R: Concise Statistical Description of a Vector, Matrix, Data...

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 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 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.

The `html` method mimics the LaTeX output. This is useful in the context of Rmarkdown html and html notebook output.

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, Gini's mean difference is computed for numeric variables. This is a robust measure of dispersion that is the mean absolute difference between any pairs of observations. In the 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, ...)
    ## 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, ...)
    ## 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, ...)

### 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 after it, in parentheses. 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.
`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` |
`subset` |
`na.action` | These are used if a formula is specified. `na.action` defaults to `na.retain` which does not delete any `NA`s 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.
`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.
`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
`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.

### Author(s)

Frank Harrell
Vanderbilt University
f.harrell@vanderbilt.edu

### See Also

`sas.get`, `quantile`, `GiniMd`, `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)

[Package Hmisc version 4.2-0 Index]