Packaged: 2020-02-13 07:08:29 UTC; kassambara

Packaged: 2018-11-15 03:11:31 UTC; kassambara

rlang, rstatix (>= 0.4.0), tibble, magrittr
Packaged: 2020-05-03 21:15:34 UTC; kassambara

rlang, rstatix (>= 0.6.0), tibble, magrittr
Packaged: 2020-06-27 05:43:55 UTC; kassambara

rlang (>= 0.4.6), rstatix (>= 0.7.1), tibble, magrittr
Suggests: grDevices, knitr, RColorBrewer, gtable, testthat
Packaged: 2022-11-14 20:58:34 UTC; kassambara

Title: Annotate Statistical Tests for 'ggplot2'
Authors@R: person("Jun", "Cheng", email = "s6juncheng@gmail.com", role = c("aut", "cre"))
Description: Automatically perform desired statistical tests (e.g. wilcox.test(), t.test()) to compare between groups, and add test p-values to the plot with annotation bar.
Visualizing group differences are frequently performed by boxplots, violin plots etc..
Statistical test results are often needed to be annotated on the plots. This package provide a convenient function that work on 'ggplot2' objects,
perform desired statistical test between groups of interest and annotate the test results on the plot.

Description: Automatically performs desired statistical tests (e.g. wilcox.test(), t.test()) to compare between groups,
and adds the resulting p-values to the plot with an annotation bar.
Visualizing group differences are frequently performed by boxplots, bar plots, etc.
Statistical test results are often needed to be annotated on these plots.
This package provides a convenient function that works on 'ggplot2' objects,
performs the desired statistical test between groups of interest and annotates the test results on the plot.

Suggests: knitr, rmarkdown, markdown, testthat

Title: Quickly Explore Your Data Using 'ggplot2' and Summary Tables
person("Michael", "Sachs", email = "sachsmc@gmail.com",
role = c("aut"), comment = "provided ggkm code"),
person("Benjamin", "Rich", email = "mail@benjaminrich.net",
role = c("aut"), comment = "provided table1 code")
Description: Quickly and easily perform exploratory data analysis by uploading your
data as a 'csv' file. Start generating insights using 'ggplot2' plots and
tables with descriptive stats, all using an easy-to-use point and click
Imports: colourpicker, dplyr, DT, Formula, ggplot2, ggrepel (>= 0.7.0),
grDevices, grid, gridExtra, Hmisc, lazyeval, markdown, methods,
plotly, quantreg, rlang, scales, shiny (>= 1.0.4), shinyjs,
Michael Sachs [aut] (provided ggkm code),
Benjamin Rich [aut] (provided table1 code)