# Tools for making latex tables in R

Thanks Joris for creating this question. Hopefully, it will be made into a community wiki.

The booktabs packages in latex produces nice looking tables. Here is a blog post on how to use xtable to create latex tables that use booktabs

I would also add the apsrtable package to the mix as it produces nice looking regression tables.

Another Idea: Some of these packages (esp. memisc and apsrtable) allow easy extensions of the code to produce tables for different regression objects. One such example is the lme4 memisc code shown in the question. It might make sense to start a github repository to collect such code snippets, and over time maybe even add it to the memisc package. Any takers?

Question

On general request, a community wiki on producing latex tables in R. In this post I'll give an overview of the most commonly used packages and blogs with code for producing latex tables from less straight-forward objects. Please feel free to add any I missed, and/or give tips, hints and little tricks on how to produce nicely formatted latex tables with R.

## Packages :

• xtable : for standard tables of most simple objects. A nice gallery with examples can be found here.
• memisc : tool for management of survey data, contains some tools for latex tables of (basic) regression model estimates.
• Hmisc contains a function latex() that creates a tex file containing the object of choice. It is pretty flexible, and can also output longtable latex tables. There's a lot of info in the help file ?latex
• miscFuncs has a neat function 'latextable' that converts matrix data with mixed alphabetic and numeric entries into a LaTeX table and prints them to the console, so they can be copied and pasted into a LaTeX document.
• texreg package (JSS paper) converts statistical model output into LaTeX tables. Merges multiple models. Can cope with about 50 different model types, including network models and multilevel models (lme and lme4).
• reporttools package (JSS paper) is another option for descriptive statistics on continuous, categorical and date variables.
• tables package is perhaps the most general LaTeX table making package in R for descriptive statistics
• stargazer package makes nice comparative statistical model summary tables

## Related questions :

... and Trick #3 Multiline entries in an Xtable

Generate some more data

moredata<-data.frame(Nominal=c(1:5), n=rep(5,5),
MeanLinBias=signif(rnorm(5, mean=0, sd=10), digits=4),
LinCI=paste("(",signif(rnorm(5,mean=-2, sd=5), digits=4),
", ", signif(rnorm(5, mean=2, sd=5), digits=4),")",sep=""),
", ", signif(rnorm(5, mean=2, sd=5), digits=4),")",sep=""))



Now produce our xtable, using the sanitize function to replace column names with the correct Latex newline commands (including double backslashes so R is happy)

<<label=multilinetable, results=tex, echo=FALSE>>=
foo<-xtable(moredata)
align(foo) <- c( rep('c',3),'p{1.8in}','p{2in}','p{1.8in}','p{2in}' )
print(foo,
floating=FALSE,
include.rownames=FALSE,
sanitize.text.function = function(str) {
str<-gsub("\n","\\\\", str, fixed=TRUE)

return(str)
},
sanitize.colnames.function = function(str) {
str<-c("Nominal", "n","\\centering Linear Model\\\\ \\% Bias","\\centering Linear \\\\ 95\\%CI", "\\centering Quadratic Model\\\\ \\%Bias", "\\centering Quadratic \\\\ 95\\%CI \\tabularnewline")
return(str)
})
@


(although this isn't perfect, as we need \tabularnewline so the table is formatted correctly, and Xtable still puts in a final \, so we end up with a blank line below the table header.)

Another R package for aggregating multiple regression models into LaTeX tables is texreg.

I have a few tricks and work arounds to interesting 'features' of xtable and Latex that I'll share here.

Trick #1: Removing Duplicates in Columns and Trick #2: Using Booktabs

First, load packages and define my clean function

<<label=first, include=FALSE, echo=FALSE>>=
library(xtable)
library(plyr)

cleanf <- function(x){
oldx <- c(FALSE, x[-1]==x[-length(x)])
# is the value equal to the previous?
res <- x
res[oldx] <- NA
return(res)}


Now generate some fake data

data<-data.frame(animal=sample(c("elephant", "dog", "cat", "fish", "snake"), 100,replace=TRUE),
colour=sample(c("red", "blue", "green", "yellow"), 100,replace=TRUE),
size=rnorm(100,mean=500, sd=150),
age=rlnorm(100, meanlog=3, sdlog=0.5))

#generate a table
datatable<-ddply(data, .(animal, colour), function(df) {
return(data.frame(size=mean(df$size), age=mean(df$age)))
})


Now we can generate a table, and use the clean function to remove duplicate entries in the label columns.

cleandata<-datatable
cleandata$animal<-cleanf(cleandata$animal)
cleandata$colour<-cleanf(cleandata$colour)
@


this is a normal xtable

<<label=normal, results=tex, echo=FALSE>>=
print(
xtable(
datatable
),
tabular.environment='longtable',
latex.environments=c("center"),
floating=FALSE,
include.rownames=FALSE
)
@


this is a normal xtable where a custom function has turned duplicates to NA

<<label=cleandata, results=tex, echo=FALSE>>=
print(
xtable(
cleandata
),
tabular.environment='longtable',
latex.environments=c("center"),
floating=FALSE,
include.rownames=FALSE
)
@


This table uses the booktab package (and needs a \usepackage{booktabs} in the headers)

\begin{table}[!h]
\centering
\caption{table using booktabs.}
\label{tab:mytable}
<<label=booktabs, echo=F,results=tex>>=
mat <- xtable(cleandata,digits=rep(2,ncol(cleandata)+1))
foo<-0:(length(mat$animal)) bar<-foo[!is.na(mat$animal)]
print(mat,
sanitize.text.function = function(x){x},
floating=FALSE,
include.rownames=FALSE,
hline.after=NULL,