Geographical distance by group - Applying a function on each pair of rows

Question

I want to calculate the average geographical distance between a number of houses per province.

Suppose I have the following data.

df1 <- data.fram         


        

Answer 1

My 10 cents. You can:

# subset the province
df1 <- df1[which(df1$province==1),]

# get all combinations
all <- combn(df1$house, 2, FUN = NULL, simplify = TRUE)

# run your function and get distances for all combinations
distances <- c()
for(col in 1:ncol(all)) {
  a <- all[1, col]
  b <- all[2, col]
  dist <- distm(c(df1$lon[a], df1$lat[a]), c(df1$lon[b], df1$lat[b]), fun = distHaversine)
  distances <- c(distances, dist)
  }

# calculate mean:
mean(distances)
# [1] 15379.21

This gives you the mean value for the province, which you can compare with the results of other methods. For example sapply which was mentioned in the comments:

df1 <- df1[which(df1$province==1),]
mean(sapply(split(df1, df1$province), dist))
# [1] 1.349036

As you can see, it gives different results, cause dist function can calculate the distances of different type (like euclidean) and cannot do haversine or other "geodesic" distances. The package geodist seems to have options which could bring you closer to sapply:

library(geodist)
library(magrittr)

# defining the data
df1 <- data.frame(province = c(1, 1, 1, 2, 2, 2),
                  house = c(1, 2, 3, 4, 5, 6),
                  lat = c(-76.6, -76.5, -76.4, -75.4, -80.9, -85.7), 
                  lon = c(39.2, 39.1, 39.3, 60.8, 53.3, 40.2))

# defining the function 
give_distance <- function(resultofsplit){
  distances <- c()
  for (i in 1:length(resultofsplit)){
    sdf <- resultofsplit
    sdf <- sdf[[i]]
    sdf <- sdf[c("lon", "lat", "province", "house")]

    sdf2 <- as.matrix(sdf)
    sdf3 <- geodist(x=sdf2, measure="haversine")
    sdf4 <- unique(as.vector(sdf3))
    sdf4 <- sdf4[sdf4 != 0]        # this is to remove the 0-distances 
    mean_dist <- mean(sdf4)
    distances <- c(distances, mean_dist)
    }  
    return(distances)
}

split(df1, df1$province) %>% give_distance()
#[1]  15379.21 793612.04

E.g. the function will give you the mean distance values for each province. Now, I didn´t manage to get give_distance work with sapply, but this should already be more efficient.