PowerPoint Presentation

Using stargazer to report

regression output and

descriptive statistics in R

(v. 1.0)

Oscar Torres-Reyna

otorres@princeton.edu

May 2014 http://www.princeton.edu/~otorres/

Introduction

As anything with R, there are many ways of exporting output into nice tables (but mostly for

LaTeX users). Some packages are: apsrtable, xtable, texreg, memisc, outreg

…and counting.

At the moment, the “new kid on the block” is stargazer. Released by Marek Hlavac on March

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, 2014, version 5.0 offers a very nice, smart, and easy-to-use alternative to non-LaTeX users, in

particular, the ability to import editable tables into a Word document.

This presentation will show some of the options stargazer offers, the contents are based on

the documentation from the package available in the following links:

•http://cran.r-project.org/web/packages/stargazer/stargazer.pdf

•http://cran.r-project.org/web/packages/stargazer/vignettes/stargazer.pdf

The default setting produces LaTeX code, the additional alternatives are:

•Output as text, which allows a quick view of results

•Output as html, which produce editable tables for Word documents.

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OUTPUT IN TEXT FORMAT

For a quick view of the results use the “text” format option

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Descriptive statistics: in text format

mydata <- mtcars

install.packages("stargazer") #Use this to install it, do this only once

library(stargazer)

stargazer(mydata, type = "text", title="Descriptive statistics", digits=1, out="table1.txt")

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Descriptive statistics

==============================================================

Statistic mpg cyl disp hp drat wt qsec vs am gear carb

N 32 32 32 32 32 32 32 32 32 32 32

Mean 20.1 6.2 230.7 146.7 3.6 3.2 17.8 0.4 0.4 3.7 2.8

St. Dev. 6.0 1.8 123.9 68.6 0.5 1.0 1.8 0.5 0.5 0.7 1.6

Min 10.4 4 71.1 52 2.8 1.5 14.5 0 0 3 1

Max 33.9 8 472.0 335 4.9 5.4 22.9 1 1 5 8

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

Descriptive statistics

======================================

Statistic N Mean St. Dev. Min Max

mpg 32 20.1 6.0 10.4 33.9

cyl 32 6.2 1.8 4 8

disp 32 230.7 123.9 71.1 472.0

hp 32 146.7 68.6 52 335

drat 32 3.6 0.5 2.8 4.9

wt 32 3.2 1.0 1.5 5.4

qsec 32 17.8 1.8 14.5 22.9

vs 32 0.4 0.5 0 1

am 32 0.4 0.5 0 1

gear 32 3.7 0.7 3 5

carb 32 2.8 1.6 1 8

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

The table will be saved in the working

directory with whatever name you

write in the out option. You can open

this file with any word processor

# Same output, transposed (variables in columns)

stargazer(mydata, type = "text", title="Descriptive statistics", digits=1, out="table1.txt", flip=TRUE)

Use this option if you want

the variables in columns

For more details/options type ?stargazer

stargazer will automatically

recognize the type of object, and will

produce the appropriate output. In

the case of data frames, it will display

summary statistics .

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Descriptive statistics: in text format, replacing variable names with labels

mydata <- mtcars

install.packages("stargazer") #Use this to install it, do this only once

library(stargazer)

stargazer(mydata, type = "text", title="Descriptive statistics", digits=1, out="table1.txt",

covariate.labels=c("Miles/(US)gallon","No. of cylinders","Displacement (cu.in.)",

"Gross horsepower","Rear axle ratio","Weight (lb/1000)",

"1/4 mile time","V/S","Transmission (0=auto, 1=manual)",

"Number of forward gears","Number of carburetors"))

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The table will be saved in the working

directory with whatever name you write

in the out option. You can open this file

with any word processor

Descriptive statistics

============================================================

Statistic N Mean St. Dev. Min Max

Miles/(US)gallon 32 20.1 6.0 10.4 33.9

No. of cylinders 32 6.2 1.8 4 8

Displacement (cu.in.) 32 230.7 123.9 71.1 472.0

Gross horsepower 32 146.7 68.6 52 335

Rear axle ratio 32 3.6 0.5 2.8 4.9

Weight (lb/1000) 32 3.2 1.0 1.5 5.4

1/4 mile time 32 17.8 1.8 14.5 22.9

V/S 32 0.4 0.5 0 1

Transmission (0=auto, 1=manual) 32 0.4 0.5 0 1

Number of forward gears 32 3.7 0.7 3 5

Number of carburetors 32 2.8 1.6 1 8

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

Use the option covariate.labels

to replace variable names with variable

labels. Must be in same order as in the

dataset.

For more details/options type ?stargazer

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Descriptive statistics: in text format, selected variables

mydata <- mtcars

install.packages("stargazer") #Use this to install it, do this only once

library(stargazer)

stargazer(mydata[c("mpg","hp","drat")], type = "text",

title="Descriptive statistics/selected variables", digits=1, out="table2.txt")

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#same output transposed and with labels instead of variable names

stargazer(mydata[c("mpg","hp","drat")], type = "text",

title="Descriptive statistics/selected variables", digits=1, out="table2.txt", flip=TRUE,

covariate.labels=c("Miles/(US)gallon","Gross horsepower","Rear axle ratio"))

Descriptive statistics/selected variables

===========================================================

Statistic Miles/(US)gallon Gross horsepower Rear axle ratio

N 32 32 32

Mean 20.1 146.7 3.6

St. Dev. 6.0 68.6 0.5

Min 10.4 52 2.8

Max 33.9 335 4.9

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

Descriptive statistics/selected variables

=====================================

Statistic N Mean St. Dev. Min Max

mpg 32 20.1 6.0 10.4 33.9

hp 32 146.7 68.6 52 335

drat 32 3.6 0.5 2.8 4.9

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

The table will be saved in the working

directory with whatever name you

write in the out option. You can open

this file with any word processor

Use this option if you want

the variables in columns

Use the option

covariate.labels

to replace variable

names with variable

labels. Must be in same

order as in the dataset.

For more details/options type ?stargazer

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Descriptive statistics: in text format, selected variables, and by group

mydata <- mtcars

install.packages("stargazer") #Use this to install it, do this only once

library(stargazer)

# Descriptive statistics for cars with automatic transmission

stargazer(subset(mydata[c("mpg","hp","drat")], mydata$am==0),

title="Automatic transmission", type = "text", digits=1, out="table3.txt")

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# Descriptive statistics for cars with manual transmission

stargazer(subset(mydata[c("mpg","hp","drat")], mydata$am==1),

title="Manual transmission", type = "text", digits=1, out="table4.txt")

Manual transmission

=====================================

Statistic N Mean St. Dev. Min Max

mpg 13 24.4 6.2 15.0 33.9

hp 13 126.8 84.1 52 335

drat 13 4.0 0.4 3.5 4.9

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

Automatic transmission

=====================================

Statistic N Mean St. Dev. Min Max

mpg 19 17.1 3.8 10.4 24.4

hp 19 160.3 53.9 62 245

drat 19 3.3 0.4 2.8 3.9

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

The table will be saved in the working

directory with whatever name you

write in the out option. You can open

this file with any word processor

For more details/options type ?stargazer

Use subset() to

select the category

Use subset() to

select the category

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Regression models: in text format

mydata$fast <- as.numeric((mydata$mpg > 20.1)) #Creating a dummy variable 1 = fast car

m1 <- lm(mpg ~ hp, data=mydata)

m2 <- lm(mpg ~ hp + drat, data=mydata)

m3 <- lm(mpg ~ hp + drat + factor(gear), data=mydata)

m4 <- glm(fast ~ hp + drat + am, family=binomial(link="logit"), data=mydata)

stargazer(m1, m2, m3, m4, type="text",

dep.var.labels=c("Miles/(US) gallon","Fast car (=1)"),

covariate.labels=c("Gross horsepower","Rear axle ratio","Four foward gears",

"Five forward gears","Type of transmission (manual=1)"), out="models.txt")

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The table will be saved in the

working directory with whatever

name you write in the out

option. You can open this file

with any word processor

For the predictors, you have the option to use variable labels instead of variable names (in order they appear)

For more details/options type

?stargazer

For the output, you

have the option to

use variable labels

instead of variable

names (according

to the type of

model)

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OUTPUT IN HTML FORMAT

For a nice presentation use the “html” option, open it with any word processor

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Regression models: in html format

mydata$fast <- as.numeric((mydata$mpg > 20.1)) #Creating a dummy variable 1 = fast car

m1 <- lm(mpg ~ hp, data=mydata)

m2 <- lm(mpg ~ hp + drat, data=mydata)

m3 <- lm(mpg ~ hp + drat + factor(gear), data=mydata)

m4 <- glm(fast ~ hp + drat + am, family=binomial(link="logit"), data=mydata)

stargazer(m1, m2, m3, m4, type="html",

dep.var.labels=c("Miles/(US) gallon","Fast car (=1)"),

covariate.labels=c("Gross horsepower","Rear axle ratio","Four foward gears",

"Five forward gears","Type of transmission (manual=1)"), out="models.htm")

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The table will be saved in the

working directory with whatever

name you write in the out

option. You can open this file

with any word processor

For more details/options type ?stargazer

In the type option write html to export R

results to html. It may be a good idea to

use the appropriate extension in the out

option, in this example the results will be

saved in the file models.htm.

Word can easily read *.htm files , making

tables easily editable. Files should look like

the example shown here.

Same apply to the other procedures

described in the previous section.

For the output, you

have the option to

use variable labels

instead of variable

names (according

to the type of

model)

For the predictors, you have the

option to use variable labels

instead of variable names (in order

they appear)

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