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

```
descr(
x,
digits = 4,
errorOnFactor = FALSE,
include = c("central tendency", "spread", "range", "distribution shape", "sample size"),
maxModes = 1,
t = FALSE,
conf.level = 0.95,
quantileType = 2
)
# S3 method for default
descr(
x,
digits = 4,
errorOnFactor = FALSE,
include = c("central tendency", "spread", "range", "distribution shape", "sample size"),
maxModes = 1,
t = FALSE,
conf.level = 0.95,
quantileType = 2
)
# S3 method for descr
print(
x,
digits = attr(x, "digits"),
t = attr(x, "transpose"),
row.names = FALSE,
...
)
# S3 method for descr
pander(x, headerPrefix = "", headerStyle = "**", ...)
# S3 method for descr
as.data.frame(x, row.names = NULL, optional = FALSE, ...)
# S3 method for data.frame
descr(x, ...)
```

- x
The vector for which to return descriptives.

- digits
The number of digits to round the results to when showing them.

- errorOnFactor
Whether to show an error when the vector is a factor, or just show the frequencies instead.

- include
Which elements to include when showing the results.

- maxModes
Maximum number of modes to display: displays "multi" if more than this number of modes if found.

- t
Whether to transpose the dataframes when printing them to the screen (this is easier for users relying on screen readers).

- 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.- row.names
Whether to show row names (

`TRUE`

) or not (`FALSE`

).- ...
Additional arguments are passed to the default

`print`

and`pander`

methods.- headerPrefix
The prefix for the heading; can be used to insert hashes (

`#`

) to create Markdown headings.- headerStyle
A string to insert before and after the heading (to make stuff bold or italic in Markdown).

- optional
Provided for compatibility with the default

`as.data.frame()`

method - see that help page for details.

A list of dataframes with the requested values.

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. multidimensionality, 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).

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.

```
descr(mtcars$mpg);
#> ###### Descriptives for mtcars$mpg
#>
#> Describing the central tendency:
#> mean median mode 95% CI mean
#> 20.09 19.2 (multi) [17.92; 22.26]
#>
#> Describing the spread:
#> var sd iqr se
#> 36.32 6.027 7.45 1.065
#>
#> Describing the range:
#> min q1 q3 max
#> 10.4 15.2 22.8 33.9
#>
#> Describing the distribution shape:
#> skewness kurtosis dip
#> 0.6724 -0.02201 0.0569
#>
#> Describing the sample size:
#> total NA. valid
#> 32 0 32
#>
```