suppose I have a dataset like this
df <- data.frame(group = c(rep(1,3),rep(2,2), rep(3,2),rep(4,3),rep(5, 2)), score = c(30, 10, 22, 44, 6, 5, 20, 35, 2, 60, 14,5))
group score
1 1 30
2 1 10
3 1 22
4 2 44
5 2 6
6 3 5
7 3 20
8 4 35
9 4 2
10 4 60
11 5 14
12 5 5
I want to remove the first row for each group, the expected out put should look like this:
group score
1 1 10
2 1 22
3 2 6
4 3 20
5 4 2
6 4 60
7 5 5
Is there a simple way to do this?
An option with dplyr is to select rows ignoring 1st row
library(dplyr)
df %>%
group_by(group) %>%
slice(2:n())
# group score
# <dbl> <dbl>
#1 1.00 10.0
#2 1.00 22.0
#3 2.00 6.00
#4 3.00 20.0
#5 4.00 2.00
#6 4.00 60.0
#7 5.00 5.00
Another way is shown by @Rich Scriven in now deleted answer
df %>%
group_by(group) %>%
slice(-1)
2
Quite simple with duplicated
df[duplicated(df$group),]
group score 2 1 10 3 1 22 5 2 6 7 3 20 9 4 2 10 4 60 12 5 5
Another base R option would be to check the adjacent elements
df[c(FALSE,df$group[-1]==df$group[-nrow(df)]),]
# group score
#2 1 10
#3 1 22
#5 2 6
#7 3 20
#9 4 2
#10 4 60
#12 5 5
Here I removed the first observation in ‘group’ (df$group[-1]) and compared (==) with the vector in which last observation is removed (df$group[-nrow(df)])). As the length of the comparison is one less than the nrow of the dataset, we pad with FALSE at the top and use this as logical index to subset the dataset.
dplyr::filter(df, group == lag(group))
group score
1 1 10
2 1 22
3 2 6
4 3 20
5 4 2
6 4 60
7 5 5
See lead and lag of package dplyr for more information:
https://dplyr.tidyverse.org/reference/lead-lag.html
Now that dplyr has slice_tail and supports the by= paramter, you can do
df %>% dplyr::slice_tail(n=-1, by=group)