My data is similar to the following:
# A tibble: 7 x 3
# Groups: offense [7]
lon lat offense
<dbl> <dbl> <fct>
1 -95.3 29.8 aggravated assault
2 -95.4 29.9 auto theft
3 -95.3 29.8 burglary
4 -95.5 29.7 murder
5 -95.4 30.0 rape
6 -95.5 29.8 robbery
7 -95.4 29.8 theft
I can run the following
cbind(df, X = rowSums(distm(df[,1:2], fun = distHaversine) / 1000 <= 10))
# A tibble: 7 x 4
# Groups: offense [7]
lon lat offense X
<dbl> <dbl> <fct> <dbl>
1 -95.3 29.8 aggravated assault 3
2 -95.4 29.9 auto theft 2
3 -95.3 29.8 burglary 3
4 -95.5 29.7 murder 1
5 -95.4 30.0 rape 2
6 -95.5 29.8 robbery 1
7 -95.4 29.8 theft 3
Which gives me the number of points within a radius of 10km according to this SO post.
What I would like to know is how to modify that function to give me which rows correspond to each point within a radius. The first row has a value of 3, this value might be made up of an observation from rows 2, 4 and 7 for example.
It might look like:
lon lat offense X points
1 -95.3 29.8 aggravated assault 3 c(2,4,7)
2 -95.4 29.9 auto theft 2 c(2,3)
3 -95.3 29.8 burglary 3 c(4,5,7)
Once I have these I would like to create lists such that row 1 would be a list containing lists of 1, 2, 4 and 7. (However this might be a different quesiton)
Data:
library(geosphere)
library(ggmap)
df <- crime %>%
group_by(offense) %>%
sample_n(1) %>%
select(lon, lat, offense)
