library("knitr") options(knitr.table.format = 'markdown') opts_chunk$set(message = FALSE, warning = FALSE, fig.path = "graphics/fig-", out.width = "480px") set.seed(1234)
This is a short tutorial that covers some of the main features of the R package papeR.
The main goal of the package is to ease statistical reporting and thus to ease
reproducible research. By relying on powerful tools such as the Sweave
, or the
packages knitr and xtable, the package can be easily integrated in
existing workflows.
First of all, the package provides an infrastructure to handle variable labels which
are used in all other functions (labels()
).
The package allows to create (complex) summary tables of the data sets (summarize()
) and
to easily plot the data (plot()
for labeled data.frame
s).
Finally, the package allows to enhance summary tables of statistical models by
(possibly) adding confidence intervals, significance stars, odds ratios, etc. and
by separating variable names and factor levels (prettify()
).
Before we start, we need to install the package. The package can be easily obtained from CRAN, e.g. via the command
install.packages("papeR")
To install the latest development version, one can use devtools to install packages from GitHub. Therefore we need to install and load devtools before we can install papeR:
install.packages("devtools") library("devtools") install_github("hofnerb/papeR")
Now we can load the package
library("papeR")
To be able to use all features of the package, we first need to create a labeled
data frame. We need labeled data frames to use the special plot()
function (see below).
All other functions do not strictly require labeled data frames but can exploit
the labels.
Labels in papeR are stored as attributes of the variables, i.e., each variable
in a labeld data frame has an attribute "variable.label"
, and the data set gets an
additional class 'ldf'
. Other packages store variable labels differently. E.g.
the function read.spss()
from the package foreign stores variable labels as
a single attribute of the data set. The package papeR is also capable of using
these labels. For details see the section "Conversion to labeled data frames".
If we create a new data.frame
we can extract and set variable labels using the
function labels()
. We use the Orthodont
data package nlme throughout this
tutorial. First load the data
data(Orthodont, package = "nlme") ## keep the original data set for later use Orthodont_orig <- Orthodont
To check if the data set is a labeled data set (i.e., of class 'ldf'
), we can use
is.ldf(Orthodont)
Despite the fact that we do not have a labeled data frame, we can query the labels. In this case, we simply get the variable names as no labels were set so far
labels(Orthodont)
This is a convenient feature, as we thus can relly on the fact that we will always have some variable labels.
To explicitly set labels, which are usually more descriptive than the variable names,
we can simply assign a vector of labels. We use some of the information which is
given on the help page of the Orthodont
data and use it as labels:
labels(Orthodont) <- c("fissure distance (mm)", "age (years)", "Subject", "Sex")
If we now query if Orthodont
is a labeled data frame and extract the labels, we get
is.ldf(Orthodont) class(Orthodont)
We see that by setting variable labels, we also add the class 'ldf'
to the data frame.
Now, the labels are
labels(Orthodont)
We can also set or ectract labels for a subset of the variables using the option
which
, which can either be a vector of variable names or indices. Let's capitalize
the labels of distance
and age
to make it consitent with Subject
and Sex
:
## set labels for distance and age labels(Orthodont, which = c("distance", "age")) <- c("Fissure distance (mm)", "Age (years)") ## extract labels for age only labels(Orthodont, which = "age") ## or for the first two variables (i.e., distance and age) labels(Orthodont, which = 1:2)
Instead of manually setting labels, we can simply convert a data frame to a
labeled data frame, either with the function as.ldf()
or with convert.labels()
.
Actually, both calls reference the same function (for an object of class data.frame
).
While as.ldf()
can be seen as the classical counterpart of is.ldf()
, the
function name convert.labels()
is inspired by the fact that these functions either
convert the variable names to labels or convert other variable labels to papeR-type
variable labels. Hence, these functions can, for example, be used to convert labels
from data sets which are imported via the function read.spss()
to papeR-type
variable labels.
If no variable labels are specified, the original variable names are used.
Orthodont2 <- convert.labels(Orthodont_orig) class(Orthodont2) labels(Orthodont2)
For data frames of class 'ldf'
, there exist special plotting functions:
par(mfrow = c(2, 2)) plot(Orthodont)
As one can see, the plot type is automatically determined
based on the data type and the axis label is defined by
the labels()
.
To obtain group comparisons, we can use grouped plots. To plot all variable in the
groups of Sex
one can use
par(mfrow = c(1, 3)) plot(Orthodont, by = "Sex")
We can as well plot everything against the metrical variable distance
par(mfrow = c(1, 3)) plot(Orthodont, with = "distance")
To plot only a subset of the data, say all but Subject
, against distance
and
suppress the regression line we can use
par(mfrow = c(1, 2)) plot(Orthodont, variables = -3, with = "distance", regression.line = FALSE)
Note that again we can use either variable names or indices to specify the variables which are to be plotted.
One can use the command summarize()
to automatically produce summary tables for
either numerical variables (i.e., variables where is.numeric()
is TRUE
) or
categorical variables (where is.factor()
is TRUE
). We now extract a summary
table for numerical variables of the Orthodont
data set:
data(Orthodont, package = "nlme") summarize(Orthodont, type = "numeric")
Similarly, we can extract summaries for all factor variables. As one of the factors
is the Subject
which has r nlevels(Orthodont$Subject)
levels, each with
r unique(table(Orthodont$Subject))
observations, we exclude this from the summary
table and only have a look at Sex
summarize(Orthodont, type = "factor", variables = "Sex")
Again, as for the plots, one can specify group
s to obtain grouped statistics:
summarize(Orthodont, type = "numeric", group = "Sex", test = FALSE)
Per default, one also gets test
s for group differences:
summarize(Orthodont, type = "numeric", group = "Sex")
So far, we only got standard R output. Yet, any of these summary tables can be
easily converted to LaTeX code using the package xtable. In papeR two
special functions xtable.summary()
and print.xtable.summary()
are defined
for easy and pretty conversion. In Sweave
we can use
<<echo = FALSE, results = tex>>= xtable(summarize(Orthodont, type = "numeric")) xtable(summarize(Orthodont, type = "factor", variables = "Sex")) xtable(summarize(Orthodont, type = "numeric", group = "Sex")) @
and in knitr we can use
<<echo = FALSE, results = 'asis'>>= xtable(summarize(Orthodont, type = "numeric")) xtable(summarize(Orthodont, type = "factor", variables = "Sex")) xtable(summarize(Orthodont, type = "numeric", group = "Sex")) @
to get the following PDF output
Note that per default, booktabs
is set to TRUE
in print.xtable.summary
, and
thus \usepackage{booktabs}
is needed in the header of the LaTeX report. For details
on LaTeX summary tables see the dedicated vignette, which can be obtained, e.g., via
vignette("papeR\_with\_latex", package = "papeR")
. See also there for more details
on summary tables in general.
To obtain markdown output we can use, for example, the function kable()
from
package knitr on the summary objects:
```{r, echo = FALSE, results = 'asis'} library("knitr") kable(summarize(Orthodont, type = "numeric")) kable(summarize(Orthodont, type = "factor", variables = "Sex", cumulative = TRUE)) kable(summarize(Orthodont, type = "numeric", group = "Sex", test = FALSE)) ```
which gives the following results
library("knitr") kable(summarize(Orthodont, type = "numeric")) kable(summarize(Orthodont, type = "factor", variables = "Sex", cumulative = TRUE)) kable(summarize(Orthodont, type = "numeric", group = "Sex"))
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 formatting for factors.
linmod <- lm(distance ~ age + Sex, data = Orthodont) ## Extract pretty summary (pretty_lm <- prettify(summary(linmod)))
The resulting table can now be formatted for printing using packages like
xtable for LaTeX which can be used in .Rnw
files with the option
results='asis'
(in knitr) or results = tex
(in Sweave
)
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)
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, version < 1.0)merMod
(linear mixed models; implemented in package lme4, version >= 1.0)anova
(anova objects)The package is intended to ease reporting of standard data analysis tasks such as descriptive statistics, simple test results, plots and to prettify the output of various statistical models.
papeR is under active development. Feature requests, bug reports, or patches, which either add new features or fix bugs, are always welcome. Please use the GitHub page.
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