Sum across multiple columns with dplyr
dplyr, r
Solution
dplyr >= 1.0.0 using across
sum up each row using `rowSums` (`rowwise` works for any aggreation, but is slower)
df %>%
replace(is.na(.), 0) %>%
mutate(sum = rowSums(across(where(is.numeric))))
sum down each column
df %>%
summarise(across(everything(), ~ sum(., na.rm = TRUE)))
dplyr < 1.0.0
sum up each row
df %>%
replace(is.na(.), 0) %>%
mutate(sum = rowSums(.[1:5]))
sum down each column using superseeded `summarise_all`:
df %>%
replace(is.na(.), 0) %>%
summarise_all(funs(sum))
Problem
My question involves summing up values across multiple columns of a data frame and creating a new column corresponding to this summation using `dplyr`. The data entries in the columns are binary(0,1). I am thinking of a row-wise analog of the `summarise_each` or `mutate_each` function of `dplyr`. Below is a minimal example of the data frame: ``` library(dplyr) df=data.frame( x1=c(1,0,0,NA,0,1,1,NA,0,1), x2=c(1,1,NA,1,1,0,NA,NA,0,1), x3=c(0,1,0,1,1,0,NA,NA,0,1), x4=c(1,0,NA,1,0,0,NA,0,0,1), x5=c(1,1,NA,1,1,1,NA,1,0,1)) > df x1 x2 x3 x4 x5 1 1 1 0 1 1 2 0 1 1 0 1 3 0 NA 0 NA NA 4 NA 1 1 1 1 5 0 1 1 0 1 6 1 0 0 0 1 7 1 NA NA NA NA 8 NA NA NA 0 1 9 0 0 0 0 0 10 1 1 1 1 1 ``` I could use something like: ``` df <- df %>% mutate(sumrow= x1 + x2 + x3 + x4 + x5) ``` but this would involve writing out the names of each of the columns. I have like 50 columns. In addition, the column names change at different iterations of the loop in which I want to implement this operation so I would like to try avoid having to give any column names. How can I do that most efficiently? Any assistance would be greatly appreciated.