Convert Panel Data to Long in R - r

Question

My current data is for missiles between 1920 and 2018. The goal is to measure a nation’s ability to deploy missiles of different kinds for each year from 1920 to 2018. The problems that arise are that the data has multiple observations per nation and often per year. This creates issues because for instance if a nation adopted a missile in 1970 that is Air to Air and imported then developed one in 1980 that is Air to Air and Air to Ground and produced domestically, that change needs to be reflected. The goal is to have a unique row/observation for each year for every nation. Also it should be noted that it is assumed if the nation can produced Air to air for instance in 1970 they can do so until 2018.
Current:
YearAcquired CountryCode CountryName Domestic AirtoAir
2014 670 Saudi Arabia 0 1
2017 670 Saudi Arabia 1 1
2016 2 United States 1 1
Desired:
YearAcquired CountryCode CountryName Domestic AirtoAir
2014 670 Saudi Arabia 0 1
2015 670 Saudi Arabia 0 1
2016 670 Saudi Arabia 0 1
2017 670 Saudi Arabia 1 1
2018 670 Saudi Arabia 1 1
2016 2 United States 0 1
2017 2 United States 0 1
2018 2 United States 0 1
Note: There are many entries and so I would like it to generate from 1920 to 2018 for every country even if they will have straight zeroes. That is not necessary but it would be a great bit!

Answer 1

You can do this via several steps:
Create the combination of all years and countries (a CROSS JOIN in SQL)
LEFT JOIN these combinations with the available data
Use a function like zoo::na.locf() to replace NA values by the last known ones per country.
The first step is common:
df <- read.table(text = 'YearAcquired CountryCode CountryName Domestic AirtoAir
2014 670 "Saudi Arabia" 0 1
2017 670 "Saudi Arabia" 1 1
2016 2 "United States" 1 1', header = TRUE, stringsAsFactors = FALSE)
combinations <- merge(data.frame(YearAcquired = seq(1920, 2018, 1)),
unique(df[,2:3]), by = NULL)
For steps 2 and 3 here a solution using dplyr
library(dplyr)
library(zoo)
df <- left_join(combinations, df) %>%
group_by(CountryCode) %>%
mutate(Domestic = na.locf(Domestic, na.rm = FALSE),
AirtoAir = na.locf(AirtoAir, na.rm = FALSE))
And one solution using data.table:
library(data.table)
library(zoo)
setDT(df)
setDT(combinations)
df <- df[combinations, on = c("YearAcquired", "CountryCode", "CountryName")]
df <- df[, na.locf(.SD, na.rm = FALSE), by = "CountryCode"]

Answer 2

You could create a new dataframe using the country names and codes available and perform a left join with your existing data. This would give you 1920 to 2018 for each country and code, leaving NA's in where you don't have data available but you could easily replace them given how you want your data structured.
# df is your initial dataframe
countries <- df$CountryName
codes <- df
new_df <- data.frame(YearAcquired = seq(1920, 2018, 1),
CountryName = df$CountryName
CountryCode = df$CountryCode)
new_df <- left_join(new_df, df)

Answer 3

Using tidyverse (dplyr and tidyr)...
If you only need to fill in internal years per country...
df <- read.table(header = TRUE, as.is = TRUE, text = "
YearAcquired countrycode CountryName Domestic AirtoAir
2014 670 'Saudi Arabia' 0 1
2017 670 'Saudi Arabia' 1 1
2016 2 'United States' 1 1
")
library(dplyr)
library(tidyr)
df %>%
group_by(countrycode) %>%
complete(YearAcquired = full_seq(YearAcquired, 1), countrycode, CountryName) %>%
arrange(countrycode, YearAcquired) %>%
fill(Domestic, AirtoAir)
#> # A tibble: 5 x 5
#> # Groups: countrycode [2]
#> YearAcquired countrycode CountryName Domestic AirtoAir
#> <dbl> <int> <chr> <int> <int>
#> 1 2016 2 United States 1 1
#> 2 2014 670 Saudi Arabia 0 1
#> 3 2015 670 Saudi Arabia 0 1
#> 4 2016 670 Saudi Arabia 0 1
#> 5 2017 670 Saudi Arabia 1 1
If you want to expand each country to all years found in the dataset...
df <- read.table(header = TRUE, as.is = TRUE, text = "
YearAcquired countrycode CountryName Domestic AirtoAir
2014 670 'Saudi Arabia' 0 1
2017 670 'Saudi Arabia' 1 1
2016 2 'United States' 1 1
")
library(dplyr)
library(tidyr)
df %>%
complete(YearAcquired = full_seq(YearAcquired, 1),
nesting(countrycode, CountryName)) %>%
group_by(countrycode) %>%
arrange(countrycode, YearAcquired) %>%
fill(Domestic, AirtoAir) %>%
mutate_at(vars(Domestic, AirtoAir), funs(if_else(is.na(.), 0L, .)))
#> # A tibble: 8 x 5
#> # Groups: countrycode [2]
#> YearAcquired countrycode CountryName Domestic AirtoAir
#> <dbl> <int> <chr> <int> <int>
#> 1 2014 2 United States 0 0
#> 2 2015 2 United States 0 0
#> 3 2016 2 United States 1 1
#> 4 2017 2 United States 1 1
#> 5 2014 670 Saudi Arabia 0 1
#> 6 2015 670 Saudi Arabia 0 1
#> 7 2016 670 Saudi Arabia 0 1
#> 8 2017 670 Saudi Arabia 1 1