Warning to existing users
This version introduces a new API that should make the creation of tables more flexible.
The old API is still present but in a deprecated mode.
See the roadmap below, and the website for the new usage.
Suggestions about this change are welcome !

Introduction

Desctable aims to be a simple and expressive interface to building statistical tables in R.

Installation

Install from CRAN with

install.packages("desctable")

or install the development version from github with

devtools::install_github("desctable/desctable")

Basic usage

Load the package

library(desctable)

Simply apply desc_table on a dataframe or a grouped dataframe to get a statistical table

iris %>%
  desc_table()

##                   Variables   N        % Min  Q1  Med     Mean  Q3 Max
## 1              Sepal.Length 150       NA 4.3 5.1 5.80 5.843333 6.4 7.9
## 2               Sepal.Width 150       NA 2.0 2.8 3.00 3.057333 3.3 4.4
## 3              Petal.Length 150       NA 1.0 1.6 4.35 3.758000 5.1 6.9
## 4               Petal.Width 150       NA 0.1 0.3 1.30 1.199333 1.8 2.5
## 5               **Species** 150       NA  NA  NA   NA       NA  NA  NA
## 6     **Species**: *setosa*  50 33.33333  NA  NA   NA       NA  NA  NA
## 7 **Species**: *versicolor*  50 33.33333  NA  NA   NA       NA  NA  NA
## 8  **Species**: *virginica*  50 33.33333  NA  NA   NA       NA  NA  NA
##          sd IQR
## 1 0.8280661 1.3
## 2 0.4358663 0.5
## 3 1.7652982 3.5
## 4 0.7622377 1.5
## 5        NA  NA
## 6        NA  NA
## 7        NA  NA
## 8        NA  NA

Declare the statistics you want to see, and give them the name of your choice

iris %>%
  desc_table("N" = length,
             "%" = percent,
             mean,
             sd)

##                   Variables   N        %     mean        sd
## 1              Sepal.Length 150       NA 5.843333 0.8280661
## 2               Sepal.Width 150       NA 3.057333 0.4358663
## 3              Petal.Length 150       NA 3.758000 1.7652982
## 4               Petal.Width 150       NA 1.199333 0.7622377
## 5               **Species** 150       NA       NA        NA
## 6     **Species**: *setosa*  50 33.33333       NA        NA
## 7 **Species**: *versicolor*  50 33.33333       NA        NA
## 8  **Species**: *virginica*  50 33.33333       NA        NA

Create comparative tables, compute statistical tests and output to pander for crisp markdown rendering!

mtcars %>%
  dplyr::mutate(cyl = factor(cyl),
                vs = factor(vs, labels = c("V-shaped", "straight")),
                am = factor(am, labels = c("automatic", "manual"))) %>%
  group_by(am) %>%
  desc_table(N = length,
             "%" = percent,
             "Median" = median,
             IQR) %>%
  desc_tests(vs = ~chisq.test) %>%
  desc_output("pander")

	am = manual (N = 13) N	%	Median	IQR	am = automatic (N = 19) N	%	Median	IQR	p	test
mpg	13		23	9.4	19		17	4.2	< 0.01	wilcox.test
cyl	13				19				< 0.01	fisher.test
4	8	62			3	16
6	3	23			4	21
8	2	15			12	63
disp	13		120	81	19		276	164	< 0.01	wilcox.test
hp	13		109	47	19		175	76	0.046	wilcox.test
drat	13		4.1	0.37	19		3.1	0.63	< 0.01	wilcox.test
wt	13		2.3	0.84	19		3.5	0.41	< 0.01	wilcox.test
qsec	13		17	2.1	19		18	2	0.27	wilcox.test
vs	13				19				0.56	chisq.test
V-shaped	6	46			12	63
straight	7	54			7	37
gear	13		4	1	19		3	0	< 0.01	wilcox.test
carb	13		2	3	19		3	2	0.74	wilcox.test