rosettaDescr: descr (or descriptives)
In rosetta: Parallel Use of Statistical Packages in Teaching

Description Usage Arguments Details Value Author(s) References See Also Examples

This function provides a number of descriptives about your data, similar to what SPSS's DESCRIPTIVES (often called with DESCR) does.

descr(
  x,
  items = names(x),
  varLabels = NULL,
  mean = TRUE,
  meanCI = TRUE,
  median = TRUE,
  mode = TRUE,
  var = TRUE,
  sd = TRUE,
  se = FALSE,
  min = TRUE,
  max = TRUE,
  q1 = FALSE,
  q3 = FALSE,
  IQR = FALSE,
  skewness = TRUE,
  kurtosis = TRUE,
  dip = TRUE,
  totalN = TRUE,
  missingN = TRUE,
  validN = TRUE,
  histogram = FALSE,
  boxplot = FALSE,
  digits = 2,
  errorOnFactor = FALSE,
  convertFactor = FALSE,
  maxModes = 1,
  maxPlotCols = 4,
  t = FALSE,
  headingLevel = 3,
  conf.level = 0.95,
  quantileType = 2
)

rosettaDescr_partial(
  x,
  digits = attr(x, "digits"),
  show = attr(x, "show"),
  headingLevel = attr(x, "headingLevel"),
  maxPlotCols = attr(x, "maxPlotCols"),
  echoPartial = FALSE,
  partialFile = NULL,
  quiet = TRUE,
  ...
)

## S3 method for class 'rosettaDescr'
knit_print(
  x,
  digits = attr(x, "digits"),
  show = attr(x, "show"),
  headingLevel = attr(x, "headingLevel"),
  maxPlotCols = attr(x, "maxPlotCols"),
  echoPartial = FALSE,
  partialFile = NULL,
  quiet = TRUE,
  ...
)

## S3 method for class 'rosettaDescr'
print(
  x,
  digits = attr(x, "digits"),
  show = attr(x, "show"),
  maxPlotCols = attr(x, "maxPlotCols"),
  headingLevel = attr(x, "headingLevel"),
  forceKnitrOutput = FALSE,
  ...
)

`x`	The object to print (i.e. as produced by `descr`).
`items`	Optionally, if `x` is a data frame, the variable names for which to produce the descriptives.
`varLabels`	Optionally, a named vector with 'pretty labels' to show for the variables. This has to be a vector of the same length as `items`, and if it is not a named vector with the names corresponding to the `items`, it has to be in the same order.
`mean, meanCI, median, mode`	Whether to compute the mean, its confidence interval, the median, and/or the mode (all logical, so `TRUE` or `FALSE`).
`var, sd, se`	Whether to compute the variance, standard deviation, and standard error (all logical, so `TRUE` or `FALSE`).
`min, max, q1, q3, IQR`	Whether to compute the minimum, maximum, first and third quartile, and inter-quartile range (all logical, so `TRUE` or `FALSE`).
`skewness, kurtosis, dip`	Whether to compute the skewness, kurtosis and dip test (all logical, so `TRUE` or `FALSE`).
`totalN, missingN, validN`	Whether to show the total sample size, the number of missing values, and the number of valid (i.e. non-missing) values (all logical, so `TRUE` or `FALSE`).
`histogram, boxplot`	Whether to show a histogram and/or boxplot
`digits`	The number of digits to round the results to when showing them.
`errorOnFactor, convertFactor`	If `errorOnFactor` is `TRUE`, factors throw an error. If not, if `convertFactor` is TRUE, they will be converted to numeric values using `as.numeric(as.character(x))`, and then the same output will be generated as for numeric variables. If `convertFactor` is false, the frequency table will be produced.
`maxModes`	Maximum number of modes to display: displays "multi" if more than this number of modes if found.
`maxPlotCols`	The maximum number of columns when plotting multiple histograms and/or boxplots.
`t`	Whether to transpose the dataframes when printing them to the screen (this is easier for users relying on screen readers). Note: this functionality has not yet been implemented!
`headingLevel`	The number of hashes to print in front of the headings when printing while knitting
`conf.level`	Confidence of confidence interval around the mean in the central tendency measures.
`quantileType`	The type of quantiles to be used to compute the interquartile range (IQR). See `quantile` for more information.
`show`	A vector of elements to show in the results, based on the arguments that activate/deactivate the descriptives (from `mean` to `validN`).
`echoPartial`	Whether to show the executed code in the R Markdown partial (`TRUE`) or not (`FALSE`).
`partialFile`	This can be used to specify a custom partial file. The file will have object `x` available.
`quiet`	Passed on to `knitr::knit()` whether it should b chatty (`FALSE`) or quiet (`TRUE`).
`...`	Any additional arguments are passed to the default print method by the print method, and to `rmdpartials::partial()` when knitting an RMarkdown partial.
`forceKnitrOutput`	Force knitr output.

Note that R (of course) has many similar functions, such as summary, psych::describe() in the excellent psych::psych package.

The Hartigans' Dip Test may be unfamiliar to users; it is a measure of uni- vs. multimodality, computed by diptest::dip.test() from the dip.test package. Depending on the sample size, values over .025 can be seen as mildly indicative of multimodality, while values over .05 probably warrant closer inspection (the p-value can be obtained using diptest::dip.test(); also see Table 1 of Hartigan & Hartigan (1985) for an indication as to critical values).

A list of dataframes with the requested values.

Gjalt-Jorn Peters

Maintainer: Gjalt-Jorn Peters gjalt-jorn@userfriendlyscience.com

Hartigan, J. A.; Hartigan, P. M. The Dip Test of Unimodality. Ann. Statist. 13 (1985), no. 1, 70–84. doi:10.1214/aos/1176346577. https://projecteuclid.org/euclid.aos/1176346577.

summary, [psych::describe()

### Simplest example with default settings
descr(mtcars$mpg);

### Also requesting a histogram and boxplot
descr(mtcars$mpg, histogram=TRUE, boxplot=TRUE);

### To show the output as Rmd Partial in the viewer
rosetta::rosettaDescr_partial(
  rosetta::descr(
    mtcars$mpg
  )
);

### Multiple variables, including one factor
rosetta::rosettaDescr_partial(
  rosetta::descr(
    iris
  )
);