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When calculating the sum of two data tables, NA+n=NA.
> dt1 <- data.table(Name=c("Joe","Ann"), "1"=c(0,NA), "2"=c(3,NA))
> dt1
Name 1 2
1: Joe 0 3
2: Ann NA NA
> dt2 <- data.table(Name=c("Joe","Ann"), "1"=c(0,NA), "2"=c(2,3))
> dt2
Name 1 2
1: Joe 0 2
2: Ann NA 3
> dtsum <- rbind(dt1, dt2)[, lapply(.SD, sum), by=Name]
> dtsum
Name 1 2
1: Joe 0 5
2: Ann NA NA
I don't want to substitute all NA's with 0. What I want is NA+NA=NA and NA+n=n to get the following result:
Name 1 2
1: Joe 0 5
2: Ann NA 3
How is this done in R?
UPDATE: removed typo in dt1
774
To find the sum of non-missing values in an R data frame column, we can simply use sum function and set the na. rm to TRUE. For example, if we have a data frame called df that contains a column say x which has some missing values then the sum of the non-missing values can be found by using the command sum(df$x,na.
How do I add an empty column to a DataFrame in R? The easiest way to add an empty column to a dataframe in R is to use the add_column() method: dataf %>% add_column(new_col = NA) . Note, that this includes installing dplyr or tidyverse.
You can define your own function to act as you want
plus <- function(x) {
if(all(is.na(x))){
c(x[0],NA)} else {
sum(x,na.rm = TRUE)}
}
rbind(dt1, dt2)[,lapply(.SD, plus), by = Name]
dtsum <- rbind(dt1, dt2)[, lapply(.SD, function(x) ifelse(all(is.na(x)), as.numeric(NA), sum(x, na.rm=T))), by=Name]
(includes @Arun's suggestion)
na.rm=TRUE is very useful to remember
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