describe: Concise Statistical Description of a Vector, Matrix, Data...
In harrelfe/Hmisc: Harrell Miscellaneous
describe R Documentation
Concise Statistical Description of a Vector, Matrix, Data Frame,
or Formula
Description
describe is a generic method that invokes describe.data.frame,
describe.matrix, describe.vector, or
describe.formula. describe.vector is the basic
function for handling a single variable.
This function determines whether the variable is character, factor,
category, binary, discrete numeric, and continuous numeric, and prints
a concise statistical summary according to each. A numeric variable is
deemed discrete if it has <= 10 distinct values. In this case,
quantiles are not printed. A frequency table is printed
for any non-binary variable if it has no more than 20 distinct
values. For any variable for which the frequency table is not printed,
the 5 lowest and highest values are printed. This behavior can be
overriden for long character variables with many levels using the
listunique parameter, to get a complete tabulation.
describe is especially useful for
describing data frames created by *.get, as labels, formats,
value labels, and (in the case of sas.get) frequencies of special
missing values are printed.
For a binary variable, the sum (number of 1's) and mean (proportion of
1's) are printed. If the first argument is a formula, a model frame
is created and passed to describe.data.frame. If a variable
is of class "impute", a count of the number of imputed values is
printed. If a date variable has an attribute partial.date
(this is set up by sas.get), counts of how many partial dates are
actually present (missing month, missing day, missing both) are also presented.
If a variable was created by the special-purpose function substi (which
substitutes values of a second variable if the first variable is NA),
the frequency table of substitutions is also printed.
For numeric variables, describe adds an item called Info
which is a relative information measure using the relative efficiency of
a proportional odds/Wilcoxon test on the variable relative to the same
test on a variable that has no ties. Info is related to how
continuous the variable is, and ties are less harmful the more untied
values there are. The formula for Info is one minus the sum of
the cubes of relative frequencies of values divided by one minus the
square of the reciprocal of the sample size. The lowest information
comes from a variable having only one distinct value following by a
highly skewed binary variable. Info is reported to
two decimal places.
A latex method exists for converting the describe object to a
LaTeX file. For numeric variables having more than 20 distinct values,
describe saves in its returned object the frequencies of 100
evenly spaced bins running from minimum observed value to the maximum.
When there are less than or equal to 20 distinct values, the original
values are maintained.
latex and html insert a spike histogram displaying these
frequency counts in the tabular material using the LaTeX picture
environment. For example output see
https://hbiostat.org/doc/rms/book/chapter7edition1.pdf.
Note that the latex method assumes you have the following styles
installed in your latex installation: setspace and relsize.
The html method mimics the LaTeX output. This is useful in the
context of Quarto/Rmarkdown html and html notebook output.
If options(prType='html') is in effect, calling print on
an object that is the result of running describe on a data frame
will result in rendering the HTML version. If run from the console a
browser window will open. When which is specified to
print, whether or not prType='html' is in effect, a
gt package html table will be produced containing only
the types of variables requested. When which='both' a list with
element names Continuous and Categorical is produced,
making it convenient for the user to print as desired, or to pass the
list directed to the qreport maketabs function when using Quarto.
The plot method is for describe objects run on data
frames. It produces spike histograms for a graphic of
continuous variables and a dot chart for categorical variables, showing
category proportions. The graphic format is ggplot2 if the user
has not set options(grType='plotly') or has set the grType
option to something other than 'plotly'. Otherwise plotly
graphics that are interactive are produced, and these can be placed into
an Rmarkdown html notebook. The user must install the plotly
package for this to work. When the use hovers the mouse over a bin for
a raw data value, the actual value will pop-up (formatted using
digits). When the user hovers over the minimum data value, most
of the information calculated by describe will pop up. For each
variable, the number of missing values is used to assign the color to
the histogram or dot chart, and a legend is drawn. Color is not used if
there are no missing values in any variable. For categorical variables,
hovering over the leftmost point for a variable displays details, and
for all points proportions, numerators, and denominators are displayed
in the popup. If both continuous and categorical variables are present
and which='both' is specified, the plot method returns an
unclassed list containing two objects, named 'Categorical'
and 'Continuous', in that order.
Sample weights may be specified to any of the functions, resulting
in weighted means, quantiles, and frequency tables.
Note: As discussed in Cox and Longton (2008), Stata Technical Bulletin 8(4)
pp. 557, the term "unique" has been replaced with "distinct" in the
output (but not in parameter names).
When weights are not used, the pseudomedian and Gini's mean difference are computed for
numeric variables. The pseudomedian is labeled pMedian and is the median of all possible pairwise averages. It is a robust and efficient measure of location that equals the mean and median for symmetric distributions. It is also called the Hodges-Lehmann one-sample estimator. Gini's mean difference is a robust measure of dispersion that is the
mean absolute difference between any pairs of observations. In simple
output Gini's difference is labeled Gmd.
formatdescribeSingle is a service function for latex,
html, and print methods for single variables that is not
intended to be called by the user.
Usage
## S3 method for class 'vector'
describe(x, descript, exclude.missing=TRUE, digits=4,
listunique=0, listnchar=12,
weights=NULL, normwt=FALSE, minlength=NULL, shortmChoice=TRUE,
rmhtml=FALSE, trans=NULL, lumptails=0.01, ...)
## S3 method for class 'matrix'
describe(x, descript, exclude.missing=TRUE, digits=4, ...)
## S3 method for class 'data.frame'
describe(x, descript, exclude.missing=TRUE,
digits=4, trans=NULL, ...)
## S3 method for class 'formula'
describe(x, descript, data, subset, na.action,
digits=4, weights, ...)
## S3 method for class 'describe'
print(x, which = c('both', 'categorical', 'continuous'), ...)
## S3 method for class 'describe'
latex(object, title=NULL,
file=paste('describe',first.word(expr=attr(object,'descript')),'tex',sep='.'),
append=FALSE, size='small', tabular=TRUE, greek=TRUE,
spacing=0.7, lspace=c(0,0), ...)
## S3 method for class 'describe.single'
latex(object, title=NULL, vname,
file, append=FALSE, size='small', tabular=TRUE, greek=TRUE,
lspace=c(0,0), ...)
## S3 method for class 'describe'
html(object, size=85, tabular=TRUE,
greek=TRUE, scroll=FALSE, rows=25, cols=100, ...)
## S3 method for class 'describe.single'
html(object, size=85,
tabular=TRUE, greek=TRUE, ...)
formatdescribeSingle(x, condense=c('extremes', 'frequencies', 'both', 'none'),
lang=c('plain', 'latex', 'html'), verb=0, lspace=c(0, 0),
size=85, ...)
## S3 method for class 'describe'
plot(x, which=c('both', 'continuous', 'categorical'),
what=NULL,
sort=c('ascending', 'descending', 'none'),
n.unique=10, digits=5, bvspace=2, ...)
Arguments
x
a data frame, matrix, vector, or formula. For a data frame, the
describe.data.frame
function is automatically invoked. For a matrix, describe.matrix is
called. For a formula, describe.data.frame(model.frame(x))
is invoked. The formula may or may not have a response variable. For
print, latex, html, or
formatdescribeSingle, x is an object created by
describe.
descript
optional title to print for x. The default is the name of the argument
or the "label" attributes of individual variables. When the first argument
is a formula, descript defaults to a character representation of
the formula.
exclude.missing
set toTRUE to print the names of variables that contain only missing values.
This list appears at the bottom of the printout, and no space is taken
up for such variables in the main listing.
digits
number of significant digits to print. For plot.describe is
the number of significant digits to put in hover text for
plotly when showing raw variable values.
listunique
For a character variable that is not an mChoice variable, that
has its longest string length greater than listnchar, and that
has no more than listunique distinct values, all values are
listed in alphabetic order. Any value having more than one occurrence
has the frequency of occurrence included. Specify
listunique equal to some value at least as large as the number
of observations to ensure that all character variables will have all
their values listed. For purposes of tabulating character strings,
multiple white spaces of any kind are translated to a single space,
leading and trailing white space are ignored, and case is ignored.
listnchar
see listunique
weights
a numeric vector of frequencies or sample weights. Each observation
will be treated as if it were sampled weights times.
minlength
value passed to summary.mChoice
shortmChoice
set to FALSE to have summary of
mChoice variables use actual levels everywhere, instead of
abbreviating to integers and printing of all original labels at the
top
rmhtml
set to TRUE to strip html from variable labels
trans
for describe.vector is a list specifying how to
transform x for constructing the frequency distribution used in
spike histograms. The first element of the list is a character string
describing the transformation, the second is the transformation
function, and the third argument is the inverse of this function that
is used in labeling points on the original scale,
e.g. trans=list('log', log, exp). For
describe.data.frame trans is a list of such lists, with
the name of each list being name of the variable to which the
transformation applies. See
https://hbiostat.org/rmsc/impred.html#data for an example.
lumptails
specifies the quantile to use (its complement is also
used) for grouping observations in the tails so that outliers have
less chance of distorting the variable's range for sparkline spike
histograms. The default is 0.01, i.e., observations below the 0.01
quantile are grouped together in the leftmost bin, and observations
above the 0.99 quantile are grouped to form the last bin.
normwt
The default, normwt=FALSE results in the use of weights as
weights in computing various statistics. In this case the sample size
is assumed to be equal to the sum of weights. Specify
normwt=TRUE to divide
weights by a constant so that weights sum to the number of
observations (length of vectors specified to describe). In this
case the number of observations is taken to be the actual number of
records given to describe.
object
a result of describe
title
unused
data
a data frame, data table, or list
subset
a subsetting expression
na.action
These are used if a formula is specified. na.action defaults to
na.retain which does not delete any NAs from the data frame.
Use na.action=na.omit or na.delete to drop any observation with
any NA before processing.
...
arguments passed to describe.default which are passed to calls
to format for numeric variables. For example if using R
POSIXct or Date date/time formats, specifying
describe(d,format='%d%b%y') will print date/time variables as
"01Jan2000". This is useful for omitting the time
component. See the help file for format.POSIXct or
format.Date for more
information. For plot methods, ... is ignored.
For html and latex methods, ... is used to pass
optional arguments to formatdescribeSingle, especially the
condense argument. For the print method when
which= is given, possible
arguments to use for tabulating continuous variable output are
sparkwidth (the width of the spike histogram sparkline in pixels,
defaulting to 200), qcondense (set to FALSE to devote
separate columns to all quantiles), extremes (set to
TRUE to print the 5 lowest and highest values in the table of
continuous variables). For categorical variable output, the argument
freq can be used to specify how frequency tables are rendered:
'chart' (the default; an interactive sparkline frequency bar chart) or
freq='table' for small tables. sort is another argument
passed to html_describe_cat. For sparkline frequency charts
the default is to sort non-numeric categories in descending order of
frequency. Set code=FALSE to use the original data order. The
w argument also applies to categorical variable output.
file
name of output file (should have a suffix of .tex). Default name is
formed from the first word of the descript element of the
describe object, prefixed by "describe". Set
file="" to send LaTeX code to standard output instead of a file.
append
set to TRUE to have latex append text to an existing file
named file
size
LaTeX text size ("small", the default, or "normalsize",
"tiny", "scriptsize", etc.) for the describe output
in LaTeX. For html is the percent of the prevailing font size to use for
the output.
tabular
set to FALSE to use verbatim rather than tabular (or html
table) environment for the summary statistics output. By default,
tabular is used if the output is not too wide.
greek
By default, the latex and html methods
will change names of greek letters that appear in variable
labels to appropriate LaTeX symbols in math mode, or html symbols, unless
greek=FALSE.
spacing
By default, the latex method for describe run
on a matrix or data frame uses the setspace LaTeX package with a
line spacing of 0.7 so as to no waste space. Specify spacing=0
to suppress the use of the setspace's spacing environment,
or specify another positive value to use this environment with a
different spacing.
lspace
extra vertical scape, in character size units (i.e., "ex"
as appended to the space). When using certain font sizes, there is
too much space left around LaTeX verbatim environments. This
two-vector specifies space to remove (i.e., the values are negated in
forming the vspace command) before (first element) and after
(second element of lspace) verbatims
scroll
set to TRUE to create an html scrollable box for
the html output
rows, cols
the number of rows or columns to allocate for the
scrollable box
vname
unused argument in latex.describe.single
which
specifies whether to plot numeric continuous or
binary/categorical variables, or both. When "both" a list with
two elements is created. Each element is a ggplot2 or
plotly object. If there are no variables of a given type, a
single ggplot2 or plotly object is returned, ready to
print. For print.describe may be "categorical" or
"continuous", causing a gt table to be created with the
categorical or continuous variable describe results.
what
character or numeric vector specifying which variables to
plot; default is to plot all
sort
specifies how and whether variables are sorted in order of
the proportion of positives when which="categorical". Specify
sort="none" to leave variables in the order they appear in the
original data.
n.unique
the minimum number of distinct values a numeric variable
must have before plot.describe uses it in a continuous variable
plot
bvspace
the between-variable spacing for categorical variables.
Defaults to 2, meaning twice the amount of vertical space as what is
used for between-category spacing within a variable
condense
specifies whether to condense the output with regard to
the 5 lowest and highest values ("extremes") and the frequency table
lang
specifies the markup language
verb
set to 1 if a verbatim environment is already in effect for LaTeX
Details
If options(na.detail.response=TRUE)
has been set and na.action is "na.delete" or
"na.keep", summary statistics on
the response variable are printed separately for missing and non-missing
values of each predictor. The default summary function returns
the number of non-missing response values and the mean of the last
column of the response values, with a names attribute of
c("N","Mean").
When the response is a Surv object and the mean is used, this will
result in the crude proportion of events being used to summarize
the response. The actual summary function can be designated through
options(na.fun.response = "function name").
If you are modifying LaTex parskip or certain other parameters,
you may need to shrink the area around tabular and
verbatim environments produced by latex.describe. You can
do this using for example
\usepackage{etoolbox}\makeatletter\preto{\@verbatim}{\topsep=-1.4pt
\partopsep=0pt}\preto{\@tabular}{\parskip=2pt
\parsep=0pt}\makeatother in the LaTeX preamble.
Value
a list containing elements descript, counts,
values. The list is of class describe. If the input
object was a matrix or a data
frame, the list is a list of lists, one list for each variable
analyzed. latex returns a standard latex object. For numeric
variables having at least 20 distinct values, an additional component
intervalFreq. This component is a list with two elements, range
(containing two values) and count, a vector of 100 integer frequency
counts. print with which= returns a 'gt' table object.
The user can modify the table by piping formatting changes, column
removals, and other operations, before final rendering.
Author(s)
Frank Harrell
Vanderbilt University
fh@fharrell.com
See Also
spikecomp, sas.get, quantile,
GiniMd, pMedian,
table, summary,
model.frame.default,
naprint, lapply, tapply,
Surv, na.delete,
na.keep,
na.detail.response, latex
Examples
set.seed(1)
describe(runif(200),dig=2) #single variable, continuous
#get quantiles .05,.10,\dots
dfr <- data.frame(x=rnorm(400),y=sample(c('male','female'),400,TRUE))
describe(dfr)
## Not run:
options(grType='plotly')
d <- describe(mydata)
p <- plot(d) # create plots for both types of variables
p[[1]]; p[[2]] # or p$Categorical; p$Continuous
plotly::subplot(p[[1]], p[[2]], nrows=2) # plot both in one
plot(d, which='categorical') # categorical ones
d <- sas.get(".","mydata",special.miss=TRUE,recode=TRUE)
describe(d) #describe entire data frame
attach(d, 1)
describe(relig) #Has special missing values .D .F .M .R .T
#attr(relig,"label") is "Religious preference"
#relig : Religious preference Format:relig
# n missing D F M R T distinct
# 4038 263 45 33 7 2 1 8
#
#0:none (251, 6%), 1:Jewish (372, 9%), 2:Catholic (1230, 30%)
#3:Jehovah's Witnes (25, 1%), 4:Christ Scientist (7, 0%)
#5:Seventh Day Adv (17, 0%), 6:Protestant (2025, 50%), 7:other (111, 3%)
# Method for describing part of a data frame:
describe(death.time ~ age*sex + rcs(blood.pressure))
describe(~ age+sex)
describe(~ age+sex, weights=freqs) # weighted analysis
fit <- lrm(y ~ age*sex + log(height))
describe(formula(fit))
describe(y ~ age*sex, na.action=na.delete)
# report on number deleted for each variable
options(na.detail.response=TRUE)
# keep missings separately for each x, report on dist of y by x=NA
describe(y ~ age*sex)
options(na.fun.response="quantile")
describe(y ~ age*sex) # same but use quantiles of y by x=NA
d <- describe(my.data.frame)
d$age # print description for just age
d[c('age','sex')] # print description for two variables
d[sort(names(d))] # print in alphabetic order by var. names
d2 <- d[20:30] # keep variables 20-30
page(d2) # pop-up window for these variables
# Test date/time formats and suppression of times when they don't vary
library(chron)
d <- data.frame(a=chron((1:20)+.1),
b=chron((1:20)+(1:20)/100),
d=ISOdatetime(year=rep(2003,20),month=rep(4,20),day=1:20,
hour=rep(11,20),min=rep(17,20),sec=rep(11,20)),
f=ISOdatetime(year=rep(2003,20),month=rep(4,20),day=1:20,
hour=1:20,min=1:20,sec=1:20),
g=ISOdate(year=2001:2020,month=rep(3,20),day=1:20))
describe(d)
# Make a function to run describe, latex.describe, and use the kdvi
# previewer in Linux to view the result and easily make a pdf file
ldesc <- function(data) {
options(xdvicmd='kdvi')
d <- describe(data, desc=deparse(substitute(data)))
dvi(latex(d, file='/tmp/z.tex'), nomargins=FALSE, width=8.5, height=11)
}
ldesc(d)
## End(Not run)
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| describe | R Documentation |
Concise Statistical Description of a Vector, Matrix, Data Frame, or Formula
Description
describe is a generic method that invokes describe.data.frame,
describe.matrix, describe.vector, or
describe.formula. describe.vector is the basic
function for handling a single variable.
This function determines whether the variable is character, factor,
category, binary, discrete numeric, and continuous numeric, and prints
a concise statistical summary according to each. A numeric variable is
deemed discrete if it has <= 10 distinct values. In this case,
quantiles are not printed. A frequency table is printed
for any non-binary variable if it has no more than 20 distinct
values. For any variable for which the frequency table is not printed,
the 5 lowest and highest values are printed. This behavior can be
overriden for long character variables with many levels using the
listunique parameter, to get a complete tabulation.
describe is especially useful for
describing data frames created by *.get, as labels, formats,
value labels, and (in the case of sas.get) frequencies of special
missing values are printed.
For a binary variable, the sum (number of 1's) and mean (proportion of
1's) are printed. If the first argument is a formula, a model frame
is created and passed to describe.data.frame. If a variable
is of class "impute", a count of the number of imputed values is
printed. If a date variable has an attribute partial.date
(this is set up by sas.get), counts of how many partial dates are
actually present (missing month, missing day, missing both) are also presented.
If a variable was created by the special-purpose function substi (which
substitutes values of a second variable if the first variable is NA),
the frequency table of substitutions is also printed.
For numeric variables, describe adds an item called Info
which is a relative information measure using the relative efficiency of
a proportional odds/Wilcoxon test on the variable relative to the same
test on a variable that has no ties. Info is related to how
continuous the variable is, and ties are less harmful the more untied
values there are. The formula for Info is one minus the sum of
the cubes of relative frequencies of values divided by one minus the
square of the reciprocal of the sample size. The lowest information
comes from a variable having only one distinct value following by a
highly skewed binary variable. Info is reported to
two decimal places.
A latex method exists for converting the describe object to a
LaTeX file. For numeric variables having more than 20 distinct values,
describe saves in its returned object the frequencies of 100
evenly spaced bins running from minimum observed value to the maximum.
When there are less than or equal to 20 distinct values, the original
values are maintained.
latex and html insert a spike histogram displaying these
frequency counts in the tabular material using the LaTeX picture
environment. For example output see
https://hbiostat.org/doc/rms/book/chapter7edition1.pdf.
Note that the latex method assumes you have the following styles
installed in your latex installation: setspace and relsize.
The html method mimics the LaTeX output. This is useful in the
context of Quarto/Rmarkdown html and html notebook output.
If options(prType='html') is in effect, calling print on
an object that is the result of running describe on a data frame
will result in rendering the HTML version. If run from the console a
browser window will open. When which is specified to
print, whether or not prType='html' is in effect, a
gt package html table will be produced containing only
the types of variables requested. When which='both' a list with
element names Continuous and Categorical is produced,
making it convenient for the user to print as desired, or to pass the
list directed to the qreport maketabs function when using Quarto.
The plot method is for describe objects run on data
frames. It produces spike histograms for a graphic of
continuous variables and a dot chart for categorical variables, showing
category proportions. The graphic format is ggplot2 if the user
has not set options(grType='plotly') or has set the grType
option to something other than 'plotly'. Otherwise plotly
graphics that are interactive are produced, and these can be placed into
an Rmarkdown html notebook. The user must install the plotly
package for this to work. When the use hovers the mouse over a bin for
a raw data value, the actual value will pop-up (formatted using
digits). When the user hovers over the minimum data value, most
of the information calculated by describe will pop up. For each
variable, the number of missing values is used to assign the color to
the histogram or dot chart, and a legend is drawn. Color is not used if
there are no missing values in any variable. For categorical variables,
hovering over the leftmost point for a variable displays details, and
for all points proportions, numerators, and denominators are displayed
in the popup. If both continuous and categorical variables are present
and which='both' is specified, the plot method returns an
unclassed list containing two objects, named 'Categorical'
and 'Continuous', in that order.
Sample weights may be specified to any of the functions, resulting in weighted means, quantiles, and frequency tables.
Note: As discussed in Cox and Longton (2008), Stata Technical Bulletin 8(4) pp. 557, the term "unique" has been replaced with "distinct" in the output (but not in parameter names).
When weights are not used, the pseudomedian and Gini's mean difference are computed for
numeric variables. The pseudomedian is labeled pMedian and is the median of all possible pairwise averages. It is a robust and efficient measure of location that equals the mean and median for symmetric distributions. It is also called the Hodges-Lehmann one-sample estimator. Gini's mean difference is a robust measure of dispersion that is the
mean absolute difference between any pairs of observations. In simple
output Gini's difference is labeled Gmd.
formatdescribeSingle is a service function for latex,
html, and print methods for single variables that is not
intended to be called by the user.
Usage
## S3 method for class 'vector'
describe(x, descript, exclude.missing=TRUE, digits=4,
listunique=0, listnchar=12,
weights=NULL, normwt=FALSE, minlength=NULL, shortmChoice=TRUE,
rmhtml=FALSE, trans=NULL, lumptails=0.01, ...)
## S3 method for class 'matrix'
describe(x, descript, exclude.missing=TRUE, digits=4, ...)
## S3 method for class 'data.frame'
describe(x, descript, exclude.missing=TRUE,
digits=4, trans=NULL, ...)
## S3 method for class 'formula'
describe(x, descript, data, subset, na.action,
digits=4, weights, ...)
## S3 method for class 'describe'
print(x, which = c('both', 'categorical', 'continuous'), ...)
## S3 method for class 'describe'
latex(object, title=NULL,
file=paste('describe',first.word(expr=attr(object,'descript')),'tex',sep='.'),
append=FALSE, size='small', tabular=TRUE, greek=TRUE,
spacing=0.7, lspace=c(0,0), ...)
## S3 method for class 'describe.single'
latex(object, title=NULL, vname,
file, append=FALSE, size='small', tabular=TRUE, greek=TRUE,
lspace=c(0,0), ...)
## S3 method for class 'describe'
html(object, size=85, tabular=TRUE,
greek=TRUE, scroll=FALSE, rows=25, cols=100, ...)
## S3 method for class 'describe.single'
html(object, size=85,
tabular=TRUE, greek=TRUE, ...)
formatdescribeSingle(x, condense=c('extremes', 'frequencies', 'both', 'none'),
lang=c('plain', 'latex', 'html'), verb=0, lspace=c(0, 0),
size=85, ...)
## S3 method for class 'describe'
plot(x, which=c('both', 'continuous', 'categorical'),
what=NULL,
sort=c('ascending', 'descending', 'none'),
n.unique=10, digits=5, bvspace=2, ...)
Arguments
x |
a data frame, matrix, vector, or formula. For a data frame, the
|
descript |
optional title to print for x. The default is the name of the argument
or the "label" attributes of individual variables. When the first argument
is a formula, |
exclude.missing |
set toTRUE to print the names of variables that contain only missing values. This list appears at the bottom of the printout, and no space is taken up for such variables in the main listing. |
digits |
number of significant digits to print. For |
listunique |
For a character variable that is not an |
listnchar |
see |
weights |
a numeric vector of frequencies or sample weights. Each observation
will be treated as if it were sampled |
minlength |
value passed to summary.mChoice |
shortmChoice |
set to |
rmhtml |
set to |
trans |
for |
lumptails |
specifies the quantile to use (its complement is also used) for grouping observations in the tails so that outliers have less chance of distorting the variable's range for sparkline spike histograms. The default is 0.01, i.e., observations below the 0.01 quantile are grouped together in the leftmost bin, and observations above the 0.99 quantile are grouped to form the last bin. |
normwt |
The default, |
object |
a result of |
title |
unused |
data |
a data frame, data table, or list |
subset |
a subsetting expression |
na.action |
These are used if a formula is specified. |
... |
arguments passed to |
file |
name of output file (should have a suffix of .tex). Default name is
formed from the first word of the |
append |
set to |
size |
LaTeX text size ( |
tabular |
set to |
greek |
By default, the |
spacing |
By default, the |
lspace |
extra vertical scape, in character size units (i.e., "ex"
as appended to the space). When using certain font sizes, there is
too much space left around LaTeX verbatim environments. This
two-vector specifies space to remove (i.e., the values are negated in
forming the |
scroll |
set to |
rows, cols |
the number of rows or columns to allocate for the scrollable box |
vname |
unused argument in |
which |
specifies whether to plot numeric continuous or
binary/categorical variables, or both. When |
what |
character or numeric vector specifying which variables to plot; default is to plot all |
sort |
specifies how and whether variables are sorted in order of
the proportion of positives when |
n.unique |
the minimum number of distinct values a numeric variable
must have before |
bvspace |
the between-variable spacing for categorical variables. Defaults to 2, meaning twice the amount of vertical space as what is used for between-category spacing within a variable |
condense |
specifies whether to condense the output with regard to
the 5 lowest and highest values ( |
lang |
specifies the markup language |
verb |
set to 1 if a verbatim environment is already in effect for LaTeX |
Details
If options(na.detail.response=TRUE)
has been set and na.action is "na.delete" or
"na.keep", summary statistics on
the response variable are printed separately for missing and non-missing
values of each predictor. The default summary function returns
the number of non-missing response values and the mean of the last
column of the response values, with a names attribute of
c("N","Mean").
When the response is a Surv object and the mean is used, this will
result in the crude proportion of events being used to summarize
the response. The actual summary function can be designated through
options(na.fun.response = "function name").
If you are modifying LaTex parskip or certain other parameters,
you may need to shrink the area around tabular and
verbatim environments produced by latex.describe. You can
do this using for example
\usepackage{etoolbox}\makeatletter\preto{\@verbatim}{\topsep=-1.4pt
\partopsep=0pt}\preto{\@tabular}{\parskip=2pt
\parsep=0pt}\makeatother in the LaTeX preamble.
Value
a list containing elements descript, counts,
values. The list is of class describe. If the input
object was a matrix or a data
frame, the list is a list of lists, one list for each variable
analyzed. latex returns a standard latex object. For numeric
variables having at least 20 distinct values, an additional component
intervalFreq. This component is a list with two elements, range
(containing two values) and count, a vector of 100 integer frequency
counts. print with which= returns a 'gt' table object.
The user can modify the table by piping formatting changes, column
removals, and other operations, before final rendering.
Author(s)
Frank Harrell
Vanderbilt University
fh@fharrell.com
See Also
spikecomp, sas.get, quantile,
GiniMd, pMedian,
table, summary,
model.frame.default,
naprint, lapply, tapply,
Surv, na.delete,
na.keep,
na.detail.response, latex
Examples
set.seed(1)
describe(runif(200),dig=2) #single variable, continuous
#get quantiles .05,.10,\dots
dfr <- data.frame(x=rnorm(400),y=sample(c('male','female'),400,TRUE))
describe(dfr)
## Not run:
options(grType='plotly')
d <- describe(mydata)
p <- plot(d) # create plots for both types of variables
p[[1]]; p[[2]] # or p$Categorical; p$Continuous
plotly::subplot(p[[1]], p[[2]], nrows=2) # plot both in one
plot(d, which='categorical') # categorical ones
d <- sas.get(".","mydata",special.miss=TRUE,recode=TRUE)
describe(d) #describe entire data frame
attach(d, 1)
describe(relig) #Has special missing values .D .F .M .R .T
#attr(relig,"label") is "Religious preference"
#relig : Religious preference Format:relig
# n missing D F M R T distinct
# 4038 263 45 33 7 2 1 8
#
#0:none (251, 6%), 1:Jewish (372, 9%), 2:Catholic (1230, 30%)
#3:Jehovah's Witnes (25, 1%), 4:Christ Scientist (7, 0%)
#5:Seventh Day Adv (17, 0%), 6:Protestant (2025, 50%), 7:other (111, 3%)
# Method for describing part of a data frame:
describe(death.time ~ age*sex + rcs(blood.pressure))
describe(~ age+sex)
describe(~ age+sex, weights=freqs) # weighted analysis
fit <- lrm(y ~ age*sex + log(height))
describe(formula(fit))
describe(y ~ age*sex, na.action=na.delete)
# report on number deleted for each variable
options(na.detail.response=TRUE)
# keep missings separately for each x, report on dist of y by x=NA
describe(y ~ age*sex)
options(na.fun.response="quantile")
describe(y ~ age*sex) # same but use quantiles of y by x=NA
d <- describe(my.data.frame)
d$age # print description for just age
d[c('age','sex')] # print description for two variables
d[sort(names(d))] # print in alphabetic order by var. names
d2 <- d[20:30] # keep variables 20-30
page(d2) # pop-up window for these variables
# Test date/time formats and suppression of times when they don't vary
library(chron)
d <- data.frame(a=chron((1:20)+.1),
b=chron((1:20)+(1:20)/100),
d=ISOdatetime(year=rep(2003,20),month=rep(4,20),day=1:20,
hour=rep(11,20),min=rep(17,20),sec=rep(11,20)),
f=ISOdatetime(year=rep(2003,20),month=rep(4,20),day=1:20,
hour=1:20,min=1:20,sec=1:20),
g=ISOdate(year=2001:2020,month=rep(3,20),day=1:20))
describe(d)
# Make a function to run describe, latex.describe, and use the kdvi
# previewer in Linux to view the result and easily make a pdf file
ldesc <- function(data) {
options(xdvicmd='kdvi')
d <- describe(data, desc=deparse(substitute(data)))
dvi(latex(d, file='/tmp/z.tex'), nomargins=FALSE, width=8.5, height=11)
}
ldesc(d)
## End(Not run)
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