11 Lab 5 (R)
11.1 Lab Goals & Instructions
Today we are back to using the billionaires dataset.
Research Question: What characteristics are associated with wealth among billionaires?
Goals
 Produce journalready tables in Stata (or at least close to journalready)
Instructions
 Download the data and the script file from the lab files below.
 Run through the script file and reference the explanations on this page if you get stuck.
 Work on the challenge activity if you have time.
Libraries
# Load libraries
library(tidyverse)
# The three libraries we'll be using in this lab
# You should also install the package "flextable"
library(gtsummary)
library(stargazer)
library(huxtable)
# install.packages("flextable")
Jump Links to Commands in this Lab:
11.2 Saving Models
In Stata we have to download a new package to save the results from our regressions. In R, we save results as objects in the environment pretty regularly. To make building tables easy, make sure you save your results as objects.
< lm(wealthworthinbillions ~ female + age, data = mydata)
m1 summary(m1)
Call:
lm(formula = wealthworthinbillions ~ female + age, data = mydata)
Residuals:
Min 1Q Median 3Q Max
3.764 2.217 1.507 0.091 72.480
Coefficients:
Estimate Std. Error t value Pr(>t)
(Intercept) 1.461568 0.557531 2.622 0.00881 **
female 0.430905 0.389897 1.105 0.26920
age 0.035492 0.008692 4.083 4.6e05 ***

Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 5.388 on 2226 degrees of freedom
(385 observations deleted due to missingness)
Multiple Rsquared: 0.007916, Adjusted Rsquared: 0.007025
Fstatistic: 8.881 on 2 and 2226 DF, pvalue: 0.000144
11.3 Building Tables
In this section, we will focus on three packages you can use to build and export tables: stargazer, gtsummary, and huxtable. We’ll cover their basics, as well as changes you can make to the visual of the table.
Here are some more resources for each:
 stargazer: https://www.jakeruss.com/cheatsheets/stargazer/
 gtsummary: https://www.danieldsjoberg.com/gtsummary/
 huxtable: https://ramorel.github.io/posts/regtableshuxtable/
11.3.1 Tables with stargazer
Stargazer is a relatively straightforward package that can build nice tables. It’s pros are its simplicity and ability to be used in R markdown. It’s cons are that it’s hard to export in word format and not quite as flexible as our final option. This is the package I see recommended most for building regression tables.
Regression tables with stargazer
To build a regression table in this package, we use the function stargazer()
and putting your saved model results into the function.
You will also need to specify a type in the command, either “text” or “html.”
Text will allow you to see the output in a regular script file. Html will
show up better in an Rmarkdown file.
Let’s start by producing a simple table from the model we already saved in text format:
stargazer(m1, type = "text")
===============================================
Dependent variable:

wealthworthinbillions

female 0.431
(0.390)
age 0.035***
(0.009)
Constant 1.462***
(0.558)

Observations 2,229
R2 0.008
Adjusted R2 0.007
Residual Std. Error 5.388 (df = 2226)
F Statistic 8.881*** (df = 2; 2226)
===============================================
Note: *p<0.1; **p<0.05; ***p<0.01
Text format isn’t the best option for inserting a table into word, but if you wanted to
save it as a text file, you specify the file to create with the out = "filepath"
option.
stargazer(m1, type = "text", out = "figs_output/example.txt")
Now let’s try an html version. The output we see in our code is just html code. To look at it we need to save it to an html file. Here is our simple table in html format and how it will look when you open the file:
stargazer(m1, type = "html", out = "figs_output/example.htm")
If you want to insert this table into a word document you can take a screenshot and save the table as an image. For this reason, stargazer is not my favorite package
Descriptive statistic tables with stargazer
Stargazer can also create descriptive statistic tables with the same stargazer()
function. Instead of listing the model results in the function, you put in the name of your dataframe with the variables you want to appear in the table.
For some reason, this command only works if you explicitly save your dataframe as a dataframe.
< as.data.frame(mydata) %>%
mydata mutate(wealthtype2 = factor(wealthtype2,
labels = c("Executive",
"Founder (nonfinance)",
"Inherited",
"Privatized",
"Selfmade (finance)")))
Here’s a basic descriptive statistics table in text format:
stargazer(mydata, type = "text")
==========================================================================================================================
Statistic N Mean St. Dev. Min Pctl(25) Pctl(75) Max

rank 2,614 599.673 467.886 1 215 988 1,565
year 2,614 2,008.412 7.484 1,996 2,001 2,014 2,014
companyfounded 2,614 1,924.712 243.777 0 1,936 1,985 2,012
demographicsage 2,614 53.341 25.333 42 47 70 98
locationgdp 2,614 1,769,103,031,873.000 3,547,082,560,202.000 0 0 725,000,000,000 10,600,000,000,000
wealthworthinbillions 2,614 3.532 5.089 1.000 1.400 3.500 76.000
wealthhowinherited2 2,614 4.386 1.194 1 4 5 6
wealthhowcategory2 2,613 4.662 1.651 1.000 3.000 6.000 9.000
wealthhowindustry2 2,613 8.715 4.637 1.000 4.000 13.000 19.000
locationregion2 2,614 4.236 1.720 1 3 6 8
locationcountrycode2 2,614 45.674 24.173 1 23 71 74
locationcitizenship2 2,614 46.764 23.861 1 23 71 73
demographicsgender2 2,580 1.905 0.298 1.000 2.000 2.000 3.000
companysector2 2,591 271.851 143.659 1.000 132.000 402.000 520.000
companyrelationship2 2,568 45.266 18.268 1.000 32.000 66.000 74.000
age 2,229 62.576 13.135 12.000 53.000 72.000 98.000
female 2,577 0.097 0.296 0.000 0.000 0.000 1.000

You can also select specific variables using the tidyverse pipe (%>% select()
) and which specific statistics should display. Here are the statistics options you have available:
n
: Number of observationsmean
: Meansd
: Standard devaitionmin
: Minimum valuemax
: Maximum valuemedian
: Medianp25
: 25th percentile valuep75
: 75th percentile value
These are all options in the command itself that you can set to TRUE
or FALSE
.
stargazer(
%>% select(wealthworthinbillions, wealthtype2, female, age),
mydata summary.stat = c("n", "mean", "sd", "min", "max"),
type = "text"
)
=========================================================
Statistic N Mean St. Dev. Min Max

wealthworthinbillions 2,614 3.532 5.089 1.000 76.000
female 2,577 0.097 0.296 0.000 1.000
age 2,229 62.576 13.135 12.000 98.000

Variations on your stargazer tables
You can also add numerous options to your stargazer command to improve the look of the overall table. Some of the ones you’ll use most often are:
title
: Add a table titlecovariate.labels
: Add a label to the variables in your table instead of its name in your dataframe.dept.var.labels
: Label the dependent variable in your modelcolumn.labels
: When you have multiple models in your regression table, label each model (aka column).digits
: Round to a certain number of decimals.
Here’s an example of a descriptive table with some specifications:
stargazer(
%>% select(wealthworthinbillions, wealthtype2, female, age),
mydata summary.stat = c("n", "mean", "sd", "min", "max"),
type = "text",
digits = 2, # round to 2 decimal points
title = "Table 1. Descriptive Statistics", # add a title
covariate.labels = c("Wealth (in billions)", "Wealth Type", "Female", "Age" )
)
Table 1. Descriptive Statistics
=====================================================
Statistic N Mean St. Dev. Min Max

Wealth (in billions) 2,614 3.53 5.09 1.00 76.00
Wealth Type 2,577 0.10 0.30 0.00 1.00
Female 2,229 62.58 13.13 12.00 98.00

Note this does not handle categorical variables well. It is treating wealth type as a numeric variable.
Regression tables with multiple models with stargazer
Most journal tables will include more than one model. Stargazer has this capability, as do all the options we are looking at in this lab.
First, let’s run and save a second regression:
< lm(wealthworthinbillions ~ female + age + wealthtype2, data = mydata) m2
Here’s the basic table with two models:
stargazer(m1, m2, type = "text")
================================================================================
Dependent variable:

wealthworthinbillions
(1) (2)

female 0.431 0.228
(0.390) (0.414)
age 0.035*** 0.039***
(0.009) (0.009)
wealthtype2Founder (nonfinance) 1.294***
(0.467)
wealthtype2Inherited 1.236***
(0.466)
wealthtype2Privatized 1.447***
(0.555)
wealthtype2Selfmade (finance) 0.413
(0.487)
Constant 1.462*** 0.275
(0.558) (0.696)

Observations 2,229 2,227
R2 0.008 0.015
Adjusted R2 0.007 0.012
Residual Std. Error 5.388 (df = 2226) 5.377 (df = 2220)
F Statistic 8.881*** (df = 2; 2226) 5.583*** (df = 6; 2220)
================================================================================
Note: *p<0.1; **p<0.05; ***p<0.01
And here’s the two model table with specifications:
stargazer(m1, m2,
type = "text",
digits = 2,
dep.var.labels = c("Wealth (in billions)"), # Label dep variable
# Covariate labels
covariate.labels = c("Female", "Age", "Wealth Type: Founder",
"Wealth Type: Inherited",
"Wealth Type: Privatized",
"Wealth Type: Selfmade (finance)"),
# Name the models
column.labels = c("Model 1", "Model 2")
)
==============================================================================
Dependent variable:

Wealth (in billions)
Model 1 Model 2
(1) (2)

Female 0.43 0.23
(0.39) (0.41)
Age 0.04*** 0.04***
(0.01) (0.01)
Wealth Type: Founder 1.29***
(0.47)
Wealth Type: Inherited 1.24***
(0.47)
Wealth Type: Privatized 1.45***
(0.55)
Wealth Type: Selfmade (finance) 0.41
(0.49)
Constant 1.46*** 0.28
(0.56) (0.70)

Observations 2,229 2,227
R2 0.01 0.01
Adjusted R2 0.01 0.01
Residual Std. Error 5.39 (df = 2226) 5.38 (df = 2220)
F Statistic 8.88*** (df = 2; 2226) 5.58*** (df = 6; 2220)
==============================================================================
Note: *p<0.1; **p<0.05; ***p<0.01
11.3.2 Tables with gtsummary
gtsummary
is another relatively straightforward table building package.
It has the advantage of showing a nice html output It is also a great option for building summary statistics tables because it handles categorical variables much better. Cons are the options are much more complicated and split between in function options and
piped options and it can’t be exported directly to word.
Regression tables with gtsummary
The basic command we’ll be using from this package for regression tables is tbl_regression()
and the saved model results.
tbl_regression(m2)
Characteristic  Beta  95% CI^{1}  pvalue 

female  0.23  0.58, 1.0  0.6 
age  0.04  0.02, 0.06  <0.001 
wealthtype2  
Executive  —  —  
Founder (nonfinance)  1.3  0.38, 2.2  0.006 
Inherited  1.2  0.32, 2.2  0.008 
Privatized  1.4  0.36, 2.5  0.009 
Selfmade (finance)  0.41  0.54, 1.4  0.4 
^{
1
}
CI = Confidence Interval

Here is how you add labels to the covariates:
tbl_regression(m2,
# Add variable labels
label = c(female ~ "Female",
~ "Age",
age ~ "Wealth Type")
wealthtype2 )
Characteristic  Beta  95% CI^{1}  pvalue 

Female  0.23  0.58, 1.0  0.6 
Age  0.04  0.02, 0.06  <0.001 
Wealth Type  
Executive  —  —  
Founder (nonfinance)  1.3  0.38, 2.2  0.006 
Inherited  1.2  0.32, 2.2  0.008 
Privatized  1.4  0.36, 2.5  0.009 
Selfmade (finance)  0.41  0.54, 1.4  0.4 
^{
1
}
CI = Confidence Interval

Regression tables with multiple models with gtsummary
To put two models together with this package, you have to save the separate tables as objects and then combine them with the tbl_merge()
function.
So here we save the two models:
< tbl_regression(m1,
tbl1 # Add variable labels
label = c(female ~ "Female",
~ "Age"))
age < tbl_regression(m2,
tbl2 # Add variable labels
label = c(female ~ "Female",
~ "Age",
age ~ "Wealth Type")) wealthtype2
And here we put them together:
tbl_merge(
list(tbl1, tbl2),
tab_spanner = c("**Model 1**", "**Model 2**")
)
Characteristic  Model 1  Model 2  

Beta  95% CI^{1}  pvalue  Beta  95% CI^{1}  pvalue  
Female  0.43  0.33, 1.2  0.3  0.23  0.58, 1.0  0.6 
Age  0.04  0.02, 0.05  <0.001  0.04  0.02, 0.06  <0.001 
Wealth Type  
Executive  —  —  
Founder (nonfinance)  1.3  0.38, 2.2  0.006  
Inherited  1.2  0.32, 2.2  0.008  
Privatized  1.4  0.36, 2.5  0.009  
Selfmade (finance)  0.41  0.54, 1.4  0.4  
^{
1
}
CI = Confidence Interval

Descriptive statistics tables with gtsummary
To create descriptive statistics tables with this package, we use the function tbl_summary
and list the dataframe with the variables we want to summarize.
Here is a simple summary table, with selected variables from our data frame:
tbl_summary(
%>% select(wealthworthinbillions, female, age, wealthtype2)
mydata )
Characteristic  N = 2,614^{1} 

wealthworthinbillions  2.0 (1.4, 3.5) 
female  249 (9.7%) 
Unknown  37 
age  62 (53, 72) 
Unknown  385 
wealthtype2  
Executive  190 (7.3%) 
Founder (nonfinance)  713 (28%) 
Inherited  953 (37%) 
Privatized  236 (9.1%) 
Selfmade (finance)  500 (19%) 
Unknown  22 
^{
1
}
Median (IQR); n (%)

Here is a table with these options specified:
type = all_continuous()
: Allow multiple lines of descriptive statistics for continuous variables.statistic = list(all_continuous() ~ c(), all_categorical())
: Specificy the statistics used separately for continouous and categorical variables.missing
: Remove missing rows.modify_header(label = "")
: Modify the header in the table.
tbl_summary(
%>% select(wealthworthinbillions, female, age, wealthtype2),
mydata # Specify continuous statistics
type = all_continuous() ~ "continuous2", # Allows multiple lines
statistic = list(all_continuous() ~ c("{mean} ({sd})",
"{min}, {max}"),
all_categorical() ~ "{p}%"),
# Add labels
label = c(female ~ "Female",
~ "Age",
age ~ "Wealth Type",
wealthtype2 ~ "Wealth (in billions)"),
wealthworthinbillions # Remove missing rows
missing = "no"
%>%
) # Modify header (the asterics keep the label bolded)
modify_header(label = "**Variable**")
Variable  N = 2,614^{1} 

Wealth (in billions)  
Mean (SD)  3.5 (5.1) 
Range  1.0, 76.0 
Female  9.7% 
Age  
Mean (SD)  63 (13) 
Range  12, 98 
Wealth Type  
Executive  7.3% 
Founder (nonfinance)  28% 
Inherited  37% 
Privatized  9.1% 
Selfmade (finance)  19% 
^{
1
}
%

Variations on your gtsummary tables
There are SO many options to gtsummary
that I can’t list here, so here are some more resources that you can use to explore this package:
 Descriptive statistics: https://www.danieldsjoberg.com/gtsummary/articles/tbl_summary.html
 Regression tables: https://www.danieldsjoberg.com/gtsummary/articles/tbl_regression.html
11.3.3 Tables with huxtable
Huxtable is the most flexible and considered by some to be the least painful option for building tables in R. It also exports directly to word. Cons are that it doesn’t create summary statistic tables (that I can find).
Regression tables with huxtable
The basic command that you will use to create regression tables with this package is huxreg()
with the saved model results listed.
Here’s a basic regression table with huxreg
:
huxreg(m2)
(1)  

(Intercept)  0.275 
(0.696)  
female  0.228 
(0.414)  
age  0.039 *** 
(0.009)  
wealthtype2Founder (nonfinance)  1.294 ** 
(0.467)  
wealthtype2Inherited  1.236 ** 
(0.466)  
wealthtype2Privatized  1.447 ** 
(0.555)  
wealthtype2Selfmade (finance)  0.413 
(0.487)  
N  2227 
R2  0.015 
logLik  6902.389 
AIC  13820.777 
*** p < 0.001; ** p < 0.01; * p < 0.05. 
You can also save the table directly to a word document!
huxreg(m2) %>%
quick_docx(file = "figs_output/example_huxreg.docx")
Or you can just turn it into an html object in the RStudio console:
huxreg(m2) %>%
as_flextable()
(1)  
(Intercept)  0.275 
(0.696)  
female  0.228 
(0.414)  
age  0.039 *** 
(0.009)  
wealthtype2Founder (nonfinance)  1.294 ** 
(0.467)  
wealthtype2Inherited  1.236 ** 
(0.466)  
wealthtype2Privatized  1.447 ** 
(0.555)  
wealthtype2Selfmade (finance)  0.413 
(0.487)  
N  2227 
R2  0.015 
logLik  6902.389 
AIC  13820.777 
*** p < 0.001; ** p < 0.01; * p < 0.05. 
Variations on your huxtable
tables
Like the other packages, there are many many options you can use to change how your tables look. Here are some basic options:
statistics = c()
: Specify the statistics shown at the bottom of the table.note = ""
: Add a note to the bottom (hint: include {stars} in your note to keep the explanation of the pvalues).coeffs = c()
: Label your variables.set_caption()
: Add a title or other note to your table (this comes after the main function with a pipe).set_caption_pos()
: Specify the position of your title or caption (this comes after the main function with a pipe).
Here’s what it all looks like together:
huxreg("Model 2" = m2, #name the model
# Specify statistics at the bottom
statistics = c('N' = "nobs", 'Rsquared' = "r.squared", 'AIC' = "aic"),
# Add a note (but keep the pvalue expanations)
note = "{stars}
Source: Forbes Billionaire Data",
# Add variable names
coefs = c("Female" = "female",
"Age" = "age",
"Founder (nonfinance)" = "wealthtype2Founder (nonfinance)",
"Inherited" = "wealthtype2Inherited",
"Privatized" = "wealthtype2Privatized",
"Selfmade" = "wealthtype2Selfmade (finance)")
%>%
) # Add a title with a pipe
set_caption("Table 2. Model Results") %>%
set_caption_pos("topleft")
Model 2  

Female  0.228 
(0.414)  
Age  0.039 *** 
(0.009)  
Founder (nonfinance)  1.294 ** 
(0.467)  
Inherited  1.236 ** 
(0.466)  
Privatized  1.447 ** 
(0.555)  
Selfmade  0.413 
(0.487)  
N  2227 
Rsquared  0.015 
*** p < 0.001; ** p < 0.01; * p < 0.05 Source: Forbes Billionaire Data 
Regression tables with multiple models with huxtable
To create a table with multiple models we have to save the models in a list object, and then run the huxreg()
command.
First let’s create a list of the models you want. We will also name them to add column names to our table:
< list("Model 1" = m1, "Model 2" = m2) models
And now we can build the table with some specifications:
huxreg(models,
statistics = c('N' = "nobs", 'Rsquared' = "r.squared", 'AIC' = "AIC"),
note = "{stars}
Source: Forbes Billionaire Data",
coefs = c("Female" = "female",
"Age" = "age",
"Founder (nonfinance)" = "wealthtype2Founder (nonfinance)",
"Inherited" = "wealthtype2Inherited",
"Privatized" = "wealthtype2Privatized",
"Selfmade" = "wealthtype2Selfmade (finance)"))
Model 1  Model 2  

Female  0.431  0.228 
(0.390)  (0.414)  
Age  0.035 ***  0.039 *** 
(0.009)  (0.009)  
Founder (nonfinance)  1.294 **  
(0.467)  
Inherited  1.236 **  
(0.466)  
Privatized  1.447 **  
(0.555)  
Selfmade  0.413  
(0.487)  
N  2229  2227 
Rsquared  0.008  0.015 
AIC  13838.863  13820.777 
*** p < 0.001; ** p < 0.01; * p < 0.05 Source: Forbes Billionaire Data 
11.4 Challenge Activity
Look back at your last homework assignment. If you did not use one of these commands to create your regression tables, try to store your models and create a nested regression table.