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 <= 5="" 10="" 20="" 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="" with="" at="" least="" values,="" the="" lowest="" and="" highest="" values="" sas.get, as SAS labels, formats,
value labels, and 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.
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
Sample weights may be specified to any of the functions, resulting in weighted means, quantiles, and frequency tables.
## S3 method for class 'vector':
describe(x, descript, exclude.missing=TRUE, digits=4,
weights, normwt, \dots)
## S3 method for class 'matrix':
describe(x, descript, exclude.missing=TRUE, digits=4, \dots)
## S3 method for class 'data.frame':
describe(x, descript, exclude.missing=TRUE,
digits=4, \dots)
## S3 method for class 'formula':
describe(x, descript, data, subset, na.action,
digits=4, weights, \dots)
## S3 method for class 'describe':
print(x, condense=TRUE, \dots)
## 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, ...)
## S3 method for class 'describe.single':
latex(object, title=NULL, condense=TRUE, vname,
file, append=FALSE, size='small', tabular=TRUE, \dots)describe.data.frame
function is automatically invoked. For a matrix, describe.matrix is
called. For a formula, describe.data.frame(model.frame(x))
is invdescript defaults to a character representation of
the formula.weights times.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 describena.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 wdescribe.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%ydescript element of the
describe object, prefixed by "describe". Set
file="" to send LaTeX code to TRUE to have latex append text to an existing file
named file"small", the default, or "normalsize", "tiny",
"scriptsize", etc.) for the describe output in LaTeX.FALSE to use verbatim rather than tabular environment
for the summary statistics output. By default, tabular is used if the
output is not too wide.latex.describe.singledescript, 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.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").sas.get, quantile, table, summary, model.frame.default,
naprint, lapply, tapply, Surv, na.delete, na.keep,
na.detail.response, latexset.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)
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)Run the code above in your browser using DataLab