I have a dataframe of data which I need to convert to use for a panel study. I want to convert my rows into columns, then stack the columns into a single column. My current data:
STATE 1970 1971 1972 1973...2018
State-A X X X X X
State-B X X X X X
State-C X X X X X
What I need:
Year State Data
1970 A X
1971 A X
...
2018 A X
1970 B X
1971 B X
etc...
I have tried the stack, and melt, and reshape functions to no avail. I also tried to list my data then flatten it, which was a total disaster. Any help is much appreciated!
We can use 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
With the updated example, change is
df1 %>%
pivot_longer(cols = -STATE, names_to = 'Year', values_to = 'Data')
If package version of tidyr
is old, use gather
df1 %>%
gather(name, Data, -Year) %>%
separate(Year, into = c('other', 'State')) %>%
select(Year = name, State, Data)
Or with melt
library(data.table)
melt(setDT(df1), id.var = 'Year', value.name = 'Data')[,
.(State = sub('.*-', '', Year), Year = variable, 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))