row_sums() simply wraps
row_means() simply wraps
however, the argument-structure of both functions is designed
to work nicely within a pipe-workflow and allows select-helpers
for selecting variables, the default for
and the return value is always a tibble (with one variable).
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A vector or data frame.
Optional, unquoted names of variables that should be selected for
further processing. Required, if
Name of new the variable with the row sums or means.
May either be
If a row's sum of valid values is less than
n, must be a numeric value from
a row in
x 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(x) * n
non-missing values for the row mean to be calculated. See 'Examples'.
row_sums(), a tibble with a new variable: the row sums from
row_means(), a tibble with a new variable: the row
append = FALSE, only the new variable
with row sums resp. row means is returned.
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data(efc) efc %>% row_sums(c82cop1:c90cop9, append = FALSE) library(dplyr) row_sums(efc, contains("cop"), 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_means(dat, c1:c4, n = 4) # at least 40% non-missing row_means(dat, c1:c4, n = .4) # create sum-score of COPE-Index, and append to data efc %>% select(c82cop1:c90cop9) %>% row_sums()
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