data1=data.frame("Grade"=c(1,2,3,1,2,3),
"Group"=c(A,A,A,B,B,B),
"Score"=c(5,7,10,7,7,8))
data2=data.frame("Grade"=c(1,2,3),
"Combine"=c(12,14,18),
"A"=c(5,7,10),
"B"=c(7,7,8))
I have 'data1' and wish for 'data2' where you transpose Group from 'data1' into 'A' and 'B' and then finally add 'Combine' which sums 'A' and 'B'
You can do
library(tidyverse)
data1 %>%
spread(Group, Score) %>%
mutate(Combine = A+B)
Grade A B Combine
1 1 5 7 12
2 2 7 7 14
3 3 10 8 18
in Base R
data2 <- data.frame("Grade" = 1:3)
grade.locations <- lapply(1:3,grep,data1$Grade)
for(i in 1:3){
data2$Combine[i] <- sum(data1[grade.locations[[i]],3])
data2$A[i] <- data1[grade.locations[[i]][1],3]
data2$B[i] <- data1[grade.locations[[i]][2],3]
}
You tagged this with data.table, so here's a data.table approach.
library(data.table)
data1 <- as.data.table(data1)
data2 <- dcast(data1,Grade ~ Group)
data2[,Combine := A + B]
data2
Grade A B Combine
1: 1 5 7 12
2: 2 7 7 14
3: 3 10 8 18
We can use pivot_wider
from tidyr
library(dplyr)
library(tidyr)
data1 %>%
pivot_wider(names_from = Group, values_from = Score) %>%
mutate(Combine = A + B)
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