Creates a Table1 for Descriptive Statistics with tidyverse utils

Installation

devtools::install_github("cities-lab/tidytable1")

Usage

library(tidytable1)
library(dplyr)
library(pander)

iris_tbl1 <- tidytable1(iris,
                        calc_cols=list(n=function(x) length(x)),
                        num_cols=c(min=min, median=median, max=max)) %>%
  pp_table1(pp_vars=c("n")) 

iris_tbl1 %>%
  pander(missing="")

Name	min	median	max	Category	freq	%	n
Sepal.Length	4.3	5.8	7.9				150
Sepal.Width	2	3	4.4				150
Petal.Length	1	4.35	6.9				150
Petal.Width	0.1	1.3	2.5				150
Species							150
				setosa	50	33.33
				versicolor	50	33.33
				virginica	50	33.33

library(htmlTable)

# tidytable1 differentiate numeric and non-numeric variables
mtcars_df <- mtcars %>%
  mutate(cyl=factor(cyl), vs=factor(vs), am=factor(am, labels = c("automatic", "manual"))) %>%
  as_tibble()

num_vars <- mtcars_df %>% select_if(is.numeric) %>% names
cat_vars <- mtcars_df %>% select_if(~!is.numeric(.)) %>% names            
row_order <- list(Name=c(num_vars, cat_vars))

## organized variables by numeric + categorical
mtcars_tbl1 <- tidytable1(mtcars_df,
                       info_cols=list(Label=c(mpg="Miles/(US) gallon", cyl="Number of cylinders", 
                                              disp="Displacement (cu.in.)", hp="Gross horsepower",
                                              drat="Rear axle ratio", wt="Weight (1000 lbs)",
                                              qsec="1/4 mile time", vs="V/S", 
                                              am="Transmission", gear="Number of forward gears",
                                              carb="Number of carburetors"),
                                      Source=c(mpg="1974 Motor Trend")),
                       num_cols=c(mean=mean, sd=sd),
                       row_order = row_order) %>%
  pp_table1(pp_vars=c("Label", "Source", "#missing"))

mtcars_tbl1 %>% 
  htmlTable(rnames = FALSE,
            align="lll rrc rrr",
            rgroup = c("Numeric Variables", "Categorical Variables"),
            n.rgroup = c(length(num_vars), length(cat_vars)))

Name	Label	Source	mean	sd	Category	freq	%	\#missing
Numeric Variables
mpg	Miles/(US) gallon	1974 Motor Trend	20.09	6.03				0
disp	Displacement (cu.in.)		230.72	123.94				0
hp	Gross horsepower		146.69	68.56				0
drat	Rear axle ratio		3.6	0.53				0
wt	Weight (1000 lbs)		3.22	0.98				0
qsec	1/4 mile time		17.85	1.79				0
gear	Number of forward gears		3.69	0.74				0
carb	Number of carburetors		2.81	1.62				0
Categorical Variables
cyl	Number of cylinders							0
					4	11	34.38
					6	7	21.88
					8	14	43.75
vs	V/S							0
					0	18	56.25
					1	14	43.75
am	Transmission							0
					automatic	19	59.38
					manual	13	40.62

TODO ====

• Enable stats by a grouping variable similar the behavior in tableone
• Add hypothesis testing functionality

Related Projects

• Gmisc
• tableone
• stargazer

Build Status