row_means: Row means (optionally with minimum amount of valid values)

In datawizard: Easy Data Wrangling and Statistical Transformations

Row means (optionally with minimum amount of valid values)

Description

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).

Usage

row_means(
  data,
  select = NULL,
  exclude = NULL,
  min_valid = NULL,
  digits = NULL,
  ignore_case = FALSE,
  regex = FALSE,
  remove_na = FALSE,
  verbose = TRUE
)

Arguments

data	A data frame with at least two columns, where row means are applied.
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.
min_valid	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.
digits	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.
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.
remove_na	Logical, if TRUE (default), removes missing (NA) values before calculating row means. Only applies if min_valuid is not specified.
verbose	Toggle warnings.

Details

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'.

Value

A vector with row means for those rows with at least n valid values.

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

datawizard documentation built on Sept. 15, 2023, 9:06 a.m.