Reputation: 1304
I have a dataset that looks something like this
df <- data.frame("id" = c("Alpha", "Alpha", "Alpha","Alpha","Beta","Beta","Beta","Beta"),
"Year" = c(1970,1970,1970,1971,1980,1980,1981,1982),
"Val" = c(2,3,-2,5,2,5,3,5))
I have mulple observations for each id and time identifier - e.g. I have 3 different alpha 1970 values. I would like to retain only one observation per id/year most notably the last one that appears in for each id/year. the final dataset should look something like this:
final <- data.frame("id" = c("Alpha","Alpha","Beta","Beta","Beta"),
"Year" = c(1970,1971,1980,1981,1982),
"Val" = c(-2,5,5,3,5))
Does anyone know how I can approach the problem?
Thanks a lot in advance for your help
Upvotes: 1
Views: 63
Reputation: 1267
How about this?
library(tidyverse)
df <- data.frame("id" = c("Alpha", "Alpha", "Alpha","Alpha","Beta","Beta","Beta","Beta"),
"Year" = c(1970,1970,1970,1971,1980,1980,1981,1982),
"Val" = c(2,3,-2,5,2,5,3,5))
final <-
df %>%
group_by(id, Year) %>%
slice(n()) %>%
ungroup()
final
#> # A tibble: 5 x 3
#> id Year Val
#> <fct> <dbl> <dbl>
#> 1 Alpha 1970 -2
#> 2 Alpha 1971 5
#> 3 Beta 1980 5
#> 4 Beta 1981 3
#> 5 Beta 1982 5
Created on 2019-09-29 by the reprex package (v0.3.0)
Translates to "within each id-Year group, take only the row where the row number is equal to the size of the group, i.e. it's the last row under the current ordering."
You could also use either filter()
, e.g. filter(row_number() == n())
, or distinct()
(and then you wouldn't even have to group), e.g. distinct(id, Year, .keep_all = TRUE)
- but distinct
functions take the first distinct row, so you'd need to reverse the row ordering here first.
Upvotes: 1
Reputation: 887203
An option with base R
aggregate(Val ~ ., df, tail, 1)
# id Year Val
#1 Alpha 1970 -2
#2 Alpha 1971 5
#3 Beta 1980 5
#4 Beta 1981 3
#5 Beta 1982 5
If we need to select the first row
aggregate(Val ~ ., df, head, 1)
Upvotes: 1
Reputation: 6234
If you are open to a data.table
solution, this can be done quite concisely:
library(data.table)
setDT(df)[, .SD[.N], by = c("id", "Year")]
#> id Year Val
#> 1: Alpha 1970 -2
#> 2: Alpha 1971 5
#> 3: Beta 1980 5
#> 4: Beta 1981 3
#> 5: Beta 1982 5
by = c("id", "Year")
groups the data.table by id
and Year
, and .SD[.N]
then returns the last row within each such group.
Upvotes: 2