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Computes summary table of means by groups.

Usage

means_by_group(x, ...)

# S3 method for class 'numeric'
means_by_group(
  x,
  by = NULL,
  ci = 0.95,
  weights = NULL,
  digits = NULL,
  group = NULL,
  ...
)

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

Arguments

x

A vector or a data frame.

...

Currently not used

by

If x is a numeric vector, by should be a factor that indicates the group-classifying categories. If x is a data frame, by should be a character string, naming the variable in x that is used for grouping. Numeric vectors are coerced to factors. Not that by 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.

group

Deprecated. Use by instead.

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. extract_column_names(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.

Value

A data frame with information on mean and further summary statistics for each sub-group.

Details

This function is comparable to aggregate(x, by, mean), but provides some further information, including summary statistics from a One-Way-ANOVA using x as dependent and by 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