library(dplyr)
a <- data_frame(id = c("A","A","A","B","B","B"),
b = c(1.2, 1.5, 1.8, 1.1, 1.6, 1.4))
Now, I´d like to retrieve the values closest to 1.43 for each of the catergories in id. I thought I could use:
a %>% group_by(id) %>% nth(which.min(abs(.$b-1.43)))
but dplyr states
Error: Don't know how to generate default for object of class grouped_df/tbl_df/tbl/data.frame
which.min() returns the index of the (first) minimum or maximum of a numeric (or logical) vector. If there are multiple equal values as close to 1.43 as each other and you want to keep all of them, you can use filter():
a %>% group_by(id) %>% filter(abs(b - 1.43) == min(abs(b - 1.43)))
#Source: local data frame [2 x 2]
#Groups: id [2]
# id b
# <chr> <dbl>
#1 A 1.5
#2 B 1.4
If you prefer sticking with the nth() function, and it is OK to have only one value for each group, you can wrap it within a summarize function so that it will be applied to each group, and also according to ?nth(), you need to pass the vector to the function as an argument as well:
a %>% group_by(id) %>% summarise(b = nth(b, which.min(abs(b-1.43))))
# A tibble: 2 × 2
# id b
# <chr> <dbl>
#1 A 1.5
#2 B 1.4
There are a few ways to do this.
Here's a dplyr solution (found using this answer):
a %>%
group_by(id) %>%
slice(which.min(abs(b - 1.43)))
id b
<chr> <dbl>
1 A 1.5
2 B 1.4
Here's a base solution:
do.call('rbind', by(a, a$id, function(x) x[which.min(abs(x$b - 1.43)), ]))
id b
<chr> <dbl>
1 A 1.5
2 B 1.4
Here's a hacky dplyr solution:
a %>%
mutate(AbsDiff = abs(b - 1.43)) %>%
group_by(id) %>%
mutate(AbsDiff_r = rank(AbsDiff, ties.method = 'first')) %>%
filter(AbsDiff_r == 1)
id b AbsDiff AbsDiff_r
<chr> <dbl> <dbl> <int>
1 A 1.5 0.07 1
2 B 1.4 0.03 1
Not too far from what you had
a %>% group_by(id) %>% summarise(which.min(abs(b-1.43)))
# A tibble: 2 × 2
# id `which.min(abs(b - 1.43))`
# <chr> <int>
# 1 A 2
# 2 B 3
Or if you need the values, rather than the indices:
a %>% group_by(id) %>% summarise(b[which.min(abs(b-1.43))])
# A tibble: 2 × 2
# id `b[which.min(abs(b - 1.43))]`
# <chr> <dbl>
# 1 A 1.5
# 2 B 1.4
Here is a version with data.table
library(data.table)
setDT(a)[, .(b= b[which.min(abs(b-1.43))]) , id]
# id b
#1: A 1.5
#2: B 1.4
