I have the following dput:
structure(list(ID = c("ID_1", "ID_1", "ID_1", "ID_1", "ID_1", "ID_1", "ID_1",
"ID_1", "ID_1", "ID_1", "ID_2", "ID_2", "ID_2", "ID_2",
"ID_2", "ID_2", "ID_2", "ID_2", "ID_2", "ID_2"),
Team = c("Team_A1", "Team_B1", "Team_A2", "Team_B2", "Team_A3",
"Team_B3", "Team_A4", "Team_B4", "Team_A5", "Team_B5",
"Team_C1", "Team_D1", "Team_C2", "Team_D2", "Team_C3",
"Team_D3", "Team_C4", "Team_D4", "Team_C5", "Team_D5"),
Price = c(-155L, 130L, -145L, 115L, -135L, 115L, -135L, 115L,
-145L, 122L, -185L, 150L, 140L, -170L, -195L, 165L,
140L, -165L, -190L, 158L)),
row.names = c(NA, -20L),
class = c("tbl_df", "tbl", "data.frame"))
Which returns the following tibble
# A tibble: 20 × 3
ID Team Price
<chr> <chr> <int>
1 ID_1 Team_A1 -155
2 ID_1 Team_B1 130
3 ID_1 Team_A2 -145
4 ID_1 Team_B2 115
5 ID_1 Team_A3 -135
6 ID_1 Team_B3 115
7 ID_1 Team_A4 -135
8 ID_1 Team_B4 115
9 ID_1 Team_A5 -145
10 ID_1 Team_B5 122
11 ID_2 Team_C1 -185
12 ID_2 Team_D1 150
13 ID_2 Team_C2 140
14 ID_2 Team_D2 -170
15 ID_2 Team_C3 -195
16 ID_2 Team_D3 165
17 ID_2 Team_C4 140
18 ID_2 Team_D4 -165
19 ID_2 Team_C5 -190
20 ID_2 Team_D5 158
I have a function that uses the Price values from every 2 rows, one for each Team. How can I apply this function to the tibble? My guess is that I will need to group_by the ID column and then use map or apply to execute the function against every 2 rows. I am having trouble finding the syntax. This is the desired output of the tibble. As you can see the New_Value contains the calculation of every 2 rows.
# A tibble: 20 × 3
ID Team Price New Value
<chr> <chr> <int> <int>
1 ID_1 Team_A1 -155 A1B1
2 ID_1 Team_B1 130 A1B1
3 ID_1 Team_A2 -145 A2B2
4 ID_1 Team_B2 115 A2B2
5 ID_1 Team_A3 -135 A3B3
6 ID_1 Team_B3 115 A3B3
7 ID_1 Team_A4 -135 A4B4
8 ID_1 Team_B4 115 A4B4
9 ID_1 Team_A5 -145 A5B5
10 ID_1 Team_B5 122 A5B5
11 ID_2 Team_C1 -185 C1D1
12 ID_2 Team_D1 150 C1D1
13 ID_2 Team_C2 140 C2D2
14 ID_2 Team_D2 -170 C2D2
15 ID_2 Team_C3 -195 C3D3
16 ID_2 Team_D3 165 C3D3
17 ID_2 Team_C4 140 C4D4
18 ID_2 Team_D4 -165 C4D4
19 ID_2 Team_C5 -190 C5D5
20 ID_2 Team_D5 158 C5D5
Here is the function that is being called.
mutate(New_Value = proprietary_function(Price[1], Price[2]))
The key is to group_by every two rows. To strictly follow your example and your desired output, the code can be like:
library(dplyr)
proprietary_function <- function(x, y) {
paste0(sub(".*_", "", x), sub(".*_", "", y), collapse = "")
}
df %>%
group_by(rn = ceiling(row_number()/2)) %>%
mutate(new_value = proprietary_function(Team[1], Team[2])) %>%
ungroup() %>%
select(-rn)
# A tibble: 20 × 4
ID Team Price new_value
<chr> <chr> <int> <chr>
1 ID_1 Team_A1 -155 A1B1
2 ID_1 Team_B1 130 A1B1
3 ID_1 Team_A2 -145 A2B2
4 ID_1 Team_B2 115 A2B2
5 ID_1 Team_A3 -135 A3B3
6 ID_1 Team_B3 115 A3B3
7 ID_1 Team_A4 -135 A4B4
8 ID_1 Team_B4 115 A4B4
9 ID_1 Team_A5 -145 A5B5
10 ID_1 Team_B5 122 A5B5
11 ID_2 Team_C1 -185 C1D1
12 ID_2 Team_D1 150 C1D1
13 ID_2 Team_C2 140 C2D2
14 ID_2 Team_D2 -170 C2D2
15 ID_2 Team_C3 -195 C3D3
16 ID_2 Team_D3 165 C3D3
17 ID_2 Team_C4 140 C4D4
18 ID_2 Team_D4 -165 C4D4
19 ID_2 Team_C5 -190 C5D5
20 ID_2 Team_D5 158 C5D5