Computes summary table of means by groups.

## Usage

```
means_by_group(x, ...)
# S3 method for numeric
means_by_group(x, group = NULL, ci = 0.95, weights = NULL, digits = NULL, ...)
# S3 method for data.frame
means_by_group(
x,
select = NULL,
group = NULL,
ci = 0.95,
weights = NULL,
digits = NULL,
exclude = NULL,
ignore_case = FALSE,
regex = FALSE,
verbose = TRUE,
...
)
```

## Arguments

- x
A vector or a data frame.

- ...
Currently not used

- group
If

`x`

is a numeric vector,`group`

should be a factor that indicates the group-classifying categories. If`x`

is a data frame,`group`

should be a character string, naming the variable in`x`

that is used for grouping. Numeric vectors are coerced to factors. Not that`group`

should only refer to a single variable.- ci
Level of confidence interval for mean estimates. Default is

`0.95`

. Use`ci = NA`

to suppress confidence intervals.- weights
If

`x`

is a numeric vector,`weights`

should be a vector of weights that will be applied to weight all observations. If`x`

is a data frame,`weights`

can also be a character string indicating the name of the variable in`x`

that should be used for weighting. Default is`NULL`

, so no weights are used.- digits
Optional scalar, indicating the amount of digits after decimal point when rounding estimates and values.

- select
Variables that will be included when performing the required tasks. Can be either

a variable specified as a literal variable name (e.g.,

`column_name`

),a string with the variable name (e.g.,

`"column_name"`

), or a character vector of variable names (e.g.,`c("col1", "col2", "col3")`

),a formula with variable names (e.g.,

`~column_1 + column_2`

),a vector of positive integers, giving the positions counting from the left (e.g.

`1`

or`c(1, 3, 5)`

),a vector of negative integers, giving the positions counting from the right (e.g.,

`-1`

or`-1:-3`

),one of the following select-helpers:

`starts_with()`

,`ends_with()`

,`contains()`

, a range using`:`

or`regex("")`

.`starts_with()`

,`ends_with()`

, and`contains()`

accept several patterns, e.g`starts_with("Sep", "Petal")`

.or a function testing for logical conditions, e.g.

`is.numeric()`

(or`is.numeric`

), or any user-defined function that selects the variables for which the function returns`TRUE`

(like:`foo <- function(x) mean(x) > 3`

),ranges specified via literal variable names, select-helpers (except

`regex()`

) and (user-defined) functions can be negated, i.e. return non-matching elements, when prefixed with a`-`

, e.g.`-ends_with("")`

,`-is.numeric`

or`-(Sepal.Width:Petal.Length)`

.**Note:**Negation means that matches are*excluded*, and thus, the`exclude`

argument can be used alternatively. For instance,`select=-ends_with("Length")`

(with`-`

) is equivalent to`exclude=ends_with("Length")`

(no`-`

). In case negation should not work as expected, use the`exclude`

argument instead.

If

`NULL`

, selects all columns. Patterns that found no matches are silently ignored, e.g.`find_columns(iris, select = c("Species", "Test"))`

will just return`"Species"`

.- exclude
See

`select`

, however, column names matched by the pattern from`exclude`

will be excluded instead of selected. If`NULL`

(the default), excludes no columns.- ignore_case
Logical, if

`TRUE`

and when one of the select-helpers or a regular expression is used in`select`

, ignores lower/upper case in the search pattern when matching against variable names.- regex
Logical, if

`TRUE`

, the search pattern from`select`

will be treated as regular expression. When`regex = TRUE`

, select*must*be a character string (or a variable containing a character string) and is not allowed to be one of the supported select-helpers or a character vector of length > 1.`regex = TRUE`

is comparable to using one of the two select-helpers,`select = contains("")`

or`select = regex("")`

, however, since the select-helpers may not work when called from inside other functions (see 'Details'), this argument may be used as workaround.- verbose
Toggle warnings.

## Details

This function is comparable to `aggregate(x, group, mean)`

, but provides
some further information, including summary statistics from a One-Way-ANOVA
using `x`

as dependent and `group`

as independent variable. `emmeans::contrast()`

is used to get p-values for each sub-group. P-values indicate whether each
group-mean is significantly different from the total mean.

## Examples

```
data(efc)
means_by_group(efc, "c12hour", "e42dep")
#> # Mean of average number of hours of care per week by elder's dependency
#>
#> Category | Mean | N | SD | 95% CI | p
#> ----------------------------------------------------------------------
#> independent | 17.00 | 2 | 11.31 | [-68.46, 102.46] | 0.573
#> slightly dependent | 34.25 | 4 | 29.97 | [-26.18, 94.68] | 0.626
#> moderately dependent | 52.75 | 28 | 51.83 | [ 29.91, 75.59] | > .999
#> severely dependent | 106.97 | 63 | 65.88 | [ 91.74, 122.19] | 0.001
#> Total | 86.46 | 97 | 66.40 | |
#>
#> Anova: R2=0.186; adj.R2=0.160; F=7.098; p<.001
data(iris)
means_by_group(iris, "Sepal.Width", "Species")
#> # Mean of Sepal.Width by Species
#>
#> Category | Mean | N | SD | 95% CI | p
#> ------------------------------------------------------
#> setosa | 3.43 | 50 | 0.38 | [3.33, 3.52] | < .001
#> versicolor | 2.77 | 50 | 0.31 | [2.68, 2.86] | < .001
#> virginica | 2.97 | 50 | 0.32 | [2.88, 3.07] | 0.035
#> Total | 3.06 | 150 | 0.44 | |
#>
#> Anova: R2=0.401; adj.R2=0.393; F=49.160; p<.001
# weighting
efc$weight <- abs(rnorm(n = nrow(efc), mean = 1, sd = .5))
means_by_group(efc, "c12hour", "e42dep", weights = "weight")
#> # Mean of average number of hours of care per week by elder's dependency
#>
#> Category | Mean | N | SD | 95% CI | p
#> ----------------------------------------------------------------------
#> independent | 16.64 | 2 | 11.31 | [-57.96, 91.23] | 0.397
#> slightly dependent | 28.67 | 5 | 28.40 | [-22.45, 79.79] | 0.397
#> moderately dependent | 51.64 | 30 | 51.25 | [ 30.42, 72.86] | 0.987
#> severely dependent | 108.67 | 57 | 64.86 | [ 93.44, 123.90] | < .001
#> Total | 84.17 | 97 | 66.01 | |
#>
#> Anova: R2=0.227; adj.R2=0.202; F=9.119; p<.001
```