row_means() compute row sums or means
for at least
n valid values per row. The functions are designed
to work nicely within a pipe-workflow and allow select-helpers
for selecting variables.
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row_sums(x, ...) ## Default S3 method: row_sums(x, ..., n, var = "rowsums", append = TRUE) ## S3 method for class 'mids' row_sums(x, ..., var = "rowsums", append = TRUE) row_means(x, ...) total_mean(x, ...) ## Default S3 method: row_means(x, ..., n, var = "rowmeans", append = TRUE) ## S3 method for class 'mids' row_means(x, ..., var = "rowmeans", append = TRUE)
A vector or data frame.
Optional, unquoted names of variables that should be selected for
further processing. Required, if
May either be
If a row's sum of valid (i.e. non-
Name of new the variable with the row sums or means.
n, must be a numeric value from
a row in
x has at least
n non-missing values, the
row mean or sum 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(x) * n
non-missing values for the row mean or sum to be calculated. See 'Examples'.
row_sums(), a data frame with a new variable: the row sums from
row_means(), a data frame with a new variable: the row
append = FALSE, only the new variable
with row sums resp. row means is returned.
the mean of all values from all specified columns in a data frame.
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data(efc) efc %>% row_sums(c82cop1:c90cop9, n = 3, append = FALSE) library(dplyr) row_sums(efc, contains("cop"), n = 2, append = FALSE) 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), c5 = c(1,7,5,3) ) dat row_means(dat, n = 4) row_sums(dat, n = 4) row_means(dat, c1:c4, n = 4) # at least 40% non-missing row_means(dat, c1:c4, n = .4) row_sums(dat, c1:c4, n = .4) # total mean of all values in the data frame total_mean(dat) # create sum-score of COPE-Index, and append to data efc %>% select(c82cop1:c90cop9) %>% row_sums(n = 1) # if data frame has only one column, this column is returned row_sums(dat[, 1, drop = FALSE], n = 0)
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