# Determining the best location based on distances between two DataFrames in R

I'm working on an R project involving the analysis of two dataFrames: `df` and `df`2. The `df` contains information about properties grouped into clusters, while `df2` contains geographical coordinates of points of interest. My goal is to determine the best location to choose based on the distances between properties within each cluster in `df` and all points in `df2`. Please, use `distm` function. For example, for cluster 1 the best point is `id = 3`, for cluster 2 is `id = 1`, and so. This is idea

``````df <- structure(list(ID = c("A", "B", "C", "D", "E", "F", "G"),
Longitude = c(-46.585970687, -51.037570982, -49.656622, -46.730269,
-49.255356686, -48.982128819, -50.177308),
Latitude = c(-23.611080198, -21.659062133, -21.24666, -21.948196,
-22.880896112, -22.429487865, -21.578706),
Cluster = c(1L, 1L, 1L, 2L, 2L, 2L, 2L)),
row.names = c(NA, -7L),
class = c("tbl_df", "data.frame"))

ID Longitude  Latitude Cluster
1  A -46.58597 -23.61108       1
2  B -51.03757 -21.65906       1
3  C -49.65662 -21.24666       1
4  D -46.73027 -21.94820       2
5  E -49.25536 -22.88090       2
6  F -48.98213 -22.42949       2
7  G -50.17731 -21.57871       2

df2 <- structure(list(id = c(1, 2, 3, 4, 5, 6, 7, 8), Longitude = c(-52.810111532,
-52.810111532, -52.710111532, -52.710111532, -52.710111532, -52.610111532,
-52.610111532, -52.610111532), Latitude = c(-22.479655795, -22.579655795,
-22.379655795, -22.479655795, -22.579655795, -22.279655795, -22.379655795,
-22.479655795)), row.names = c(NA, -8L), class = c("tbl_df",
"tbl", "data.frame"))

id Longitude Latitude
<dbl>     <dbl>    <dbl>
1     1     -52.8    -22.5
2     2     -52.8    -22.6
3     3     -52.7    -22.4
4     4     -52.7    -22.5
5     5     -52.7    -22.6
6     6     -52.6    -22.3
7     7     -52.6    -22.4
8     8     -52.6    -22.5
``````

Do you need to take in to account road, walking, public transport networks? There are many routing packages that use OSM and other data.