Stargazer Package

Introduction

This project aims to give a short tutorial on the stargazer package and to provide a basic understanding on how to create regression tables.

What is `stargazer` package?

The Stargazer package is a great way to create tables to neatly represent your regression outputs nicely.
The package gives options to output tables in multiple formats: .txt, LaTex code, and as .html. Using the output table as text (.txt) gives a quick view of results. -Printing the output table as .html, produces tables in Word document.

How to use `stargazer` package?

The first step is to install the “stargazer” package in R, and load it.

#Installing the "stargazer" package
#install.packages( "stargazer")
#Load the package by calling the library
library(stargazer)

## Warning: package 'stargazer' was built under R version 4.0.3

## 
## Please cite as:

##  Hlavac, Marek (2018). stargazer: Well-Formatted Regression and Summary Statistics Tables.

##  R package version 5.2.2. https://CRAN.R-project.org/package=stargazer

stargazer(mtcars, type = "html")


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

The mtcars data set will be used for demonstration.

mtcars

##                      mpg cyl  disp  hp drat    wt  qsec vs am gear carb
## Mazda RX4           21.0   6 160.0 110 3.90 2.620 16.46  0  1    4    4
## Mazda RX4 Wag       21.0   6 160.0 110 3.90 2.875 17.02  0  1    4    4
## Datsun 710          22.8   4 108.0  93 3.85 2.320 18.61  1  1    4    1
## Hornet 4 Drive      21.4   6 258.0 110 3.08 3.215 19.44  1  0    3    1
## Hornet Sportabout   18.7   8 360.0 175 3.15 3.440 17.02  0  0    3    2
## Valiant             18.1   6 225.0 105 2.76 3.460 20.22  1  0    3    1
## Duster 360          14.3   8 360.0 245 3.21 3.570 15.84  0  0    3    4
## Merc 240D           24.4   4 146.7  62 3.69 3.190 20.00  1  0    4    2
## Merc 230            22.8   4 140.8  95 3.92 3.150 22.90  1  0    4    2
## Merc 280            19.2   6 167.6 123 3.92 3.440 18.30  1  0    4    4
## Merc 280C           17.8   6 167.6 123 3.92 3.440 18.90  1  0    4    4
## Merc 450SE          16.4   8 275.8 180 3.07 4.070 17.40  0  0    3    3
## Merc 450SL          17.3   8 275.8 180 3.07 3.730 17.60  0  0    3    3
## Merc 450SLC         15.2   8 275.8 180 3.07 3.780 18.00  0  0    3    3
## Cadillac Fleetwood  10.4   8 472.0 205 2.93 5.250 17.98  0  0    3    4
## Lincoln Continental 10.4   8 460.0 215 3.00 5.424 17.82  0  0    3    4
## Chrysler Imperial   14.7   8 440.0 230 3.23 5.345 17.42  0  0    3    4
## Fiat 128            32.4   4  78.7  66 4.08 2.200 19.47  1  1    4    1
## Honda Civic         30.4   4  75.7  52 4.93 1.615 18.52  1  1    4    2
## Toyota Corolla      33.9   4  71.1  65 4.22 1.835 19.90  1  1    4    1
## Toyota Corona       21.5   4 120.1  97 3.70 2.465 20.01  1  0    3    1
## Dodge Challenger    15.5   8 318.0 150 2.76 3.520 16.87  0  0    3    2
## AMC Javelin         15.2   8 304.0 150 3.15 3.435 17.30  0  0    3    2
## Camaro Z28          13.3   8 350.0 245 3.73 3.840 15.41  0  0    3    4
## Pontiac Firebird    19.2   8 400.0 175 3.08 3.845 17.05  0  0    3    2
## Fiat X1-9           27.3   4  79.0  66 4.08 1.935 18.90  1  1    4    1
## Porsche 914-2       26.0   4 120.3  91 4.43 2.140 16.70  0  1    5    2
## Lotus Europa        30.4   4  95.1 113 3.77 1.513 16.90  1  1    5    2
## Ford Pantera L      15.8   8 351.0 264 4.22 3.170 14.50  0  1    5    4
## Ferrari Dino        19.7   6 145.0 175 3.62 2.770 15.50  0  1    5    6
## Maserati Bora       15.0   8 301.0 335 3.54 3.570 14.60  0  1    5    8
## Volvo 142E          21.4   4 121.0 109 4.11 2.780 18.60  1  1    4    2

dat1 <- mtcars

The syntax for stargazer has four main arguments.

The first one indicates the data frame (dat1),
The second one asks the output type (“text”),
The third argument asks for the title (“Summary Statistics”),
The fourth one specifies the file name for your exported table (“dat1.txt”).
It also provides the standard deviation, minimum, mean and maximum values.

stargazer(dat1, type= "html", title= "Summary Statistics", out= "dat1.text")

**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

If you only want to select certain variables, then use subset() to select. Lets choose cars with manual transmission.

stargazer(dat1[c("mpg","hp","drat")],
          title="Cars with Manual Transmission", 
          type= "html", digits =1, out="dat2.text")

**Cars with Manual Transmission**

Statistic	N	Mean	St. Dev.	Min	Pctl(25)	Pctl(75)	Max

mpg	32	20.1	6.0	10	15.4	22.8	34
hp	32	146.7	68.6	52	96.5	180	335
drat	32	3.6	0.5	2.8	3.1	3.9	4.9

The stargazer package has options to customize the appearance of the output which creates neat looking tables. By creating a vector with the covariate.labels argument will display an output with relabeled variables.

stargazer(dat1[c("mpg", "hp", "drat")], type = "html",
          title = "Summary Statistic for Cars with Manual Transmission",           out = "dat3.text", digits = 1, covariate.labels = c("Miles per           Gallon", "Horsepower", "Rear axle ratio"))

**Summary Statistic for Cars with Manual Transmission**

Statistic	N	Mean	St. Dev.	Min	Pctl(25)	Pctl(75)	Max

Miles per Gallon	32	20.1	6.0	10	15.4	22.8	34
Horsepower	32	146.7	68.6	52	96.5	180	335
Rear axle ratio	32	3.6	0.5	2.8	3.1	3.9	4.9

Regression Output with stargazer

So far, we have seen that by passing a data frame to stargazer package creates a summary statistic table. This package is also extremely practical when it comes to creating regression models by simply passing a regression object.

m1 <- lm(mpg ~ hp, mtcars)
m2 <- lm(mpg~ drat, mtcars)
m3 <- lm(mpg ~ hp + drat, mtcars)
stargazer(m1, m2, m3,
          type = "html",
          digits = 1,
          header = FALSE,
          title= "Regression Results",
          covariate.labels = c("Miles per Gallon", "Horsepower", "Rear axle ratio"))

**Regression Results**

	Dependent variable:

	mpg
	(1)	(2)	(3)

Miles per Gallon	-0.1^***		-0.1^***
	(0.01)		(0.01)

Horsepower		7.7^***	4.7^***
		(1.5)	(1.2)

Rear axle ratio	30.1^***	-7.5	10.8^**
	(1.6)	(5.5)	(5.1)


Observations	32	32	32
R²	0.6	0.5	0.7
Adjusted R²	0.6	0.4	0.7
Residual Std. Error	3.9 (df = 30)	4.5 (df = 30)	3.2 (df = 29)
F Statistic	45.5^*** (df = 1; 30)	26.0^*** (df = 1; 30)	41.5^*** (df = 2; 29)

Note:	p<0.1; p<0.05; p<0.01

References:

The R Markdown website

Stargazer Package

Alev Yildiz

4/20/2021

Introduction

What is `stargazer` package?

How to use `stargazer` package?

Regression Output with stargazer

Stargazer Package

Alev Yildiz

4/20/2021

Introduction

What is stargazer package?

How to use stargazer package?

Regression Output with stargazer

What is `stargazer` package?

How to use `stargazer` package?