README.md

In cities-lab/tidytable1: Creates Table1 Descriptive Statistics with tidyverse packages

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

cities-lab/tidytable1 documentation built on May 9, 2019, 4:54 a.m.