Output each factor level as dummy variable in stargazer summary statistics table

Question

I'm using the R package stargazer to create high-quality regression tables, and I would like to use it to create a summary statistics table. I have a factor variable in my data, and I would like the summary table to show me the percent in each category of the factor -- in effect, separate the factor into a set of mutually exclusive logical (dummy) variables, and then display those in the table. Here's an example:

> library(car)
> library(stargazer)
> data(Blackmore)
> stargazer(Blackmore[, c("age", "exercise", "group")], type = "text")

==========================================
Statistic  N   Mean  St. Dev.  Min   Max  
------------------------------------------
age       945 11.442  2.766   8.000 17.920
exercise  945 2.531   3.495   0.000 29.960
------------------------------------------

But I'm trying to get an additional row that shows me the percent in each group (% control and/or % patient, in these data). I'm sure this is just an option somewhere in stargazer, but I can't find it. Does anyone know what it is?

Edit: car::Blackmoor has updated spelling to car::Blackmore.

Answer 1

Since Stargazer can't do this directly, you can create your own summary table as a data frame and output that using pander, xtable, or any other package. For example, here's how you can use dplyr and tidyr to create a summary table:

library(dplyr)
library(tidyr)

fancy.summary <- Blackmoor %>%
  select(-subject) %>%  # Remove the subject column
  group_by(group) %>%  # Group by patient and control
  summarise_each(funs(mean, sd, min, max, length)) %>%  # Calculate summary statistics for each group
  mutate(prop = age_length / sum(age_length)) %>%  # Calculate proportion
  gather(variable, value, -group, -prop) %>%  # Convert to long
  separate(variable, c("variable", "statistic")) %>%  # Split variable column
  mutate(statistic = ifelse(statistic == "length", "n", statistic)) %>%
  spread(statistic, value) %>%  # Make the statistics be actual columns
  select(group, variable, n, mean, sd, min, max, prop)  # Reorder columns

Which results in this if you use pander:

library(pander)

pandoc.table(fancy.summary)

------------------------------------------------------
 group   variable   n   mean   sd    min   max   prop 
------- ---------- --- ------ ----- ----- ----- ------
control    age     359 11.26  2.698   8   17.92 0.3799

control  exercise  359 1.641  1.813   0   11.54 0.3799

patient    age     586 11.55  2.802   8   17.92 0.6201

patient  exercise  586 3.076  4.113   0   29.96 0.6201
------------------------------------------------------

Answer 2

Another workaround is to use model.matrix to create dummy variables in a separate step, and then use stargazer to create a table from that. To show this with the example:

> library(car)
> library(stargazer)
> data(Blackmore)
> 
> options(na.action = "na.pass")  # so that we keep missing values in the data
> X <- model.matrix(~ age + exercise + group - 1, data = Blackmore)
> X.df <- data.frame(X)  # stargazer only does summary tables of data.frame objects
> names(X) <- colnames(X)
> stargazer(X.df, type = "text")

=============================================
Statistic     N   Mean  St. Dev.  Min   Max  
---------------------------------------------
age          945 11.442  2.766   8.000 17.920
exercise     945 2.531   3.495   0.000 29.960
groupcontrol 945 0.380   0.486     0     1   
grouppatient 945 0.620   0.486     0     1   
---------------------------------------------

Edit: car::Blackmoor has updated spelling to car::Blackmore.

Answer 3

The package tables can be useful for this task.

library(car)
library(tables)
data(Blackmore)

# percent only:
(x <- tabular((Factor(group, "") ) ~ (Pct=Percent()) * Format(digits=4), 
    data=Blackmore))
##              
##         Pct  
## control 37.99
## patient 62.01

# percent and counts:
(x <- tabular((Factor(group, "") ) ~ ((n=1) + (Pct=Percent())) * Format(digits=4), 
    data=Blackmore))
##                      
##         n      Pct   
## control 359.00  37.99
## patient 586.00  62.01

Then it's straightforward to output this to LaTeX:

> latex(x)
\begin{tabular}{lcc}
\hline
  & n & \multicolumn{1}{c}{Pct} \\ 
\hline
control  & $359.00$ & $\phantom{0}37.99$ \\
patient  & $586.00$ & $\phantom{0}62.01$ \\
\hline 
\end{tabular}