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)
Run the code above in your browser using DataCamp Workspace