将各因子水平输出为观星汇总统计表中的虚拟变量 [英] Output each factor level as dummy variable in stargazer summary statistics table

问题描述

我正在使用 R 包 stargazer 创建高质量的回归表，我想用它来创建一个汇总统计表.我的数据中有一个因子变量，我希望汇总表显示因子在每个类别中的百分比——实际上，将因子分成一组互斥的逻辑(虚拟)变量，然后显示表中的那些.举个例子:

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
------------------------------------------

但我试图获得一个额外的行，显示每组中的百分比(在这些数据中，% 控制和/或 % 患者).我确定这只是观星者某个地方的一个选项，但我找不到它.有人知道是什么吗?

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?

car::Blackmoor 已将拼写更新为 car::Blackmore.

推荐答案

由于 Stargazer 无法直接执行此操作，因此您可以创建自己的汇总表作为数据框并使用 pander、xtable 或任何其他包进行输出.例如，以下是如何使用 dplyr 和 tidyr 创建汇总表:

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

如果你使用 pander 会导致这个:

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
------------------------------------------------------

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