| row_sums {sjmisc} | R Documentation |
row_sums() simply wraps rowSums, while
row_means() simply wraps mean_n,
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 na.rm is TRUE,
and the return value is always a tibble (with one variable).
row_sums(x, ..., na.rm = TRUE, var = "rowsums", append = TRUE) row_means(x, ..., n, var = "rowmeans", append = TRUE)
x |
A vector or data frame. |
... |
Optional, unquoted names of variables that should be selected for
further processing. Required, if |
na.rm |
Logical, |
var |
Name of new the variable with the row sums or means. |
append |
Logical, if |
n |
May either be
If a row's sum of valid values is less than |
For n, must be a numeric value from 0 to ncol(x). If
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'.
For row_sums(), a tibble with a new variable: the row sums from
x; for row_means(), a tibble with a new variable: the row
means from x. If append = FALSE, only the new variable
with row sums resp. row means is returned.
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()