin 0 -> 68427 bytes man/figures/README-overview.png | Bin 0 -> 136244 bytes man/figures/README-text.png | Bin 0 -> 126441 bytes vignettes/.gitignore | 2 + vignettes/img/overview.png | Bin 0 -> 69663 bytes vignettes/lighthouse-codebook.Rmd | 174 +++++++++++++++++++++++++++++ vignettes/lighthouse-codebook.md | 136 ++++++++++++++++++++++ 16 files changed, 490 insertions(+), 3 deletions(-) create mode 100644 README.Rmd create mode 100644 man/figures/README-categorical.png create mode 100644 man/figures/README-numeric 1.png create mode 100644 man/figures/README-numeric.png create mode 100644 man/figures/README-overview 1.png create mode 100644 man/figures/README-overview 2.png create mode 100644 man/figures/README-overview.png create mode 100644 man/figures/README-text.png create mode 100644 vignettes/.gitignore create mode 100644 vignettes/img/overview.png create mode 100644 vignettes/lighthouse-codebook.Rmd create mode 100644 vignettes/lighthouse-codebook.md diff --git a/.Rbuildignore b/.Rbuildignore index a8fb882..9acafe0 100644 --- a/.Rbuildignore +++ b/.Rbuildignore @@ -5,3 +5,4 @@ ^docs$ ^pkgdown$ ^\.github$ +^README\.Rmd$ diff --git a/.gitignore b/.gitignore index 457525e..8a6f2db 100644 --- a/.gitignore +++ b/.gitignore @@ -5,3 +5,4 @@ .DS_Store .quarto docs +inst/doc diff --git a/DESCRIPTION b/DESCRIPTION index 2c73089..933f04d 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -35,3 +35,7 @@ Roxygen: list(markdown = TRUE) RoxygenNote: 7.3.3 URL: https://github.com/ccsarapas/lighthouse.codebook, https://ccsarapas.github.io/lighthouse.codebook/ BugReports: https://github.com/ccsarapas/lighthouse.codebook/issues +Suggests: + knitr, + rmarkdown +VignetteBuilder: knitr diff --git a/README.Rmd b/README.Rmd new file mode 100644 index 0000000..2146de7 --- /dev/null +++ b/README.Rmd @@ -0,0 +1,100 @@ +--- +output: github_document +--- + + + +```{r, include = FALSE} +knitr::opts_chunk$set( + collapse = TRUE, + comment = "#", + out.width = "100%", + fig.align = "center", + fig.path = "man/figures/", + eval = FALSE +) +``` + +# lighthouse.codebook + +The lighthouse.codebook package includes tools to summarize a dataset into a +formatted Excel workbook, including a data dictionary and summaries. It +incorporates external metadata (such as variable labels, value labels, and user +missing / non-response codes), with convenience functions for using metadata from +SPSS and REDCap datasets. Codebooks can be customized in a number of ways, +including options for grouped summaries. + +## Installation + +You can install lighthouse.codebook by running: + +``` r +# install.packages("remotes") +remotes::install_github("ccsarapas/lighthouse.codebook") +``` + +## Creating a codebook + + + +Creating a codebook involves two general steps: + +1) Create a a "codebook" object in R from a data frame (and, optionally, metadata), + using `cb_create()` or a specialized variant (such as `cb_create_spss()` or `cb_create_redcap()`). + +2) Write the codebook to disk using `cb_write()`. + +Thus, in the simplest case, to create a codebook and write it to disk:. +```r +gain_q4 |> cb_create() |> cb_write("q4.xlsx") +``` + +`cb_create()` and `cb_write()` include a number of options for controlling how data +is interpreted, summarized, and presented. + + +## Codebook contents +The codebook written to disk will include an Overview tab listing all variables +in the dataset; Summary tabs for numeric, categorical, and text variables; and, +if grouping variables are specified, Grouped Summary tabs for numeric and categorical +variables. + +The Overview tab includes one row for each variable in the dataset, with information +on variable types, labels, values, and missingness. By default, each variable is +hyperlinked to its location on the relevant summary tab. + +```{r, overview, echo = FALSE, eval = TRUE} +knitr::include_graphics("man/figures/README-overview.png") +``` + +The numeric summary tab includes descriptive statistics for all numeric variables +in the dataset: + +```{r, numeric, echo = FALSE, eval = TRUE} +knitr::include_graphics("man/figures/README-numeric.png") +``` + +The categorical summary tab includes frequencies for all categorical variables, +optionally with separate rows for user missing values: + +```{r, categorical, echo = FALSE, eval = TRUE} +knitr::include_graphics("man/figures/README-categorical.png") +``` + +The text summary tab includes frequencies for the most common values for all text +variables in the dataset. + +```{r, text, echo = FALSE, eval = TRUE} +knitr::include_graphics("man/figures/README-text.png") +``` + + +## SPSS extension + +Functionality from this package is also available as an SPSS extension command [here](https://github.com/ccsarapas/lighthouse.codebook.spss). \ No newline at end of file diff --git a/README.md b/README.md index 3e38f4e..b1e4e41 100644 --- a/README.md +++ b/README.md @@ -1,16 +1,85 @@ + + # lighthouse.codebook -The lighthouse.codebook package includes tools for summarizing datasets used by staff at the [Lighthouse Institute](https://www.chestnut.org/lighthouse-institute/), the research division of Chestnut Health Systems. +The lighthouse.codebook package includes tools to summarize a dataset +into a formatted Excel workbook, including a data dictionary and +summaries. It incorporates external metadata (such as variable labels, +value labels, and user missing / non-response codes), with convenience +functions for using metadata from SPSS and REDCap datasets. Codebooks +can be customized in a number of ways, including options for grouped +summaries. ## Installation -Install lighthouse.codebook by running: +You can install lighthouse.codebook by running: ``` r # install.packages("remotes") remotes::install_github("ccsarapas/lighthouse.codebook") ``` + +## Creating a codebook + + + + + +Creating a codebook involves two general steps: + +1. Create a a “codebook” object in R from a data frame (and, + optionally, metadata), using `cb_create()` or a specialized variant + (such as `cb_create_spss()` or `cb_create_redcap()`). + +2. Write the codebook to disk using `cb_write()`. + +Thus, in the simplest case, to create a codebook and write it to disk:. + +``` r +gain_q4 |> cb_create() |> cb_write("q4.xlsx") +``` + +`cb_create()` and `cb_write()` include a number of options for +controlling how data is interpreted, summarized, and presented. + + +## Codebook contents + +The codebook written to disk will include an Overview tab listing all +variables in the dataset; Summary tabs for numeric, categorical, and +text variables; and, if grouping variables are specified, Grouped +Summary tabs for numeric and categorical variables. + +The Overview tab includes one row for each variable in the dataset, with +information on variable types, labels, values, and missingness. By +default, each variable is hyperlinked to its location on the relevant +summary tab. + + + +The numeric summary tab includes descriptive statistics for all numeric +variables in the dataset: + + + +The categorical summary tab includes frequencies for all categorical +variables, optionally with separate rows for user missing values: + + + +The text summary tab includes frequencies for the most common values for +all text variables in the dataset. + + + ## SPSS extension -Functionality from this package is also available as an SPSS extension command [here](https://github.com/ccsarapas/lighthouse.codebook.spss). \ No newline at end of file +Functionality from this package is also available as an SPSS extension +command [here](https://github.com/ccsarapas/lighthouse.codebook.spss). diff --git a/man/figures/README-categorical.png b/man/figures/README-categorical.png new file mode 100644 index 0000000000000000000000000000000000000000..3766ac36ff286780d196844ffd1589f468beaee5 GIT binary 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zNgiWAKCvBxigZ}qYOF95`6gkpmO?`JH^=MpN@uanRmH(GDe>K4)c|*l z4l09Vg~CiG-pp0v<+5upJMz5}-AHKiG>w2Q`R@M6>r(&*9xQ;*?up@B45#s1`koQY zCLn)5VAXR$964V$L2~luXi=N;QZ_X+u>n1(aZ*bmtQ-H7*xhrq(b9Qck;o4Q)-7V! zIos}jlM{>*LgU1--XYlR;>ccabp1yhrhRiiacBPfuQ@2``=-JG$7Fr7fzB>hi*J1c zuQ0uAGtGH^^+C>vx^J}w#nzW*3|Hdu=8L+b*_RfK&rWrlys@nh6w{zrb6~9|nX?6D z0`OvggX=z9y62%T29_5~sQ)h0jKRWaiY`cqPyaIJ^PLhxRNP0XxXQ~OerEB6z~90W zsLq(vx?uTC&TkSpmb);W^FxyZV}&&AWm9?JCr`DX@oU-2+k&w3Up?elW1%!A>?>dn zt&t?>T=5|@^Z0e7PD})cQ!>1ujd*&z<7&l}<1|(I+oK!gW+bm~25BsAa+m&I>18>v zQlKnaoaoOlZ#25j_ghk;1xwlzJlY<7$LbGCDsW7Wk2I5Y;N~h6c&|2-GtMSKJv8w) z|0#V~Av?epr|}fS_F1E@J-gx8aVw@D-hYORxl*RQjGO!}1jl)$=)@Hv`zM{$6#9WV z;y_PpPj`c zp*j$AA^9e~C(M@4iMYIF6&>frzb6PwtKasMBh;fvy!onZ7Z{MTq|9|$F#xe3A<-6A zlB*U#w&Y(O05ZIRG&iN@CB%&?^v)CDCaaJO$3$t-Z0;9JgD2E>F*ww``hLA6_bf0M a*dpmAYF3W3&p + %\VignetteIndexEntry{Introduction to lighthouse.codebook} + %\VignetteEngine{knitr::rmarkdown} + %\VignetteEncoding{UTF-8} +--- + +```{r, include = FALSE} +knitr::opts_chunk$set( + eval = FALSE, + comment = "#", + out.width = "100%" +) +``` +Creating a codebook involves two general steps: + +1) Create a a "codebook" object in R from a data frame (and, optionally, metadata), + using `cb_create()` or a specialized variant (such as `cb_create_spss()` or `cb_create_redcap()`). + +2) Write the codebook to disk using `cb_write()`. + +## Creating a codebook +In the simplest case, to create a codebook and write it to disk:. +```r +gain_q4 |> cb_create() |> cb_write("q4.xlsx") +``` + +Creating a codebook using the lighthouse.codebook package involves two steps. + +1) Create a a "codebook" object from a data frame (and, optionally, metadata) using +`cb_create()` or a specialized variant (such as `cb_create_spss()` or `cb_create_redcap()`). + +2) Write the codebook to disk using `cb_write()`. + +Thus, in the simplest case, to create a codebook and write it to disk:. +```r +gain_q4 |> cb_create() |> cb_write("q4.xlsx") +``` +`cb_create()` and `cb_write()` include a number of options for controlling how data +is interpreted, summarized, and presented. +- The "Creating Codebooks" vignette covers options for controlling how data and +metadata are _interpreted,_ such as by applying value labels, specifying user missing +or nonresponse codes, and taking advantage of specialized metadata (e.g., from SPSS +or REDCap data). +- The "Writing Codebooks" vignette covers how data is _summarized and presented_ +in the codebook written to disk, including options for grouped summaries and missing +data. + +friends include options for + +The codebook written to disk will include an Overview tab listing all variables +in the dataset; Summary tabs for numeric, categorical, and text variables; and, +if grouping variables are specified, Grouped Summary tabs for numeric and categorical +variables. + +The codebook object can also be used to create other objects in R, such +as transformed data (see `cb_get_data()`) and summaries (see `cb_summarize_numeric()`, +`cb_summarize_categorical()`, `cb_summarize_text()`). +```r +cb <- cb_create(gain_q4) + +# data with SPSS-style variable and value labels +q4_haven <- cb_get_data(cb, format = "haven") + +# descriptives for numeric variables +q4_summary_num <- cb_summarize_numeric(cb) +``` +## Codebook contents +* Is this section necessary? * +A codebook written to disk will include an Overview tab as well as Summary tabs +for numeric, categorical, and text variables. The Overview tab includes one row +for each variable in the dataset, with information on variable type, label, values, +and missingness. By default, each variable is hyperlinked to its location on the +relevant summary tab. + +```{r echo = FALSE, eval = TRUE} +knitr::include_graphics("img/overview.png") +``` + +The numeric summary tab includes descriptive statistics for all numeric variables in the dataset: +[image] + +The categorical summary tab includes frequencies for all categorical variables in the dataset: +[image] + +The text summary tab includes the number of unique values, and frequencies for the most common values, for all text variables in the dataset. (The number of values for which frequencies are shown can be specified using the `n_text_vals` argument to `cb_write()`; by default, the 5 most common values are shown.) +[image] + +## Creating codebooks + +1. In simplest case, use `cb_create()` with "basic" dataframe. + - \*\*\* make a simpler example dataframe for this + - simple eg code +2. Other options - probably show *very* briefly, then link to separate vignette for each: + - Use `cb_create()` with user-supplied metadata (link to vignette). + - Use `cb_create_redcap()` with data and metadata exported from REDCap (link to vignette). + - Use `cb_create_spss()` with SPSS data imported using haven (link to vignette). + +### Variable types +Data summaries are produced for "numeric," "categorical," and "text" variables. For a given variable `x`, +- `x` is treated as categorical if (1) it is a factor, ordered factor, or logical vector, _or_ (2) it has associated value labels other than missing value codes (specified in metadata or, for SPSS data, in a `"haven_labelled"` vector). +- `x` is treated as numeric if (1) it is numeric (i.e., `is.numeric(x)` is `TRUE`) _and_ (2) it has no associated value labels other than missing value codes. +- `x` is treated as text if (1) it is a character vector _and_ (2) it has no associated value labels other than missing value codes. + +Thus, you can change the way a variable is summarized by changing its class. For +instance, to get complete frequencies for a numeric or character variable, convert +it to factor; to get only the top frequencies for a factor with many levels, convert +it to character. + +Variables of other classes, such as dates, datetimes, and lists, are not currently included on summary tabs. (Summaries for dates and datetimes are planned for a future release). + +### Missing values + +User missing values (also known as nonresponse codes, reserve codes, or special values) can be specified in the call to `cb_create()` with the `.user_missing` argument. Missing codes are specified using a formula or list of formulas; formulas should have variable(s) on the left-hand side (as a [tidyselect expression](https://dplyr.tidyverse.org/reference/dplyr_tidy_select.html)) and user missing values on the right-hand side. +** ...adapt subsection on missing values from `cb_create()`. ** +If the left-hand side is empty, the specified values will be treated as user missing across all compatible variables in the dataset. +```r +cb <- cb_create(dat, .user_missing = ~ -99) +``` + +User missing values are dropped for computation of summary statistics for numeric variables. By default, user missing values are individually tabulated on ungrouped categorical and text summary tabs: +```r +missing_codes <- c( + "Not Asked" = -3, "Missing" = -4, "Confidential" = -6, "Refused" = -7, + "Don't Know" = -8, "Legitimate Skip" = -9 +) + +dat |> + cb_create(.user_missing = ~ missing_codes) |> + cb_write("cb.xlsx") +``` +[image] +...but can be collapsed with system missing values into a single "(Missing)" row by setting `detail_missing` to `FALSE`: +```r +dat |> + cb_create(.user_missing = ~ missing_codes) |> + cb_write("cb.xlsx", detail_missing = FALSE) +``` +However, user missing values are always collapsed (as though `detail_missing = FALSE`) on _grouped_ summary tabs. +``` +### Grouped summaries +Grouped summaries can be generated by specifying one or more grouping variables in the `group_by` argument to `cb_write()`. The workbook will contain both ungrouped summary tabs and tabs grouped by the specified variables. By default, grouping is done in columns, using decked heads when multiple grouping variables are specified. +```r +dat |> + cb_create() |> + cb_write(group_by = c(XRA, XOBS)) +``` +[image] +For numeric variables, grouping can instead be done in rows by passing some or all grouping variables to `group_rows_numeric`: +```r +dat |> + cb_create() |> + cb_write(group_by = c(XRA, XOBS), group_rows_numeric = XOBS) +``` +[image] + +### Handling long / repetitive variable labels + +- Adapt section on `.split_var_labels` from NEWS. + +... + + + + +## `cb_create()` vignette on +#### User-supplied metadata + - by default, `cb_create()` expects columns `"name"` with variable names, `"label"` with variable labels, and `"val_labels"` with value labels. + - however, only `"name"` is required. + - alternate names in `metadata` can be specified using `.name`, `.var_label`, and `.val_labels` arg. (or you can just change the names in `metadata`.) + - value labels should be a single string per variable; specify separators + - can keep additional columns from metadata by passing to `...` \ No newline at end of file diff --git a/vignettes/lighthouse-codebook.md b/vignettes/lighthouse-codebook.md new file mode 100644 index 0000000..7b92170 --- /dev/null +++ b/vignettes/lighthouse-codebook.md @@ -0,0 +1,136 @@ +--- +title: "Introduction to lighthouse.codebook" +output: rmarkdown::html_vignette +vignette: > + %\VignetteIndexEntry{Introduction to lighthouse.codebook} + %\VignetteEngine{knitr::rmarkdown} + %\VignetteEncoding{UTF-8} +--- + +```{r, include = FALSE} +knitr::opts_chunk$set( + eval = FALSE, + comment = "#" +) +``` +### Workflow overview + +Creating a codebook using the lighthouse.codebook package generally involves two steps. First, create a "codebook" object from a data frame (and, optionally, metadata) using `cb_create()` or a specialized variant (such as `cb_create_spss()` or `cb_create_redcap()`). Second, write the codebook to disk using `cb_write()`. + +In many cases, this can be done in a single pipeline. +```r +library(lighthouse.codebook) + +# using example dataset included with package +gain_q4 |> cb_create() |> cb_write("q4.xlsx") +``` + + +A codebook written to disk will include an overview tab as well as summary tabs for numeric, categorical, and text variables. The overview tab lists all variables with their labels and missing value information. By default, each variable is hyperlinked to its location on a Summary tab. +```{r eval = TRUE} +knitr::include_graphics("img/overview.png") +``` + +The numeric summary tab includes descriptive statistics for all numeric variables in the dataset: +[image] + +The categorical summary tab includes frequencies for all categorical variables in the dataset: +[image] + +The text summary tab includes the number of unique values, and frequencies for the most common values, for all text variables in the dataset. (The number of values for which frequencies are shown can be specified using the `n_text_vals` argument to `cb_write()`; by default, the 5 most common values are shown.) +[image] + +The codebook object can also be used to create other objects in R, such +as transformed data (see `cb_get_data()`) and summaries (see `cb_summarize_numeric()`, +`cb_summarize_categorical()`, `cb_summarize_text()`). +```r +cb <- cb_create(gain_q4) + +# data with SPSS-style variable and value labels +q4_haven <- cb_get_data(cb, format = "haven") + +# descriptives for numeric variables +q4_summary_num <- cb_summarize_numeric(cb) +``` + +### Creating codebooks + +1. In simplest case, use `cb_create()` with "basic" dataframe. + - \*\*\* make a simpler example dataframe for this + - simple eg code +2. Other options - probably show *very* briefly, then link to separate vignette for each: + - Use `cb_create()` with user-supplied metadata (link to vignette). + - Use `cb_create_redcap()` with data and metadata exported from REDCap (link to vignette). + - Use `cb_create_spss()` with SPSS data imported using haven (link to vignette). + +### Variable types +Data summaries are produced for "numeric," "categorical," and "text" variables. For a given variable `x`, +- `x` is treated as categorical if (1) it is a factor, ordered factor, or logical vector, _or_ (2) it has associated value labels other than missing value codes (specified in metadata or, for SPSS data, in a `"haven_labelled"` vector). +- `x` is treated as numeric if (1) it is numeric (i.e., `is.numeric(x)` is `TRUE`) _and_ (2) it has no associated value labels other than missing value codes. +- `x` is treated as text if (1) it is a character vector _and_ (2) it has no associated value labels other than missing value codes. + +Thus, you can change the way a variable is summarized by changing its class. For instance, to get complete frequencies for a numeric or character variable, convert it to factor; or to get only the top frequencies for a factor with many levels, convert it to character. + +Variables of other classes, such as dates, datetimes, and lists, are not currently included on summary tabs. (Summaries for dates and datetimes are planned for a future release). + +### Missing values + +User missing values (also known as nonresponse codes, reserve codes, or special values) can be specified in the call to `cb_create()` with the `.user_missing` argument. Missing codes are specified using a formula or list of formulas; formulas should have variable(s) on the left-hand side (as a [tidyselect expression](https://dplyr.tidyverse.org/reference/dplyr_tidy_select.html)) and user missing values on the right-hand side. +** ...adapt subsection on missing values from `cb_create()`. ** +If the left-hand side is empty, the specified values will be treated as user missing across all compatible variables in the dataset. +```r +cb <- cb_create(dat, .user_missing = ~ -99) +``` + +User missing values are dropped for computation of summary statistics for numeric variables. By default, user missing values are individually tabulated on ungrouped categorical and text summary tabs: +```r +missing_codes <- c( + "Not Asked" = -3, "Missing" = -4, "Confidential" = -6, "Refused" = -7, + "Don't Know" = -8, "Legitimate Skip" = -9 +) + +dat |> + cb_create(.user_missing = ~ missing_codes) |> + cb_write("cb.xlsx") +``` +[image] +...but can be collapsed with system missing values into a single "(Missing)" row by setting `detail_missing` to `FALSE`: +```r +dat |> + cb_create(.user_missing = ~ missing_codes) |> + cb_write("cb.xlsx", detail_missing = FALSE) +``` +However, user missing values are always collapsed (as though `detail_missing = FALSE`) on _grouped_ summary tabs. +``` +### Grouped summaries +Grouped summaries can be generated by specifying one or more grouping variables in the `group_by` argument to `cb_write()`. The workbook will contain both ungrouped summary tabs and tabs grouped by the specified variables. By default, grouping is done in columns, using decked heads when multiple grouping variables are specified. +```r +dat |> + cb_create() |> + cb_write(group_by = c(XRA, XOBS)) +``` +[image] +For numeric variables, grouping can instead be done in rows by passing some or all grouping variables to `group_rows_numeric`: +```r +dat |> + cb_create() |> + cb_write(group_by = c(XRA, XOBS), group_rows_numeric = XOBS) +``` +[image] + +### Handling long / repetitive variable labels + +- Adapt section on `.split_var_labels` from NEWS. + +... + + + + +## `cb_create()` vignette on +#### User-supplied metadata + - by default, `cb_create()` expects columns `"name"` with variable names, `"label"` with variable labels, and `"val_labels"` with value labels. + - however, only `"name"` is required. + - alternate names in `metadata` can be specified using `.name`, `.var_label`, and `.val_labels` arg. (or you can just change the names in `metadata`.) + - value labels should be a single string per variable; specify separators + - can keep additional columns from metadata by passing to `...` \ No newline at end of file From 40bbfb11b5c9f1e9ca6c7a9e106192871d95e249 Mon Sep 17 00:00:00 2001 From: ccsarapas Date: Wed, 4 Feb 2026 09:46:54 -0600 Subject: [PATCH 02/13] implement row grouping for categorical summaries --- DESCRIPTION | 2 +- NEWS.md | 10 ++++++++++ R/cb_summarize.r | 11 +++++++--- R/cb_write.r | 52 +++++++++++++++++++++++++++++------------------- R/utils.r | 19 ++++++++++++++++-- man/cb_write.Rd | 21 ++++++++++++------- 6 files changed, 81 insertions(+), 34 deletions(-) diff --git a/DESCRIPTION b/DESCRIPTION index 45a37b1..413d02b 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -1,6 +1,6 @@ Package: lighthouse.codebook Title: Summarize Datasets for Lighthouse Institute Projects -Version: 0.2.3 +Version: 0.2.9000 Authors@R: c( person("Casey", "Sarapas", email = "ccsarapas@chestnut.org", diff --git a/NEWS.md b/NEWS.md index d928098..da7bcc0 100644 --- a/NEWS.md +++ b/NEWS.md @@ -1,3 +1,13 @@ +# lighthouse.codebook 0.3.0 + +## New feature + +* Added options to `cb_write()` to show grouping variables for categorical summaries + in rows (which was previously only possible for numeric summaries.) `cb_write()` + now includes three arguments for showing some or all grouping variables in rows: + `group_rows` controls both numeric and categorical summaries, while `group_rows_numeric` + and `group_rows_categorical` control numeric and categorical summaries, respectively. + # lighthouse.codebook 0.2.3 * `cb_create_spss()` now accepts `.rmv_html` and `.rmv_line_breaks` arguments, consistent diff --git a/R/cb_summarize.r b/R/cb_summarize.r index fb22b3d..d2c4650 100644 --- a/R/cb_summarize.r +++ b/R/cb_summarize.r @@ -152,7 +152,8 @@ cb_summarize_categorical_impl <- function(cb, prefixed = TRUE, detail_missing = missing(group_by), detail_na_label = "NA", - warn_if_none = FALSE) { + warn_if_none = FALSE, + group_rows = NULL) { force(detail_missing) data <- attr(cb, "data_labelled") data_dt <- data.table::as.data.table(data) @@ -307,14 +308,18 @@ cb_summarize_categorical_impl <- function(cb, ) freqs <- freqs[, cols_out, with = FALSE] + group_cols <- setdiff(group_by, group_rows) + if (!length(group_cols)) group_cols <- NULL + freqs |> tibble::as_tibble() |> set_attrs( detail_missing = detail_missing, id_cols = c("name", label_cols, "value"), group_by = group_by, - group_cols = group_by, - group_counts = group_counts(cb, group_by) + group_rows = group_rows, + group_cols = group_cols, + group_counts = group_counts(cb, group_cols) ) } diff --git a/R/cb_write.r b/R/cb_write.r index 748e140..4b95649 100644 --- a/R/cb_write.r +++ b/R/cb_write.r @@ -21,12 +21,16 @@ #' to corresponding rows on summary tabs and vice versa. #' @param group_by <[`tidy-select`][dplyr_tidy_select]> Column or columns to group #' by. If specified, additional numeric and categorical summary tabs will be included -#' with grouped summaries. Subgroups are shown in columns by default. For the numeric -#' summary tab, subgroups for some or all grouping variables can instead be shown -#' in rows if specified in `group_rows_numeric`. -#' @param group_rows_numeric <[`tidy-select`][dplyr_tidy_select]> Column or columns -#' to group by in rows on the grouped numeric summary tab. All columns must also -#' be specified in `group_by`. +#' with grouped summaries. Subgroups are shown in columns by default. Some or all +#' grouping variables can instead be shown in rows if specified in `group_rows`, +#' `group_rows_numeric`, or `group_rows_categorical`. +#' @param group_rows <[`tidy-select`][dplyr_tidy_select]> Column or columns to group +#' by in rows on grouped summary tabs. All columns must also be specified in `group_by`. +#' Will apply to both numeric and categorical summary tabs unless otherwise specified +#' in `group_rows_numeric` or `group_rows_categorical`. +#' @param group_rows_numeric,group_rows_categorical <[`tidy-select`][dplyr_tidy_select]> +#' Column or columns to group by in rows on grouped numeric or categorical summary +#' tab. #' @param detail_missing Include detailed missing value information on ungrouped #' categorical and text summary tabs? (Detailed missing information for grouped #' summary tabs is not currently supported.) @@ -58,7 +62,9 @@ cb_write <- function(cb, incl_dims = TRUE, hyperlinks = TRUE, group_by = NULL, - group_rows_numeric = NULL, + group_rows = NULL, + group_rows_numeric = group_rows, + group_rows_categorical = group_rows, detail_missing = c("if_any_user_missing", "yes", "no"), n_text_vals = 5, overwrite = TRUE) { @@ -77,26 +83,24 @@ cb_write <- function(cb, ) ) group_by <- cb_untidyselect(cb, {{ group_by }}) + group_rows <- cb_untidyselect(cb, {{ group_rows }}) group_rows_numeric <- cb_untidyselect(cb, {{ group_rows_numeric }}) - if (!is.null(group_rows_numeric)) { - if (is.null(group_by)) { - cli::cli_abort( - "If `group_rows_numeric` is specified, `group_by` must also be specified." - ) - } - if (length(setdiff(group_rows_numeric, group_by))) { - cli::cli_abort( - "All columns specified in `group_rows_numeric` must also be included in `group_by`." - ) - } - } + group_rows_categorical <- cb_untidyselect(cb, {{ group_rows_categorical }}) + check_group_rows_arg(group_rows, group_by) + check_group_rows_arg(group_rows_numeric, group_by) + check_group_rows_arg(group_rows_categorical, group_by) + if (!is.null(group_by)) { summaries$num_grp <- cb_summarize_numeric_impl( cb, group_by = group_by, group_rows = group_rows_numeric ) - summaries$cat_grp <- cb_summarize_categorical_impl(cb, group_by = group_by) + summaries$cat_grp <- cb_summarize_categorical_impl( + cb, + group_by = group_by, + group_rows = group_rows_categorical + ) } cb_write_codebook( cb, summaries, @@ -768,7 +772,13 @@ cb_write_codebook <- function(cb, summaries$cat_grp <- summaries$cat_grp |> cb_format_names(skip = group_by, attrs = "id_cols") cols_pct <- untidyselect(summaries$cat_grp, tidyselect::starts_with("%")) - clear_repeats <- setdiff(attr(summaries$cat_grp, "id_cols"), "Value") + group_rows <- attr(summaries$cat_grp, "group_rows") + id_cols <- attr(summaries$cat_grp, "id_cols") + clear_repeats <- c(setdiff(id_cols, "Value"), group_rows) + if (!is.null(group_rows)) { + attr(summaries$cat_grp, "group_rows") <- c(group_rows, "Value") + attr(summaries$cat_grp, "id_cols") <- setdiff(id_cols, "Value") + } sheet_nms$cat_grp <- paste0("Grouped ", sheet_nms$cat) headers$cat_grp <- c(headers$cat, paste("By ", toString(group_by))) params$cat_grp <- summaries$cat_grp |> diff --git a/R/utils.r b/R/utils.r index 22226cc..dbf68e6 100644 --- a/R/utils.r +++ b/R/utils.r @@ -8,13 +8,28 @@ check_codebook <- function(x) { check_user_missing_arg <- function(x) { arg <- as.character(rlang::ensym(x)) if (!( - rlang::is_formula(x) || (is.list(x) && all(sapply(x, rlang::is_formula))) - )) { + rlang::is_formula(x) || (is.list(x) && all(sapply(x, rlang::is_formula))) + )) { cli::cli_abort("{.arg {arg}} must be a formula or list of formulas.") } if (rlang::is_formula(x)) x <- list(x) x } +check_group_rows_arg <- function(group_rows, group_by) { + arg <- as.character(rlang::ensym(group_rows)) + if (!is.null(group_rows)) { + if (is.null(group_by)) { + cli::cli_abort( + "If {.arg {arg}} is specified, {.arg group_by} must also be specified." + ) + } + if (length(setdiff(group_rows, group_by))) { + cli::cli_abort( + "All columns specified in {.arg {arg}} must also be included in {.arg group_by}." + ) + } + } +} set_attrs <- function(x, ...) { dots <- rlang::list2(...) for (nm in names(dots)) attr(x, nm) <- dots[[nm]] diff --git a/man/cb_write.Rd b/man/cb_write.Rd index 0018f5b..727a809 100644 --- a/man/cb_write.Rd +++ b/man/cb_write.Rd @@ -12,7 +12,9 @@ cb_write( incl_dims = TRUE, hyperlinks = TRUE, group_by = NULL, - group_rows_numeric = NULL, + group_rows = NULL, + group_rows_numeric = group_rows, + group_rows_categorical = group_rows, detail_missing = c("if_any_user_missing", "yes", "no"), n_text_vals = 5, overwrite = TRUE @@ -34,13 +36,18 @@ to corresponding rows on summary tabs and vice versa.} \item{group_by}{<\code{\link[dplyr:dplyr_tidy_select]{tidy-select}}> Column or columns to group by. If specified, additional numeric and categorical summary tabs will be included -with grouped summaries. Subgroups are shown in columns by default. For the numeric -summary tab, subgroups for some or all grouping variables can instead be shown -in rows if specified in \code{group_rows_numeric}.} +with grouped summaries. Subgroups are shown in columns by default. Some or all +grouping variables can instead be shown in rows if specified in \code{group_rows}, +\code{group_rows_numeric}, or \code{group_rows_categorical}.} -\item{group_rows_numeric}{<\code{\link[dplyr:dplyr_tidy_select]{tidy-select}}> Column or columns -to group by in rows on the grouped numeric summary tab. All columns must also -be specified in \code{group_by}.} +\item{group_rows}{<\code{\link[dplyr:dplyr_tidy_select]{tidy-select}}> Column or columns to group +by in rows on grouped summary tabs. All columns must also be specified in \code{group_by}. +Will apply to both numeric and categorical summary tabs unless otherwise specified +in \code{group_rows_numeric} or \code{group_rows_categorical}.} + +\item{group_rows_numeric, group_rows_categorical}{<\code{\link[dplyr:dplyr_tidy_select]{tidy-select}}> +Column or columns to group by in rows on grouped numeric or categorical summary +tab.} \item{detail_missing}{Include detailed missing value information on ungrouped categorical and text summary tabs? (Detailed missing information for grouped From d3072e344ea059f83ef3eaf635bcd2736324b496 Mon Sep 17 00:00:00 2001 From: ccsarapas Date: Mon, 9 Feb 2026 10:58:08 -0600 Subject: [PATCH 03/13] add details on compatible classes for `.user_missing` --- R/cb_create.r | 10 ++++++---- 1 file changed, 6 insertions(+), 4 deletions(-) diff --git a/R/cb_create.r b/R/cb_create.r index 901e21e..0e77ec3 100644 --- a/R/cb_create.r +++ b/R/cb_create.r @@ -99,10 +99,12 @@ #' If labels set in `.user_missing` conflict with those in `metadata`, `.user_missing_conflict` #' controls which labels are used. #' -#' User missing values are not compatible with logical, date, or datetime (POSIXt) -#' variables. By default, these variables will be ignored if specified in `.user_missing`. -#' (i.e., user missing values will be applied only to compatible variables.) This behavior -#' can be changed using the `.user_missing_incompatible` argument. +#' User missings may be set for numeric, character, factor/ordered factor, and haven_labelled/haven_labelled_spss +#' vectors. For factors, user missings are set based on factor labels (not the underlying +#' integer codes). For `"haven_labelled"` vectors, user missings are set based on +#' values (not value labels). By default, variables with incompatible classes (e.g., +#' logical, Date, POSIXt) will be ignored if specified in `.user_missing`. This +#' behavior can be changed using the `.user_missing_incompatible` argument. #' #' @examples #' diamonds2 <- ggplot2::diamonds |> From 8df316c61427d22ea06f35ace287cf7148c5adff Mon Sep 17 00:00:00 2001 From: ccsarapas Date: Mon, 9 Feb 2026 17:05:36 -0600 Subject: [PATCH 04/13] add intro vignette --- .Rbuildignore | 2 + .gitignore | 2 + _pkgdown.yml | 2 +- vignettes/lighthouse-codebook.Rmd | 449 +++++++++++++++++++++++------- vignettes/lighthouse-codebook.md | 136 --------- 5 files changed, 346 insertions(+), 245 deletions(-) delete mode 100644 vignettes/lighthouse-codebook.md diff --git a/.Rbuildignore b/.Rbuildignore index 9acafe0..8721688 100644 --- a/.Rbuildignore +++ b/.Rbuildignore @@ -6,3 +6,5 @@ ^pkgdown$ ^\.github$ ^README\.Rmd$ +^doc$ +^Meta$ diff --git a/.gitignore b/.gitignore index 8a6f2db..54f27cb 100644 --- a/.gitignore +++ b/.gitignore @@ -6,3 +6,5 @@ .quarto docs inst/doc +/doc/ +/Meta/ diff --git a/_pkgdown.yml b/_pkgdown.yml index d98e4d4..8221a9a 100644 --- a/_pkgdown.yml +++ b/_pkgdown.yml @@ -10,4 +10,4 @@ authors: Casey Sarapas: href: "https://chestnut.org/li/scientists-and-project-directors/category/research-scientists/profile/casey-sarapas-phd" Chestnut Health Systems: - href: "https://chestnut.org/" \ No newline at end of file + href: "https://chestnut.org/" diff --git a/vignettes/lighthouse-codebook.Rmd b/vignettes/lighthouse-codebook.Rmd index 9b45721..4c37a0f 100644 --- a/vignettes/lighthouse-codebook.Rmd +++ b/vignettes/lighthouse-codebook.Rmd @@ -14,91 +14,228 @@ knitr::opts_chunk$set( out.width = "100%" ) ``` + +## Creating codebooks + + + Creating a codebook involves two general steps: -1) Create a a "codebook" object in R from a data frame (and, optionally, metadata), - using `cb_create()` or a specialized variant (such as `cb_create_spss()` or `cb_create_redcap()`). +1. Create a a “codebook” object in R from a data frame (and, + optionally, metadata), using `cb_create()` or a specialized variant + (such as `cb_create_spss()` or `cb_create_redcap()`). -2) Write the codebook to disk using `cb_write()`. +2. Write the codebook to disk using `cb_write()`. -## Creating a codebook -In the simplest case, to create a codebook and write it to disk:. -```r -gain_q4 |> cb_create() |> cb_write("q4.xlsx") -``` +``` r +library(lighthouse.codebook) -Creating a codebook using the lighthouse.codebook package involves two steps. +# create and write a codebook without metadata +dat |> + cb_create() |> + cb_write("cb.xlsx") -1) Create a a "codebook" object from a data frame (and, optionally, metadata) using -`cb_create()` or a specialized variant (such as `cb_create_spss()` or `cb_create_redcap()`). +# with metadata +dat |> + cb_create(metadata = dat1_metadata) |> + cb_write("cb.xlsx") -2) Write the codebook to disk using `cb_write()`. +# from SPSS data +dat_spss <- haven::read_sav("dat_spss.sav", user_na = TRUE) -Thus, in the simplest case, to create a codebook and write it to disk:. -```r -gain_q4 |> cb_create() |> cb_write("q4.xlsx") -``` -`cb_create()` and `cb_write()` include a number of options for controlling how data -is interpreted, summarized, and presented. -- The "Creating Codebooks" vignette covers options for controlling how data and -metadata are _interpreted,_ such as by applying value labels, specifying user missing -or nonresponse codes, and taking advantage of specialized metadata (e.g., from SPSS -or REDCap data). -- The "Writing Codebooks" vignette covers how data is _summarized and presented_ -in the codebook written to disk, including options for grouped summaries and missing -data. +dat_spss |> + cb_create_spss() |> + cb_write("cb_spss.xlsx") -friends include options for +# from REDCap data +dat_rc <- REDCapR::redcap_read(redcap_uri = rc_uri, token = rc_token) +meta_rc <- REDCapR::redcap_metadata_read(redcap_uri = rc_uri, token = rc_token) +dat_rc$data |> + cb_create_redcap(metadata = meta_rc$data) |> + cb_write("cb_rc.xlsx") +``` The codebook written to disk will include an Overview tab listing all variables in the dataset; Summary tabs for numeric, categorical, and text variables; and, if grouping variables are specified, Grouped Summary tabs for numeric and categorical variables. -The codebook object can also be used to create other objects in R, such -as transformed data (see `cb_get_data()`) and summaries (see `cb_summarize_numeric()`, -`cb_summarize_categorical()`, `cb_summarize_text()`). +## Customizing codebooks + +There are many options for controlling how data is interpreted, summarized, and +presented. This section shows a few of the most useful options. Further options +are detailed in the documentation for `cb_create()` and `cb_write()`. + +### Grouped summaries + +Numeric and categorical data summaries can be grouped by one or more variables by +specifying them in the `group_by` argument to `cb_write()`. ```r -cb <- cb_create(gain_q4) +cb_create(data, metadata) |> + cb_write("cb.xlsx", group_by = treatment_group) -# data with SPSS-style variable and value labels -q4_haven <- cb_get_data(cb, format = "haven") +cb_create(data, metadata) |> + cb_write("cb.xlsx", group_by = c(treatment_group, timepoint, age_group)) +``` -# descriptives for numeric variables -q4_summary_num <- cb_summarize_numeric(cb) +By default, values for each subgroup are shown in separate columns, with decked +heads if more than one grouping variables is specified. However, some or all grouping +variables can instead be shown in rows using the `group_rows` argument. +```r +# show `treatment_group` in columns and `timepoint` in rows +cb_create(data, metadata) |> + cb_write( + "cb.xlsx", + group_by = c(treatment_group, timepoint), + group_rows = timepoint + ) +``` +Different row grouping behavior can be specified for numeric versus categorical +summary tabs using the `group_rows_numeric` and `group_rows_categorical` arguments. +```r +# for numeric summary, show `treatment_group` in columns and `timepoint` in rows; +# for categorical summary, show all grouping variables in columns +cb_create(data, metadata) |> + cb_write( + "cb.xlsx", + group_by = c(treatment_group, timepoint), + group_rows_numeric = timepoint + ) + +# for numeric summary, show all grouping vars in rows for numeric summary; +# for categorical summary, show `treatment_group` in rows +cb_create(data, metadata) |> + cb_write( + "cb.xlsx", + group_by = c(treatment_group, timepoint), + group_rows_numeric = c(treatment_group, timepoint), + group_rows_categorical = treatment_group + ) ``` -## Codebook contents -* Is this section necessary? * -A codebook written to disk will include an Overview tab as well as Summary tabs -for numeric, categorical, and text variables. The Overview tab includes one row -for each variable in the dataset, with information on variable type, label, values, -and missingness. By default, each variable is hyperlinked to its location on the -relevant summary tab. -```{r echo = FALSE, eval = TRUE} -knitr::include_graphics("img/overview.png") +### User missing values + +User missing values (also known as nonresponse codes, reserve codes, or special +values) can be specified using the `.user_missing` argument to `cb_create()`. +Missing values are specified using a formula or list of formulas, with variables +on the left-hand side (as names or [tidyselect](https://dplyr.tidyverse.org/reference/dplyr_tidy_select.html) +expressions) and values on the right-hand side. If the left-hand side is empty, +user missings will be set for all compatible variables in the dataset. + +```r +# set a single missing value for a single variable +cb <- cb_create(data, metadata, .user_missing = var1 ~ 99) + +# for variables `var1` through `var5` +cb <- cb_create(data, metadata, .user_missing = var1:var5 ~ c(98, 99)) + +# for all numeric variables, plus `var6` and `var7` +cb <- cb_create( + data, + metadata, + .user_missing = c(where(is.numeric), var6, var7) ~ c(-9, -8, -7) +) + +# for all compatible variables in dataset +cb <- cb_create(data, metadata, .user_missing = ~ c(98, 99)) ``` -The numeric summary tab includes descriptive statistics for all numeric variables in the dataset: -[image] +If the user missing values are named, the names will be treated as value labels +in data summaries. -The categorical summary tab includes frequencies for all categorical variables in the dataset: -[image] +```r +cb <- cb_create( + data, + metadata, + .user_missing = var1:var5 ~ c("Declined" = 98, "Not applicable" = 99) +) +``` -The text summary tab includes the number of unique values, and frequencies for the most common values, for all text variables in the dataset. (The number of values for which frequencies are shown can be specified using the `n_text_vals` argument to `cb_write()`; by default, the 5 most common values are shown.) -[image] +To apply different user missings for different variables, pass a list of formulas. +```r +cb <- cb_create( + data, + metadata, + .user_missing = list( + starts_with("status") ~ c("Declined" = 98, "Not applicable" = 99), + var7:var10 ~ c("Don't know" = -4, "Not applicable" = -5) + ) +) +``` +### Missing value handling -## Creating codebooks +On numeric summary tabs, missing values (including both user missing values and `NA`) +are dropped for computation of summary statistics. -1. In simplest case, use `cb_create()` with "basic" dataframe. - - \*\*\* make a simpler example dataframe for this - - simple eg code -2. Other options - probably show *very* briefly, then link to separate vignette for each: - - Use `cb_create()` with user-supplied metadata (link to vignette). - - Use `cb_create_redcap()` with data and metadata exported from REDCap (link to vignette). - - Use `cb_create_spss()` with SPSS data imported using haven (link to vignette). +On ungrouped categorical and text summary tabs, by default, user missing values +are individually tabulated. For example, if `.user_missing = ~ c("Declined" = 98, +"Not applicable" = 99)`, then categorical and text summary tabs will include rows +giving counts for `"[98] Declined"` and `"[99] Not applicable"`. All user missing +values and `NA` can instead be collapsed into a single `"(Missing)"` row using the +`detail_missing` argument to `cb_write()`. +```r +dat |> + cb_create(.user_missing = ~ ~ c("Declined" = 98, "Not applicable" = 99)) |> + cb_write("cb.xlsx", detail_missing = FALSE) +``` +Finally, user missing values are always collapsed (as though `detail_missing = FALSE`) +on _grouped_ summary tabs. + +### Splitting long variable labels + +Sets of related variables sometimes share a common prefix. Using the `.split_var_labels` +argument to `cb_create()`, this prefix can be extracted into a separate column, +making it easier to see at a glance what is unique about each variable. +For example, given a set of variable labels that all begin with `"What colors do +you like? Select all that apply: "`: + +| Name | Label | +| ---- | ----- | +| age | How old are you today? | +| colors1 | What colors do you like? Select all that apply: Red | +| colors2 | What colors do you like? Select all that apply: Green | +| colors3 | What colors do you like? Select all that apply: Blue | +| colors4 | What colors do you like? Select all that apply: Orange | +| height | What is your height in inches? | + +You can split the labels for these variables, specifying them using a [tidyselect](https://dplyr.tidyverse.org/reference/dplyr_tidy_select.html) +expression: + +```r +cb_create( + data, + metadata, + .split_var_labels = starts_with("colors") + ) |> + cb_write("cb.xlsx") +``` + +| Name | Label Stem | Label | +| ---- | ---------- | ----- | +| age | | How old are you today? | +| colors1 | What colors do you like? Select all that apply: | Red | +| colors2 | What colors do you like? Select all that apply: | Green | +| colors3 | What colors do you like? Select all that apply: | Blue | +| colors4 | What colors do you like? Select all that apply: | Orange | +| height | | What is your height in inches? | -### Variable types +Multiple sets of variables with common prefixes can be specified by passing a +list of tidyselect expressions. + +```r +cb_create( + data, + metadata, + .split_var_labels = list( + starts_with("colors"), + fav_food2:fav_food9, + c(rating1, rating4:rating7, rating9) + ) + ) |> + cb_write("cb.xlsx") +``` + +## Variable typing Data summaries are produced for "numeric," "categorical," and "text" variables. For a given variable `x`, - `x` is treated as categorical if (1) it is a factor, ordered factor, or logical vector, _or_ (2) it has associated value labels other than missing value codes (specified in metadata or, for SPSS data, in a `"haven_labelled"` vector). - `x` is treated as numeric if (1) it is numeric (i.e., `is.numeric(x)` is `TRUE`) _and_ (2) it has no associated value labels other than missing value codes. @@ -109,66 +246,162 @@ instance, to get complete frequencies for a numeric or character variable, conve it to factor; to get only the top frequencies for a factor with many levels, convert it to character. -Variables of other classes, such as dates, datetimes, and lists, are not currently included on summary tabs. (Summaries for dates and datetimes are planned for a future release). - -### Missing values +Variables of other classes, such as dates, datetimes, and lists, are not currently +included on summary tabs. Summaries for dates and datetimes are planned for a future +release. -User missing values (also known as nonresponse codes, reserve codes, or special values) can be specified in the call to `cb_create()` with the `.user_missing` argument. Missing codes are specified using a formula or list of formulas; formulas should have variable(s) on the left-hand side (as a [tidyselect expression](https://dplyr.tidyverse.org/reference/dplyr_tidy_select.html)) and user missing values on the right-hand side. -** ...adapt subsection on missing values from `cb_create()`. ** -If the left-hand side is empty, the specified values will be treated as user missing across all compatible variables in the dataset. -```r -cb <- cb_create(dat, .user_missing = ~ -99) -``` +## Other uses for the codebook object +The `"lighthouse_codebook"` object created by `cb_create()` will most commonly be +used to write an Excel codebook to disk using `cb_write()`. However, it can also +be used to create other objects in R. -User missing values are dropped for computation of summary statistics for numeric variables. By default, user missing values are individually tabulated on ungrouped categorical and text summary tabs: ```r -missing_codes <- c( - "Not Asked" = -3, "Missing" = -4, "Confidential" = -6, "Refused" = -7, - "Don't Know" = -8, "Legitimate Skip" = -9 +# example data +q4_subset <- gain_q4 |> + subset(select = c(XPID, XOBS, XRA, B17, SU4a, SU4b, SU1f99v)) + +# create codebook +cb <- cb_create( + q4_subset, + metadata = q4_metadata, + .user_missing = ~ c("Not Asked" = -3, + "Missing" = -4, + "Confidential" = -6, + "Refused" = -7, + "Don't Know" = -8, + "Legitimate Skip" = -9) ) -dat |> - cb_create(.user_missing = ~ missing_codes) |> - cb_write("cb.xlsx") +cb +# # A tibble: 7 × 6 +# name type label values user_missings missing +# +# 1 XPID text Participant ID NA [-9] Legitim… 0 +# 2 XOBS categorical Observation Wave [0] I… [-9] Legitim… 0 +# 3 XRA categorical Random assignment [0] C… [-9] Legitim… 0 +# 4 B17 categorical Pregnant [0] N… [-9] Legitim… 0.221 +# 5 SU4a numeric PPS - P90 days alcohol use NA [-9] Legitim… 0.394 +# 6 SU4b numeric PPS - P90 days drunk or 5+ d… NA [-9] Legitim… 0.442 +# 7 SU1f99v text QCS - P90 Days Other AOD Tx … NA [-9] Legitim… 0.923 ``` -[image] -...but can be collapsed with system missing values into a single "(Missing)" row by setting `detail_missing` to `FALSE`: +### Extract transformed data +Use `cb_get_data()` to extract transformed data based in several formats. `format += "factors"` yields a dataset with all variables with value labels converted to +factors and user missings converted to `NA`. ```r -dat |> - cb_create(.user_missing = ~ missing_codes) |> - cb_write("cb.xlsx", detail_missing = FALSE) +cb_get_data(cb, format = "factors") +# # A tibble: 104 × 7 +# XPID XOBS XRA B17 SU4a SU4b SU1f99v +# +# 1 001 Intake Treatment No NA NA NA +# 2 002 Intake Control No 10 4 NA +# 3 003 Intake Treatment No 10 1 Peer counselor +# 4 003 3-month Treatment No 39 15 NA +# 5 003 6-month Treatment No NA NA NA +# 6 004 Intake Control No 10 2 NA +# 7 004 3-month Control No 55 63 NA +# 8 004 6-month Control No 10 1 NA +# 9 005 Intake Control Yes 35 0 Social worker +# 10 005 3-month Control Yes 55 39 NA +# # ℹ 94 more rows ``` -However, user missing values are always collapsed (as though `detail_missing = FALSE`) on _grouped_ summary tabs. +Whereas `format = "haven"` yields a dataset with SPSS-style variable labels, value +labels, and user missings encoded using the `"haven_labelled_spss"` class. +```r +cb_get_data(cb, format = "haven") +# # A tibble: 104 × 7 +# XPID XOBS XRA B17 SU4a SU4b SU1f99v +# +# 1 001 0 [Intake] 1 [Treatment] 0 [No] -9 (NA) -9 (NA) -9 (NA) +# 2 002 0 [Intake] 0 [Control] 0 [No] 10 4 -9 (NA) +# 3 003 0 [Intake] 1 [Treatment] 0 [No] 10 1 Peer couns… +# 4 003 1 [3-month] 1 [Treatment] 0 [No] 39 15 -4 (NA) +# 5 003 2 [6-month] 1 [Treatment] 0 [No] -4 (NA) -4 (NA) -4 (NA) +# 6 004 0 [Intake] 0 [Control] 0 [No] 10 2 -9 (NA) +# 7 004 1 [3-month] 0 [Control] 0 [No] 55 63 -9 (NA) +# 8 004 2 [6-month] 0 [Control] 0 [No] 10 1 -9 (NA) +# 9 005 0 [Intake] 0 [Control] 1 [Yes] 35 0 Social wor… +# 10 005 1 [3-month] 0 [Control] 1 [Yes] 55 39 -9 (NA) +# # ℹ 94 more rows ``` -### Grouped summaries -Grouped summaries can be generated by specifying one or more grouping variables in the `group_by` argument to `cb_write()`. The workbook will contain both ungrouped summary tabs and tabs grouped by the specified variables. By default, grouping is done in columns, using decked heads when multiple grouping variables are specified. +### Get data summaries +`cb_summarize_numeric()`, `cb_summarize_categorical()`, and `cb_summarize_text()` +return summaries for all variables of their respective types. These are the basis +of the summary tabs generated by `cb_write()`. ```r -dat |> - cb_create() |> - cb_write(group_by = c(XRA, XOBS)) +cb_summarize_numeric(cb) +# # A tibble: 2 × 8 +# name label valid_n valid_pct mean SD median MAD +# +# 1 SU4a PPS - P90 days alcohol use 63 0.606 23.4 21.2 20 25.2 +# 2 SU4b PPS - P90 days drunk or 5+ d… 58 0.558 8.09 13.6 2 2.97 +# # ℹ 5 more variables: min , max , range , skew , kurt + +cb_summarize_categorical(cb) |> +# # A tibble: 9 × 7 +# name label is_missing value n pct_of_all pct_of_valid +# +# 1 XOBS Observation Wave FALSE [0] Intake 42 0.404 0.404 +# 2 XOBS Observation Wave FALSE [1] 3-month 34 0.327 0.327 +# 3 XOBS Observation Wave FALSE [2] 6-month 28 0.269 0.269 +# 4 XRA Random assignment FALSE [0] Control 50 0.481 0.481 +# 5 XRA Random assignment FALSE [1] Treatment 54 0.519 0.519 +# 6 B17 Pregnant FALSE [1] Yes 6 0.0577 0.0741 +# 7 B17 Pregnant FALSE [0] No 75 0.721 0.926 +# 8 B17 Pregnant TRUE [-9] Legitim… 22 0.212 NA +# 9 B17 Pregnant TRUE [-4] Missing 1 0.00962 NA +# # ℹ 1 more variable: pct_of_missing + +cb_summarize_text(cb) +# # A tibble: 14 × 7 +# name label is_missing unique_n value n pct_of_all +# +# 1 XPID Participant ID FALSE 42 003 3 0.0288 +# 2 XPID Participant ID FALSE 42 004 3 0.0288 +# 3 XPID Participant ID FALSE 42 005 3 0.0288 +# 4 XPID Participant ID FALSE 42 006 3 0.0288 +# 5 XPID Participant ID FALSE 42 010 3 0.0288 +# 6 XPID Participant ID FALSE 42 (37 … 89 0.856 +# 7 SU1f99v QCS - P90 Days Other AOD … FALSE 8 AA 1 0.00962 +# 8 SU1f99v QCS - P90 Days Other AOD … FALSE 8 Alco… 1 0.00962 +# 9 SU1f99v QCS - P90 Days Other AOD … FALSE 8 Case… 1 0.00962 +# 10 SU1f99v QCS - P90 Days Other AOD … FALSE 8 Group 1 0.00962 +# 11 SU1f99v QCS - P90 Days Other AOD … FALSE 8 NA 1 0.00962 +# 12 SU1f99v QCS - P90 Days Other AOD … FALSE 8 (3 o… 3 0.0288 +# 13 SU1f99v QCS - P90 Days Other AOD … TRUE NA [-4]… 44 0.423 +# 14 SU1f99v QCS - P90 Days Other AOD … TRUE NA [-9]… 52 0.5 +# # ℹ 2 more variables: pct_of_valid , pct_of_missing ``` -[image] -For numeric variables, grouping can instead be done in rows by passing some or all grouping variables to `group_rows_numeric`: +`cb_summarize_numeric()` and `cb_summarize_categorical()` can also return grouped +summaries: ```r -dat |> - cb_create() |> - cb_write(group_by = c(XRA, XOBS), group_rows_numeric = XOBS) +cb_summarize_numeric(cb, group_by = XOBS) +# # A tibble: 6 × 8 +# XOBS name label valid_n valid_pct mean SD median +# +# 1 Intake SU4a PPS - P90 days alcohol use 30 0.714 28.8 20.1 26.5 +# 2 3-month SU4a PPS - P90 days alcohol use 20 0.588 21.4 21.0 12.5 +# 3 6-month SU4a PPS - P90 days alcohol use 13 0.464 13.9 21.5 6 +# 4 Intake SU4b PPS - P90 days drunk or 5… 30 0.714 7.13 10.7 4 +# 5 3-month SU4b PPS - P90 days drunk or 5… 15 0.441 16.1 20.0 3 +# 6 6-month SU4b PPS - P90 days drunk or 5… 13 0.464 1 0.707 1 +# # ℹ 6 more variables: MAD , min , max , range , skew , +# # kurt + +cb_summarize_categorical(cb, group_by = XRA) +# # A tibble: 12 × 7 +# XRA name label value n pct_of_all pct_of_valid +# +# 1 Control XOBS Observation Wave [0] Intake 20 0.4 0.4 +# 2 Control XOBS Observation Wave [1] 3-month 16 0.32 0.32 +# 3 Control XOBS Observation Wave [2] 6-month 14 0.28 0.28 +# 4 Treatment XOBS Observation Wave [0] Intake 22 0.407 0.407 +# 5 Treatment XOBS Observation Wave [1] 3-month 18 0.333 0.333 +# 6 Treatment XOBS Observation Wave [2] 6-month 14 0.259 0.259 +# 7 Control B17 Pregnant [1] Yes 3 0.06 0.0811 +# 8 Control B17 Pregnant [0] No 34 0.68 0.919 +# 9 Control B17 Pregnant (Missing) 13 0.26 NA +# 10 Treatment B17 Pregnant [1] Yes 3 0.0556 0.0682 +# 11 Treatment B17 Pregnant [0] No 41 0.759 0.932 +# 12 Treatment B17 Pregnant (Missing) 10 0.185 NA ``` -[image] - -### Handling long / repetitive variable labels - -- Adapt section on `.split_var_labels` from NEWS. - -... - - - - -## `cb_create()` vignette on -#### User-supplied metadata - - by default, `cb_create()` expects columns `"name"` with variable names, `"label"` with variable labels, and `"val_labels"` with value labels. - - however, only `"name"` is required. - - alternate names in `metadata` can be specified using `.name`, `.var_label`, and `.val_labels` arg. (or you can just change the names in `metadata`.) - - value labels should be a single string per variable; specify separators - - can keep additional columns from metadata by passing to `...` \ No newline at end of file diff --git a/vignettes/lighthouse-codebook.md b/vignettes/lighthouse-codebook.md deleted file mode 100644 index 7b92170..0000000 --- a/vignettes/lighthouse-codebook.md +++ /dev/null @@ -1,136 +0,0 @@ ---- -title: "Introduction to lighthouse.codebook" -output: rmarkdown::html_vignette -vignette: > - %\VignetteIndexEntry{Introduction to lighthouse.codebook} - %\VignetteEngine{knitr::rmarkdown} - %\VignetteEncoding{UTF-8} ---- - -```{r, include = FALSE} -knitr::opts_chunk$set( - eval = FALSE, - comment = "#" -) -``` -### Workflow overview - -Creating a codebook using the lighthouse.codebook package generally involves two steps. First, create a "codebook" object from a data frame (and, optionally, metadata) using `cb_create()` or a specialized variant (such as `cb_create_spss()` or `cb_create_redcap()`). Second, write the codebook to disk using `cb_write()`. - -In many cases, this can be done in a single pipeline. -```r -library(lighthouse.codebook) - -# using example dataset included with package -gain_q4 |> cb_create() |> cb_write("q4.xlsx") -``` - - -A codebook written to disk will include an overview tab as well as summary tabs for numeric, categorical, and text variables. The overview tab lists all variables with their labels and missing value information. By default, each variable is hyperlinked to its location on a Summary tab. -```{r eval = TRUE} -knitr::include_graphics("img/overview.png") -``` - -The numeric summary tab includes descriptive statistics for all numeric variables in the dataset: -[image] - -The categorical summary tab includes frequencies for all categorical variables in the dataset: -[image] - -The text summary tab includes the number of unique values, and frequencies for the most common values, for all text variables in the dataset. (The number of values for which frequencies are shown can be specified using the `n_text_vals` argument to `cb_write()`; by default, the 5 most common values are shown.) -[image] - -The codebook object can also be used to create other objects in R, such -as transformed data (see `cb_get_data()`) and summaries (see `cb_summarize_numeric()`, -`cb_summarize_categorical()`, `cb_summarize_text()`). -```r -cb <- cb_create(gain_q4) - -# data with SPSS-style variable and value labels -q4_haven <- cb_get_data(cb, format = "haven") - -# descriptives for numeric variables -q4_summary_num <- cb_summarize_numeric(cb) -``` - -### Creating codebooks - -1. In simplest case, use `cb_create()` with "basic" dataframe. - - \*\*\* make a simpler example dataframe for this - - simple eg code -2. Other options - probably show *very* briefly, then link to separate vignette for each: - - Use `cb_create()` with user-supplied metadata (link to vignette). - - Use `cb_create_redcap()` with data and metadata exported from REDCap (link to vignette). - - Use `cb_create_spss()` with SPSS data imported using haven (link to vignette). - -### Variable types -Data summaries are produced for "numeric," "categorical," and "text" variables. For a given variable `x`, -- `x` is treated as categorical if (1) it is a factor, ordered factor, or logical vector, _or_ (2) it has associated value labels other than missing value codes (specified in metadata or, for SPSS data, in a `"haven_labelled"` vector). -- `x` is treated as numeric if (1) it is numeric (i.e., `is.numeric(x)` is `TRUE`) _and_ (2) it has no associated value labels other than missing value codes. -- `x` is treated as text if (1) it is a character vector _and_ (2) it has no associated value labels other than missing value codes. - -Thus, you can change the way a variable is summarized by changing its class. For instance, to get complete frequencies for a numeric or character variable, convert it to factor; or to get only the top frequencies for a factor with many levels, convert it to character. - -Variables of other classes, such as dates, datetimes, and lists, are not currently included on summary tabs. (Summaries for dates and datetimes are planned for a future release). - -### Missing values - -User missing values (also known as nonresponse codes, reserve codes, or special values) can be specified in the call to `cb_create()` with the `.user_missing` argument. Missing codes are specified using a formula or list of formulas; formulas should have variable(s) on the left-hand side (as a [tidyselect expression](https://dplyr.tidyverse.org/reference/dplyr_tidy_select.html)) and user missing values on the right-hand side. -** ...adapt subsection on missing values from `cb_create()`. ** -If the left-hand side is empty, the specified values will be treated as user missing across all compatible variables in the dataset. -```r -cb <- cb_create(dat, .user_missing = ~ -99) -``` - -User missing values are dropped for computation of summary statistics for numeric variables. By default, user missing values are individually tabulated on ungrouped categorical and text summary tabs: -```r -missing_codes <- c( - "Not Asked" = -3, "Missing" = -4, "Confidential" = -6, "Refused" = -7, - "Don't Know" = -8, "Legitimate Skip" = -9 -) - -dat |> - cb_create(.user_missing = ~ missing_codes) |> - cb_write("cb.xlsx") -``` -[image] -...but can be collapsed with system missing values into a single "(Missing)" row by setting `detail_missing` to `FALSE`: -```r -dat |> - cb_create(.user_missing = ~ missing_codes) |> - cb_write("cb.xlsx", detail_missing = FALSE) -``` -However, user missing values are always collapsed (as though `detail_missing = FALSE`) on _grouped_ summary tabs. -``` -### Grouped summaries -Grouped summaries can be generated by specifying one or more grouping variables in the `group_by` argument to `cb_write()`. The workbook will contain both ungrouped summary tabs and tabs grouped by the specified variables. By default, grouping is done in columns, using decked heads when multiple grouping variables are specified. -```r -dat |> - cb_create() |> - cb_write(group_by = c(XRA, XOBS)) -``` -[image] -For numeric variables, grouping can instead be done in rows by passing some or all grouping variables to `group_rows_numeric`: -```r -dat |> - cb_create() |> - cb_write(group_by = c(XRA, XOBS), group_rows_numeric = XOBS) -``` -[image] - -### Handling long / repetitive variable labels - -- Adapt section on `.split_var_labels` from NEWS. - -... - - - - -## `cb_create()` vignette on -#### User-supplied metadata - - by default, `cb_create()` expects columns `"name"` with variable names, `"label"` with variable labels, and `"val_labels"` with value labels. - - however, only `"name"` is required. - - alternate names in `metadata` can be specified using `.name`, `.var_label`, and `.val_labels` arg. (or you can just change the names in `metadata`.) - - value labels should be a single string per variable; specify separators - - can keep additional columns from metadata by passing to `...` \ No newline at end of file From af75f6fea6c96d0795af894854f66ffd5604d3f9 Mon Sep 17 00:00:00 2001 From: ccsarapas Date: Mon, 9 Feb 2026 17:06:47 -0600 Subject: [PATCH 05/13] expand README --- README.Rmd | 81 ++++++++++++++++++++++++++++++++++++++---------------- README.md | 75 ++++++++++++++++++++++++++++++++++++-------------- 2 files changed, 111 insertions(+), 45 deletions(-) diff --git a/README.Rmd b/README.Rmd index 2146de7..944502f 100644 --- a/README.Rmd +++ b/README.Rmd @@ -17,40 +17,69 @@ knitr::opts_chunk$set( # lighthouse.codebook -The lighthouse.codebook package includes tools to summarize a dataset into a -formatted Excel workbook, including a data dictionary and summaries. It -incorporates external metadata (such as variable labels, value labels, and user -missing / non-response codes), with convenience functions for using metadata from -SPSS and REDCap datasets. Codebooks can be customized in a number of ways, -including options for grouped summaries. +The lighthouse.codebook package includes tools to summarize a dataset into a formatted +Excel workbook, including a data dictionary and summaries. It incorporates external +metadata (such as variable labels, value labels, and user missing / non-response codes), +with functions for using metadata from SPSS and REDCap datasets. Codebooks can be +customized in a number of ways, including options for grouped summaries. ## Installation You can install lighthouse.codebook by running: -``` r +```r # install.packages("remotes") remotes::install_github("ccsarapas/lighthouse.codebook") ``` -## Creating a codebook +## Creating codebooks Creating a codebook involves two general steps: -1) Create a a "codebook" object in R from a data frame (and, optionally, metadata), - using `cb_create()` or a specialized variant (such as `cb_create_spss()` or `cb_create_redcap()`). +1. Create a a “codebook” object in R from a data frame (and, + optionally, metadata), using `cb_create()` or a specialized variant + (such as `cb_create_spss()` or `cb_create_redcap()`). -2) Write the codebook to disk using `cb_write()`. +2. Write the codebook to disk using `cb_write()`. -Thus, in the simplest case, to create a codebook and write it to disk:. -```r -gain_q4 |> cb_create() |> cb_write("q4.xlsx") +``` r +library(lighthouse.codebook) + +# create and write a codebook without metadata +dat |> + cb_create() |> + cb_write("cb.xlsx") + +# with metadata +dat |> + cb_create(metadata = dat1_metadata) |> + cb_write("cb.xlsx") + +# from SPSS data +dat_spss <- haven::read_sav("dat_spss.sav", user_na = TRUE) + +dat_spss |> + cb_create_spss() |> + cb_write("cb_spss.xlsx") + +# from REDCap data +dat_rc <- REDCapR::redcap_read(redcap_uri = rc_uri, token = rc_token) +meta_rc <- REDCapR::redcap_metadata_read(redcap_uri = rc_uri, token = rc_token) + +dat_rc$data |> + cb_create_redcap(metadata = meta_rc$data) |> + cb_write("cb_rc.xlsx") ``` -`cb_create()` and `cb_write()` include a number of options for controlling how data -is interpreted, summarized, and presented. +## Customizing codebooks + +There are many options for controlling how data is interpreted, summarized, and +presented. See [**Introduction to lighthouse.codebook**](lighthouse-codebook.html) for +some of the most useful options, including grouped data summaries and specifying +user missing codes. Further options are detailed in the documentation for `cb_create()` +and `cb_write()`. ## Codebook contents -The codebook written to disk will include an Overview tab listing all variables -in the dataset; Summary tabs for numeric, categorical, and text variables; and, -if grouping variables are specified, Grouped Summary tabs for numeric and categorical + +The codebook written to disk will include an _overview_ tab listing all variables +in the dataset; _summary_ tabs for numeric, categorical, and text variables; and, +if grouping variables are specified, _grouped summary_ tabs for numeric and categorical variables. -The Overview tab includes one row for each variable in the dataset, with information +The _overview_ tab includes one row for each variable in the dataset, with information on variable types, labels, values, and missingness. By default, each variable is hyperlinked to its location on the relevant summary tab. @@ -73,27 +103,30 @@ hyperlinked to its location on the relevant summary tab. knitr::include_graphics("man/figures/README-overview.png") ``` -The numeric summary tab includes descriptive statistics for all numeric variables +The _numeric summary_ tab includes descriptive statistics for all numeric variables in the dataset: ```{r, numeric, echo = FALSE, eval = TRUE} knitr::include_graphics("man/figures/README-numeric.png") ``` -The categorical summary tab includes frequencies for all categorical variables, +The _categorical summary_ tab includes frequencies for all categorical variables, optionally with separate rows for user missing values: ```{r, categorical, echo = FALSE, eval = TRUE} knitr::include_graphics("man/figures/README-categorical.png") ``` -The text summary tab includes frequencies for the most common values for all text -variables in the dataset. +Finally, the _text summary_ tab includes frequencies for the most common values for all +text variables in the dataset. (The number of values shown can be adjusted using +the `n_text_vals` argument to `cb_write()`.) ```{r, text, echo = FALSE, eval = TRUE} knitr::include_graphics("man/figures/README-text.png") ``` +If `group_by` is specified in `cb_write()`, additional numeric and categorical summary +tabs will be included grouped by the specified variables. ## SPSS extension diff --git a/README.md b/README.md index b1e4e41..b12e223 100644 --- a/README.md +++ b/README.md @@ -6,10 +6,9 @@ The lighthouse.codebook package includes tools to summarize a dataset into a formatted Excel workbook, including a data dictionary and summaries. It incorporates external metadata (such as variable labels, -value labels, and user missing / non-response codes), with convenience -functions for using metadata from SPSS and REDCap datasets. Codebooks -can be customized in a number of ways, including options for grouped -summaries. +value labels, and user missing / non-response codes), with functions for +using metadata from SPSS and REDCap datasets. Codebooks can be +customized in a number of ways, including options for grouped summaries. ## Installation @@ -20,7 +19,7 @@ You can install lighthouse.codebook by running: remotes::install_github("ccsarapas/lighthouse.codebook") ``` -## Creating a codebook +## Creating codebooks @@ -34,14 +33,43 @@ Creating a codebook involves two general steps: 2. Write the codebook to disk using `cb_write()`. -Thus, in the simplest case, to create a codebook and write it to disk:. - ``` r -gain_q4 |> cb_create() |> cb_write("q4.xlsx") +library(lighthouse.codebook) + +# create and write a codebook without metadata +dat |> + cb_create() |> + cb_write("cb.xlsx") + +# with metadata +dat |> + cb_create(metadata = dat1_metadata) |> + cb_write("cb.xlsx") + +# from SPSS data +dat_spss <- haven::read_sav("dat_spss.sav", user_na = TRUE) + +dat_spss |> + cb_create_spss() |> + cb_write("cb_spss.xlsx") + +# from REDCap data +dat_rc <- REDCapR::redcap_read(redcap_uri = rc_uri, token = rc_token) +meta_rc <- REDCapR::redcap_metadata_read(redcap_uri = rc_uri, token = rc_token) + +dat_rc$data |> + cb_create_redcap(metadata = meta_rc$data) |> + cb_write("cb_rc.xlsx") ``` -`cb_create()` and `cb_write()` include a number of options for -controlling how data is interpreted, summarized, and presented. +## Customizing codebooks + +There are many options for controlling how data is interpreted, +summarized, and presented. See [**Introduction to +lighthouse.codebook**](lighthouse-codebook.html) for some of the most +useful options, including grouped data summaries and specifying user +missing codes. Further options are detailed in the documentation for +`cb_create()` and `cb_write()`. ## Codebook contents -The codebook written to disk will include an Overview tab listing all -variables in the dataset; Summary tabs for numeric, categorical, and -text variables; and, if grouping variables are specified, Grouped -Summary tabs for numeric and categorical variables. +The codebook written to disk will include an *overview* tab listing all +variables in the dataset; *summary* tabs for numeric, categorical, and +text variables; and, if grouping variables are specified, *grouped +summary* tabs for numeric and categorical variables. -The Overview tab includes one row for each variable in the dataset, with -information on variable types, labels, values, and missingness. By +The *overview* tab includes one row for each variable in the dataset, +with information on variable types, labels, values, and missingness. By default, each variable is hyperlinked to its location on the relevant summary tab. -The numeric summary tab includes descriptive statistics for all numeric -variables in the dataset: +The *numeric summary* tab includes descriptive statistics for all +numeric variables in the dataset: -The categorical summary tab includes frequencies for all categorical +The *categorical summary* tab includes frequencies for all categorical variables, optionally with separate rows for user missing values: -The text summary tab includes frequencies for the most common values for -all text variables in the dataset. +Finally, the *text summary* tab includes frequencies for the most common +values for all text variables in the dataset. (The number of values +shown can be adjusted using the `n_text_vals` argument to `cb_write()`.) +If `group_by` is specified in `cb_write()`, additional numeric and +categorical summary tabs will be included grouped by the specified +variables. + ## SPSS extension Functionality from this package is also available as an SPSS extension From 910929d5117ce7da9c8aaae8c806141c1f3d3724 Mon Sep 17 00:00:00 2001 From: ccsarapas Date: Thu, 12 Feb 2026 09:44:00 -0600 Subject: [PATCH 06/13] clean up README comments and unused imgs --- README.Rmd | 3 --- README.md | 4 ---- man/figures/README-numeric 1.png | Bin 279633 -> 0 bytes man/figures/README-overview 1.png | Bin 69663 -> 0 bytes man/figures/README-overview 2.png | Bin 68427 -> 0 bytes vignettes/img/overview.png | Bin 69663 -> 0 bytes 6 files changed, 7 deletions(-) delete mode 100644 man/figures/README-numeric 1.png delete mode 100644 man/figures/README-overview 1.png delete mode 100644 man/figures/README-overview 2.png delete mode 100644 vignettes/img/overview.png diff --git a/README.Rmd b/README.Rmd index 944502f..9fb6fa8 100644 --- a/README.Rmd +++ b/README.Rmd @@ -33,9 +33,6 @@ remotes::install_github("ccsarapas/lighthouse.codebook") ``` ## Creating codebooks - - - Creating a codebook involves two general steps: 1. Create a a “codebook” object in R from a data frame (and, diff --git a/README.md b/README.md index b12e223..ffa5dec 100644 --- a/README.md +++ b/README.md @@ -21,10 +21,6 @@ remotes::install_github("ccsarapas/lighthouse.codebook") ## Creating codebooks - - - - Creating a codebook involves two general steps: 1. 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It incorporates external +Excel workbook, including a data dictionary and vrariable summaries. It incorporates external metadata (such as variable labels, value labels, and user missing / non-response codes), with functions for using metadata from SPSS and REDCap datasets. Codebooks can be customized in a number of ways, including options for grouped summaries. @@ -35,13 +35,13 @@ remotes::install_github("ccsarapas/lighthouse.codebook") ## Creating codebooks Creating a codebook involves two general steps: -1. Create a a “codebook” object in R from a data frame (and, - optionally, metadata), using `cb_create()` or a specialized variant - (such as `cb_create_spss()` or `cb_create_redcap()`). +1. Create a "codebook" object in R from a data frame (and, + optionally, metadata) using `cb_create()` or a specialized variant + (such as `cb_create_spss()` or `cb_create_redcap()`). -2. Write the codebook to disk using `cb_write()`. +2. Write the codebook to disk using `cb_write()`. -``` r +```r library(lighthouse.codebook) # create and write a codebook without metadata @@ -73,10 +73,10 @@ dat_rc$data |> ## Customizing codebooks There are many options for controlling how data is interpreted, summarized, and -presented. See [**Introduction to lighthouse.codebook**](lighthouse-codebook.html) for -some of the most useful options, including grouped data summaries and specifying -user missing codes. Further options are detailed in the documentation for `cb_create()` -and `cb_write()`. +presented. See `vignette("lighthouse-codebook")` for some of the most useful options, +including grouped data summaries and specifying user missing codes. Further options +are detailed in the help pages for `cb_create()` and `cb_write()`. + - - + Creating a codebook involves two general steps: -1. Create a a “codebook” object in R from a data frame (and, - optionally, metadata), using `cb_create()` or a specialized variant - (such as `cb_create_spss()` or `cb_create_redcap()`). +1. Create a “codebook” object in R from a data frame (and, optionally, metadata), + using `cb_create()` or a specialized variant (such as `cb_create_spss()` or `cb_create_redcap()`). -2. Write the codebook to disk using `cb_write()`. +2. Write the codebook to disk using `cb_write()`. -``` r +```r library(lighthouse.codebook) # create and write a codebook without metadata @@ -55,9 +52,9 @@ dat_rc$data |> cb_create_redcap(metadata = meta_rc$data) |> cb_write("cb_rc.xlsx") ``` -The codebook written to disk will include an Overview tab listing all variables -in the dataset; Summary tabs for numeric, categorical, and text variables; and, -if grouping variables are specified, Grouped Summary tabs for numeric and categorical +The codebook written to disk will include an overview tab listing all variables +in the dataset; summary tabs for numeric, categorical, and text variables; and, +if grouping variables are specified, grouped summary tabs for numeric and categorical variables. ## Customizing codebooks @@ -79,7 +76,7 @@ cb_create(data, metadata) |> ``` By default, values for each subgroup are shown in separate columns, with decked -heads if more than one grouping variables is specified. However, some or all grouping +heads if more than one grouping variable is specified. However, some or all grouping variables can instead be shown in rows using the `group_rows` argument. ```r # show `treatment_group` in columns and `timepoint` in rows @@ -102,7 +99,7 @@ cb_create(data, metadata) |> group_rows_numeric = timepoint ) -# for numeric summary, show all grouping vars in rows for numeric summary; +# for numeric summary, show all grouping variables in rows; # for categorical summary, show `treatment_group` in rows cb_create(data, metadata) |> cb_write( @@ -183,9 +180,11 @@ on _grouped_ summary tabs. ### Splitting long variable labels -Sets of related variables sometimes share a common prefix. Using the `.split_var_labels` -argument to `cb_create()`, this prefix can be extracted into a separate column, -making it easier to see at a glance what is unique about each variable. +Variable labels for sets of related variables sometimes share a common prefix. Using +the `.split_var_labels` argument to `cb_create()`, this prefix can be extracted +into a separate column, making it easier to see at a glance what is unique about +each variable. + For example, given a set of variable labels that all begin with `"What colors do you like? Select all that apply: "`: @@ -237,13 +236,13 @@ cb_create( ## Variable typing Data summaries are produced for "numeric," "categorical," and "text" variables. For a given variable `x`, -- `x` is treated as categorical if (1) it is a factor, ordered factor, or logical vector, _or_ (2) it has associated value labels other than missing value codes (specified in metadata or, for SPSS data, in a `"haven_labelled"` vector). -- `x` is treated as numeric if (1) it is numeric (i.e., `is.numeric(x)` is `TRUE`) _and_ (2) it has no associated value labels other than missing value codes. -- `x` is treated as text if (1) it is a character vector _and_ (2) it has no associated value labels other than missing value codes. +* `x` is treated as categorical if (1) it is a factor, ordered factor, or logical vector, _or_ (2) it has associated value labels other than missing value codes (specified in metadata or, for SPSS data, in a `"haven_labelled"` vector). +* `x` is treated as numeric if (1) it is numeric (i.e., `is.numeric(x)` is `TRUE`) _and_ (2) it has no associated value labels other than missing value codes. +* `x` is treated as text if (1) it is a character vector _and_ (2) it has no associated value labels other than missing value codes. -Thus, you can change the way a variable is summarized by changing its class. For +Thus, you can change how a variable is summarized by changing its class. For instance, to get complete frequencies for a numeric or character variable, convert -it to factor; to get only the top frequencies for a factor with many levels, convert +it to a factor; to get only the top frequencies for a factor with many levels, convert it to character. Variables of other classes, such as dates, datetimes, and lists, are not currently From 0dffcbe3a10e5bfc00122612aff03eac019a3166 Mon Sep 17 00:00:00 2001 From: ccsarapas Date: Thu, 12 Feb 2026 11:34:19 -0600 Subject: [PATCH 08/13] remove `format = "values"` from `cb_get_data()` (closes #26) --- NEWS.md | 4 +++ R/cb_create.r | 76 ++++++++++++++++++++++++++-------------------- man/cb_create.Rd | 12 +++++--- man/cb_get_data.Rd | 17 ++++++----- 4 files changed, 63 insertions(+), 46 deletions(-) diff --git a/NEWS.md b/NEWS.md index 99d3756..3573882 100644 --- a/NEWS.md +++ b/NEWS.md @@ -14,6 +14,10 @@ * Expanded the README. +## Lifecycle changes + +* The `format = "values"` option in `cb_get_data()` has been removed (see #26). + # lighthouse.codebook 0.2.3 * `cb_create_spss()` now accepts `.rmv_html` and `.rmv_line_breaks` arguments, consistent diff --git a/R/cb_create.r b/R/cb_create.r index 6e446d9..0c7e223 100644 --- a/R/cb_create.r +++ b/R/cb_create.r @@ -23,17 +23,17 @@ #' right-hand side. If left-hand side is omitted, defaults to `tidyselect::everything()`. #' See "Specifying user missing values" below for examples. #' @param .split_var_labels A [`tidyselect`][dplyr_tidy_select] expression or list of tidyselect -#' expressions, indicating (sets of) variable labels with a common stem that should +#' expressions, indicating (sets of) variable labels with a common stem that should #' be extracted into a separate column. -#' @param .include_r_classes Include a column listing class(es) of each variable? +#' @param .include_r_classes Include a column listing class(es) of each variable? #' (e.g., `"factor"`, `"POSIXct, POSIXt"`.) #' @param .include_types Include a column listing simplified type for each variable? #' (e.g,. `"categorical"`, `"date-time"`.) #' @param .val_labs_sep1,.val_labs_sep2 Regex patterns separating value labels -#' in `metadata`. `.val_labs_sep1` separates values from labels, and `.val_labs_sep2` +#' in `metadata`. `.val_labs_sep1` separates values from labels, and `.val_labs_sep2` #' separates value/label pairs. e.g., if value labels are in format `"1, First label|2, Second label"`, #' set `.val_labs_sep1` to `","` and `.val_labs_sep2` to `"\\|"`. -#' @param .rmv_html Should HTML tags be removed from metadata (e.g., from variable +#' @param .rmv_html Should HTML tags be removed from metadata (e.g., from variable #' and value labels)? #' @param .rmv_line_breaks Should line breaks be removed from metadata (e.g., from #' variable and value labels)? If `TRUE`, line breaks will be replaced with `" / "`. @@ -42,9 +42,9 @@ #' specified for at least one variable. #' @param .user_missing_conflict If different labels for a value are provided in #' metadata and user missings, which should be used? -#' @param .user_missing_incompatible How to handle variables specified in `.user_missing` +#' @param .user_missing_incompatible How to handle variables specified in `.user_missing` #' that aren't compatible with user missing values (e.g., logical, Date, or POSIXt)? -#' +#' #' @return #' An `"li_codebook"` object, consisting of (1) a tibble summarizing the passed #' dataset and (2) attributes containing the passed dataset (in several formats) @@ -53,7 +53,7 @@ #' - `name`: variable name #' - `type`: optional column containing simplified variable type #' - `class`: optional column containing class(es) of each variable -#' - `label_stem`: optional column containing variable label stems, if any variables +#' - `label_stem`: optional column containing variable label stems, if any variables #' are specified in `.split_var_labels` #' - `label`: variable label #' - `values`: values, with labels if applicable @@ -72,7 +72,7 @@ #' \preformatted{ #' cb <- cb_create(data, metadata, .user_missing = var1 ~ 99) #' } -#' The same user missings can be applied to multiple variables using [tidyselect][dplyr_tidy_select] +#' The same user missings can be applied to multiple variables using [tidyselect][dplyr_tidy_select] #' expressions. #' \preformatted{ #' # for variables `var1` through `var5` @@ -80,7 +80,7 @@ #' #' # for all numeric variables, plus `var6` and `var7` #' .user_missing = c(where(is.numeric), var6, var7) ~ c(-9, -8, -7) -#' +#' #' # omitted left-hand side defaults to `tidyselect::everything()` #' .user_missing = ~ -99 #' } @@ -98,14 +98,14 @@ #' } #' If labels set in `.user_missing` conflict with those in `metadata`, `.user_missing_conflict` #' controls which labels are used. -#' -#' User missings may be set for numeric, character, factor/ordered factor, and haven_labelled/haven_labelled_spss -#' vectors. For factors, user missings are set based on factor labels (not the underlying -#' integer codes). For `"haven_labelled"` vectors, user missings are set based on -#' values (not value labels). By default, variables with incompatible classes (e.g., -#' logical, Date, POSIXt) will be ignored if specified in `.user_missing`. This +#' +#' User missings may be set for numeric, character, factor/ordered factor, and haven_labelled/haven_labelled_spss +#' vectors. For factors, user missings are set based on factor labels (not the underlying +#' integer codes). For `"haven_labelled"` vectors, user missings are set based on +#' values (not value labels). By default, variables with incompatible classes (e.g., +#' logical, Date, POSIXt) will be ignored if specified in `.user_missing`. This #' behavior can be changed using the `.user_missing_incompatible` argument. -#' +#' #' @examples #' diamonds2 <- ggplot2::diamonds |> #' transform( @@ -117,18 +117,18 @@ #' right = FALSE #' )) #' ) -#' +#' #' # basic codebook #' cb_create(diamonds2) -#' +#' #' # convert variables to factor to treat as categorical #' diamonds2 |> #' transform( #' carat_group = factor(carat_group), #' price_group = factor(price_group) -#' ) |> +#' ) |> #' cb_create() -#' +#' #' # provide metadata for variable and value labels #' diamonds_meta <- data.frame( #' name = names(diamonds2), @@ -153,7 +153,7 @@ #' "1 = <$500; 2 = $500-$999; 3 = $1,000-$1,999; 4 = $2,000-$4,999; 5 = $5,000-$9,999; 6 = $10,000+" #' ) #' ) -#' +#' #' cb_create( #' diamonds2, diamonds_meta, #' .val_labs_sep1 = " = ", .val_labs_sep2 = "; " @@ -208,27 +208,37 @@ cb_create <- function(data, #' #' @param cb An object of class `"li_codebook"` as produced by [`cb_create()`] or #' a variant. -#' @param format Format of the returned data; see below for details. +#' @param format Format of the returned data, either `"factors"` or `"haven"`; +#' see below for details. #' #' @return #' A tibble with variables formatted based on the `format` argument. -#' - For `format = "values"`, all variables retain the same values as the original -#' dataset, including values for user missings. The data may reflect transformations -#' made by variants of [`cb_create()`] -- e.g., for [`cb_create_redcap()`], integer coercion -#' and propagation of user missings across checkbox variables. -#' - For `"haven"`, value labels and user missings are encoded using class -#' [`"haven_labelled"`][haven::labelled]` #' - For `"factors"`, all variables with value labels are converted to factors, #' and all user missings are converted to `NA`. +#' - For `"haven"`, variable labels, value labels, and user missings are encoded +#' using class [`"haven_labelled_spss"`][haven::labelled]`. +#' +#' Both formats may also reflect transformations made by variants of [`cb_create()`]. +#' In particular, for codebooks created using [`cb_create_redcap()`], integer coercion +#' and propagation of user missings across checkbox variables. #' #' @export -cb_get_data <- function(cb, format = c("factors", "haven", "values")) { +cb_get_data <- function(cb, format = c("factors", "haven")) { check_codebook(cb) - switch(match.arg(format), - factors = attr(cb, "data_zapped"), - haven = attr(cb, "data_labelled"), - values = attr(cb, "data") + tryCatch( + format <- match.arg(format), + error = \(e) { + if (format == "values") { + cli::cli_abort( + '`format = "values"` is no longer supported.', + call = parent.frame(4) + ) + } + stop(e) + } ) + if (format == "factors") attr(cb, "data_zapped") + else attr(cb, "data_labelled") } cb_init <- function(data, diff --git a/man/cb_create.Rd b/man/cb_create.Rd index c8872c6..3ddc27f 100644 --- a/man/cb_create.Rd +++ b/man/cb_create.Rd @@ -144,10 +144,12 @@ User missing values may optionally be named to set value labels: If labels set in \code{.user_missing} conflict with those in \code{metadata}, \code{.user_missing_conflict} controls which labels are used. -User missing values are not compatible with logical, date, or datetime (POSIXt) -variables. By default, these variables will be ignored if specified in \code{.user_missing}. -(i.e., user missing values will be applied only to compatible variables.) This behavior -can be changed using the \code{.user_missing_incompatible} argument. +User missings may be set for numeric, character, factor/ordered factor, and haven_labelled/haven_labelled_spss +vectors. For factors, user missings are set based on factor labels (not the underlying +integer codes). For \code{"haven_labelled"} vectors, user missings are set based on +values (not value labels). By default, variables with incompatible classes (e.g., +logical, Date, POSIXt) will be ignored if specified in \code{.user_missing}. This +behavior can be changed using the \code{.user_missing_incompatible} argument. } \examples{ @@ -170,7 +172,7 @@ diamonds2 |> transform( carat_group = factor(carat_group), price_group = factor(price_group) - ) |> + ) |> cb_create() # provide metadata for variable and value labels diff --git a/man/cb_get_data.Rd b/man/cb_get_data.Rd index 6cab0a9..d876368 100644 --- a/man/cb_get_data.Rd +++ b/man/cb_get_data.Rd @@ -4,26 +4,27 @@ \alias{cb_get_data} \title{Extract data from a codebook object} \usage{ -cb_get_data(cb, format = c("factors", "haven", "values")) +cb_get_data(cb, format = c("factors", "haven")) } \arguments{ \item{cb}{An object of class \code{"li_codebook"} as produced by \code{\link[=cb_create]{cb_create()}} or a variant.} -\item{format}{Format of the returned data; see below for details.} +\item{format}{Format of the returned data, either \code{"factors"} or \code{"haven"}; +see below for details.} } \value{ A tibble with variables formatted based on the \code{format} argument. \itemize{ -\item For \code{format = "values"}, all variables retain the same values as the original -dataset, including values for user missings. The data may reflect transformations -made by variants of \code{\link[=cb_create]{cb_create()}} -- e.g., for \code{\link[=cb_create_redcap]{cb_create_redcap()}}, integer coercion -and propagation of user missings across checkbox variables. -\item For \code{"haven"}, value labels and user missings are encoded using class -\code{\link[haven:labelled]{"haven_labelled"}}` \item For \code{"factors"}, all variables with value labels are converted to factors, and all user missings are converted to \code{NA}. +\item For \code{"haven"}, variable labels, value labels, and user missings are encoded +using class \code{\link[haven:labelled]{"haven_labelled_spss"}}`. } + +Both formats may also reflect transformations made by variants of \code{\link[=cb_create]{cb_create()}}. +In particular, for codebooks created using \code{\link[=cb_create_redcap]{cb_create_redcap()}}, integer coercion +and propagation of user missings across checkbox variables. } \description{ Codebook objects created by \code{\link[=cb_create]{cb_create()}} and friends contain several transformed From 893b70dd6be949d683ec9441cf119168df56e11d Mon Sep 17 00:00:00 2001 From: ccsarapas Date: Fri, 13 Feb 2026 11:13:35 -0600 Subject: [PATCH 09/13] implement `.options` arg for `cb_create()` and variants --- NAMESPACE | 2 + NEWS.md | 48 ++++++++- R/cb_create.r | 139 +++++++++++++++---------- R/cb_create_redcap.r | 217 +++++++++++++++++++++++---------------- R/cb_create_spss.r | 60 +++++------ R/cb_write.r | 14 +-- R/utils.r | 23 ++++- man/cb_create.Rd | 68 ++++-------- man/cb_create_options.Rd | 89 ++++++++++++++++ man/cb_create_redcap.Rd | 114 +++++--------------- man/cb_create_spss.Rd | 52 ++-------- man/cb_write.Rd | 18 ++-- 12 files changed, 465 insertions(+), 379 deletions(-) create mode 100644 man/cb_create_options.Rd diff --git a/NAMESPACE b/NAMESPACE index 7a19b66..af707b1 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -4,7 +4,9 @@ S3method(nan_to_na,data.frame) S3method(nan_to_na,default) S3method(nan_to_na,list) export(cb_create) +export(cb_create_options) export(cb_create_redcap) +export(cb_create_redcap_options) export(cb_create_spss) export(cb_get_data) export(cb_summarize_categorical) diff --git a/NEWS.md b/NEWS.md index 3573882..5e0ed4d 100644 --- a/NEWS.md +++ b/NEWS.md @@ -1,6 +1,6 @@ # lighthouse.codebook 0.3.0 -## New feature +## Added * Added options to `cb_write()` to show grouping variables for categorical summaries in rows (which was previously only possible for numeric summaries.) `cb_write()` @@ -8,13 +8,53 @@ `group_rows` controls both numeric and categorical summaries, while `group_rows_numeric` and `group_rows_categorical` control numeric and categorical summaries, respectively. -## New documentation - * Added an introductory vignette (see `vignette("lighthouse-codebook")`). * Expanded the README. + +## Changed + +* `cb_create()`, `cb_create_spss()`, and `cb_create_redcap()` now use a single `.options` + argument for less commonly used settings. Arguments for those settings have been + moved into an options object created with `cb_create_options()` or `cb_create_redcap_options()`. + ```r + # previously + cb <- cb_create( + dat, metadata = metadata, + .rmv_html = FALSE, .include_r_classes = TRUE + ) + + # now + cb <- cb_create( + dat, metadata = metadata, + .options = cb_create_options(rmv_html = FALSE, include_r_classes = TRUE) + ) + + ### `cb_create_spss()` also uses `cb_create_options()` + # previously + cb_spss <- cb_create_spss(dat_spss, .rmv_line_breaks = FALSE) + + # now + cb_spss <- cb_create_spss( + dat_spss, + .options = cb_create_options(rmv_line_breaks = FALSE) + ) + + ### Note that `cb_create_redcap()` has its own options constructor + # previously + cb <- cb_create_redcap( + dat_rc, metadata = metadata_rc, + .form = NULL, .rmv_html = FALSE + ) -## Lifecycle changes + # now + cb <- cb_create_redcap( + dat_rc, metadata = metadata_rc, + .options = cb_create_redcap_options(form = NULL, rmv_html = FALSE) + ) + ``` + +## Removed * The `format = "values"` option in `cb_get_data()` has been removed (see #26). diff --git a/R/cb_create.r b/R/cb_create.r index 0c7e223..424f6f7 100644 --- a/R/cb_create.r +++ b/R/cb_create.r @@ -8,8 +8,8 @@ #' [`cb_summarize_text()`]). #' #' @param data A data frame. -#' @param metadata A data frame containing metadata, such as variable labels and value -#' labels. +#' @param metadata A data frame containing metadata, such as variable labels and +#' value labels. #' @param ... Additional columns from `metadata` to preserve in the final codebook. #' New names can be assigned by passing named arguments. Columns for variable #' name, form, variable label, and value labels are included by default. @@ -25,45 +25,31 @@ #' @param .split_var_labels A [`tidyselect`][dplyr_tidy_select] expression or list of tidyselect #' expressions, indicating (sets of) variable labels with a common stem that should #' be extracted into a separate column. -#' @param .include_r_classes Include a column listing class(es) of each variable? -#' (e.g., `"factor"`, `"POSIXct, POSIXt"`.) -#' @param .include_types Include a column listing simplified type for each variable? -#' (e.g,. `"categorical"`, `"date-time"`.) #' @param .val_labs_sep1,.val_labs_sep2 Regex patterns separating value labels #' in `metadata`. `.val_labs_sep1` separates values from labels, and `.val_labs_sep2` -#' separates value/label pairs. e.g., if value labels are in format `"1, First label|2, Second label"`, -#' set `.val_labs_sep1` to `","` and `.val_labs_sep2` to `"\\|"`. -#' @param .rmv_html Should HTML tags be removed from metadata (e.g., from variable -#' and value labels)? -#' @param .rmv_line_breaks Should line breaks be removed from metadata (e.g., from -#' variable and value labels)? If `TRUE`, line breaks will be replaced with `" / "`. -#' @param .user_missing_col Include value labels for user missing values in a separate -#' column? The default, `"if_any"`, adds the column only if user missings are -#' specified for at least one variable. -#' @param .user_missing_conflict If different labels for a value are provided in -#' metadata and user missings, which should be used? -#' @param .user_missing_incompatible How to handle variables specified in `.user_missing` -#' that aren't compatible with user missing values (e.g., logical, Date, or POSIXt)? +#' separates value/label pairs from one another. e.g., if value labels are in +#' the format `"1, First label|2, Second label"`, set `.val_labs_sep1` to `","` +#' and `.val_labs_sep2` to `"\\|"`. +#' @param .options Additional options to use for codebook creation. Must be the result +#' from a call to `cb_create_options()`. See that function's help page for available +#' options. #' #' @return -#' An `"li_codebook"` object, consisting of (1) a tibble summarizing the passed -#' dataset and (2) attributes containing the passed dataset (in several formats) -#' and additional metadata. Specifically: -#' - A tibble with columns: -#' - `name`: variable name -#' - `type`: optional column containing simplified variable type -#' - `class`: optional column containing class(es) of each variable -#' - `label_stem`: optional column containing variable label stems, if any variables -#' are specified in `.split_var_labels` -#' - `label`: variable label -#' - `values`: values, with labels if applicable -#' - `user_missing`: optional column, depending on value of `.user_missing_col`, -#' showing user missing values, with labels if applicable -#' - `missing`: proportion missing -#' - additional columns if specified in `...` -#' - Attributes: -#' - Transformed versions of the passed dataset. See [`cb_get_data()`] -#' - Lookup tables and other metadata used internally. +#' An `"li_codebook"` object, consisting of a tibble summarizing the passed +#' dataset and attributes containing additional metadata. The tibble includes columns: +#' - `name`: variable name +#' - `type`: column containing simplified variable type +#' - `class`: optional column containing class(es) of each variable +#' - `label_stem`: optional column containing variable label stems, if any variables +#' are specified in `.split_var_labels` +#' - `label`: variable label +#' - `values`: values, with labels if applicable +#' - `user_missing`: optional column showing user missing values, with labels +#' if applicable. By default, this column is included only if user missings +#' are specified for at least one variable. This behavior can be changed using +#' the `user_missing_col` argument to `cb_create_options()`. +#' - `missing`: proportion missing +#' - additional columns if specified in `...` #' #' @section Specifying user missing values: #' User missing values are defined by passing a formula or list of formulas to the @@ -96,15 +82,15 @@ #' \preformatted{ #' .user_missing = ~ c(Declined = -98, "Not applicable" = -99) #' } -#' If labels set in `.user_missing` conflict with those in `metadata`, `.user_missing_conflict` -#' controls which labels are used. +#' If labels set in `.user_missing` conflict with those in `metadata`, the `user_missing_conflict` +#' argument to `cb_create_options()` controls which labels are used. #' #' User missings may be set for numeric, character, factor/ordered factor, and haven_labelled/haven_labelled_spss #' vectors. For factors, user missings are set based on factor labels (not the underlying #' integer codes). For `"haven_labelled"` vectors, user missings are set based on #' values (not value labels). By default, variables with incompatible classes (e.g., #' logical, Date, POSIXt) will be ignored if specified in `.user_missing`. This -#' behavior can be changed using the `.user_missing_incompatible` argument. +#' behavior can be changed using the `.user_missing_incompatible` argument to `cb_create_options()`. #' #' @examples #' diamonds2 <- ggplot2::diamonds |> @@ -167,40 +153,80 @@ cb_create <- function(data, .val_labels = val_labels, .user_missing = NULL, .split_var_labels = NULL, - .include_types = !.include_r_classes, - .include_r_classes = FALSE, .val_labs_sep1 = NULL, .val_labs_sep2 = NULL, - .rmv_html = TRUE, - .rmv_line_breaks = TRUE, - .user_missing_col = c("if_any", "yes", "no"), - .user_missing_conflict = c("metadata", "missing_label"), - .user_missing_incompatible = c("ignore", "warn", "error") - ) { + .options = cb_create_options()) { + check_options(.options) data |> cb_init( metadata, meta_var_name = {{ .name }}, meta_var_label = {{ .var_label }}, meta_val_labels = {{ .val_labels }}, ... ) |> - cb_clean_fields(rmv_html = .rmv_html, rmv_line_breaks = .rmv_line_breaks) |> + cb_clean_fields( + rmv_html = .options$rmv_html, + rmv_line_breaks = .options$rmv_line_breaks + ) |> cb_user_missings( user_missing = .user_missing, - incompatible = .user_missing_incompatible + incompatible = .options$user_missing_incompatible ) |> cb_add_lookups(sep1 = .val_labs_sep1, sep2 = .val_labs_sep2) |> - cb_label_data(conflict = .user_missing_conflict) |> + cb_label_data(conflict = .options$user_missing_conflict) |> cb_zap_data() |> cb_add_dims() |> - cb_add_val_labels_col(user_missing_col = .user_missing_col) |> + cb_add_val_labels_col(user_missing_col = .options$user_missing_col) |> cb_add_type_col( - include_r_classes = .include_r_classes, - include_types = .include_types + include_r_classes = .options$include_r_classes, + include_types = .options$include_types ) |> cb_add_missing_col() |> cb_split_labels_col(split_var_labels = rlang::enexpr(.split_var_labels)) } +#' Additional options for codebook creation +#' +#' @description +#' Additional options for use by `cb_create()`. +#' +#' @inheritParams rlang::args_dots_empty +#' @param include_types Include a column listing simplified type for each variable? +#' (e.g,. `"categorical"`, `"date-time"`.) +#' @param include_r_classes Include a column listing class(es) of each variable? +#' (e.g., `"factor"`, `"POSIXct, POSIXt"`.) +#' @param rmv_html Should HTML tags be removed from metadata (e.g., from variable +#' and value labels)? +#' @param rmv_line_breaks Should line breaks be removed from metadata (e.g., from +#' variable and value labels)? If `TRUE`, line breaks will be replaced with `" / "`. +#' @param user_missing_col Include value labels for user missing values in a separate +#' column? The default, `"if_any"`, adds the column only if user missings are +#' specified for at least one variable. +#' @param user_missing_conflict If labels passed to `.user_missing` conflict with +#' value labels in metadata, which should be used? +#' @param user_missing_incompatible How to handle variables specified in `.user_missing` +#' that aren't compatible with user missing values (e.g., logical, Date, or POSIXt)? +#' +#' @export +cb_create_options <- function( + ..., + include_types = TRUE, + include_r_classes = FALSE, + rmv_html = TRUE, + rmv_line_breaks = TRUE, + user_missing_col = c("if_any", "yes", "no"), + user_missing_conflict = c("metadata", "missing_label"), + user_missing_incompatible = c("ignore", "warn", "error")) { + rlang::check_dots_empty() + out <- list( + include_types = include_types, include_r_classes = include_r_classes, + rmv_html = rmv_html, rmv_line_breaks = rmv_line_breaks, + user_missing_col = user_missing_col, + user_missing_conflict = user_missing_conflict, + user_missing_incompatible = user_missing_incompatible + ) + structure(out, class = "cb_create_options") +} + #' Extract data from a codebook object #' #' Codebook objects created by [`cb_create()`] and friends contain several transformed @@ -266,8 +292,8 @@ cb_init <- function(data, out <- out |> dplyr::mutate(values = NA_character_) } + out <- structure(out, class = c("li_codebook", class(out))) out <- set_attrs(out, data = data) - class(out) <- c("li_codebook", class(out)) out } @@ -305,7 +331,7 @@ cb_user_missings_by_var <- function(cb, ) if (length(bad_vars) > 4) bad_vars <- c(head(bad_vars, 3), "...") bad_vars <- paste(bad_vars, collapse = ", ") - msg <- "{n_bad} variable{?s} specified in {.arg .user_missing} are not compatible with user missing values" + msg <- "{n_bad} variable{?s} specified in {.arg .user_missing} {?is/are} not compatible with user missing values" if (incompatible == "error") cli::cli_abort(c("!" = msg, "*" = bad_vars)) cli::cli_warn(c("!" = paste0(msg, " and will be ignored"), "*" = bad_vars)) } @@ -468,7 +494,6 @@ reconcile_missing_labels <- function(val_labs, } cb_label_data <- function(cb, conflict = c("metadata", "missing_label")) { - conflict <- match.arg(conflict) data <- attr(cb, "data") vals_by_label <- attr(cb, "vals_by_label") factors <- attr(cb, "factors") diff --git a/R/cb_create_redcap.r b/R/cb_create_redcap.r index a2d7c1c..2e7cb85 100644 --- a/R/cb_create_redcap.r +++ b/R/cb_create_redcap.r @@ -1,5 +1,5 @@ #' Generate a codebook object from REDCap data -#' +#' #' @description #' `cb_create_redcap()` builds an object of class `"li_codebook"` from a dataset and #' corresponding codebook exported from REDCap. The resulting object can be used @@ -14,156 +14,199 @@ #' - Unpacking, labelling, and optional missing propagation for checkbox data #' - Optional coercion for character variables marked as "integer" in `metedata$text_validation_type_or_show_slider_number` #' +#' All of these behaviors can be controlled using the `.options` argument. +#' #' @inheritParams cb_create #' @param data A data frame exported or retrieved from REDCap. #' @param metadata A data frame containing the REDCap codebook associated with `data`. #' @param ... Additional columns from `metadata` to preserve in the final codebook. #' New names can be assigned by passing named arguments. Columns for variable #' name, form, variable label, and value labels are included by default. -#' @param .name,.var_label,.val_labels Columns in `metadata` containing variable -#' name, variable label, and value labels, respectively. -#' @param .form Column in `metadata` containing form names. (Set to `NULL` to omit.) #' @param .user_missing A formula or list of formulas specifying user missing values. #' Formulas should specify variables on the left-hand side (as variable names #' or [tidyselect][dplyr_tidy_select] expressions), and missing values on the #' right-hand side. If left-hand side is omitted, defaults to `tidyselect::everything()`. #' See "Specifying user missing values" in [`cb_create()`] documentation for examples. -#' @param .coerce_integers Should variables listed as "integer" in `metedata$text_validation_type_or_show_slider_number` -#' be coerced to integer? -#' @param .checkbox_resp_values Should checkbox values use labels in `metadata` -#' (`TRUE`) or "Yes" / "No" (`FALSE`)? See "Checkbox data handling" below. -#' @param .propagate_checkbox_missings Should user missing values in a checkbox -#' group be propagated across all variables in the group? See "Checkbox data handling" -#' below. +#' @param .options Additional options to use for codebook creation. Must be the result +#' from a call to `cb_create_redcap_options()` or `cb_create_options()`. See `?cb_create_redcap_options` +#' for available options. #' #' @return -#' An `"li_codebook"` object, consisting of (1) a tibble summarizing the passed -#' dataset and (2) attributes containing the passed dataset (in several formats) -#' and additional metadata. Specifically: -#' - A tibble with columns: -#' - `name`: variable name -#' - `form`: form name -#' - `type`: optional column containing simplified variable type -#' - `class`: optional column containing class(es) of each variable -#' - `label_stem`: optional column containing variable label stems, if any variables -#' are specified in `.split_var_labels` -#' - `label`: variable label -#' - `values`: values, with labels if applicable -#' - `user_missing`: optional column, depending on value of `.user_missing_col`, -#' showing user missing values, with labels if applicable -#' - `missing`: proportion missing -#' - additional columns if specified in `...` -#' - Attributes: -#' - Transformed versions of the passed dataset. See [`cb_get_data()`]. -#' - Lookup tables and other metadata used internally. +#' An `"li_codebook"` object, consisting of a tibble summarizing the passed +#' dataset and attributes containing additional metadata. The tibble includes columns: +#' - `name`: variable name +#' - `form`: form name +#' - `type`: column containing simplified variable type +#' - `class`: optional column containing class(es) of each variable +#' - `label_stem`: optional column containing variable label stems, if any variables +#' are specified in `.split_var_labels` +#' - `label`: variable label +#' - `values`: values, with labels if applicable +#' - `user_missing`: optional column showing user missing values, with labels +#' if applicable. By default, this column is included only if user missings +#' are specified for at least one variable. This behavior can be changed using +#' the `user_missing_col` argument to `cb_create_options()`. +#' - `missing`: proportion missing +#' - additional columns if specified in `...` #' #' @section Checkbox data handling: #' ## Value labels #' Data from REDCap checkboxes yields one variable in the dataset for each response -#' option. These will be labelled generically with `"Yes"` or `"No"`, unless `.checkbox_resp_values` -#' is `TRUE`, in which case response-specific labels from `metadata` will be used. -#' For example, if a checkbox group has options "In the past year," "More than a +#' option. By default, these will be labelled generically with `"Yes"` or `"No"`. +#' For example, consider a checkbox group with options "In the past year," "More than a #' year ago," and "Never," corresponding to variables `chk_var1___0`, `chk_var1___1`, -#' and `chk_var1___2`: if `.checkbox_resp_values` is `FALSE`, all of these will -#' have values: +#' and `chk_var1___2`. By default, all of these will be given the same value labels: #' - `chk_var1___0`, `chk_var1___1`, `chk_var1___2`: 0 = "No"; 1 = "Yes". -#' -#' If `.checkbox_resp_values` is `TRUE`, each variable will have unique labels: +#' This behavior can be changed by setting `checkbox_resp_values = TRUE` in `cb_create_options()`. +#' In this case, response-specific labels from `metadata` will be used, so that +#' each variable will have unique labels: #' - `chk_var1___0`: 0 = "Not selected," 1 = "In the past year" #' - `chk_var1___1`: 0 = "Not selected," 1 = "More than a year ago" #' - `chk_var1___2`: 0 = "Not selected," 0 = "Never" #' #' ## Missing value propagation -#' If `.propagate_checkbox_missings` is `TRUE`, missing values in a checkbox group -#' variable will be propagated to all variables in the group. For example, given -#' a checkbox group with options "Pregnant," "Not pregnant," and "Not applicable," -#' corresponding to variables `chk_preg_0___0`, `chk_preg_0___1`, and `chk_preg_0____9`, -#' and assuming that `-9` is specified as a user missing value. If `.propagate_checkbox_missings` -#' is `TRUE`, `chk_preg_0___0` and `chk_preg_0___1` will be set to `-9` if `chk_preg_0____9` -#' is `1`. Otherwise, these columns will remain as `0` where `chk_preg_0____9` is `1`. +#' By default, missing values in a checkbox group will be propagated to all variables +#' in the group. For example, consider a checkbox group with options "Pregnant," +#' "Not pregnant," and "Not applicable," corresponding to variables `chk_preg_0___0`, +#' `chk_preg_0___1`, and `chk_preg_0____9`, and assuming that `-9` is specified +#' as a user missing value. By default, `chk_preg_0___0` and `chk_preg_0___1` will +#' be set to `-9` if `chk_preg_0____9` is `1`. This behavior can be overridden by +#' setting `propagate_checkbox_missings = FALSE` in `cb_create_options()`, in which +#' case no values will be changed. #' #' @export cb_create_redcap <- function(data, metadata, ..., - .name = field_name, - .var_label = field_label, - .val_labels = select_choices_or_calculations, - .form = form_name, .user_missing = NULL, .split_var_labels = NULL, - .include_types = !.include_r_classes, - .include_r_classes = FALSE, - .val_labs_sep1 = ", ", - .val_labs_sep2 = "\\|", - .rmv_html = TRUE, - .rmv_line_breaks = TRUE, - .coerce_integers = TRUE, - .checkbox_resp_values = FALSE, - .propagate_checkbox_missings = TRUE, - .user_missing_col = c("if_any", "yes", "no"), - .user_missing_conflict = c("metadata", "missing_label"), - .user_missing_incompatible = c("ignore", "warn", "error") - ) { - .user_missing_col <- match.arg(.user_missing_col) - .user_missing_conflict <- match.arg(.user_missing_conflict) - meta <- meta_expand_checkboxes_rc(metadata, data) + .options = cb_create_redcap_options()) { + check_options(.options, redcap = TRUE) + meta <- meta_expand_checkboxes_rc( + metadata, data, + name = !!.options$name, type = !!.options$type + ) cb <- data |> cb_init( meta, - meta_var_name = {{ .name }}, meta_var_label = {{ .var_label }}, - meta_val_labels = {{ .val_labels }}, form = {{ .form }}, ..., - ..rc_type = field_type, + meta_var_name = !!.options$name, meta_var_label = !!.options$var_label, + meta_val_labels = !!.options$val_labels, form = !!.options$form, ..., + ..rc_type = !!.options$type, ..rc_validate_type = text_validation_type_or_show_slider_number, ) - if (.coerce_integers) cb <- cb_coerce_integers_rc(cb) + if (.options$coerce_integers) cb <- cb_coerce_integers_rc(cb) cb$..rc_validate_type <- NULL cb <- cb |> - cb_clean_fields(rmv_html = .rmv_html, rmv_line_breaks = .rmv_line_breaks) |> + cb_clean_fields( + rmv_html = .options$rmv_html, + rmv_line_breaks = .options$rmv_line_breaks + ) |> cb_user_missings( user_missing = .user_missing, - incompatible = .user_missing_incompatible + incompatible = .options$user_missing_incompatible ) |> - cb_add_lookups(sep1 = .val_labs_sep1, sep2 = .val_labs_sep2) |> - cb_relabel_checkboxes_rc(use_resp_values = .checkbox_resp_values) + cb_add_lookups( + sep1 = .options$val_labs_sep1, + sep2 = .options$val_labs_sep2 + ) |> + cb_relabel_checkboxes_rc(use_resp_values = .options$checkbox_resp_values) if ("form" %in% names(cb)) cb <- cb_complete_label_rc(cb) - if (.propagate_checkbox_missings) { + if (.options$propagate_checkbox_missings) { cb <- cb_propagate_user_missing_checkboxes_rc(cb) } cb |> - cb_label_data(conflict = .user_missing_conflict) |> + cb_label_data(conflict = .options$user_missing_conflict) |> cb_zap_data() |> cb_add_dims() |> - cb_add_val_labels_col(user_missing_col = .user_missing_col) |> + cb_add_val_labels_col(user_missing_col = .options$user_missing_col) |> cb_add_type_col( - include_r_classes = .include_r_classes, - include_types = .include_types + include_r_classes = .options$include_r_classes, + include_types = .options$include_types ) |> cb_add_missing_col() |> cb_split_labels_col(split_var_labels = rlang::enexpr(.split_var_labels)) |> dplyr::relocate(any_of(c("form", "type", "class")), .after = name) } -## `field_name` and `field_type` are hard-coded -- do they always have these names? -meta_expand_checkboxes_rc <- function(meta, data) { - if (!("checkbox" %in% meta$field_type)) return(meta) +#' @rdname cb_create_options +#' +#' @param name,var_label,val_labels,type For REDCap data, columns in `metadata` containing variable +#' name, variable label, value labels, and variable type, respectively. +#' @param form For REDCap data, column in `metadata` containing form names. (Set to `NULL` to omit.) +#' @param val_labs_sep1,val_labs_sep2 For REDCap data, regex patterns separating value labels +#' in `metadata`. `val_labs_sep1` separates values from labels, and `val_labs_sep2` +#' separates value/label pairs from one another. e.g., if value labels are in +#' the format `"1, First label|2, Second label"`, set `val_labs_sep1` to `","` +#' and `val_labs_sep2` to `"\\|"`. +#' @param coerce_integers For REDCap data, should variables listed as "integer" in `metedata$text_validation_type_or_show_slider_number` +#' be coerced to integer? +#' @param checkbox_resp_values For REDCap data, should checkbox values use labels in `metadata` (`TRUE`) +#' or "Yes" / "No" (`FALSE`)? See "Checkbox data handling" on the `cb_create_redcap()` +#' help page. +#' @param propagate_checkbox_missings For REDCap data, should user missing values in a checkbox group +#' be propagated across all variables in the group? See "Checkbox data handling" +#' on the `cb_create_redcap()` help page. +#' +#' @export +cb_create_redcap_options <- function( + ..., + include_types = TRUE, + include_r_classes = FALSE, + rmv_html = TRUE, + rmv_line_breaks = TRUE, + user_missing_col = c("if_any", "yes", "no"), + user_missing_conflict = c("metadata", "missing_label"), + user_missing_incompatible = c("ignore", "warn", "error"), + name = field_name, + var_label = field_label, + val_labels = select_choices_or_calculations, + type = field_type, + form = form_name, + val_labs_sep1 = ", ", + val_labs_sep2 = "\\|", + coerce_integers = TRUE, + checkbox_resp_values = FALSE, + propagate_checkbox_missings = TRUE) { + rlang::check_dots_empty() + out <- list( + include_types = include_types, include_r_classes = include_r_classes, + rmv_html = rmv_html, rmv_line_breaks = rmv_line_breaks, + user_missing_col = user_missing_col, + user_missing_conflict = user_missing_conflict, + user_missing_incompatible = user_missing_incompatible, + name = rlang::enquo(name), var_label = rlang::enquo(var_label), + val_labels = rlang::enquo(val_labels), type = rlang::enquo(type), + form = rlang::enquo(form), val_labs_sep1 = val_labs_sep1, + val_labs_sep2 = val_labs_sep2, coerce_integers = coerce_integers, + checkbox_resp_values = checkbox_resp_values, + propagate_checkbox_missings = propagate_checkbox_missings + ) + structure(out, class = "cb_create_redcap_options") +} + +meta_expand_checkboxes_rc <- function(meta, data, name, type) { + name_chr <- as.character(rlang::ensym(name)) + type_chr <- as.character(rlang::ensym(type)) + if (!("checkbox" %in% meta[[type_chr]])) return(meta) datanames <- names(data) + meta <- dplyr::rename(meta, ..name = {{ name }}, ..type = {{ type }}) checkbox_names <- meta |> - dplyr::filter(field_type == "checkbox") |> - dplyr::select(field_name) |> + dplyr::filter(..type == "checkbox") |> + dplyr::select(..name) |> dplyr::reframe( .chk_name = datanames[ - stringr::str_starts(datanames, stringr::str_c(field_name, "___")) + stringr::str_starts(datanames, stringr::str_c(..name, "___")) ], - .by = field_name + .by = ..name ) meta |> - dplyr::left_join(checkbox_names, dplyr::join_by(field_name)) |> + dplyr::left_join(checkbox_names, dplyr::join_by(..name)) |> dplyr::mutate( # .chk_name_stem = ifelse(!is.na(.chk_name), field_name, NA), - field_name = dplyr::coalesce(.chk_name, field_name), + ..name = dplyr::coalesce(.chk_name, ..name), .keep = "unused" - ) + ) |> + dplyr::rename("{name_chr}" := ..name, "{type_chr}" := ..type) } cb_coerce_integers_rc <- function(cb) { diff --git a/R/cb_create_spss.r b/R/cb_create_spss.r index b366a46..17bdf6b 100644 --- a/R/cb_create_spss.r +++ b/R/cb_create_spss.r @@ -19,61 +19,47 @@ #' or [tidyselect][dplyr_tidy_select] expressions), and missing values on the #' right-hand side. If left-hand side is omitted, defaults to `tidyselect::everything()`. #' See "Specifying user missing values" in [`cb_create()`] documentation for examples. -#' @param .rmv_html Should HTML tags be removed from variable and value labels? -#' @param .rmv_line_breaks Should line breaks be removed from variable and value -#' labels? If `TRUE`, line breaks will be replaced with `" / "`. -#' @param .user_missing_conflict If labels passed to `.user_missing` conflicts with -#' a value label in `data`, which should be used? #' #' @return -#' An `"li_codebook"` object, consisting of (1) a tibble summarizing the passed -#' dataset and (2) attributes containing the passed dataset (in several formats) -#' and additional metadata. Specifically: -#' - A tibble with columns: -#' - `name`: variable name -#' - `type`: optional column containing simplified variable type -#' - `class`: optional column containing class(es) of each variable -#' - `label_stem`: optional column containing variable label stems, if any variables -#' are specified in `.split_var_labels` -#' - `label`: variable label -#' - `values`: values, with labels if applicable -#' - `user_missing`: optional column, depending on value of `.user_missing_col`, -#' showing user missing values, with labels if applicable -#' - `missing`: proportion missing -#' - Attributes: -#' - Transformed versions of the passed dataset. See [`cb_get_data()`]. -#' - Lookup tables and other metadata used internally. +#' An `"li_codebook"` object, consisting of a tibble summarizing the passed +#' dataset and attributes containing additional metadata. The tibble includes columns: +#' - `name`: variable name +#' - `type`: column containing simplified variable type +#' - `class`: optional column containing class(es) of each variable +#' - `label_stem`: optional column containing variable label stems, if any variables +#' are specified in `.split_var_labels` +#' - `label`: variable label +#' - `values`: values, with labels if applicable +#' - `user_missing`: optional column showing user missing values, with labels +#' if applicable. By default, this column is included only if user missings +#' are specified for at least one variable. This behavior can be changed using +#' the `user_missing_col` argument to `cb_create_options()`. +#' - `missing`: proportion missing #' #' @export cb_create_spss <- function(data, .user_missing = NULL, .split_var_labels = NULL, - .include_types = !.include_r_classes, - .include_r_classes = FALSE, - .rmv_html = TRUE, - .rmv_line_breaks = TRUE, - .user_missing_col = c("if_any", "yes", "no"), - .user_missing_conflict = c("val_label", "missing_label"), - .user_missing_incompatible = c("ignore", "warn", "error") - ) { + .options = cb_create_options()) { + check_options(.options) data |> cb_init() |> cb_clean_fields_spss( - rmv_html = .rmv_html, - rmv_line_breaks = .rmv_line_breaks + rmv_html = .options$rmv_html, + rmv_line_breaks = .options$rmv_line_breaks ) |> cb_add_label_col_spss() |> cb_update_labels_spss( user_missing = .user_missing, - user_missing_conflict = .user_missing_conflict, - user_missing_incompatible = .user_missing_incompatible + user_missing_conflict = .options$user_missing_conflict, + user_missing_incompatible = .options$user_missing_incompatible ) |> cb_zap_data_spss() |> cb_add_dims() |> - cb_add_val_labels_col(user_missing_col = .user_missing_col) |> + cb_add_val_labels_col(user_missing_col = .options$user_missing_col) |> cb_add_type_col( - include_r_classes = .include_r_classes, - include_types = .include_types + include_r_classes = .options$include_r_classes, + include_types = .options$include_types ) |> cb_add_missing_col() |> cb_split_labels_col(split_var_labels = rlang::enexpr(.split_var_labels)) diff --git a/R/cb_write.r b/R/cb_write.r index 4b95649..15be5a2 100644 --- a/R/cb_write.r +++ b/R/cb_write.r @@ -15,10 +15,6 @@ #' a variant. #' @param file Path to write to. #' @param dataset_name Name of the dataset to display in workbook headers. -#' @param incl_date,incl_dims Should the date and/or dataset dimensions be included -#' in the Overview tab header? -#' @param hyperlinks If `TRUE`, variable names on the Overview sheet will link -#' to corresponding rows on summary tabs and vice versa. #' @param group_by <[`tidy-select`][dplyr_tidy_select]> Column or columns to group #' by. If specified, additional numeric and categorical summary tabs will be included #' with grouped summaries. Subgroups are shown in columns by default. Some or all @@ -37,6 +33,10 @@ #' @param n_text_vals On the text summary tab, how many unique non-missing values #' should be included for each variable? If there are more than `n_text_vals` + 1 #' unique values, the `n_text_vals` most common non-missing values will be included. +#' @param incl_date,incl_dims Should the date and/or dataset dimensions be included +#' in the Overview tab header? +#' @param hyperlinks If `TRUE`, variable names on the Overview sheet will link +#' to corresponding rows on summary tabs and vice versa. #' @param overwrite Overwrite existing file? #' #' @return @@ -58,15 +58,15 @@ cb_write <- function(cb, file, dataset_name = NULL, - incl_date = TRUE, - incl_dims = TRUE, - hyperlinks = TRUE, group_by = NULL, group_rows = NULL, group_rows_numeric = group_rows, group_rows_categorical = group_rows, detail_missing = c("if_any_user_missing", "yes", "no"), n_text_vals = 5, + incl_date = TRUE, + incl_dims = TRUE, + hyperlinks = TRUE, overwrite = TRUE) { check_codebook(cb) detail_missing <- match.arg(detail_missing) diff --git a/R/utils.r b/R/utils.r index dbf68e6..6f49877 100644 --- a/R/utils.r +++ b/R/utils.r @@ -1,10 +1,31 @@ -is_codebook <- function(x) "li_codebook" %in% class(x) +is_codebook <- function(x) inherits(x, "li_codebook") check_codebook <- function(x) { arg <- as.character(rlang::ensym(x)) if (!is_codebook(x)) { cli::cli_abort('{.arg {arg}} must be an object of class `"li_codebook"`.') } } +check_options <- function(x, redcap = FALSE) { + if (redcap) { + opts_class <- "cb_create_redcap_options" + opts_class_wrong <- "cb_create_options" + } else { + opts_class <- "cb_create_options" + opts_class_wrong <- "cb_create_redcap_options" + } + if (inherits(x, opts_class_wrong)) { + msg <- c( + "!" = "`.options` must be created from `{opts_class}()`, not `{opts_class_wrong}()`." + ) + if (!redcap) { + msg <- c(msg, "i" = "Did you mean to call `cb_create_redcap()`?") + } + cli::cli_abort(msg) + } + if (!inherits(x, opts_class)) { + cli::cli_abort("`.options` must be created from `{opts_class}()`") + } +} check_user_missing_arg <- function(x) { arg <- as.character(rlang::ensym(x)) if (!( diff --git a/man/cb_create.Rd b/man/cb_create.Rd index 3ddc27f..0112709 100644 --- a/man/cb_create.Rd +++ b/man/cb_create.Rd @@ -13,22 +13,16 @@ cb_create( .val_labels = val_labels, .user_missing = NULL, .split_var_labels = NULL, - .include_types = !.include_r_classes, - .include_r_classes = FALSE, .val_labs_sep1 = NULL, .val_labs_sep2 = NULL, - .rmv_html = TRUE, - .rmv_line_breaks = TRUE, - .user_missing_col = c("if_any", "yes", "no"), - .user_missing_conflict = c("metadata", "missing_label"), - .user_missing_incompatible = c("ignore", "warn", "error") + .options = cb_create_options() ) } \arguments{ \item{data}{A data frame.} -\item{metadata}{A data frame containing metadata, such as variable labels and value -labels.} +\item{metadata}{A data frame containing metadata, such as variable labels and +value labels.} \item{...}{Additional columns from \code{metadata} to preserve in the final codebook. New names can be assigned by passing named arguments. Columns for variable @@ -49,58 +43,34 @@ See "Specifying user missing values" below for examples.} expressions, indicating (sets of) variable labels with a common stem that should be extracted into a separate column.} -\item{.include_types}{Include a column listing simplified type for each variable? -(e.g,. \code{"categorical"}, \code{"date-time"}.)} - -\item{.include_r_classes}{Include a column listing class(es) of each variable? -(e.g., \code{"factor"}, \code{"POSIXct, POSIXt"}.)} - \item{.val_labs_sep1, .val_labs_sep2}{Regex patterns separating value labels in \code{metadata}. \code{.val_labs_sep1} separates values from labels, and \code{.val_labs_sep2} -separates value/label pairs. e.g., if value labels are in format \code{"1, First label|2, Second label"}, -set \code{.val_labs_sep1} to \code{","} and \code{.val_labs_sep2} to \code{"\\\\|"}.} - -\item{.rmv_html}{Should HTML tags be removed from metadata (e.g., from variable -and value labels)?} - -\item{.rmv_line_breaks}{Should line breaks be removed from metadata (e.g., from -variable and value labels)? If \code{TRUE}, line breaks will be replaced with \code{" / "}.} - -\item{.user_missing_col}{Include value labels for user missing values in a separate -column? The default, \code{"if_any"}, adds the column only if user missings are -specified for at least one variable.} +separates value/label pairs from one another. e.g., if value labels are in +the format \code{"1, First label|2, Second label"}, set \code{.val_labs_sep1} to \code{","} +and \code{.val_labs_sep2} to \code{"\\\\|"}.} -\item{.user_missing_conflict}{If different labels for a value are provided in -metadata and user missings, which should be used?} - -\item{.user_missing_incompatible}{How to handle variables specified in \code{.user_missing} -that aren't compatible with user missing values (e.g., logical, Date, or POSIXt)?} +\item{.options}{Additional options to use for codebook creation. Must be the result +from a call to \code{cb_create_options()}. See that function's help page for available +options.} } \value{ -An \code{"li_codebook"} object, consisting of (1) a tibble summarizing the passed -dataset and (2) attributes containing the passed dataset (in several formats) -and additional metadata. Specifically: -\itemize{ -\item A tibble with columns: +An \code{"li_codebook"} object, consisting of a tibble summarizing the passed +dataset and attributes containing additional metadata. The tibble includes columns: \itemize{ \item \code{name}: variable name -\item \code{type}: optional column containing simplified variable type +\item \code{type}: column containing simplified variable type \item \code{class}: optional column containing class(es) of each variable \item \code{label_stem}: optional column containing variable label stems, if any variables are specified in \code{.split_var_labels} \item \code{label}: variable label \item \code{values}: values, with labels if applicable -\item \code{user_missing}: optional column, depending on value of \code{.user_missing_col}, -showing user missing values, with labels if applicable +\item \code{user_missing}: optional column showing user missing values, with labels +if applicable. By default, this column is included only if user missings +are specified for at least one variable. This behavior can be changed using +the \code{user_missing_col} argument to \code{cb_create_options()}. \item \code{missing}: proportion missing \item additional columns if specified in \code{...} } -\item Attributes: -\itemize{ -\item Transformed versions of the passed dataset. See \code{\link[=cb_get_data]{cb_get_data()}} -\item Lookup tables and other metadata used internally. -} -} } \description{ \code{cb_create()} builds an object of class \code{"li_codebook"} from a dataset and optional @@ -141,15 +111,15 @@ User missing values may optionally be named to set value labels: \preformatted{ .user_missing = ~ c(Declined = -98, "Not applicable" = -99) } -If labels set in \code{.user_missing} conflict with those in \code{metadata}, \code{.user_missing_conflict} -controls which labels are used. +If labels set in \code{.user_missing} conflict with those in \code{metadata}, the \code{user_missing_conflict} +argument to \code{cb_create_options()} controls which labels are used. User missings may be set for numeric, character, factor/ordered factor, and haven_labelled/haven_labelled_spss vectors. For factors, user missings are set based on factor labels (not the underlying integer codes). For \code{"haven_labelled"} vectors, user missings are set based on values (not value labels). By default, variables with incompatible classes (e.g., logical, Date, POSIXt) will be ignored if specified in \code{.user_missing}. This -behavior can be changed using the \code{.user_missing_incompatible} argument. +behavior can be changed using the \code{.user_missing_incompatible} argument to \code{cb_create_options()}. } \examples{ diff --git a/man/cb_create_options.Rd b/man/cb_create_options.Rd new file mode 100644 index 0000000..6ee1f59 --- /dev/null +++ b/man/cb_create_options.Rd @@ -0,0 +1,89 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/cb_create.r, R/cb_create_redcap.r +\name{cb_create_options} +\alias{cb_create_options} +\alias{cb_create_redcap_options} +\title{Additional options for codebook creation} +\usage{ +cb_create_options( + ..., + include_types = TRUE, + include_r_classes = FALSE, + rmv_html = TRUE, + rmv_line_breaks = TRUE, + user_missing_col = c("if_any", "yes", "no"), + user_missing_conflict = c("metadata", "missing_label"), + user_missing_incompatible = c("ignore", "warn", "error") +) + +cb_create_redcap_options( + ..., + include_types = TRUE, + include_r_classes = FALSE, + rmv_html = TRUE, + rmv_line_breaks = TRUE, + user_missing_col = c("if_any", "yes", "no"), + user_missing_conflict = c("metadata", "missing_label"), + user_missing_incompatible = c("ignore", "warn", "error"), + name = field_name, + var_label = field_label, + val_labels = select_choices_or_calculations, + type = field_type, + form = form_name, + val_labs_sep1 = ", ", + val_labs_sep2 = "\\\\|", + coerce_integers = TRUE, + checkbox_resp_values = FALSE, + propagate_checkbox_missings = TRUE +) +} +\arguments{ +\item{...}{These dots are for future extensions and must be empty.} + +\item{include_types}{Include a column listing simplified type for each variable? +(e.g,. \code{"categorical"}, \code{"date-time"}.)} + +\item{include_r_classes}{Include a column listing class(es) of each variable? +(e.g., \code{"factor"}, \code{"POSIXct, POSIXt"}.)} + +\item{rmv_html}{Should HTML tags be removed from metadata (e.g., from variable +and value labels)?} + +\item{rmv_line_breaks}{Should line breaks be removed from metadata (e.g., from +variable and value labels)? If \code{TRUE}, line breaks will be replaced with \code{" / "}.} + +\item{user_missing_col}{Include value labels for user missing values in a separate +column? The default, \code{"if_any"}, adds the column only if user missings are +specified for at least one variable.} + +\item{user_missing_conflict}{If labels passed to \code{.user_missing} conflict with +value labels in metadata, which should be used?} + +\item{user_missing_incompatible}{How to handle variables specified in \code{.user_missing} +that aren't compatible with user missing values (e.g., logical, Date, or POSIXt)?} + +\item{name, var_label, val_labels, type}{For REDCap data, columns in \code{metadata} containing variable +name, variable label, value labels, and variable type, respectively.} + +\item{form}{For REDCap data, column in \code{metadata} containing form names. (Set to \code{NULL} to omit.)} + +\item{val_labs_sep1, val_labs_sep2}{For REDCap data, regex patterns separating value labels +in \code{metadata}. \code{val_labs_sep1} separates values from labels, and \code{val_labs_sep2} +separates value/label pairs from one another. e.g., if value labels are in +the format \code{"1, First label|2, Second label"}, set \code{val_labs_sep1} to \code{","} +and \code{val_labs_sep2} to \code{"\\\\|"}.} + +\item{coerce_integers}{For REDCap data, should variables listed as "integer" in \code{metedata$text_validation_type_or_show_slider_number} +be coerced to integer?} + +\item{checkbox_resp_values}{For REDCap data, should checkbox values use labels in \code{metadata} (\code{TRUE}) +or "Yes" / "No" (\code{FALSE})? See "Checkbox data handling" on the \code{cb_create_redcap()} +help page.} + +\item{propagate_checkbox_missings}{For REDCap data, should user missing values in a checkbox group +be propagated across all variables in the group? See "Checkbox data handling" +on the \code{cb_create_redcap()} help page.} +} +\description{ +Additional options for use by \code{cb_create()}. +} diff --git a/man/cb_create_redcap.Rd b/man/cb_create_redcap.Rd index 9ced111..98cc49f 100644 --- a/man/cb_create_redcap.Rd +++ b/man/cb_create_redcap.Rd @@ -8,24 +8,9 @@ cb_create_redcap( data, metadata, ..., - .name = field_name, - .var_label = field_label, - .val_labels = select_choices_or_calculations, - .form = form_name, .user_missing = NULL, .split_var_labels = NULL, - .include_types = !.include_r_classes, - .include_r_classes = FALSE, - .val_labs_sep1 = ", ", - .val_labs_sep2 = "\\\\|", - .rmv_html = TRUE, - .rmv_line_breaks = TRUE, - .coerce_integers = TRUE, - .checkbox_resp_values = FALSE, - .propagate_checkbox_missings = TRUE, - .user_missing_col = c("if_any", "yes", "no"), - .user_missing_conflict = c("metadata", "missing_label"), - .user_missing_incompatible = c("ignore", "warn", "error") + .options = cb_create_redcap_options() ) } \arguments{ @@ -37,11 +22,6 @@ cb_create_redcap( New names can be assigned by passing named arguments. Columns for variable name, form, variable label, and value labels are included by default.} -\item{.name, .var_label, .val_labels}{Columns in \code{metadata} containing variable -name, variable label, and value labels, respectively.} - -\item{.form}{Column in \code{metadata} containing form names. (Set to \code{NULL} to omit.)} - \item{.user_missing}{A formula or list of formulas specifying user missing values. Formulas should specify variables on the left-hand side (as variable names or \link[dplyr:dplyr_tidy_select]{tidyselect} expressions), and missing values on the @@ -52,69 +32,29 @@ See "Specifying user missing values" in \code{\link[=cb_create]{cb_create()}} do expressions, indicating (sets of) variable labels with a common stem that should be extracted into a separate column.} -\item{.include_types}{Include a column listing simplified type for each variable? -(e.g,. \code{"categorical"}, \code{"date-time"}.)} - -\item{.include_r_classes}{Include a column listing class(es) of each variable? -(e.g., \code{"factor"}, \code{"POSIXct, POSIXt"}.)} - -\item{.val_labs_sep1, .val_labs_sep2}{Regex patterns separating value labels -in \code{metadata}. \code{.val_labs_sep1} separates values from labels, and \code{.val_labs_sep2} -separates value/label pairs. e.g., if value labels are in format \code{"1, First label|2, Second label"}, -set \code{.val_labs_sep1} to \code{","} and \code{.val_labs_sep2} to \code{"\\\\|"}.} - -\item{.rmv_html}{Should HTML tags be removed from metadata (e.g., from variable -and value labels)?} - -\item{.rmv_line_breaks}{Should line breaks be removed from metadata (e.g., from -variable and value labels)? If \code{TRUE}, line breaks will be replaced with \code{" / "}.} - -\item{.coerce_integers}{Should variables listed as "integer" in \code{metedata$text_validation_type_or_show_slider_number} -be coerced to integer?} - -\item{.checkbox_resp_values}{Should checkbox values use labels in \code{metadata} -(\code{TRUE}) or "Yes" / "No" (\code{FALSE})? See "Checkbox data handling" below.} - -\item{.propagate_checkbox_missings}{Should user missing values in a checkbox -group be propagated across all variables in the group? See "Checkbox data handling" -below.} - -\item{.user_missing_col}{Include value labels for user missing values in a separate -column? The default, \code{"if_any"}, adds the column only if user missings are -specified for at least one variable.} - -\item{.user_missing_conflict}{If different labels for a value are provided in -metadata and user missings, which should be used?} - -\item{.user_missing_incompatible}{How to handle variables specified in \code{.user_missing} -that aren't compatible with user missing values (e.g., logical, Date, or POSIXt)?} +\item{.options}{Additional options to use for codebook creation. Must be the result +from a call to \code{cb_create_redcap_options()} or \code{cb_create_options()}. See \code{?cb_create_redcap_options} +for available options.} } \value{ -An \code{"li_codebook"} object, consisting of (1) a tibble summarizing the passed -dataset and (2) attributes containing the passed dataset (in several formats) -and additional metadata. Specifically: -\itemize{ -\item A tibble with columns: +An \code{"li_codebook"} object, consisting of a tibble summarizing the passed +dataset and attributes containing additional metadata. The tibble includes columns: \itemize{ \item \code{name}: variable name \item \code{form}: form name -\item \code{type}: optional column containing simplified variable type +\item \code{type}: column containing simplified variable type \item \code{class}: optional column containing class(es) of each variable \item \code{label_stem}: optional column containing variable label stems, if any variables are specified in \code{.split_var_labels} \item \code{label}: variable label \item \code{values}: values, with labels if applicable -\item \code{user_missing}: optional column, depending on value of \code{.user_missing_col}, -showing user missing values, with labels if applicable +\item \code{user_missing}: optional column showing user missing values, with labels +if applicable. By default, this column is included only if user missings +are specified for at least one variable. This behavior can be changed using +the \code{user_missing_col} argument to \code{cb_create_options()}. \item \code{missing}: proportion missing \item additional columns if specified in \code{...} } -\item Attributes: -\itemize{ -\item Transformed versions of the passed dataset. See \code{\link[=cb_get_data]{cb_get_data()}}. -\item Lookup tables and other metadata used internally. -} -} } \description{ \code{cb_create_redcap()} builds an object of class \code{"li_codebook"} from a dataset and @@ -131,24 +71,23 @@ and metadata, including: \item Unpacking, labelling, and optional missing propagation for checkbox data \item Optional coercion for character variables marked as "integer" in \code{metedata$text_validation_type_or_show_slider_number} } + +All of these behaviors can be controlled using the \code{.options} argument. } \section{Checkbox data handling}{ \subsection{Value labels}{ Data from REDCap checkboxes yields one variable in the dataset for each response -option. These will be labelled generically with \code{"Yes"} or \code{"No"}, unless \code{.checkbox_resp_values} -is \code{TRUE}, in which case response-specific labels from \code{metadata} will be used. -For example, if a checkbox group has options "In the past year," "More than a +option. By default, these will be labelled generically with \code{"Yes"} or \code{"No"}. +For example, consider a checkbox group with options "In the past year," "More than a year ago," and "Never," corresponding to variables \code{chk_var1___0}, \code{chk_var1___1}, -and \code{chk_var1___2}: if \code{.checkbox_resp_values} is \code{FALSE}, all of these will -have values: +and \code{chk_var1___2}. By default, all of these will be given the same value labels: \itemize{ \item \code{chk_var1___0}, \code{chk_var1___1}, \code{chk_var1___2}: 0 = "No"; 1 = "Yes". -} - -If \code{.checkbox_resp_values} is \code{TRUE}, each variable will have unique labels: -\itemize{ +This behavior can be changed by setting \code{checkbox_resp_values = TRUE} in \code{cb_create_options()}. +In this case, response-specific labels from \code{metadata} will be used, so that +each variable will have unique labels: \item \code{chk_var1___0}: 0 = "Not selected," 1 = "In the past year" \item \code{chk_var1___1}: 0 = "Not selected," 1 = "More than a year ago" \item \code{chk_var1___2}: 0 = "Not selected," 0 = "Never" @@ -157,13 +96,14 @@ If \code{.checkbox_resp_values} is \code{TRUE}, each variable will have unique l \subsection{Missing value propagation}{ -If \code{.propagate_checkbox_missings} is \code{TRUE}, missing values in a checkbox group -variable will be propagated to all variables in the group. For example, given -a checkbox group with options "Pregnant," "Not pregnant," and "Not applicable," -corresponding to variables \code{chk_preg_0___0}, \code{chk_preg_0___1}, and \code{chk_preg_0____9}, -and assuming that \code{-9} is specified as a user missing value. If \code{.propagate_checkbox_missings} -is \code{TRUE}, \code{chk_preg_0___0} and \code{chk_preg_0___1} will be set to \code{-9} if \code{chk_preg_0____9} -is \code{1}. Otherwise, these columns will remain as \code{0} where \code{chk_preg_0____9} is \code{1}. +By default, missing values in a checkbox group will be propagated to all variables +in the group. For example, consider a checkbox group with options "Pregnant," +"Not pregnant," and "Not applicable," corresponding to variables \code{chk_preg_0___0}, +\code{chk_preg_0___1}, and \code{chk_preg_0____9}, and assuming that \code{-9} is specified +as a user missing value. By default, \code{chk_preg_0___0} and \code{chk_preg_0___1} will +be set to \code{-9} if \code{chk_preg_0____9} is \code{1}. This behavior can be overridden by +setting \code{propagate_checkbox_missings = FALSE} in \code{cb_create_options()}, in which +case no values will be changed. } } diff --git a/man/cb_create_spss.Rd b/man/cb_create_spss.Rd index b67dc25..f5ab4c8 100644 --- a/man/cb_create_spss.Rd +++ b/man/cb_create_spss.Rd @@ -8,13 +8,7 @@ cb_create_spss( data, .user_missing = NULL, .split_var_labels = NULL, - .include_types = !.include_r_classes, - .include_r_classes = FALSE, - .rmv_html = TRUE, - .rmv_line_breaks = TRUE, - .user_missing_col = c("if_any", "yes", "no"), - .user_missing_conflict = c("val_label", "missing_label"), - .user_missing_incompatible = c("ignore", "warn", "error") + .options = cb_create_options() ) } \arguments{ @@ -31,51 +25,27 @@ See "Specifying user missing values" in \code{\link[=cb_create]{cb_create()}} do expressions, indicating (sets of) variable labels with a common stem that should be extracted into a separate column.} -\item{.include_types}{Include a column listing simplified type for each variable? -(e.g,. \code{"categorical"}, \code{"date-time"}.)} - -\item{.include_r_classes}{Include a column listing class(es) of each variable? -(e.g., \code{"factor"}, \code{"POSIXct, POSIXt"}.)} - -\item{.rmv_html}{Should HTML tags be removed from variable and value labels?} - -\item{.rmv_line_breaks}{Should line breaks be removed from variable and value -labels? If \code{TRUE}, line breaks will be replaced with \code{" / "}.} - -\item{.user_missing_col}{Include value labels for user missing values in a separate -column? The default, \code{"if_any"}, adds the column only if user missings are -specified for at least one variable.} - -\item{.user_missing_conflict}{If labels passed to \code{.user_missing} conflicts with -a value label in \code{data}, which should be used?} - -\item{.user_missing_incompatible}{How to handle variables specified in \code{.user_missing} -that aren't compatible with user missing values (e.g., logical, Date, or POSIXt)?} +\item{.options}{Additional options to use for codebook creation. Must be the result +from a call to \code{cb_create_options()}. See that function's help page for available +options.} } \value{ -An \code{"li_codebook"} object, consisting of (1) a tibble summarizing the passed -dataset and (2) attributes containing the passed dataset (in several formats) -and additional metadata. Specifically: -\itemize{ -\item A tibble with columns: +An \code{"li_codebook"} object, consisting of a tibble summarizing the passed +dataset and attributes containing additional metadata. The tibble includes columns: \itemize{ \item \code{name}: variable name -\item \code{type}: optional column containing simplified variable type +\item \code{type}: column containing simplified variable type \item \code{class}: optional column containing class(es) of each variable \item \code{label_stem}: optional column containing variable label stems, if any variables are specified in \code{.split_var_labels} \item \code{label}: variable label \item \code{values}: values, with labels if applicable -\item \code{user_missing}: optional column, depending on value of \code{.user_missing_col}, -showing user missing values, with labels if applicable +\item \code{user_missing}: optional column showing user missing values, with labels +if applicable. By default, this column is included only if user missings +are specified for at least one variable. This behavior can be changed using +the \code{user_missing_col} argument to \code{cb_create_options()}. \item \code{missing}: proportion missing } -\item Attributes: -\itemize{ -\item Transformed versions of the passed dataset. See \code{\link[=cb_get_data]{cb_get_data()}}. -\item Lookup tables and other metadata used internally. -} -} } \description{ \code{cb_create_spss()} builds an object of class \code{"li_codebook"} from an imported diff --git a/man/cb_write.Rd b/man/cb_write.Rd index 727a809..1699465 100644 --- a/man/cb_write.Rd +++ b/man/cb_write.Rd @@ -8,15 +8,15 @@ cb_write( cb, file, dataset_name = NULL, - incl_date = TRUE, - incl_dims = TRUE, - hyperlinks = TRUE, group_by = NULL, group_rows = NULL, group_rows_numeric = group_rows, group_rows_categorical = group_rows, detail_missing = c("if_any_user_missing", "yes", "no"), n_text_vals = 5, + incl_date = TRUE, + incl_dims = TRUE, + hyperlinks = TRUE, overwrite = TRUE ) } @@ -28,12 +28,6 @@ a variant.} \item{dataset_name}{Name of the dataset to display in workbook headers.} -\item{incl_date, incl_dims}{Should the date and/or dataset dimensions be included -in the Overview tab header?} - -\item{hyperlinks}{If \code{TRUE}, variable names on the Overview sheet will link -to corresponding rows on summary tabs and vice versa.} - \item{group_by}{<\code{\link[dplyr:dplyr_tidy_select]{tidy-select}}> Column or columns to group by. If specified, additional numeric and categorical summary tabs will be included with grouped summaries. Subgroups are shown in columns by default. Some or all @@ -57,6 +51,12 @@ summary tabs is not currently supported.)} should be included for each variable? If there are more than \code{n_text_vals} + 1 unique values, the \code{n_text_vals} most common non-missing values will be included.} +\item{incl_date, incl_dims}{Should the date and/or dataset dimensions be included +in the Overview tab header?} + +\item{hyperlinks}{If \code{TRUE}, variable names on the Overview sheet will link +to corresponding rows on summary tabs and vice versa.} + \item{overwrite}{Overwrite existing file?} } \value{ From 2b7811c6d947b121106532d5cae989f4bda9d416 Mon Sep 17 00:00:00 2001 From: ccsarapas Date: Fri, 13 Feb 2026 14:08:39 -0600 Subject: [PATCH 10/13] make `user_missing_conflict` options consistent across functions --- R/cb_create.r | 8 ++++---- R/cb_create_redcap.r | 2 +- R/cb_create_spss.r | 3 +-- man/cb_create_options.Rd | 4 ++-- 4 files changed, 8 insertions(+), 9 deletions(-) diff --git a/R/cb_create.r b/R/cb_create.r index 424f6f7..7e0fdab 100644 --- a/R/cb_create.r +++ b/R/cb_create.r @@ -214,7 +214,7 @@ cb_create_options <- function( rmv_html = TRUE, rmv_line_breaks = TRUE, user_missing_col = c("if_any", "yes", "no"), - user_missing_conflict = c("metadata", "missing_label"), + user_missing_conflict = c("val_label", "missing_label"), user_missing_incompatible = c("ignore", "warn", "error")) { rlang::check_dots_empty() out <- list( @@ -464,7 +464,7 @@ cb_add_lookups <- function(cb, sep1, sep2) { reconcile_missing_labels <- function(val_labs, missings, - conflict = c("metadata", "missing_label")) { + conflict = c("val_label", "missing_label")) { conflict <- match.arg(conflict) labs_in_missing <- val_labs[match(missings, val_labs)] @@ -485,7 +485,7 @@ reconcile_missing_labels <- function(val_labs, names(missings)[label_miss] <- lab_name[label_miss] ### if na is labelled and in vals and labels don't match # relabel based on `conflict` - if (conflict == "metadata") { + if (conflict == "val_label") { names(missings)[mismatch] <- lab_name[mismatch] } else if (conflict == "missing_label") { names(val_labs)[match(lab_val[mismatch], val_labs)] <- miss_name[mismatch] @@ -493,7 +493,7 @@ reconcile_missing_labels <- function(val_labs, list(val_labs = val_labs, missings = missings) } -cb_label_data <- function(cb, conflict = c("metadata", "missing_label")) { +cb_label_data <- function(cb, conflict = c("val_label", "missing_label")) { data <- attr(cb, "data") vals_by_label <- attr(cb, "vals_by_label") factors <- attr(cb, "factors") diff --git a/R/cb_create_redcap.r b/R/cb_create_redcap.r index 2e7cb85..ccb8c58 100644 --- a/R/cb_create_redcap.r +++ b/R/cb_create_redcap.r @@ -155,7 +155,7 @@ cb_create_redcap_options <- function( rmv_html = TRUE, rmv_line_breaks = TRUE, user_missing_col = c("if_any", "yes", "no"), - user_missing_conflict = c("metadata", "missing_label"), + user_missing_conflict = c("val_label", "missing_label"), user_missing_incompatible = c("ignore", "warn", "error"), name = field_name, var_label = field_label, diff --git a/R/cb_create_spss.r b/R/cb_create_spss.r index 17bdf6b..a4827cd 100644 --- a/R/cb_create_spss.r +++ b/R/cb_create_spss.r @@ -100,7 +100,6 @@ cb_update_labels_spss <- function(cb, cb_add_lookups() |> set_attrs(data_labelled = data) } else { - conflict <- sub("val_label", "metadata", match.arg(user_missing_conflict)) user_missing <- check_user_missing_arg(user_missing) user_missing_vars <- user_missing |> lapply(\(um) { @@ -126,7 +125,7 @@ cb_update_labels_spss <- function(cb, user_missing = attr_user_missing[names(attr_user_missing) %in% user_missing_vars], vals_by_label = attr_vals_by_label[names(attr_vals_by_label) %in% user_missing_vars] ) |> - cb_label_data(conflict = conflict) |> + cb_label_data(conflict = user_missing_conflict) |> # then restore full missing and val attributes set_attrs( user_missing = attr_user_missing, diff --git a/man/cb_create_options.Rd b/man/cb_create_options.Rd index 6ee1f59..33b114d 100644 --- a/man/cb_create_options.Rd +++ b/man/cb_create_options.Rd @@ -12,7 +12,7 @@ cb_create_options( rmv_html = TRUE, rmv_line_breaks = TRUE, user_missing_col = c("if_any", "yes", "no"), - user_missing_conflict = c("metadata", "missing_label"), + user_missing_conflict = c("val_label", "missing_label"), user_missing_incompatible = c("ignore", "warn", "error") ) @@ -23,7 +23,7 @@ cb_create_redcap_options( rmv_html = TRUE, rmv_line_breaks = TRUE, user_missing_col = c("if_any", "yes", "no"), - user_missing_conflict = c("metadata", "missing_label"), + user_missing_conflict = c("val_label", "missing_label"), user_missing_incompatible = c("ignore", "warn", "error"), name = field_name, var_label = field_label, From 33ab6e3918825228e13a736a4759ca577149bae7 Mon Sep 17 00:00:00 2001 From: ccsarapas Date: Fri, 13 Feb 2026 14:09:18 -0600 Subject: [PATCH 11/13] fix tidyeval of default args in `cb_write()` --- R/cb_write.r | 16 ++++++++++++---- 1 file changed, 12 insertions(+), 4 deletions(-) diff --git a/R/cb_write.r b/R/cb_write.r index 15be5a2..a99c2f5 100644 --- a/R/cb_write.r +++ b/R/cb_write.r @@ -77,15 +77,23 @@ cb_write <- function(cb, num = cb_summarize_numeric_impl(cb), cat = cb_summarize_categorical_impl(cb, detail_missing = detail_missing), txt = cb_summarize_text_impl( - cb, - n_text_vals = n_text_vals, + cb, + n_text_vals = n_text_vals, detail_missing = detail_missing ) ) group_by <- cb_untidyselect(cb, {{ group_by }}) group_rows <- cb_untidyselect(cb, {{ group_rows }}) - group_rows_numeric <- cb_untidyselect(cb, {{ group_rows_numeric }}) - group_rows_categorical <- cb_untidyselect(cb, {{ group_rows_categorical }}) + if (missing(group_rows_numeric)) { + group_rows_numeric <- group_rows + } else { + group_rows_numeric <- cb_untidyselect(cb, {{ group_rows_numeric }}) + } + if (missing(group_rows_categorical)) { + group_rows_categorical <- group_rows + } else { + group_rows_categorical <- cb_untidyselect(cb, {{ group_rows_categorical }}) + } check_group_rows_arg(group_rows, group_by) check_group_rows_arg(group_rows_numeric, group_by) check_group_rows_arg(group_rows_categorical, group_by) From 0028bccbcae8cba951945f6318d84b33aa5a6533 Mon Sep 17 00:00:00 2001 From: ccsarapas Date: Fri, 13 Feb 2026 15:16:15 -0600 Subject: [PATCH 12/13] update site theme --- _pkgdown.yml | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/_pkgdown.yml b/_pkgdown.yml index 8221a9a..961f921 100644 --- a/_pkgdown.yml +++ b/_pkgdown.yml @@ -1,7 +1,10 @@ url: https://ccsarapas.github.io/lighthouse.codebook/ template: bootstrap: 5 - bootswatch: sandstone + bslib: + base_font: {google: "Roboto"} + code_font: {google: "Roboto Mono"} + font_scale: 1.2 footer: structure: left: package From ec63dc2ace6054d4c76f4343a8c68e348933242c Mon Sep 17 00:00:00 2001 From: ccsarapas Date: Fri, 13 Feb 2026 15:17:58 -0600 Subject: [PATCH 13/13] bump version number --- DESCRIPTION | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/DESCRIPTION b/DESCRIPTION index 50eb44f..d99ccdd 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -1,6 +1,6 @@ Package: lighthouse.codebook Title: Summarize Datasets for Lighthouse Institute Projects -Version: 0.2.9000 +Version: 0.3.0 Authors@R: c( person("Casey", "Sarapas", email = "ccsarapas@chestnut.org",

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