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'.

## 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)
#> [1] NA 2.75 NA NA
# remove all NA before computing row means
row_means(dat, remove_na = TRUE)
#> [1] 1.500000 2.750000 7.000000 5.666667
# needs at least 4 non-missing values per row
row_means(dat, min_valid = 4) # 1 valid return value
#> [1] NA 2.75 NA NA
# needs at least 3 non-missing values per row
row_means(dat, min_valid = 3) # 2 valid return values
#> [1] NA 2.750000 NA 5.666667
# needs at least 2 non-missing values per row
row_means(dat, min_valid = 2)
#> [1] 1.500000 2.750000 NA 5.666667
# needs at least 1 non-missing value per row, for two selected variables
row_means(dat, select = c("c1", "c3"), min_valid = 1)
#> [1] 1 3 NA 4
# needs at least 50% of non-missing values per row
row_means(dat, min_valid = 0.5) # 3 valid return values
#> [1] 1.500000 2.750000 NA 5.666667
# needs at least 75% of non-missing values per row
row_means(dat, min_valid = 0.75) # 2 valid return values
#> [1] NA 2.750000 NA 5.666667
```