describe {Hmisc} | 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 unique 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 unique
values. For any variable with at least 20 unique values, 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 unique values 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 at least 20 unique values,
describe
saves in its returned object the frequencies of 100
evenly spaced bins running from minimum observed value to the maximum.
latex
inserts a spike histogram displaying these frequency counts
in the tabular material using the LaTeX picture environment. For
example output see
http://biostat.mc.vanderbilt.edu/wiki/pub/Main/Hmisc/counties.pdf.
Note that the latex method assumes you have the following styles
installed in your latex installation: setspace and relsize.
Sample weights may be specified to any of the functions, resulting in weighted means, quantiles, and frequency tables.
Usage
## S3 method for class 'vector'
describe(x, descript, exclude.missing=TRUE, digits=4,
listunique=0, listnchar=12,
weights=NULL, normwt=FALSE, minlength=NULL, ...)
## 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, ...)
## S3 method for class 'formula'
describe(x, descript, data, subset, na.action,
digits=4, weights, ...)
## S3 method for class 'describe'
print(x, condense=TRUE, ...)
## S3 method for class 'describe'
latex(object, title=NULL, condense=TRUE,
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, condense=TRUE, vname,
file, append=FALSE, size='small', tabular=TRUE, greek=TRUE,
lspace=c(0,0), ...)
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 |
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. |
normwt |
The default, |
object |
a result of |
title |
unused |
condense |
default isTRUE to condense the output with regard to the 5 lowest and highest values and the frequency table |
data |
|
subset |
|
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 |
vname |
unused argument in |
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 unique 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.
Author(s)
Frank Harrell
Vanderbilt University
f.harrell@vanderbilt.edu
See Also
sas.get
, quantile
, 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:
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 unique
# 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)