descr | R Documentation |

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 |

`items` |
Optionally, if |

`varLabels` |
Optionally, a named vector with 'pretty labels' to show
for the variables. This has to be a vector of the same length as |

`mean, meanCI, median, mode` |
Whether to compute the mean, its
confidence interval, the median, and/or the mode (all logical, so |

`var, sd, se` |
Whether to compute the variance, standard deviation, and
standard error (all logical, so |

`min, max, q1, q3, IQR` |
Whether to compute the minimum, maximum, first and
third quartile, and inter-quartile range (all logical, so |

`skewness, kurtosis, dip` |
Whether to compute the skewness, kurtosis and
dip test (all logical, so |

`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 |

`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 |

`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). |

`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 |

`show` |
A vector of elements to show in the results, based on the
arguments that activate/deactivate the descriptives (from |

`echoPartial` |
Whether to show the executed code in the R Markdown
partial ( |

`partialFile` |
This can be used to specify a custom partial file. The
file will have object |

`quiet` |
Passed on to |

`...` |
Any additional arguments are passed to the default print method
by the print method, and to |

`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 the `dip.test()`

function from the
`{diptest}`

package from the. 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
that `dip.test()`

function from `{diptest}`

; 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 ) );

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