Create a conditional count variable in R - r

Question

I want to create a count variable with the number of peoples with Z==0 in each of the given years. As Illustrated below:
PersonID Year Z Count*
1 1990 0 1
2 1990 1 1
3 1990 1 1
4 1990 2 1
5 1990 1 1
1 1991 1 3
2 1991 0 3
3 1991 1 3
4 1991 0 3
5 1991 0 3
1 1992 NA 1
2 1992 2 1
3 1992 2 1
4 1992 0 1
5 1993 1 0
1 1993 1 0
2 1993 2 0
3 1993 NA 0
4 1993 1 0
5 1994 0 5
1 1994 0 5
2 1994 0 5
3 1994 0 5
4 1994 0 5
I looked at my previous R-scripts and found this
library(dplyr)
sum_data <- data %>% group_by(PersonID) %>% summarise(Count = sum(Z, na.rm=T))
Can someone help me get this right? The count variable should basically count a total number of persons with Z==0, in the same format as I illustrated above. Thanks!!
dput(data)
structure(list(PersonID = c(1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L,
5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L),
Year = c(1990L, 1990L, 1990L, 1990L, 1990L, 1991L, 1991L,
1991L, 1991L, 1991L, 1992L, 1992L, 1992L, 1992L, 1993L, 1993L,
1993L, 1993L, 1993L, 1994L, 1994L, 1994L, 1994L, 1994L),
Z = c(0L, 1L, 1L, 2L, 1L, 1L, 0L, 1L, 0L, 0L, NA, 2L, 2L,
0L, 1L, 1L, 2L, NA, 1L, 0L, 0L, 0L, 0L, 0L)), .Names = c("PersonID",
"Year", "Z"), class = "data.frame", row.names = c(NA, -24L))

Answer 1

Here's a simple solution :
library(dplyr)
sum_data <- df %>%
mutate(Z=replace(Z, is.na(Z), 1)) %>%
mutate(temp = ifelse(Z == 0, 1, 0)) %>%
group_by(Year) %>%
summarize(count = sum(temp))
basically this is what the code is doing :
mutate(Z=replace(Z, is.na(Z), 1)) replace the NA with 1 (optional)
mutate(temp = ifelse(Z == 0, 1, 0)) create a conditional temp
variable :
ifelse(Z == 0, 1, 0) say if Z == 0 then the value is 1
else 0
group_by(Year) pretty explicite :) it group the data frame by
Year
summarize(count = sum(temp)) create a count variable with the
sum of earlier generated temp
results :
Year count
<int> <int>
1 1990 5
2 1991 5
3 1992 4
4 1993 5
5 1994 5
and if you want to join this data to the original data frame just use join :
left_join(df, sum_data)
Joining, by = "Year"
PersonID Year Z count
1 1 1990 0 1
2 2 1990 1 1
3 3 1990 1 1
4 4 1990 2 1
5 5 1990 1 1
6 1 1991 1 3
7 2 1991 0 3
8 3 1991 1 3
9 4 1991 0 3
10 5 1991 0 3
11 1 1992 NA 1
12 2 1992 2 1
13 3 1992 2 1
14 4 1992 0 1
15 5 1993 1 0
16 1 1993 1 0
17 2 1993 2 0
18 3 1993 NA 0
19 4 1993 1 0
20 5 1994 0 5
21 1 1994 0 5
22 2 1994 0 5
23 3 1994 0 5
24 4 1994 0 5

Answer 2

Try this:
library(dplyr)
df <- left_join(data, data %>% filter(Z==0) %>% group_by(Year) %>% summarise(Count = n()))
df[is.na(df$Count),]$Count <- 0
PersonID Year Z Count
1 1 1990 0 1
2 2 1990 1 1
3 3 1990 1 1
4 4 1990 2 1
5 5 1990 1 1
6 1 1991 1 3
7 2 1991 0 3
8 3 1991 1 3
9 4 1991 0 3
10 5 1991 0 3
11 1 1992 NA 1
12 2 1992 2 1
13 3 1992 2 1
14 4 1992 0 1
15 5 1993 1 0
16 1 1993 1 0
17 2 1993 2 0
18 3 1993 NA 0
19 4 1993 1 0
20 5 1994 0 5
21 1 1994 0 5
22 2 1994 0 5
23 3 1994 0 5
24 4 1994 0 5