sjmisc (version 1.2)

mean_n: Row means with min 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 n values of a row are valid (and not NA).

Usage

mean_n(dat, n, digits = 2)

Arguments

dat

data.frame with at least two columns, where row means are applied.

n

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').

I

digits

Numeric value indicating the number of decimal places to be used for rounding mean value. Negative values are allowed (see ‘Details’).

Value

• A vector with row mean values of df for those rows with at least n valid values. Else, NA is returned.

Details

Rounding to a negative number of digits means rounding to a power of ten, so for example mean_n(df, 3, digits = -2) rounds to the nearest hundred. For n, must be a numeric value from 0 to ncol(dat). If a row in dat has at least n non-missing values, the row mean is returned. If n is a non-integer value from 0 to 1, n is considered to indicate the proportion of necessary non-missing values per row. E.g., if n = .75, a row must have at least ncol(dat) * n non-missing values for the row mean to be calculated. See 'Examples'.

References

• http://candrea.ch/blog/compute-spss-like-mean-index-variables/{candrea's blog}
• http://r4stats.com/2014/09/03/adding-the-spss-mean-n-function-to-r/{r4stats.com}

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))

# needs at least 4 non-missing values per row
mean_n(dat, 4) # 1 valid return value

# needs at least 3 non-missing values per row
mean_n(dat, 3) # 2 valid return values

# needs at least 2 non-missing values per row
mean_n(dat, 2)

# needs at least 1 non-missing value per row
mean_n(dat, 1) # all means are shown

# needs at least 50\% of non-missing values per row
mean_n(dat, .5) # 3 valid return values

# needs at least 75\% of non-missing values per row
mean_n(dat, .75) # 2 valid return values