Computes summary table of means by groups.
means_by_group(x, ...)# S3 method for numeric
means_by_group(x, by = NULL, ci = 0.95, weights = NULL, digits = NULL, ...)
# S3 method for 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,
...
)
A data frame with information on mean and further summary statistics for each sub-group.
A vector or a data frame.
Currently not used
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.
Level of confidence interval for mean estimates. Default is 0.95.
Use ci = NA to suppress confidence intervals.
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.
Optional scalar, indicating the amount of digits after decimal point when rounding estimates and values.
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".
See select, however, column names matched by the pattern
from exclude will be excluded instead of selected. If NULL (the default),
excludes no columns.
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.
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.
Toggle warnings.
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.
data(efc)
means_by_group(efc, "c12hour", "e42dep")
data(iris)
means_by_group(iris, "Sepal.Width", "Species")
# weighting
efc$weight <- abs(rnorm(n = nrow(efc), mean = 1, sd = .5))
means_by_group(efc, "c12hour", "e42dep", weights = "weight")
Run the code above in your browser using DataLab