Aggregate and adding new column

Question

I have a dataset with district name, household latitude, and longitude. The dataset has 2000 household locations . I want to calculate the mean of latitude and longitude based on district name. Next, I want to add two new columns (ie Lat_mean, Long_mean) in which the mean Lat and Long will be stored for each household.

I was just able to aggregate the mean values for latitude and longitude. I don't know how to paste the summarized data as a new column for each ID (see code)

id <- c(1,2,3,4,5,6)
district <- c("A", "B", "C", "A", "A", "B")
lat <- c(28.6, 30.2, 35.9, 27.5, 27.9, 31.5)
long <- c(77.5, 85.2, 66.5, 75.0, 79.2, 88.8)
df <- data.frame(id, district, lat, long)

df_group <- df %>% group_by(district) %>% summarise_at(vars(lat:long), mean)

I am expecting the following. Lat_mean & Long_mean columns will be added to 'df' and each ID will have values based on district name. See the image below.

Answer 1

We can use mutate_at instead of summarise_at . Within the list , specify the name , so that it will create a new column with suffix as that name

library(dplyr)
df %>% 
   group_by(district) %>%
   mutate_at(vars(lat, long), list(mean = mean))
# A tibble: 6 x 6
# Groups:   district [3]
#     id district   lat  long lat_mean long_mean
#  <dbl> <fct>    <dbl> <dbl>    <dbl>     <dbl>
#1     1 A         28.6  77.5     28        77.2
#2     2 B         30.2  85.2     30.8      87  
#3     3 C         35.9  66.5     35.9      66.5
#4     4 A         27.5  75       28        77.2
#5     5 A         27.9  79.2     28        77.2
#6     6 B         31.5  88.8     30.8      87  

Answer 2

> df %>% 
    mutate(lat_mean = ave(lat, district, FUN=mean),
           lon_mean = ave(long, district, FUN=mean))
  id district  lat long lat_mean lon_mean
1  1        A 28.6 77.5    28.00 77.23333
2  2        B 30.2 85.2    30.85 87.00000
3  3        C 35.9 66.5    35.90 66.50000
4  4        A 27.5 75.0    28.00 77.23333
5  5        A 27.9 79.2    28.00 77.23333
6  6        B 31.5 88.8    30.85 87.00000