Overview

How to export tables from R depends on what word processor you use. This tutorial focuses on Word. If you use LaTeX, there are many existing R packages and tutorials that will get you started, including xtable and stargazer.

To export tables to Word, follow these general steps:

You’ll now have a basic table that you can format in Word. Below are three examples of how to use this process to create crosstabs, tables for summary statistics, and regression tables.

states <- read.csv("states.csv")

pkgs <- c("dplyr", "tidyr", "broom")
install.packages(pkgs) #install 
sapply(pkgs, require, character.only = T) #load 

## dplyr tidyr broom
##  TRUE  TRUE  TRUE

Crosstabs

Create a table showing the proportion of states that supported Bush in 2000, by region (South versus Non-South):

# Create table
t <- with(states, table(south, gb_win00))
t <- prop.table(t, margin = 1)

t #large majority of southern states supported Bush in 2000:   

##           gb_win00
## south       Bush win  Gore win
##   Nonsouth 0.4705882 0.5294118
##   South    0.8750000 0.1250000

# Write this table to a comma separated .txt file:    
write.table(t, file = "bush_south.txt", sep = ",", quote = FALSE, row.names = F)

The .txt file will end up in your working directory. Now follow steps 3 and 4 in the Overview section above to create the crosstab in Word.

Summary statistics

Here’s another example that again uses the states.csv dataset. Say we wanted to create a table with summary statistics for five of the variables in this dataset:

sumstat <- states %>%

    # Select and rename five variables 
    select(
        `Black (%)` = blkpct,
        `Attend church (%)` = attend_pct,
        `Supported Bush in 2000 (%)` = bush00,
        `Supported Obama in 2008 (%)` = obama08,
        `Women in State Legislature (%)` = womleg
        ) %>%

    # Find the mean, st. dev., min, and max for each variable 
    summarise_each(funs(mean, sd, min, max)) %>%

    # Move summary stats to columns
    gather(key, value, everything()) %>% 
    separate(key, into = c("variable", "stat"), sep = "_") %>%
    spread(stat, value) %>%

    # Set order of summary statistics 
    select(variable, mean, sd, min, max) %>%

    # Round all numeric variables to one decimal point
    mutate_each(funs(round(., 1)), -variable)

sumstat

##                         variable mean  sd  min  max
## 1              Attend church (%) 38.9 9.4 22.0 60.0
## 2                      Black (%) 10.3 9.7  0.4 36.8
## 3     Supported Bush in 2000 (%) 50.4 8.7 31.9 67.8
## 4    Supported Obama in 2008 (%) 50.5 9.5 32.5 71.8
## 5 Women in State Legislature (%) 23.2 7.3  8.8 37.8

# Write to .txt
write.table(sumstat, file = "sumstats.txt", sep = ",", quote = FALSE, row.names = F)

Again, the sumstats.txt file will end up in your working directory, and you can use steps 3 and 4 from the Overview section above to import this file into Word.

Regression tables

Say we wanted to run three OLS models to predict state-level support for Bush in 2000, where each model adds a predictor to the preceding model. We can create a regression table with all three models like so:

m1 <- tidy(lm(bush00 ~ blkpct, states))
m2 <- tidy(lm(bush00 ~ blkpct + south, data = states))
m3 <- tidy(lm(bush00 ~ blkpct + south + womleg, data = states))
# Note that tidy() from the broom package is used to convert each model to a data frame

all_models <- rbind_list(
    m1 %>% mutate(model = 1),
    m2 %>% mutate(model = 2),
    m3 %>% mutate(model = 3))

all_models

## Source: local data frame [9 x 6]
##
##          term    estimate std.error statistic      p.value model
##         (chr)       (dbl)     (dbl)     (dbl)        (dbl) (dbl)
## 1 (Intercept) 50.92242670 1.8269042 27.873617 2.674311e-31     1
## 2      blkpct -0.04645116 0.1295857 -0.358459 7.215717e-01     1
## 3 (Intercept) 51.26374042 1.7392966 29.473834 5.855540e-32     2
## 4      blkpct -0.35982687 0.1753724 -2.051788 4.578488e-02     2
## 5  southSouth  9.04563705 3.6085132  2.506749 1.570125e-02     2
## 6 (Intercept) 66.01152726 4.5218532 14.598335 6.949435e-19     3
## 7      blkpct -0.41579167 0.1585744 -2.622061 1.181097e-02     3
## 8  southSouth  6.03572838 3.3595138  1.796608 7.896601e-02     3
## 9      womleg -0.56807999 0.1634218 -3.476157 1.121456e-03     3

# Now make this data frame look more like a regression table 
ols_table <- all_models %>%
    select(-statistic, -p.value) %>%
    mutate_each(funs(round(., 2)), -term) %>% 
    gather(key, value, estimate:std.error) %>%
    spread(model, value)

ols_table

## Source: local data frame [8 x 5]
##
##          term       key     1     2     3
##         (chr)     (chr) (dbl) (dbl) (dbl)
## 1 (Intercept)  estimate 50.92 51.26 66.01
## 2 (Intercept) std.error  1.83  1.74  4.52
## 3      blkpct  estimate -0.05 -0.36 -0.42
## 4      blkpct std.error  0.13  0.18  0.16
## 5  southSouth  estimate    NA  9.05  6.04
## 6  southSouth std.error    NA  3.61  3.36
## 7      womleg  estimate    NA    NA -0.57
## 8      womleg std.error    NA    NA  0.16

# Export
write.table(ols_table, file = "olstab.txt", sep = ",", quote = FALSE, row.names = F)

Again, follow steps 3 and 4 from the Overview section above to import the content of the .txt file into Word.