Мы можем использовать tidyverse
library(dplyr)
library(tidyr)
df1 %>%
pivot_longer(cols = -Year, values_to = 'Data') %>%
c
# A tibble: 12 x 3
# Year State Data
# <chr> <chr> <chr>
# 1 1970 A X
# 2 1971 A X
# 3 1972 A X
# 4 1973 A X
# 5 1970 B X
# 6 1971 B X
# 7 1972 B X
# 8 1973 B X
# 9 1970 C X
#10 1971 C X
#11 1972 C X
#12 1973 C X
Обновление
В обновленном примере изменение будет
df1 %>%
pivot_longer(cols = -STATE, names_to = 'Year', values_to = 'Data')
Если версия пакета tidyr
устарела , используйте gather
df1 %>%
gather(name, Data, -Year) %>%
separate(Year, into = c('other', 'State')) %>%
select(Year = name, State, Data)
Или с melt
library(data.table)
melt(setDT(df1), id.var = 'Year', value.name = 'Data')[,
.(State = sub('.*-', '', Year), Year = variable, Data)]
data
df1 <- structure(list(Year = c("State-A", "State-B", "State-C"), `1970` = c("X",
"X", "X"), `1971` = c("X", "X", "X"), `1972` = c("X", "X", "X"
), `1973` = c("X", "X", "X")), class = "data.frame", row.names = c(NA,
-3L))