I have two tables like that;
Test Comp.Name
A Test Comp.1
B Test Comp.1
B Test Comp.2
C Test Comp.2
A Test Comp.3
B Test Comp.4
table1 <- data.frame("Test" = c("A Test", "B Test", "B Test", "C Test", "A Test", "B Test"),
"Comp Name" = c("Comp.1", "Comp.1", "Comp.2", "Comp.2", "Comp.3", "Comp.4"))
Test.Name Comp.1 Comp.2 Comp.3 Comp.4
A Test 5 6 7 8
B Test 10 15 20 25
C Test 2 8 3 212
table2 <- data.frame("Test Name" = c("A Test", "B Test", "C Test"),
"Comp.1" = c(5, 10, 2),
"Comp.2" = c(6, 15, 8),
"Comp.3" = c(7, 20, 3),
"Comp.4" = c(8, 25, 212))
i want to get the following table:
Test Comp.Name Test.Price
A Test Comp.1 5
B Test Comp.1 10
B Test Comp.2 15
C Test Comp.2 8
A Test Comp.3 7
B Test Comp.4 25
table3 <- data.frame("Test" = c("A Test", "B Test", "B Test", "C Test", "A Test", "B Test"),
"Comp Name" = c("Comp.1", "Comp.1", "Comp.2", "Comp.2", "Comp.3", "Comp.4"),
"Test Price" = c(5,10,15,8,7,25))
I will try inner_join and reshape functions in R, The best I've been able to do so far is something like:
:
table3 <- inner_join(table1, table2, by.x = "Test", by.y="Test Name") %>%
select("Test Name", "Comp Name", "Test Price" = "Comp Name")
I couldn't get the result I wanted.
You can first use melt on table2 and then perform a left_join with table1
This can be achieved with dplyr and tidyr.
library(dplyr)
library(tidyr)
table2_long <- table2 |>
pivot_longer(-Test.Name,
names_to="Comp.Name")
table3 <- inner_join(table1, table2_long, by= c("Test"="Test.Name", "Comp.Name"))
Output
Test Comp.Name value
1 A Test Comp.1 5
2 B Test Comp.1 10
3 B Test Comp.2 15
4 C Test Comp.2 8
5 A Test Comp.3 7
6 B Test Comp.4 25
It can be achieved as well with reshape
library(reshape2)
table2_long <- table2 |>
melt(id.cols = "Test.Name",
variable.name = "Comp.Name")
table3 <- merge(table1, table2_long, by.x = c("Test","Comp.Name"), by.y = c("Test.Name","Comp.Name"))