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Using package papeR

Benjamin Hofner
Tuesday, March 17, 2015

A short tutorial for package papeR

This is a short tutorial that covers some of the features of package papeR.

Installation

To install the latest development version, one can use devtools to install packages from GitHub.

install.packages("devtools")

Now we can load devtools to install papeR:

library("devtools")
install_github("hofnerb/papeR")

(note that this chunk is not evaluated automatically).

Loading the package

Now we can load the package:

library("papeR")

Labeled data frames

To be able to use all features of the package, we first need to create a labeled data frame. If we create a new data frame or import a data frame, we can extract and set variable labels

data_orig <- data.frame(a = 1:10, 
                        b = 10:1, 
                        c = factor(sample(c(1:2), 10, replace = TRUE)))
data <- data_orig
labels(data)
##   a   b   c 
## "a" "b" "c"

labels(data) <- c("Variable a", "Variable b", "Variable c")
is.labeled.data.frame(data)
## [1] TRUE

labels(data)
##            a            b            c 
## "Variable a" "Variable b" "Variable c"

Alternatively, we can simply coerce the data frame:

data2 <- as.labeled.data.frame(data_orig)
labels(data2)
##   a   b   c 
## "a" "b" "c"

In this case, the original labels are kept. Per default, we use the variable names as labels. If the data set originates from an SPSS data set and is imported imported via the function read.spss() from package foreign, we use the variable labels from SPSS.

However, again we need to formally convert the data to a labeled.data.frame by using the function as.labeled.data.frame().

Methods for labeled data frames

For data frames of class labeled.data.frame, there exist special plotting functions:

par(mfrow = c(2, 2))
plot(data)

As one can see, the plot type is automatically determined based on the data type and the axis label is defiened by the labels().

To obtain group comparisons, we can use grouped plots:

par(mfrow = c(1, 2))
plot(data, by = "c")

We can as well plot everything against a metrical variable:

par(mfrow = c(1, 2))
plot(data, with = "b")

Summary tables

For LaTeX based reports, one can use the commands latex.table.cont() and latex.table.fac() to automatically produce summary tables for either continuous variables or factors.

data(Orthodont, package = "nlme")
latex.table.cont(Orthodont)
latex.table.fac(Orthodont, variables = "Sex")

Again, one can specify group to obtain grouped statistcis. In this case, one also can gets tests for group differences:

latex.table.cont(Orthodont, group = "Sex", test = FALSE)

The results in LaTeX look as follows:

LaTeX Output

A non-latex version is currently under construction.

Prettify output

To prettify the output of a linear model, one can use the function prettify(). This function adds confidence intervals, properly prints p-values, adds significance stars to the output (if desired) and additionally adds pretty formating for factors.

linmod <- lm(distance ~ age + Sex, data = Orthodont)
## Extract pretty summary
(pretty_lm <- prettify(summary(linmod)))
##                 Estimate CI (lower) CI (upper) Std. Error   t value
## 1 (Intercept) 17.7067130 15.5014071 19.9120189 1.11220946 15.920304
## 2         age  0.6601852  0.4663472  0.8540231 0.09775895  6.753194
## 3 Sex: Female -2.3210227 -3.2031499 -1.4388955 0.44488623 -5.217115
##   Pr(>|t|)    
## 1   <0.001 ***
## 2   <0.001 ***
## 3   <0.001 ***

The resulting table can now be formated for printing using packages like xtable for LaTeX which can be used in .Rnw files with the option results='asis'

library("xtable")
xtable(pretty_lm)

In markdown files (.Rmd) one can instead use the function kable() with the chunk option results='asis'. The result looks as follows:

kable(pretty_lm)
Estimate CI (lower) CI (upper) Std. Error t value Pr(>|t|)
(Intercept) 17.7067130 15.5014071 19.9120189 1.1122095 15.920304 <0.001 ***
age 0.6601852 0.4663472 0.8540231 0.0977589 6.753194 <0.001 ***
Sex: Female -2.3210227 -3.2031499 -1.4388955 0.4448862 -5.217115 <0.001 ***

Supported objects

The function prettify is currently implemented for objects of the following classes:

  • lm (linear models)
  • glm (generalized linear models)
  • coxph (Cox proportional hazards models)
  • lme (linear mixed models; implemented in package nlme)
  • mer (linear mixed models; implemented in package lme4, vers < 1.0)
  • merMod (linear mixed models; implemented in package lme4, vers. >= 1.0)
  • anova (anova objects)

Outlook

This package is currently under ative development. Feature requests, bug reports, or patches that either add new features or fix bugs are always welcome. Please use the GitHub page.