This repository holds digital assets associated with the article "An R data parser for the gPhone gravity meter" [1]. That article describes R Markdown designed to parse and import gPhone 74 gravity meter data into RStudio. Users may then inspect and analyse the data further using RStudio's Data Viewer or R scripting.
Figure 1. Example gPhone 74 gravity meter data parsed into an R dataframe.
| File | Notes |
|---|---|
data/gphone-074-test-data.tsf |
gPhone 74 test data (14.6 MB). |
src/gphone-import.Rmd |
R Markdown. |
| Software | Notes |
|---|---|
| R | Available here. Free. |
| RStudio | Available here. Free and fee-based options. |
Please ensure the R environment has the following packages installed.
- dplyr
- lubridate
- readr
- rmarkdown
- rstudioapi
Please ensure their dependencies are also installed.
Dependencies
- base64enc
- bit
- bit64
- bslib
- cachem
- clipr
- cpp11
- crayon
- digest
- evaluate
- fastmap
- fontawesome
- fs
- generics
- hms
- highr
- htmltools
- jquerylib
- jsonlite
- knitr
- memoise
- mime
- pillar
- pkgconfig
- prettyunits
- progress
- R6
- rappdirs
- sass
- tibble
- tidyselect
- timechange
- tinytex
- tzdb
- utf8
- vroom
- withr
- xfun
- yaml
The data parser was tested in the following environment.
Windows Test Environment
| Type | Component | Version |
|---|---|---|
| Platform | Operating system | Windows 11, 25H2 (OS Build 26200.8894) |
| Software | R | 4.6.1 |
| " | RStudio | 2026.07.0 (Build 139) |
| R package | dplyr | 1.2.1 |
| " | lubridate | 1.9.5 |
| " | readr | 2.2.0 |
| " | rmarkdown | 2.31 |
| " | rstudioapi | 0.19.0 |
| Data | gphone-074-test-data.tsf |
Repository dataset. |
The file gphone-import.Rmd should be run from RStudio.
The R Markdown file is designed to parse and import gPhone 74 gravimetry data in time series format (TSF). When run, the R Markdown file prompts users to select a TSF file to import.
The file gphone-074-test-data.tsf, supplied in this repository, holds example
time series data that may be used to test the parser.
This work was supported by the Australian Research Council Training Centre in Data Analytics for Resources and Environments (project ICI9010031).