This function is similar to the SPSS MEAN.n function and computes
row means from a data frame or matrix if at least min_valid values of a row are
valid (and not NA).
row_means(
data,
select = NULL,
exclude = NULL,
min_valid = NULL,
digits = NULL,
ignore_case = FALSE,
regex = FALSE,
remove_na = FALSE,
verbose = TRUE
)A vector with row means for those rows with at least n valid values.
A data frame with at least two columns, where row means are applied.
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.
Optional, a numeric value of length 1. May either be
a numeric value that indicates the amount of valid values per row to calculate the row mean;
or a value between 0 and 1, indicating a proportion of valid values per
row to calculate the row mean (see 'Details').
NULL (default), in which all cases are considered.
If a row's sum of valid values is less than min_valid, NA will be returned.
Numeric value indicating the number of decimal places to be
used for rounding mean values. Negative values are allowed (see 'Details').
By default, digits = NULL and no rounding is used.
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.
Logical, if TRUE (default), removes missing (NA) values
before calculating row means. Only applies if min_valuid is not specified.
Toggle warnings.
Rounding to a negative number of digits means rounding to a power of
ten, for example row_means(df, 3, digits = -2) rounds to the nearest hundred.
For min_valid, if not NULL, min_valid must be a numeric value from 0
to ncol(data). If a row in the data frame has at least min_valid
non-missing values, the row mean is returned. If min_valid is a non-integer
value from 0 to 1, min_valid is considered to indicate the proportion of
required non-missing values per row. E.g., if min_valid = 0.75, a row must
have at least ncol(data) * min_valid non-missing values for the row mean
to be calculated. See 'Examples'.
dat <- data.frame(
c1 = c(1, 2, NA, 4),
c2 = c(NA, 2, NA, 5),
c3 = c(NA, 4, NA, NA),
c4 = c(2, 3, 7, 8)
)
# default, all means are shown, if no NA values are present
row_means(dat)
# remove all NA before computing row means
row_means(dat, remove_na = TRUE)
# needs at least 4 non-missing values per row
row_means(dat, min_valid = 4) # 1 valid return value
# needs at least 3 non-missing values per row
row_means(dat, min_valid = 3) # 2 valid return values
# needs at least 2 non-missing values per row
row_means(dat, min_valid = 2)
# needs at least 1 non-missing value per row, for two selected variables
row_means(dat, select = c("c1", "c3"), min_valid = 1)
# needs at least 50% of non-missing values per row
row_means(dat, min_valid = 0.5) # 3 valid return values
# needs at least 75% of non-missing values per row
row_means(dat, min_valid = 0.75) # 2 valid return values
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