how do I identify rows where an element appears for the first time? - r

Question

I have the following data frame of student records. what I want is to identify students who joined a certain program in 2014 for the first time when they were in 9th grade.
names.first<-c('a','a','b','b','c','d')
names.last<-c('c','c','z','z','f','h')
year<-c(2014,2013,2014,2015,2015,2014)
grade<-c(9,8,9,10,10,10)
df<-data.frame(names.first,names.last,year,grade)
df
To do this, I have used the following statement to say that I want students where the program year==2014 and their grade ==9.
df$first.cohort<-ifelse(df$year==2014 & df$grade==9,1,0)
df
names.first names.last year grade first.cohort
1 a c 2014 9 1
2 a c 2013 8 0
3 b z 2014 9 1
4 b z 2015 10 0
5 c f 2015 10 0
6 d h 2014 10 0
However, as you can notice this would include students who didn't enter the program in year 2014 such as student awho started in 2013. How do I create a ifelse statement where I only capture students who are in 9th grade and started the program in 2014 for the first time so that the df looks like
names.first names.last year grade first.cohort
1 a c 2014 9 0
2 a c 2013 8 0
3 b z 2014 9 1
4 b z 2015 10 0
5 c f 2015 10 0
6 d h 2014 10 0

Answer 1

We can use first after arrangeing by 'name' and 'year' to create the logical expression
library(dplyr)
df %>%
arrange(names, year) %>%
group_by(names) %>%
mutate(first.cohort = as.integer(grade == 9 & first(year) == 2014))
# A tibble: 6 x 4
# Groups: names [4]
# names year grade first.cohort
# <fct> <dbl> <dbl> <int>
#1 a 2013 8 0
#2 a 2014 9 0
#3 b 2014 9 1
#4 b 2015 10 0
#5 c 2015 10 0
#6 d 2014 10 0
For keeping the same order as in the input dataset, we can create a sequence column first and then do the arrange on the column after the mutate
df %>%
mutate(rn = row_number()) %>%
arrange(names, year) %>%
group_by(names) %>%
mutate(first.cohort = as.integer(grade == 9 & first(year) == 2014)) %>%
ungroup %>%
arrange(rn) %>%
select(-rn)
Or using the same logic with data.table that have the additional advantage of keeping the same order as in the input dataset
library(data.table)
setDT(df)[order(names, year), first.cohort := as.integer(grade == 9 &
first(year) == 2014), names]
Update
With the new example in the OP's post, we do the grouping by both the 'names' column
df %>%
arrange(names.first, names.last, year) %>%
group_by(names.first, names.last) %>%
mutate(first.cohort = as.integer(grade == 9 & first(year) == 2014))
# A tibble: 6 x 5
# Groups: names.first, names.last [4]
# names.first names.last year grade first.cohort
# <fct> <fct> <dbl> <dbl> <int>
#1 a c 2013 8 0
#2 a c 2014 9 0
#3 b z 2014 9 1
#4 b z 2015 10 0
#5 c f 2015 10 0
#6 d h 2014 10 0

Answer 2

Using dplyr
library(dplyr)
df%>%group_by(names)%>%dplyr::mutate(Fc=as.numeric((year==2014&grade==9)&(min(year)==2014)))
# A tibble: 6 x 4
# Groups: names [4]
names year grade Fc
<fctr> <dbl> <dbl> <dbl>
1 a 2014 9 0
2 a 2013 8 0
3 b 2014 9 1
4 b 2015 10 0
5 c 2015 10 0
6 d 2014 10 0