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* changed unit test json Signed-off-by: Chinmay Singh <chsingh@microsoft.com> * updated domain Signed-off-by: Chinmay Singh <chsingh@microsoft.com> * custom animation for sum Signed-off-by: Chinmay Singh <chsingh@microsoft.com> * stacked layout Signed-off-by: Chinmay Singh <chsingh@microsoft.com> * adjust y-axis encoding Signed-off-by: Chinmay Singh <chsingh@microsoft.com> * automatic tasks * use existing elements for timing * grid specs for sum |
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R | ||
data | ||
data-raw | ||
datamations | ||
demo-app | ||
dist | ||
inst | ||
man | ||
notebooks | ||
renv | ||
sandbox | ||
tests | ||
vignettes | ||
.Rbuildignore | ||
.Rprofile | ||
.gitignore | ||
.pylintrc | ||
DESCRIPTION | ||
LICENSE | ||
LICENSE.md | ||
NAMESPACE | ||
PythonSettings.json | ||
README.Rmd | ||
README.md | ||
SECURITY.md | ||
_pkgdown.yml | ||
app.R | ||
azure-pipelines.yml | ||
datamations.Rproj | ||
package-lock.json | ||
renv.lock | ||
setup.py |
README.md
datamations
datamations is a framework for the automatic generation of explanation of the steps of an analysis pipeline. It automatically turns code into animations, showing the state of the data at each step of an analysis.
For more information, please visit the package website, which includes additional examples, defaults and conventions, and more.
Installation
You can install datamations from GitHub with:
# install.packages("devtools")
devtools::install_github("microsoft/datamations")
Usage
To get started, load datamations and dplyr:
A datamation shows a plot of what the data looks like at each step of a
tidyverse pipeline, animated by the transitions that lead to each state.
The following shows an example taking the built-in small_salary
data
set, grouping by Degree
, and calculating the mean Salary
.
First, define the code for the pipeline, then generate the datamation
with datamation_sanddance()
:
library(datamations)
library(dplyr)
"small_salary %>%
group_by(Degree) %>%
summarize(mean = mean(Salary))" %>%
datamation_sanddance()
datamations supports the following dplyr
functions:
group_by()
(up to three grouping variables)summarize()
/summarise()
(limited to summarizing one variable)filter()
count()
/tally