Htykuut

I am facing an thinking & programming problem. See below my question, I have no clue what a proper approach is (played with DPLYR's group_by, but without results). Many thanks in advance for trying helping me out here!

I have a data set like this:

Numbers   Area      Cluster  

1         A          1            

0.8       A          1

0.78      A          1

0.7       B          1

0.4       A          2

0         C          1 

I want to calculate two new columns:

1. Show the % of Area's occurring in a specific cluster (Column_Example_1)


2. Per Cluster, a new index of the column numbers (in a range from 1 - 0) (Column_example_2). The new ratio should be based on the column Numbers #note: in the example it is just an example, it could also done differently, but we I want to make sure that the column Numbers is leading)

The result should be like this:

Numbers   Area      Cluster  Example_1                             Example_2 

1         A          1          60%  #5x cluster 1, and 3x Area A)   1

0.8       A          1          60%                                  0.8  

0.78      A          1          60%                                  0.78

0.7       B          1          20%                                  0.7 

0.4       A          2         100%                                  1

0         C          1          20%                                  0

Since you want to keep all rows, you can calculate the relative frequencies as follows:

library(tidyverse)

df <- data.frame(numbers = c(1, .8, .78, .7, .4, 0),

                 area = c("A", "A", "A", "B", "A", "C"),

                 cluster = c(1, 1, 1, 1, 2, 1))



df %>% 

  group_by(cluster) %>%

  mutate(example_1 = n()) %>%

  group_by(area, cluster) %>%

  mutate(example_1 = n() / example_1)



# A tibble: 6 x 4

# Groups:   area, cluster [4]

  numbers area  cluster example_1

    <dbl> <fct>   <dbl>     <dbl>

1    1    A           1       0.6

2    0.8  A           1       0.6

3    0.78 A           1       0.6

4    0.7  B           1       0.2

5    0.4  A           2       1  

6    0    C           1       0.2

You can also do with data.table:

library(magrittr)

library(data.table)



df <- data.table(Numbers = c(1, .8, .78, .7, .4, 0), 

           Area = c(rep("A", 3), "B", "A", "C"), 

           Cluster = c(rep(1, 4), 2, 1))



df[, N := .N, by = c("Cluster")] %>% 

  .[, Example_1 := .N/N, by = c("Cluster", "Area")] %>% 

  .[, `:=`(N = NULL, Example_2 = Numbers)]

Output:

> df

   Numbers Area Cluster Example_1 Example_2

1:    1.00    A       1       0.6      1.00

2:    0.80    A       1       0.6      0.80

3:    0.78    A       1       0.6      0.78

4:    0.70    B       1       0.2      0.70

5:    0.40    A       2       1.0      0.40

6:    0.00    C       1       0.2      0.00