@@ -98,28 +99,31 @@ two-mode networks as well as one-mode networks.
## Tutorials
-Together with `{manynet}`, this package makes available interactive
-`{learnr}` tutorials. The easiest way to access the tutorials is via
-`run_tute()`. If no tutorial name is provided, the function will return
-a list of tutorials currently available in either package:
+This package makes available interactive `{learnr}` tutorials to help
+new and experienced users learn how they can conduct social network
+analysis using the stocnet packages. The easiest way to access the
+tutorials is via `run_tute()`. If no tutorial name is provided, the
+function will return a list of tutorials currently available in either
+package:
``` r
library(migraph)
run_tute()
-#> Checking tutorials in stocnet packages ■■■■■■■■■■■■■■■■ 50% | …
-#> # A tibble: 9 × 3
-#> package name title
-#> /R_(programming_language)-Logo.wine.png){kind=logo}
+
+[R](http://www.r-project.org/) is a programming language and environment for statistical computing and graphics.
+R is available as Free Software under the terms of the Free Software Foundation's
+GNU General Public License,
+and provides a wide variety of statistical (linear and nonlinear modelling,
+classical statistical tests, time-series analysis, classification, ...)
+and graphical techniques, and is highly extensible.
+This means that anybody can write extensions to R and make them publicly available,
+such as in the [stocnet](https://github.com/stocnet) group of packages...
+
+### What is RStudio?
+
+
+
+[RStudio](https://posit.co/products/open-source/rstudio/) is an integrated development environment (IDE) for R and Python,
+enabling researchers to interact with R (and/or Python) through a fully-functional editor
+with syntax highlighting, direct code execution, autocomplete,
+and various tools for plotting, history, debugging, package development, and workspace management.
+
+In the end, although you will need to make sure R has been downloaded and installed
+correctly on the system you are using,
+in practice you will never open it directly.
+Instead you will be using RStudio to interact with R.
+Think of R as the internals of the calculator, but RStudio as the case.
+Let's start the calculator but opening the 'calculator case' app, RStudio.
+
+## Getting started
+
+### RStudio and R scripts
+
+If we open and take a look around RStudio, we should see a window of four (4) panes.
+Among them there should be a **console**:
+this is where RStudio executes commands in R.
+You can type commands yourself (RStudio may help by suggesting autocompletions),
+but we usually write code in an R script instead,
+and then tell RStudio when to execute one or more lines from the script.
+There are basically three reasons for using a script: editing, repetition, sharing.
+You can run a command in RStudio by moving the cursor to the line or lines you want to run
+and then press Cmd-Enter (Mac) or Ctrl-Enter (Windows).
+You can try this with the following lines:
+
+```{r printing-results, exercise = TRUE}
+1 + 5 # This will print the result
+105 * 99 + 6 # An asterisk is used for multiplication
+```
+
+Note that R won't execute anything after a comment `#`.
+Remove the hash symbol at the start of this line to run it:
+
+```{r comments, exercise = TRUE}
+# 1/5 # this will still be commented...
+```
+
+In an R script you can toggle commenting for one or more lines using Cmd-Shift-C/Ctrl-Shift-C.
+If you try to run a commented out line, it will continue until it finds the next command.
+
+### Beyond a calculator
+
+Ok, wow, R is a calculator!
+But it is also much, much more than that...
+Try the following command:
+
+```{r hello-world, exercise = TRUE}
+print("Hello World")
+```
+
+You've told R to print a string of text (identified by the quotation marks) to the console.
+Much more flexible than a high school calculator!
+
+It is important to note that R is case-sensitive,
+i.e. `Print("Hello World")` will not work. Try it!
+
+```{r Print-hello-world, exercise = TRUE}
+Print("Hello World")
+```
+
+```{r case-sensitivity-question, purl=FALSE}
+question("Why does Print('Hello World') cause an error?",
+ answer("Because print is spelled with a capital P",
+ correct = TRUE),
+ answer("Because the print function has already been used"),
+ answer("Because the print function has been lost"),
+ answer("Because of face insensitivity"),
+ random_answer_order = TRUE,
+ allow_retry = TRUE
+)
+```
+
+
+This means that `james` is not the same as `JAMES` (and `Hollway` is not the same as `Holloway`...).
+In R, we can write such logical statements as:
+
+```{r equivalence, exercise = TRUE}
+"James"=="james" # Try also "James"!="james"
+# Other logical statements include: ">", ">=", "<=", "<".
+# 1 < 5 # Try also "1 <= 5"
+```
+
+Logical values are always either `TRUE` or `FALSE`,
+but can be abbreviated as `T` or `F` respectively.
+Why do we have to use two equals signs and quotation marks?
+Quotation marks tells R you are referring to a string of text and not a named _object_.
+
+## Objects
+
+### Values
+
+An object is a placeholder R uses for one or more numbers, strings, or other things.
+You can assign such things to an object using one `=` sign,
+but it's better to use `<-` to avoid mistakes related to `=` use also in logical statements.
+
+```{r assignment, exercise = TRUE}
+surname <- "Hollway"
+y.chromosome <- T # or TRUE
+siblings <- 1
+age <- NA # This is used for missing information
+# Note that these objects then appear in RStudio's environment pane (by default the top right)
+```
+
+You can then recover this information by simply calling these objects:
+
+```{r calling, exercise = TRUE, exercise.setup = "assignment"}
+surname
+y.chromosome
+siblings
+age
+```
+
+And even operate on them:
+
+```{r mult, exercise = TRUE, exercise.setup = "assignment"}
+siblings*3
+# Try multiplying the other objects by 3
+```
+
+### Vectors
+
+We can also concatenate multiple values together using the function `c()`:
+
+```{r concatenation, exercise = TRUE}
+lived <- c("New Zealand", "UK", "New Zealand", "Germany", "UK", "Switzerland")
+```
+
+And recall them. Where was the fourth place I lived?
+We use square brackets `[ ]` for indexing:
+
+```{r indexing, exercise = TRUE, exercise.setup = "concatenation"}
+lived[4]
+```
+
+There are several shortcuts for making a series of values.
+For example, consecutive numbers can be produced with:
+
+```{r series, exercise = TRUE}
+teenageyrs <- 13:19
+teenageqrtrs <- seq(13, 19.99, by = 0.25)
+# We can recall every third value from this object using a repeating vector
+teenageqrtrs
+teenageqrtrs[c(FALSE, FALSE, TRUE)]
+# teenageqrtrs[c(F, F, T)] # Also works but it is best practice to write out the logic.
+```
+
+So R can help us store and recall values and even vectors of values,
+but the key is being able to relate values and vectors together.
+For that we use objects of more complex _classes_.
+
+## Classes
+
+### Matrices
+
+Data can be aggregated in R into different formats, such as data frames and lists
+but the most common one used for network research is the matrix format.
+Matrices are created by populating a given number of rows and columns with data
+Assigning, `<-`, doesn't print any output unless you wrap the line in parentheses:
+
+```{r assign-print, exercise = TRUE}
+(my.matrix <- matrix(data = 1:9, nrow = 6, ncol = 6))
+```
+
+If you look in the help file,
+which you can access by putting a `?` before the command/function name,
+you will see matrix sets `byrow = F` by default.
+
+```{r help, exercise = TRUE}
+?matrix # Forgot the exact name of the function? Use ?? for search...
+```
+
+This means that it populates the matrix with the data by column by default,
+but we can populate it by row instead by adding an extra 'argument':
+
+```{r byrow, exercise = TRUE}
+(my.2nd.matrix <- matrix(1:9, 6, 6, byrow = T))
+```
+
+We can index cells of a matrix using square brackets with a comma `[ , ]`
+
+```{r index-cells, exercise = TRUE, exercise.setup = "byrow"}
+my.2nd.matrix[2, 2]
+```
+
+Left of the comma is the row, right of the comma is the column.
+We can even overwrite particular cells of the matrix by assigning a new value
+to those indexed cells:
+
+```{r cell-assign, exercise = TRUE, exercise.setup = "byrow"}
+my.2nd.matrix[my.2nd.matrix == 6] <- 600
+my.2nd.matrix
+```
+
+### Data frames
+
+Data frames are like matrices, but can hold different types of variables at once,
+such as logical, numeric/integer, or string/character variables.
+Replace the missing data (the NAs) with your details:
+
+```{r df-own-values, exercise = TRUE}
+mydf <- data.frame(Surname = c("Hollway", NA),
+ Born = c("New Zealand", NA),
+ Siblings = c(1, NA))
+```
+
+You can even add new variables by simply writing a new variable name:
+
+```{r df-add-variable, exercise = TRUE, exercise.setup="df-own-values"}
+mydf$Dept <- c("IRPS", NA)
+```
+
+Can you call the data frame and print to the console?
+
+```{r df-test, exercise = TRUE, exercise.setup="df-add-variable"}
+
+```
+
+```{r df-test-solution}
+mydf
+```
+
+We can recall an observation (row) or variable (column) of the data frame
+in the same way that we indexed the matrix above, e.g. `mydf[2,2]`,
+but we can also call a named variable using the `$` sign:
+
+```{r df-call-var, exercise = TRUE, exercise.setup="df-add-variable"}
+mydf$Surname
+```
+
+This can be very handy when "subsetting" the data:
+
+```{r df-subset, exercise = TRUE, exercise.setup="df-add-variable"}
+james <- mydf[mydf$Surname == "Hollway", ]
+james
+```
+
+Note, however, that data frames must have variables of equal length.
+
+### Lists
+
+Lists are a more flexible generalisation of data frames.
+
+```{r list-init, exercise = TRUE}
+mylist <- list() # Here we are initialising an empty list
+```
+
+List items can also be named, like data frame variables, but don't have to be:
+
+```{r list-names, exercise = TRUE, exercise.setup="list-init"}
+mylist$Surname <- c("Hollway", NA)
+mylist$Siblings <- c(1, NA) # Now you can add the others from above
+```
+
+You can also add lists to a list:
+
+```{r list-nest, exercise = TRUE, exercise.setup="list-names"}
+mylist$Lived <- list(c("New Zealand", "UK", "New Zealand", "Germany", "UK", "Switzerland"), NA)
+```
+
+Note that we've been using parentheses, `()`, here and not brackets, `[]`,
+as we did when we were indexing.
+Parentheses are used for _functions_.
+
+## Functions
+
+Functions are sets of actions or algorithms that are applied to values, vectors, or objects.
+
+```{r functions, exercise = TRUE}
+exp(0.09855)
+mean(c(1, 5, 8, 7, 6, 4, 22, 1, 0.9))
+```
+
+### Arguments
+
+Usually every function must be followed by `()`.
+Some functions work without any "arguments" though;
+that is, with empty parentheses.
+
+```{r functions-empty, exercise = TRUE}
+ls() # This tells you what objects are in your environment
+getwd() # This tells you the directory on your computer R is working in/on
+list.files() # This tells you what files are in your working directory
+# setwd("...") # You can set the working directory with this function
+# (or under session in RStudio)
+```
+
+Compare the above with functions like the following,
+which enables you to write an object out of R to some path on your hard-drive that you specify:
+
+```{r function-write, exercise = TRUE}
+write.csv(x = mydf, file = "~/Desktop/jamesdf.csv")
+```
+
+Two arguments are specified for this function `write.csv()`:
+`x` and `file`.
+But the function can accept other arguments as might be necessary
+for more complex data, for less common outputs, or in edge cases.
+See `?write.csv` for a list of the different arguments the function will accept.
+Usually functions include defaults so that they work even if you do not
+specify all the possible arguments though.
+In fact, we don't need to write `x = `, just `write.csv(mydf, file = "~/Desktop/jamesdf.csv`.
+That is because the function is expecting the object to be written
+to be specified as the first argument,
+so it is only where you want to be explicit or use a different ordering of the arguments
+that you might need to spell that out.
+It is good practice to be explicit whereever possible though to avoid unexpected results.
+
+### Pipes
+
+When working with multiple functions on the same object,
+we can use pipe operators `%>%` or `|>` to chain consecutive functions
+and avoid nesting multiple functions in the code.
+Pipes take the result of the code on the left of the pipe operator
+and uses it in whatever function is on the right or next line of the pipe operator.
+Note that when piping over multiple lines should,
+the pipe operator(s) should be used at the end of each line.
+
+```{r pipes, exercise = TRUE}
+example.vector <- c(1, 5, 8, 7, 6, 4, 22, 1, 0.9)
+pipe.result.1 <- example.vector |>
+ mean()
+pipe.result.1
+
+# library(dplyr)
+# pipe.result.2 <- example.vector %>%
+ # mean()
+# both pipe operators give the same result
+# pipe.result.1 == pipe.result.2
+```
+
+While `|>`is the native pipe operator since R v4.0.0,
+those using earlier versions of R may wish to use `%>%`
+from either the `{magrittr}` or `{dplyr}` packages.
+Note that in that case, the package would need to be loaded first
+before you can use the operator.
+
+## Tasks
+
+1. Create and fill in a matrix of "whom you already know" in the class:
+There are other ways to do this,
+but for this unit test I'd like you to do it in R.
+You can follow my example below (copy to a new script and uncomment):
+
+```{r egonet-eg, exercise = TRUE}
+mynetwork <- matrix(0,2,2) # this creates an empty network of 2 people
+# Next I'm going to name the matrix rows and columns:
+rownames(mynetwork) <- c("James Hollway","Korakot Janteerasakul")
+colnames(mynetwork) <- c("James Hollway","Korakot Janteerasakul")
+mynetwork[1,2] <- 1 # this means I know Korakot already
+mynetwork[2,1] <- 1 # I think I can say Korakot knows me already too...
+mynetwork["James Hollway","Korakot Janteerasakul"] <- 1 # I could also do this by name
+# mynetwork[mynetwork > 0] <- 0 # Just in case you make a mistake, this wipes it!
+```
diff --git a/inst/tutorials/tutorial8/diversity.Rmd b/inst/tutorials/tutorial8/diversity.Rmd
index ff475492f..d15e1ed20 100644
--- a/inst/tutorials/tutorial8/diversity.Rmd
+++ b/inst/tutorials/tutorial8/diversity.Rmd
@@ -50,7 +50,7 @@ learnr::random_phrases_add(language = "en",
"Bravo!",
"Super!"),
encouragement = c("Bon effort"))
-marvel_friends <- to_unsigned(ison_marvel_relationships, keep = "positive")
+marvel_friends <- to_unsigned(to_uniplex(fict_marvel, "relationship"), keep = "positive")
marvel_friends <- to_giant(marvel_friends)
marvel_friends <- marvel_friends %>% to_subgraph(Appearances >= mean(Appearances))
```
@@ -73,11 +73,12 @@ By the end of this tutorial, you will be able to:
## Initial visualisation
For this session, we'll explore a couple of different datasets.
-First, let's examine homogeneity/`r gloss("heterogeneity")` in the Marvel relationships dataset from `{manynet}`,
-`ison_marvel_relationships`.
+First, let's examine homogeneity/`r gloss("heterogeneity")` in the Marvel dataset from `{manynet}`,
+`fict_marvel`.
The dataset is quite complicated,
so to make this simpler, let's concentrate on:
+- just the relationships part of the network
- just the positive (friendship) ties and not the negative (enmity) ties
- the main (giant) component without any isolates
- just those characters that appear in the comics more than average
@@ -90,24 +91,23 @@ and can be seen in the following chunk in order:
```
```{r friends-hint-1, purl = FALSE}
+to_uniplex(____, "relationship")
+```
+
+```{r friends-hint-2, purl = FALSE}
# since the dataset is a 'signed' graph, we want to get just the
# positively signed ties to get the friendship graph
# (and lose the enmity relations)
to_unsigned(____, keep = "positive")
```
-```{r friends-hint-2, purl = FALSE}
+```{r friends-hint-3, purl = FALSE}
# to_giant() is a quick easy way to get the giant/main component
to_giant(____)
```
-```{r friends-hint-3, purl = FALSE}
-to_subgraph(____, Appearances >= mean(Appearances))
-```
-
```{r friends-hint-4, purl = FALSE}
-# don't forget to assign the results!
-marvel_friends <- ____
+to_subgraph(____, Appearances >= mean(Appearances))
```
```{r friends-hint-5, purl = FALSE}
diff --git a/inst/tutorials/tutorial9/ergm.Rmd b/inst/tutorials/tutorial9/ergm.Rmd
index bc8ed4eb9..87c77ba39 100644
--- a/inst/tutorials/tutorial9/ergm.Rmd
+++ b/inst/tutorials/tutorial9/ergm.Rmd
@@ -17,6 +17,7 @@ library(ergm)
```{r setup, include = FALSE, purl=FALSE, eval=TRUE}
library(manynet)
+library(netrics)
library(autograph)
library(migraph)
library(ergm)
@@ -89,7 +90,7 @@ What attributes are attached to the nodes of this network that we might
use in modelling?
```{r density, exercise=TRUE, exercise.setup = "ergm-data"}
-net_density(flomarriage)
+net_by_density(flomarriage)
net_node_attributes(flomarriage)
net_tie_attributes(flomarriage)
```
@@ -106,7 +107,7 @@ What do you observe? Describe the network in terms of configurations.
```{r vis-flomarriage, exercise=TRUE, exercise.setup = "ergm-data", fig.width=8}
graphr(flomarriage, node_size = "wealth")
-as_tidygraph(flomarriage) %>% mutate_nodes(Degree = node_deg()) %>%
+as_tidygraph(flomarriage) %>% mutate_nodes(Degree = node_by_deg()) %>%
mutate_ties(Triangles = tie_is_triangular()) %>%
graphr(node_size = "Degree", edge_color = "Triangles")
```
@@ -391,7 +392,7 @@ We need additional modelling!
## Markov model
```{r visflo2, echo=FALSE, purl = FALSE, fig.width=9, setup = "flom-gof"}
-p1 <- as_tidygraph(flomarriage) %>% mutate_nodes(Degree = node_deg()) %>%
+p1 <- as_tidygraph(flomarriage) %>% mutate_nodes(Degree = node_by_deg()) %>%
mutate_ties(Triangles = tie_is_triangular()) %>%
graphr(node_size = "Degree", edge_color = "Triangles") +
ggplot2::theme(legend.position = 'none')
diff --git a/man/figures/logo.png b/man/figures/logo.png
index 13092a832..0c58f72ec 100644
Binary files a/man/figures/logo.png and b/man/figures/logo.png differ
diff --git a/man/figures/logo_old.png b/man/figures/logo_old.png
index a21b608a5..13092a832 100644
Binary files a/man/figures/logo_old.png and b/man/figures/logo_old.png differ
diff --git a/man/figures/logo_older.png b/man/figures/logo_older.png
new file mode 100644
index 000000000..a21b608a5
Binary files /dev/null and b/man/figures/logo_older.png differ
diff --git a/man/mpn_bristol.Rd b/man/mpn_bristol.Rd
index ef00ec304..06ac33437 100644
--- a/man/mpn_bristol.Rd
+++ b/man/mpn_bristol.Rd
@@ -6,8 +6,8 @@
\title{Multimodal (3) Bristol protest events, 1990-2002 (Diani and Bison 2004)}
\format{
\if{html}{\out{}}\preformatted{#> -- # Bristol protest event network ---------------------------------------------
-#> # A labelled, two-mode network of 150 individuals and 1496 affiliation and
-#> participation ties
+#> # A labelled, two-mode network of 150 individuals and 114 individuals and 1496
+#> affiliation and participation ties
#>
#> -- Nodes
#> # A tibble: 264 x 3
diff --git a/man/mpn_cow.Rd b/man/mpn_cow.Rd
index af0933f4e..67456e528 100644
--- a/man/mpn_cow.Rd
+++ b/man/mpn_cow.Rd
@@ -35,7 +35,7 @@
#>
}\if{html}{\out{
}}
-\if{html}{\out{}}\preformatted{#> # A labelled, weighted, two-mode network of 152 nodes and 839 ties
+\if{html}{\out{
}}\preformatted{#> # A labelled, weighted, two-mode network of 112 nodes and 40 nodes and 839 ties
#>
#> -- Nodes
#> # A tibble: 152 x 3
diff --git a/man/mpn_elite_usa.Rd b/man/mpn_elite_usa.Rd
index 6fbed695d..27dca211b 100644
--- a/man/mpn_elite_usa.Rd
+++ b/man/mpn_elite_usa.Rd
@@ -7,7 +7,7 @@
\alias{mpn_elite_usa_money}
\title{Two-mode and three-mode American power elite database (Domhoff 2016)}
\format{
-\if{html}{\out{
}}\preformatted{#> # A labelled, two-mode network of 34 nodes and 46 ties
+\if{html}{\out{
}}\preformatted{#> # A labelled, two-mode network of 14 nodes and 20 nodes and 46 ties
#>
#> -- Nodes
#> # A tibble: 34 x 2
@@ -35,7 +35,7 @@
#>
}\if{html}{\out{
}}
-\if{html}{\out{}}\preformatted{#> # A labelled, two-mode network of 38 nodes and 103 ties
+\if{html}{\out{
}}\preformatted{#> # A labelled, two-mode network of 26 nodes and 12 nodes and 103 ties
#>
#> -- Nodes
#> # A tibble: 38 x 2
diff --git a/man/mpn_senate112.Rd b/man/mpn_senate112.Rd
index 784acebb7..2c44b358e 100644
--- a/man/mpn_senate112.Rd
+++ b/man/mpn_senate112.Rd
@@ -8,7 +8,7 @@
\alias{mpn_senate_over}
\title{Two-mode 112th Congress Senate Voting (Knoke et al. 2021)}
\format{
-\if{html}{\out{
}}\preformatted{#> # A labelled, weighted, two-mode network of 114 nodes and 2791 ties
+\if{html}{\out{
}}\preformatted{#> # A labelled, weighted, two-mode network of 51 nodes and 63 nodes and 2791 ties
#>
#> -- Nodes
#> # A tibble: 114 x 2
@@ -36,7 +36,7 @@
#>
}\if{html}{\out{
}}
-\if{html}{\out{}}\preformatted{#> # A labelled, weighted, two-mode network of 134 nodes and 3675 ties
+\if{html}{\out{
}}\preformatted{#> # A labelled, weighted, two-mode network of 62 nodes and 72 nodes and 3675 ties
#>
#> -- Nodes
#> # A tibble: 134 x 2
@@ -64,7 +64,7 @@
#>
}\if{html}{\out{
}}
-\if{html}{\out{}}\preformatted{#> # A labelled, weighted, two-mode network of 52 nodes and 614 ties
+\if{html}{\out{
}}\preformatted{#> # A labelled, weighted, two-mode network of 20 nodes and 32 nodes and 614 ties
#>
#> -- Nodes
#> # A tibble: 52 x 2
diff --git a/man/test_tutorials.Rd b/man/test_tutorials.Rd
new file mode 100644
index 000000000..2e36367df
--- /dev/null
+++ b/man/test_tutorials.Rd
@@ -0,0 +1,27 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/tutorial_test.R
+\name{test_tutorials}
+\alias{test_tutorials}
+\title{Test tutorials}
+\usage{
+test_tutorials(path, quiet = TRUE)
+}
+\arguments{
+\item{path}{Character vector of the paths to the tutorials to be knitted.}
+
+\item{quiet}{Logical, whether to suppress messages from \code{render()}.}
+}
+\value{
+No return value, called for side effects.
+}
+\description{
+For our purposes, "testing" a tutorial means being able to
+(successfully) run \code{render()} on it.
+This function renders the tutorial provided in \code{path}.
+There is no check to see if the rendered file looks OK.
+If a tutorial fails to render, then an error will be generated which will
+propagate to the caller.
+}
+\author{
+David Kane, see tutorial.helpers
+}
diff --git a/man/tests.Rd b/man/tests.Rd
index ae5196096..d882795d8 100644
--- a/man/tests.Rd
+++ b/man/tests.Rd
@@ -75,14 +75,14 @@ of the original network.
}
}
\examples{
-marvel_friends <- to_unsigned(ison_marvel_relationships)
-marvel_friends <- to_giant(marvel_friends) \%>\%
+marvel_friends <- fict_marvel \%>\% to_uniplex("relationship") \%>\%
+ to_unsigned() \%>\% to_giant() \%>\%
to_subgraph(PowerOrigin == "Human")
-(cugtest <- test_random(marvel_friends, manynet::net_heterophily, attribute = "Attractive",
+(cugtest <- test_random(marvel_friends, net_by_heterophily, attribute = "Attractive",
times = 200))
# plot(cugtest)
# (qaptest <- test_permutation(marvel_friends,
-# manynet::net_heterophily, attribute = "Attractive",
+# net_by_heterophily, attribute = "Attractive",
# times = 200))
# plot(qaptest)
}
diff --git a/man/tutorials.Rd b/man/tutorials.Rd
new file mode 100644
index 000000000..f85734157
--- /dev/null
+++ b/man/tutorials.Rd
@@ -0,0 +1,27 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/tutorial_run.R
+\name{tutorials}
+\alias{tutorials}
+\alias{run_tute}
+\alias{extract_tute}
+\title{Open and extract code from tutorials}
+\usage{
+run_tute(tute)
+
+extract_tute(tute)
+}
+\arguments{
+\item{tute}{String, name of the tutorial (e.g. "tutorial2").}
+}
+\description{
+These functions make it easy to use the tutorials
+in the \code{{manynet}} and \code{{migraph}} packages:
+\itemize{
+\item \code{run_tute()} runs a \code{{learnr}} tutorial from
+either the \code{{manynet}} or \code{{migraph}} packages,
+wraps \code{learnr::run_tutorial()} with some convenience.
+\item \code{extract_tute()} extracts and opens just the solution code
+from a \code{{manynet}} or \code{{migraph}} tutorial,
+saving the .R script to the current working directory.
+}
+}
diff --git a/pkgdown/_pkgdown.yml b/pkgdown/_pkgdown.yml
index 770f53ab2..31d85b188 100644
--- a/pkgdown/_pkgdown.yml
+++ b/pkgdown/_pkgdown.yml
@@ -52,9 +52,18 @@ reference:
- starts_with("test")
- regression
- predict
- - title: "Data"
+
+ - title: "Practicing"
+ - subtitle: "Learning"
+ desc: |
+ Functions for loading and extracting code from tutorials.
+ contents:
+ - tutorials
+ - subtitle: "Data"
desc: |
The package contains multimodal, multilevel, and multiplex network data,
used in the book _Multimodal Political Networks_ (prefixed with `mpn_`).
contents:
- starts_with("mpn_")
+
+
diff --git a/tests/testthat/helper-functions.R b/tests/testthat/helper-functions.R
index 5abaf2d2a..414f5a875 100644
--- a/tests/testthat/helper-functions.R
+++ b/tests/testthat/helper-functions.R
@@ -1,3 +1,5 @@
+knitr::opts_chunk$set(message = FALSE)
+
top3 <- function(res, dec = 4){
if(is.numeric(res)){
unname(round(res, dec))[1:3]
diff --git a/tests/testthat/test-measure_over.R b/tests/testthat/test-measure_over.R
index c758ee89d..6684d7c93 100644
--- a/tests/testthat/test-measure_over.R
+++ b/tests/testthat/test-measure_over.R
@@ -1,10 +1,10 @@
test_that("over_waves works", {
- res <- over_waves(manynet::fict_potter, manynet::net_components)
+ res <- over_waves(manynet::fict_potter, netrics::net_by_components)
# expect_equal(unname(unlist(c(res))), c(48,52,57,43,54,64))
})
test_that("over_membership works", {
- res <- over_membership(fict_potter, manynet::net_assortativity,
- membership = node_in_regular(fict_potter))
+ res <- over_membership(fict_potter, netrics::net_by_assortativity,
+ membership = netrics::node_in_regular(fict_potter))
expect_equal(unname(unlist(c(res))), c(0.490201713,NaN))
})
diff --git a/tests/testthat/test-model_tests.R b/tests/testthat/test-model_tests.R
index a7ebee70d..426e7f372 100644
--- a/tests/testthat/test-model_tests.R
+++ b/tests/testthat/test-model_tests.R
@@ -3,14 +3,14 @@ marvel_friends <- manynet::to_uniplex(manynet::fict_marvel, "relationship") %>%
manynet::to_giant() %>% manynet::to_unsigned() %>%
manynet::to_subgraph(PowerOrigin == "Human")
cugtest <- test_random(marvel_friends,
- manynet::net_heterophily,
+ netrics::net_by_heterophily,
attribute = "Attractive",
times = 200)
cugtest2 <- test_random(marvel_friends,
- manynet::net_betweenness,
+ netrics::net_by_betweenness,
times = 200)
cugtest3 <- test_random(ison_southern_women,
- manynet::net_equivalency,
+ netrics::net_by_equivalency,
times = 200)
test_that("test_random works", {
@@ -40,7 +40,7 @@ test_that("test_random works", {
# Set the qaptest up
qaptest <- test_permutation(marvel_friends,
- manynet::net_heterophily,
+ netrics::net_by_heterophily,
attribute = "Attractive",
times = 200)
@@ -57,7 +57,7 @@ test_that("test_permutation works", {
test_that("test_configuration works", {
testthat::skip_on_os("linux")
configtest <- test_configuration(marvel_friends,
- manynet::net_heterophily,
+ netrics::net_by_heterophily,
attribute = "Attractive",
times = 200)
expect_s3_class(configtest, "network_test")
diff --git a/tests/testthat/test-tutorials_autograph.R b/tests/testthat/test-tutorials_autograph.R
new file mode 100644
index 000000000..2b5aeb9e2
--- /dev/null
+++ b/tests/testthat/test-tutorials_autograph.R
@@ -0,0 +1,8 @@
+test_that("autograph tutorials work", {
+ for(tute.dir in list.dirs(system.file("tutorials", package = "autograph"),
+ recursive = F)){
+ tute.file <- list.files(tute.dir, pattern = "*.Rmd", full.names = T)
+ expect_null(test_tutorials(tute.file))
+ }
+})
+
diff --git a/tests/testthat/test-tutorials_manynet.R b/tests/testthat/test-tutorials_manynet.R
new file mode 100644
index 000000000..d2f6e55ea
--- /dev/null
+++ b/tests/testthat/test-tutorials_manynet.R
@@ -0,0 +1,7 @@
+test_that("manynet tutorials work", {
+ for(tute.dir in list.dirs(system.file("tutorials", package = "manynet"),
+ recursive = F)){
+ tute.file <- list.files(tute.dir, pattern = "*.Rmd", full.names = T)
+ expect_null(test_tutorials(tute.file))
+ }
+})
\ No newline at end of file
diff --git a/tests/testthat/test-tutorials_migraph.R b/tests/testthat/test-tutorials_migraph.R
new file mode 100644
index 000000000..1087eeab5
--- /dev/null
+++ b/tests/testthat/test-tutorials_migraph.R
@@ -0,0 +1,7 @@
+test_that("migraph tutorials work", {
+ for(tute.dir in list.dirs(system.file("tutorials", package = "migraph"),
+ recursive = F)){
+ tute.file <- list.files(tute.dir, pattern = "*.Rmd", full.names = T)
+ expect_null(test_tutorials(tute.file))
+ }
+})
\ No newline at end of file