remove NA using dplyr package & counting non-zero values in R-CodePudding

I am working with the below df:

first_column<-c(1,2,3,4)
second_column<-c(1,2,"NA",4)
df<-data.frame(first_column,second_column)
df$test=ifelse(df$first_column==df$second_column,0,1) 

> df
  first_column second_column test
1            1            1    0
2            2            2    0
3            3           NA    1
4            4            4    0

What I would like to do are 2 things, 1) to remove an entire row if there is NA in second column, how should I do with & without dplyr? 2) If I would like to have an result returning to the first column if the test column shows non-zero, that is, in this case, returning to first column # 3 based on "1" on test column. May I know how should I tackle these 2 things? Many thanks for your help.

CodePudding user response：

You can use tidyr::drop_na:

library(tidyr)
df %>%
  drop_na(second_column)

And complete.cases in base R:

df[complete.cases(df$second_column), ]

Note that in your data frame, NA is character, "NA". It should be just NA.

CodePudding user response：

We could use base R

df[!is.na(df$second_column),]