Match tables using 2 criteria in R - r

Question

I just begin coding in R , I am trying to manipulate data but I have an issue which is the following:
I have 2 different tables (simplified )
the first one (player_df) is as follows:
name experience Club age Position
luc 2 FCB 18 Goalkeeper
jean 9 Real 26 midfielder
ronaldo 14 FCB 32 Goalkeeper
jean 9 Real 26 midfielder
messi 11 Liverpool 35 midfielder
tevez 6 Chelsea 27 Attack
inzaghi 9 Juve 34 Defender
kwfni 17 Bayern 40 Attack
Blabla 9 Real 25 midfielder
wdfood 11 Liverpool 33 midfielder
player2 7 Chelsea 28 Attack
player3 10 Juve 34 Defender
fgh 17 Bayern 40 Attack
...
The second table is the salary by club and experience in million (salary_df)
*experience FCB BAYERN Juve Real Chelsea
1 1.5 1.3 1 4 3
2 2.5 2 2.4 5 4
3 3.4 3.1 3.5 6.3 5
4 5 4.5 6.7 9 6
5 7.1 6.9 9 12 7
6 9 8 10 15 10
7 10 9 12 16 15
8 14 12 13 19 16
9 14.5 17 15 20 17
10 15 19 17 23 18
..*
I would like to add a new column to my data in the first table named let say salary_estimation, and which takes into consideration 2 variables for example here experience and the club.
For example for "luc" who plays in "FCB" and has "2" years experience the output should be "2.5"
In excel its an index / match function, but in R I don't know which function should I use .
How should I approach the problem ?

Answer 1

Data:
df1 <- read.table(text = 'name experience Club age Position
luc 2 FCB 18 Goalkeeper
jean 9 Real 26 midfielder
ronaldo 14 FCB 32 Goalkeeper
jean 9 Real 26 midfielder
messi 11 Liverpool 35 midfielder
tevez 6 Chelsea 27 Attack
inzaghi 9 Juve 34 Defender
kwfni 17 Bayern 40 Attack
Blabla 9 Real 25 midfielder
wdfood 11 Liverpool 33 midfielder
player2 7 Chelsea 28 Attack
player3 10 Juve 34 Defender
fgh 17 Bayern 40 Attack', header = TRUE, stringsAsFactors = FALSE)
df2 <- read.table(text = 'experience FCB BAYERN Juve Real Chelsea
1 1.5 1.3 1 4 3
2 2.5 2 2.4 5 4
3 3.4 3.1 3.5 6.3 5
4 5 4.5 6.7 9 6
5 7.1 6.9 9 12 7
6 9 8 10 15 10
7 10 9 12 16 15
8 14 12 13 19 16
9 14.5 17 15 20 17
10 15 19 17 23 18', header = TRUE, stringsAsFactors = FALSE)
Code:
library('data.table')
setDT(df2)[, Chelsea := as.numeric(Chelsea)]
df2 <- melt(df2, id.vars = "experience", variable.name = "Club", value.name = "Salary" )
df2[df1, on = c("experience", "Club"), nomatch = NA]
Output:
# experience Club Salary name age Position
# 1: 2 FCB 2.5 luc 18 Goalkeeper
# 2: 9 Real 20.0 jean 26 midfielder
# 3: 14 FCB NA ronaldo 32 Goalkeeper
# 4: 9 Real 20.0 jean 26 midfielder
# 5: 11 Liverpool NA messi 35 midfielder
# 6: 6 Chelsea 10.0 tevez 27 Attack
# 7: 9 Juve 15.0 inzaghi 34 Defender
# 8: 17 Bayern NA kwfni 40 Attack
# 9: 9 Real 20.0 Blabla 25 midfielder
# 10: 11 Liverpool NA wdfood 33 midfielder
# 11: 7 Chelsea 15.0 player2 28 Attack
# 12: 10 Juve 17.0 player3 34 Defender
# 13: 17 Bayern NA fgh 40 Attack

Answer 2

One of possible solutions is by joining first table (let say it is player_df) with "long format" of second table salary_df using experience and club as keys. You can do it by using tidyverse package.
library(tidyverse)
player_df %>%
mutate(Club = str_to_title(Club)) %>%
left_join(
salary_df %>%
pivot_longer(-experience, names_to = "Club", values_to = "salary_estimation") %>%
mutate(Club = str_to_title(Club)) )
# Joining, by = c("experience", "Club")
# # A tibble: 13 x 6
# name experience Club age Position salary_estimation
# <chr> <dbl> <chr> <dbl> <chr> <dbl>
# 1 luc 2 Fcb 18 Goalkeeper 2.5
# 2 jean 9 Real 26 midfielder 20
# 3 ronaldo 14 Fcb 32 Goalkeeper NA
# 4 jean 9 Real 26 midfielder 20
# 5 messi 11 Liverpool 35 midfielder NA
# 6 tevez 6 Chelsea 27 Attack 10
# 7 inzaghi 9 Juve 34 Defender 15
# 8 kwfni 17 Bayern 40 Attack NA
# 9 Blabla 9 Real 25 midfielder 20
# 10 wdfood 11 Liverpool 33 midfielder NA
# 11 player2 7 Chelsea 28 Attack 15
# 12 player3 10 Juve 34 Defender 17
# 13 fgh 17 Bayern 40 Attack NA