data <- read_csv("../00_data/freedom.csv")## New names:
## Rows: 4979 Columns: 9
## -- Column specification
## -------------------------------------------------------- Delimiter: "," chr
## (3): country, Status, Region_Name dbl (6): ...1, year, CL, PR, Region_Code,
## is_ldc
## i Use `spec()` to retrieve the full column specification for this data. i
## Specify the column types or set `show_col_types = FALSE` to quiet this message.
## * `` -> `...1`data_wide <- data %>% pivot_wider(names_from = CL, values_from = PR)
data_wide## # A tibble: 4,979 x 14
## ...1 country year Status Region_Code Region_Name is_ldc `7` `6` `5`
## <dbl> <chr> <dbl> <chr> <dbl> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 1 Afghanis~ 1995 NF 142 Asia 1 7 NA NA
## 2 2 Afghanis~ 1996 NF 142 Asia 1 7 NA NA
## 3 3 Afghanis~ 1997 NF 142 Asia 1 7 NA NA
## 4 4 Afghanis~ 1998 NF 142 Asia 1 7 NA NA
## 5 5 Afghanis~ 1999 NF 142 Asia 1 7 NA NA
## 6 6 Afghanis~ 2000 NF 142 Asia 1 7 NA NA
## 7 7 Afghanis~ 2001 NF 142 Asia 1 7 NA NA
## 8 8 Afghanis~ 2002 NF 142 Asia 1 NA 6 NA
## 9 9 Afghanis~ 2003 NF 142 Asia 1 NA 6 NA
## 10 10 Afghanis~ 2004 NF 142 Asia 1 NA 5 NA
## # ... with 4,969 more rows, and 4 more variables: `4` <dbl>, `3` <dbl>,
## # `1` <dbl>, `2` <dbl>data_wide %>% pivot_longer(`7`:`2`, names_to = "CL", values_to = "PR", values_drop_na = TRUE)## # A tibble: 4,979 x 9
## ...1 country year Status Region_Code Region_Name is_ldc CL PR
## <dbl> <chr> <dbl> <chr> <dbl> <chr> <dbl> <chr> <dbl>
## 1 1 Afghanistan 1995 NF 142 Asia 1 7 7
## 2 2 Afghanistan 1996 NF 142 Asia 1 7 7
## 3 3 Afghanistan 1997 NF 142 Asia 1 7 7
## 4 4 Afghanistan 1998 NF 142 Asia 1 7 7
## 5 5 Afghanistan 1999 NF 142 Asia 1 7 7
## 6 6 Afghanistan 2000 NF 142 Asia 1 7 7
## 7 7 Afghanistan 2001 NF 142 Asia 1 7 7
## 8 8 Afghanistan 2002 NF 142 Asia 1 6 6
## 9 9 Afghanistan 2003 NF 142 Asia 1 6 6
## 10 10 Afghanistan 2004 NF 142 Asia 1 6 5
## # ... with 4,969 more rows