CODEWITHSUNDEEP
rdplyrpanel-data
Thiago
Thiago

Reputation: 163

Creating panel data, filling gaps between years and repeating the last value in the subsequent years using R

I have the following sample: original data

I am trying to turn it into the following panel data: expected

As you can see in the last image, I would like to repeat the values between years for the same country, and repeat the last value for the subsequent years until the year 2020.

Upvotes: 0

Views: 941

Answers (1)

Allan Cameron
Allan Cameron

Reputation: 173803

You can use grid.expand to get the country / year combinations you want, then left_join the main data frame to this, and finally fill the missing data, ensuring you filter out any remaining NAs.

library(dplyr)
library(tidyr)

panel <- expand.grid(year = min(df$year):2020, 
                     country = unique(df$country), 
                     stringsAsFactors = FALSE)     %>%
           left_join(df)                           %>% 
           group_by(country)                       %>% 
           fill(c("id", "regioncode", "prespowl")) %>%
           filter(!is.na(id))                      %>%
           as.data.frame()

Which gives the following result:

panel
#>    year   country id regioncode  prespowl
#> 1  2011   Albania  1     Europe 0.1817557
#> 2  2012   Albania  1     Europe 0.1817557
#> 3  2013   Albania  1     Europe 0.1817557
#> 4  2014   Albania  1     Europe 0.1817557
#> 5  2015   Albania  1     Europe 0.1817557
#> 6  2016   Albania  1     Europe 0.1817557
#> 7  2017   Albania  1     Europe 0.1817557
#> 8  2018   Albania  1     Europe 0.1411482
#> 9  2019   Albania  1     Europe 0.1411482
#> 10 2020   Albania  1     Europe 0.1411482
#> 11 2016   Algeria  2     Africa 0.3837466
#> 12 2017   Algeria  2     Africa 0.3837466
#> 13 2018   Algeria  2     Africa 0.4837466
#> 14 2019   Algeria  2     Africa 0.4837466
#> 15 2020   Algeria  2     Africa 0.4837466
#> 16 1999 Argentina  3   Americas 0.2887138
#> 17 2000 Argentina  3   Americas 0.2887138
#> 18 2001 Argentina  3   Americas 0.2887138
#> 19 2002 Argentina  3   Americas 0.2887138
#> 20 2003 Argentina  3   Americas 0.2887138
#> 21 2004 Argentina  3   Americas 0.2887138
#> 22 2005 Argentina  3   Americas 0.2887138
#> 23 2006 Argentina  3   Americas 0.4322523
#> 24 2007 Argentina  3   Americas 0.4322523
#> 25 2008 Argentina  3   Americas 0.4322523
#> 26 2009 Argentina  3   Americas 0.4322523
#> 27 2010 Argentina  3   Americas 0.4322523
#> 28 2011 Argentina  3   Americas 0.4322523
#> 29 2012 Argentina  3   Americas 0.4322523
#> 30 2013 Argentina  3   Americas 0.5453171
#> 31 2014 Argentina  3   Americas 0.5453171
#> 32 2015 Argentina  3   Americas 0.5453171
#> 33 2016 Argentina  3   Americas 0.5453171
#> 34 2017 Argentina  3   Americas 0.5453171
#> 35 2018 Argentina  3   Americas 0.5453171
#> 36 2019 Argentina  3   Americas 0.5453171
#> 37 2020 Argentina  3   Americas 0.5453171

Data used:

df <- read.table(text= 'country year    id  regioncode   prespowl
                        Albania 2011    1   Europe      0.1817557
                        Albania 2018    1   Europe      0.1411482
                        Algeria 2016    2   Africa      0.3837466
                        Algeria 2018    2   Africa      0.4837466
                      Argentina 1999    3   Americas    0.2887138
                      Argentina 2006    3   Americas    0.4322523
                      Argentina 2013    3   Americas    0.5453171
', header = TRUE, stringsAsFactors = FALSE)

Upvotes: 3

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