CODEWITHSUNDEEP
rvariablesmeanmissing-datana.rm
Emily Noyer
Emily Noyer

Reputation: 1

Creating a new column as a mean of multiple other columns while excluding cases that have more than one missing value

I am trying to create a variable using the mutate function which is the mean score of multiple other variables while excluding observations that have more than one missing value. The code I am using will just factor out the missing value from it's calculation and continue to calculate the mean.

h18lb_r <- h18lb_r %>% mutate(social_network_contact_mean = rowMeans(h18lb_r[c(QLB008A, QLB008B, QLB008C, QLB008D, QLB012A, QLB012B, QLB012C,QLB012D, QLB016A, QLB016B, QLB016C, QLB016D), na.rm=TRUE]))


Error in `mutate()`:
ℹ In argument: `social_network_contact_mean = rowMeans(...)`.
Caused by error in `[.data.frame`:
! unused argument (na.rm = TRUE)

I would like a new column with the mean score for the combined variables while excluding observations that have more than one missing value.

Upvotes: 0

Views: 24

Answers (1)

dk2311
dk2311

Reputation: 1

I think you may be subsetting the rowMeans function wrong. There are multiple ways to subset but this one worked for me!

data <- data.frame(x=c(4,3,4,4,99),
                  y=c(4,NA,3,2,4),
                  z = c(88,NA,4,4,5),
                  w = c(4,5,2,3,4))

h18lb_r <- data %>% mutate(avg_mean = rowMeans(subset(data, select = c(`x`,`y`,`z`,`w`)), na.rm = T))

Upvotes: 0

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