Desc {DescTools}  R Documentation 
Produce summaries of various types of variables. Calculate descriptive statistics for x and use Word as reporting tool for the numeric results and for descriptive plots. The appropriate statistics are chosen depending on the class of x. The general intention is to simplify the description process for lazy typers and return a quick, but rich summary.
A 2dimensional table will be described with it's relative frequencies, a short summary containing the total cases,
the dimensions of the table, chisquare tests and some association measures as phicoefficient, contingency coefficient and Cramer's V.
Tables with higher dimensions will simply be printed as flat table, with marginal sums for the first and for the last dimension.
Desc(x, ..., main = NULL, plotit = NULL, wrd = NULL)
## Default S3 method:
Desc(x, main = NULL, maxrows = NULL, ord = NULL,
conf.level = 0.95, verbose = 2, rfrq = "111", margins = c(1,2),
dprobs = NULL, mprobs = NULL, plotit = NULL, sep = NULL, digits = NULL, ...)
## S3 method for class 'data.frame'
Desc(x, main = NULL, plotit = NULL, enum = TRUE, sep = NULL, ...)
## S3 method for class 'list'
Desc(x, main = NULL, plotit = NULL, enum = TRUE, sep = NULL, ...)
## S3 method for class 'numeric'
Desc(x, main = NULL, maxrows = NULL, plotit = NULL,
sep = NULL, digits = NULL, ...)
## S3 method for class 'integer'
Desc(x, main = NULL, maxrows = NULL, plotit = NULL,
sep = NULL, digits = NULL, ...)
## S3 method for class 'factor'
Desc(x, main = NULL, maxrows = NULL, ord = NULL, plotit = NULL,
sep = NULL, digits = NULL, ...)
## S3 method for class 'ordered'
Desc(x, main = NULL, maxrows = NULL, ord = NULL, plotit = NULL,
sep = NULL, digits = NULL, ...)
## S3 method for class 'character'
Desc(x, main = NULL, maxrows = NULL, ord = NULL, plotit = NULL,
sep = NULL, digits = NULL, ...)
## S3 method for class 'logical'
Desc(x, main = NULL, ord = NULL, conf.level = 0.95, plotit = NULL,
sep = NULL, digits = NULL, ...)
## S3 method for class 'Date'
Desc(x, main = NULL, dprobs = NULL, mprobs = NULL, plotit = NULL,
sep = NULL, digits = NULL, ...)
## S3 method for class 'table'
Desc(x, main = NULL, conf.level = 0.95, verbose = 2,
rfrq = "111", margins = c(1,2), plotit = NULL, sep = NULL, digits = NULL, ...)
## S3 method for class 'formula'
Desc(formula, data = parent.frame(), subset, main = NULL,
plotit = NULL, digits = NULL, ...)
## S3 method for class 'Desc'
print(x, digits = NULL, plotit = NULL, nolabel = FALSE,
sep = NULL, nomain = FALSE, ...)
## S3 method for class 'Desc'
plot(x, main = NULL, ...)
x 
the object to be described. This can be a data.frame, a list, a table or a vector of the classes: numeric, integer, factor, ordered factor, logical. 
main 
a character vector, containing the main title(s).If this is left to 
wrd 
the pointer to a running MS Word instance, as created by 
digits 
integer. With how many digits shoud the relative frequencies be formatted? Default can be set by 
maxrows 
numeric; defines the maximum number of rows in a frequency table to be reported. For factors with many levels it is often not interesting to see
all of them. Default is set to 12 most frequent ones (resp. the first ones if Setting 
ord 
character out of 
rfrq 
a string with 3 characters, each of them being 
margins 
a vector, consisting out of 1 and/or 2. Defines the margin sums to be included.
Row margins are reported if margins is set to 1. Set it to 2 for column margins and c(1,2) for both. 
verbose 
integer out of 
conf.level 
confidence level of the interval. If set to 
dprobs , mprobs 
a vector with the probabilities for the ChiSquare test for days, resp. months, when describing a 
enum 
logical, determining if in data.frames and lists a sequential number should be included in the main title. Default is TRUE. The reason for this option is, that if a Word report with enumerated headings is created, the numbers may be redundant or inconsistent. 
plotit 
boolean. Should a plot be created? The plot type will be chosen according to the classes of variables (roughly following a
numericnumeric, numericcategorical, categoricalcategorical logic). Default can be defined by option 
sep 
character. The separator for the title. By default a line of 
nolabel 
logical, defining if labels (defined as attribute with the name 
formula 
a formula of the form 
data 
an optional matrix or data frame containing the variables in the formula 
subset 
an optional vector specifying a subset of observations to be used. 
nomain 
logical, determines if the main title of the output is printed or not, default is 
... 
further arguments to be passed to or from other methods. For the internal default method these can include:

Desc is a generic function. It dispatches to one of the methods above depending on the class of its first argument. Typing ?Desc
+ TAB at the prompt should present a choice of links: the help pages for each of these Desc
methods (at least if you're using RStudio, which anyway is recommended).
You don't need to use the full name of the method although you may if you wish; i.e.,
Desc(x) is idiomatic R but you can bypass method dispatch by going direct if you wish:
Desc.numeric(x).
This function produces a rich description of a factor, containing length, number of NAs, number of levels and
detailed frequencies of all levels.
The order of the frequency table can be chosen between descending/ascending frequency, labels or levels.
For ordered factors the order default is "level"
.
Character vectors are treated as unordered factors
Desc.char converts x to a factor an processes x as factor.
Desc.ordered does nothing more than changing the standard order for the frequencies to it's intrinsic order, which means order "level"
instead of "desc"
in the factor case.
Description interface for dates. We do here what seems reasonable for describing dates. We start with a short summary about length, number of NAs and extreme values, before we describe the frequencies of the weekdays and months, rounded up by a chisquare test.
A 2dimensional table will be described with it's relative frequencies, a short summary containing the total cases,
the dimensions of the table, chisquare tests and some association measures as phicoefficient, contingency coefficient and Cramer's V.
Tables with higher dimensions will simply be printed as flat table, with marginal sums for the first and for the last dimension.
Note that NAs cannot be handled by this interface, as tables in general come in "as.is", say basically as a matrix without any further information about potentially previously cleared NAs.
Description of a dichotomous variable. This can either be a boolean vector, a factor with two levels or a numeric variable
with only two unique values.
The confidence levels for the relative frequencies are calculated by BinomCI
, method "Wilson"
on a confidence level defined by conf.level
.
Dichotomous variables can easily be condensed in one graphical representation. Desc for a set of flags (=dichotomous variables) calculates the frequencies, a binomial confidence intervall and produces a kind of dotplot with error bars.
Motivation for this function is, that dichotomous variable in general do not contain intense information. Therefore it makes sense to condense the description of sets of dichotomous variables.
The formula interface accepts the formula operators +
, :
, *
, I()
, 1
and evaluates any function.
The left hand side and right hand side of the formula are evaluated the same way.
The variable pairs are processed in dependency of their classes.
Word This function is not thought of being directly run by the enduser. It will normally be called automatically, when
a pointer to a Word instance is passed to the function Desc
.
However DescWrd
takes some more specific arguments concerning the Word output (like font or fontsize), which can make it necessary to call the function directly.
A list containing the following components:
length 
the length of the vector (n + NAs). 
n 
the valid entries (NAs are excluded) 
NAs 
number of NAs 
unique 
number of unique values. 
0s 
number of zeros 
mean 
arithmetic mean 
MeanSE 
standard error of the mean, as calculated by 
quant 
a table of quantiles, as calculated by

sd 
standard deviation 
vcoef 
coefficient of variation: 
mad 
median absolute deviation ( 
IQR 
interquartile range 
skew 
skewness, as calculated by 
kurt 
kurtosis, as calculated by 
highlow 
the lowest and the highest values, reported with their frequencies in brackets, if > 1. 
frq 
a data.frame of absolute and relative frequencies given by 
Andri Signorell <andri@signorell.net>
opt < DescToolsOptions()
# implemented classes:
Desc(d.pizza$wrongpizza) # logical
Desc(d.pizza$driver) # factor
Desc(d.pizza$quality) # ordered factor
Desc(as.character(d.pizza$driver)) # character
Desc(d.pizza$week) # integer
Desc(d.pizza$delivery_min) # numeric
Desc(d.pizza$date) # Date
Desc(d.pizza)
Desc(d.pizza$wrongpizza, main="The wrong pizza delivered", digits=5)
Desc(table(d.pizza$area)) # 1dim table
Desc(table(d.pizza$area, d.pizza$operator)) # 2dim table
Desc(table(d.pizza$area, d.pizza$operator, d.pizza$driver)) # ndim table
# expressions
Desc(log(d.pizza$temperature))
Desc(d.pizza$temperature > 45)
# supported labels
Label(d.pizza$temperature) < "This is the temperature in degrees Celsius
measured at the time when the pizza is delivered to the client."
Desc(d.pizza$temperature)
# try as well: Desc(d.pizza$temperature, wrd=GetNewWrd())
z < Desc(d.pizza$temperature)
print(z, digits=1, plotit=FALSE)
# plot (additional arguments are passed on to the underlying plot function)
plot(z, main="The pizza's temperature in Celsius", args.hist=list(breaks=50))
# formula interface for single variables
Desc(~ uptake + Type, data = CO2, plotit = FALSE)
# bivariate
Desc(price ~ operator, data=d.pizza) # numeric ~ factor
Desc(driver ~ operator, data=d.pizza) # factor ~ factor
Desc(driver ~ area + operator, data=d.pizza) # factor ~ several factors
Desc(driver + area ~ operator, data=d.pizza) # several factors ~ factor
Desc(driver ~ week, data=d.pizza) # factor ~ integer
Desc(driver ~ operator, data=d.pizza, rfrq="111") # alle rel. frequencies
Desc(driver ~ operator, data=d.pizza, rfrq="000",
verbose=3) # no rel. frequencies
Desc(price ~ delivery_min, data=d.pizza) # numeric ~ numeric
Desc(price + delivery_min ~ operator + driver + wrongpizza,
data=d.pizza, digits=c(2,2,2,2,0,3,0,0) )
Desc(week ~ driver, data=d.pizza, digits=c(2,2,2,2,0,3,0,0)) # define digits
Desc(delivery_min + weekday ~ driver, data=d.pizza)
# without defining dataparameter
Desc(d.pizza$delivery_min ~ d.pizza$driver)
# with functions and interactions
Desc(sqrt(price) ~ operator : factor(wrongpizza), data=d.pizza)
Desc(log(price+1) ~ cut(delivery_min, breaks=seq(10,90,10)),
data=d.pizza, digits=c(2,2,2,2,0,3,0,0))
# response versus all the rest
Desc(driver ~ ., data=d.pizza[, c("temperature","wine_delivered","area","driver")])
# all the rest versus response
Desc(. ~ driver, data=d.pizza[, c("temperature","wine_delivered","area","driver")])
# pairwise Descriptions
p < CombPairs(c("area","count","operator","driver","temperature","wrongpizza","quality"), )
for(i in 1:nrow(p))
print(Desc(formula(gettextf("%s ~ %s", p$X1[i], p$X2[i])), data=d.pizza))
# get more flexibility, create the table first
tab < as.table(apply(HairEyeColor, c(1,2), sum))
tab < tab[,c("Brown","Hazel","Green","Blue")]
# display only absolute values, row and columnwise percentages
Desc(tab, row.vars=c(3, 1), rfrq="011", plotit=FALSE)
# do the plot by hand, while setting the colours for the mosaics
cols1 < SetAlpha(c("sienna4", "burlywood", "chartreuse3", "slategray1"), 0.6)
cols2 < SetAlpha(c("moccasin", "salmon1", "wheat3", "gray32"), 0.8)
plot(Desc(tab), col1=cols1, col2=cols2)
# use global format options for presentation
Fmt(abs=as.fmt(digits=0, big.mark=""))
Fmt(per=as.fmt(digits=2, fmt="%"))
Desc(area ~ driver, d.pizza, plotit=FALSE)
Fmt(abs=as.fmt(digits=0, big.mark="'"))
Fmt(per=as.fmt(digits=3, ldigits=0))
Desc(area ~ driver, d.pizza, plotit=FALSE)
# plot arguments can be fixed in detail
z < Desc(BoxCox(d.pizza$temperature, lambda = 1.5))
plot(z, mar=c(0, 2.1, 4.1, 2.1), args.rug=TRUE, args.hist=list(breaks=50),
args.dens=list(from=0))
# The default description for count variables can be inappropriate,
# the density curve does not represent the variable well.
set.seed(1972)
x < rpois(n = 500, lambda = 5)
Desc(x)
# but setting maxrows to Inf gives a better plot
Desc(x, maxrows = Inf)
# Output into word document (Windowsspecific example) 
# by simply setting wrd=GetNewWrd()
## Not run:
# create a new word instance and insert title and contents
wrd < GetNewWrd(header=TRUE)
# let's have a subset
d.sub < d.pizza[,c("driver", "date", "operator", "price", "wrongpizza")]
# do just the univariate analysis
Desc(d.sub, wrd=wrd)
## End(Not run)
DescToolsOptions(opt)