My question involves generalizing a data frame with multiple columns (50 columns) using a function summarise_eachin dplyr. The data records in the columns are binary (0 = negative, 1 = positive), and I try to get kolsums and percentage positive values. The problem is that in some columns there are NSs, and I want to exclude them in the calculation of totals and percentages. The following is a minimal example:
library(dplyr)
library(tidyr)
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
df %>%
summarise_each(funs(total.count=n(), positive.count=sum(.,na.rm=T),positive.pctg=sum(.,na.rm=T)*100/n())) %>%
gather(key,fxn,x1_total.count:x5_positive.pctg) %>%
separate(key,c("col","funcn"),sep="\\_") %>%
spread(funcn,fxn)
col positive.count positive.pctg total.count
1 x1 4 40 10
2 x2 5 50 10
3 x3 4 40 10
4 x4 3 30 10
5 x5 7 70 10
What I was hoping to get in the above table, for example, the generic (total.count) for x1 as:
length(df$x1[!is.na(df$x1)])
[1] 8
Instead, I get the equivalent of the following, which includes NA:
length(df$x1)
[1] 10
and I also want a percentage (positive.pctg) for x1 like:
sum(df$x1,na.rm=T)/length(df$x1[!is.na(df$x1)])
[1] 0.5
Instead, I get the equivalent of the following, which includes NA:
sum(df$x1,na.rm=T)/length(df$x1)
[1] 0.4
dplyr ommiting NAs? , n() length()
na.omit/na.rm/complete.cases.
.