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The Stargazer package in R is a useful tool for creating high-quality summary tables and regression output for academic research papers or presentations. To use the package, first install it and load it into your R session. Then, you can use the stargazer() function to create a summary table or regression output for your data. For example, if you have a dataset of housing prices and want to compare the mean prices for different regions, you can use stargazer to create a table that displays the mean prices for each region in a visually appealing and easy-to-read format. This table can then be included in your paper or presentation to effectively communicate your findings.
You can use the stargazer package in R to create high quality tables that can be used in publications.
The following example shows how to get started with the stargazer package by using the built-in R dataset called .
Example: How to Use the stargazer Package in R
First, we can use the following code to install and load the stargazer package:
#install stargazer package install.packages('stargazer') #load stargazer package library(stargazer)
Once we’ve loaded the package, we can use the stargazer function to produce high quality tables.
This function uses the following basic syntax:
stargazer(df, type=’text’, title=’my_title’, out=’my_data.txt’, …)
where:
- df: Name of the data frame to use
- type: Type of output to display
- title: Title to show at top of table
- out: Name of file to use when exporting table
Note that for the following examples we’ll use .txt to export text files but you can use .html if you’d like to export HTML files instead.
The stargazer function is typically used to create two different types of tables:
- A table of summary statistics for each variable in a data frame
- A table that summarizes the results of a regression model
We can use the following code to create a table that displays summary statistics for each variable in the mtcars data frame:
#create table that provide summary statistics of each variable in dataset stargazer(mtcars, type='text', title='Summary Statistics', out='mtcars_data.txt') Summary Statistics ============================================================= Statistic N Mean St. Dev. Min Pctl(25) Pctl(75) Max ------------------------------------------------------------- mpg 32 20.091 6.027 10 15.4 22.8 34 cyl 32 6.188 1.786 4 4 8 8 disp 32 230.722 123.939 71 120.8 326 472 hp 32 146.688 68.563 52 96.5 180 335 drat 32 3.597 0.535 2.760 3.080 3.920 4.930 wt 32 3.217 0.978 1.513 2.581 3.610 5.424 qsec 32 17.849 1.787 14.500 16.892 18.900 22.900 vs 32 0.438 0.504 0 0 1 1 am 32 0.406 0.499 0 0 1 1 gear 32 3.688 0.738 3 3 4 5 carb 32 2.812 1.615 1 2 4 8 -------------------------------------------------------------
Note that a table of summary statistics has been produced for each variable in the mtcars data frame.
If we’d like, we can also navigate to the location where we exported mtcars_data.txt to view the text file that contains these summary statistics.
#fit regression model fit <- lm(mpg ~ disp + hp, data=mtcars) #create table that summarizes regression model stargazer(fit, type='text', title='Regression Summary', out='mtcars_regression.txt') Regression Summary =============================================== Dependent variable: --------------------------- mpg ----------------------------------------------- disp -0.030*** (0.007) hp -0.025* (0.013) Constant 30.736*** (1.332) ----------------------------------------------- Observations 32 R2 0.748 Adjusted R2 0.731 Residual Std. Error 3.127 (df = 29) F Statistic 43.095*** (df = 2; 29) =============================================== Note: *p<0.1; **p<0.05; ***p<0.01
The output displays the coefficients for each term in the regression model along with various summary statistics near the bottom of the table.
Note: You can find the complete documentation for the stargazer package .
Additional Resources
The following tutorials explain how to perform other common operations in R:
How to Loop Through Column Names in R
How to Create an Empty Data Frame in R
How to Append Rows to a Data Frame in R