下面是一个使用sf库的例子，我转换到一个可以理解米的坐标系，然后使用st_buffer画圆，st_within计算重叠，然后使用sapply计算计数和平均度量。

library(sf)
library(dplyr)

name <- c('A', 'B', 'C', 'D', 'E','F','G','H','I','J')
lat <- runif(10,min=37,max=39)
lon <- runif(10,min=-91,max=-89)
metric <- runif(10,min=0,250)

df <- as.data.frame(cbind(name,lat,lon,metric))

# make a spatial data frame
df_sf <- st_as_sf(df, coords = c('lon', 'lat'), crs=4326)

# transform to a coordinate system which understands metres 
# the original data is near St Louis, so UTM 15 is appropriate
df_sf_transformed <- st_transform(df_sf, crs = 32615)

# make circles of radius 15km around each point
df_sf_buffers <- st_buffer(df_sf_transformed, dist = 15000) %>% st_geometry()

# work out which point is within each circle
overlaps <- st_within(y = df_sf_buffers, x = df_sf_transformed)

# count the number of overlaps for each circle
# the sapply function is like a for loop that iterates over each 
# row in the overlaps object and calls the length function to 
# calculate the length of each row
counts <- data.frame(count = sapply(overlaps, length))

# a custom function to sum the metrics for selected points
find_avg_metric <- function(sf_df, indices) {
    entries <- sf_df[indices, ]
    result <- sum(as.numeric(entries$metric))
    result
}

# iterate over the overlaps object and use the indices found to access 
# the relevant rows of the original data frame to sum up the metric column
metrics <- data.frame(avg_metric = sapply(overlaps, function(index) find_avg_metric(df_sf_transformed, index)))

# The final answer
final_result <- df %>% bind_cols(counts) %>% bind_cols(metrics)
final_result
#name              lat               lon           metric count avg_metric
#1     A 37.9383664987981  -90.567177023273 30.6012906949036     1   30.60129
#2     B 37.8796539758332 -89.6363721224479 139.638845692389     1  139.63885
#3     C 37.9519012323581 -90.8471902268939 106.271727592684     1  106.27173
#4     D 38.3774091359228 -89.1159357768483 186.965031607542     1  186.96503
#5     E 37.3993454128504 -89.7444549454376 29.5921949436888     2  194.88581
#6     F 38.6436389596201 -89.0847784169018 150.506012840196     1  150.50601
#7     G 38.4185414239764 -90.1974869910628 162.634110951331     1  162.63411
#8     H 38.2770276684314 -90.4734013564885 69.9945208616555     1   69.99452
#9     I 37.3666391288862 -89.6443270738237 165.293618629221     2  194.88581
#10    J  38.659956720192 -89.7836953452788 123.549888376147     1  123.54989