How do I duplicate and add rows between the values of two different columns? - r

Question

I'm trying to duplicate each observation for all of the years that fall between "styear" and "endyear." So, for example, there should end up being 118 USA rows with years 1898-2016.
Here's the data:
# A tibble: 14 x 9
stateabb ccode styear stmonth stday endyear endmonth endday version
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 USA 2 1898 8 13 2016 12 31 2016
2 UKG 200 1816 1 1 2016 12 31 2016
3 FRN 220 1816 1 1 1940 6 22 2016
4 FRN 220 1945 8 15 2016 12 31 2016
5 GMY 255 1816 1 1 1918 11 11 2016
6 GMY 255 1925 1 1 1945 5 7 2016
7 GMY 255 1991 12 11 2016 12 31 2016
8 AUH 300 1816 1 1 1918 11 3 2016
9 ITA 325 1860 1 1 1943 9 2 2016
10 RUS 365 1816 1 1 1917 12 5 2016
11 RUS 365 1922 1 1 2016 12 31 2016
12 CHN 710 1950 1 1 2016 12 31 2016
13 JPN 740 1895 4 1 1945 8 14 2016
14 JPN 740 1991 12 11 2016 12 31 2016
I have tried various combinations of slice() and seq() and complete(), but I think I'm just too new at coding to a) know what to do and b) how to really understand other answers to similar questions.
Ultimately, I am merging this data with other data and creating 0/1 dummy variable to indicate if a country was a "great power" in a given year. The easiest way I thought of was to do this by creating individual rows for each year a country was a great power (the data in this question) because the data I am merging it with is also in the country-year format. I am open to other options, though, if something else is easier!
Thank you!

Answer 1

I think tidyr::expand() and full_seq() can achieve what you want, with grouping on stateabb and styear since you have multiple start years for some states.
Assuming your data frame is named mydata, something like this. I have retained the column of expanded years and named it filled_year, but you may want to remove it.
library(dplyr)
library(tidyr)
new_data <- mydata %>%
group_by(stateabb, styear) %>%
tidyr::expand(stateabb, full_seq(c(styear, endyear), 1)) %>%
inner_join(mydata) %>%
rename(filled_year = `full_seq(c(styear, endyear), 1)`) %>%
ungroup()
The top and bottom of the USA rows:
new_data %>%
filter(stateabb == "USA") %>%
head()
# A tibble: 6 x 10
styear stateabb filled_year ccode stmonth stday endyear endmonth endday version
<int> <chr> <dbl> <int> <int> <int> <int> <int> <int> <int>
1 1898 USA 1898 2 8 13 2016 12 31 2016
2 1898 USA 1899 2 8 13 2016 12 31 2016
3 1898 USA 1900 2 8 13 2016 12 31 2016
4 1898 USA 1901 2 8 13 2016 12 31 2016
5 1898 USA 1902 2 8 13 2016 12 31 2016
6 1898 USA 1903 2 8 13 2016 12 31 2016
new_data %>%
filter(stateabb == "USA") %>%
tail()
# A tibble: 6 x 10
styear stateabb filled_year ccode stmonth stday endyear endmonth endday version
<int> <chr> <dbl> <int> <int> <int> <int> <int> <int> <int>
1 1898 USA 2011 2 8 13 2016 12 31 2016
2 1898 USA 2012 2 8 13 2016 12 31 2016
3 1898 USA 2013 2 8 13 2016 12 31 2016
4 1898 USA 2014 2 8 13 2016 12 31 2016
5 1898 USA 2015 2 8 13 2016 12 31 2016
6 1898 USA 2016 2 8 13 2016 12 31 2016
Your example data:
mydata <- structure(list(stateabb = c("USA", "UKG", "FRN", "FRN", "GMY",
"GMY", "GMY", "AUH", "ITA", "RUS", "RUS", "CHN", "JPN", "JPN"
), ccode = c(2L, 200L, 220L, 220L, 255L, 255L, 255L, 300L, 325L,
365L, 365L, 710L, 740L, 740L), styear = c(1898L, 1816L, 1816L,
1945L, 1816L, 1925L, 1991L, 1816L, 1860L, 1816L, 1922L, 1950L,
1895L, 1991L), stmonth = c(8L, 1L, 1L, 8L, 1L, 1L, 12L, 1L, 1L,
1L, 1L, 1L, 4L, 12L), stday = c(13L, 1L, 1L, 15L, 1L, 1L, 11L,
1L, 1L, 1L, 1L, 1L, 1L, 11L), endyear = c(2016L, 2016L, 1940L,
2016L, 1918L, 1945L, 2016L, 1918L, 1943L, 1917L, 2016L, 2016L,
1945L, 2016L), endmonth = c(12L, 12L, 6L, 12L, 11L, 5L, 12L,
11L, 9L, 12L, 12L, 12L, 8L, 12L), endday = c(31L, 31L, 22L, 31L,
11L, 7L, 31L, 3L, 2L, 5L, 31L, 31L, 14L, 31L), version = c(2016L,
2016L, 2016L, 2016L, 2016L, 2016L, 2016L, 2016L, 2016L, 2016L,
2016L, 2016L, 2016L, 2016L)), class = "data.frame", row.names = c("1",
"2", "3", "4", "5", "6", "7", "8", "9", "10", "11", "12", "13",
"14"))

Answer 2

My guess is that there is a better way to do this, but here is one way with a small reduced data set. The steps are
Make a minimal dataset.
Make function that creates the seq of dates that you want
Split the dataframe into a list of small dataframes by country with dplyr group_split.
Apply a function using purrr map that maps your list into new expanded date dataframes.
Bind list of dataframes back into one dataframe.
library(dplyr)
library(purrr )
df<-data.frame(
stringsAsFactors = FALSE,
stateabb = c("USA", "UKG"),
styear = c(1898L, 1816L),
endyear = c(2016L, 2016L)
)
expand_dates<-function(df) {
stateabb<-df %>% pluck("stateabb")
styear<-df %>% pluck("styear")
endyear<-df%>% pluck("endyear")
years=seq(styear,endyear )
data.frame(years) %>%
mutate(stateabb=stateabb,styear=styear,endyear=endyear)
}
df_new<-df %>%
group_split(stateabb)%>%
map(expand_dates) %>%
bind_rows()
head(df_new)
#> years stateabb styear endyear
#> 1 1816 UKG 1816 2016
#> 2 1817 UKG 1816 2016
#> 3 1818 UKG 1816 2016
#> 4 1819 UKG 1816 2016
#> 5 1820 UKG 1816 2016
#> 6 1821 UKG 1816 2016
Created on 2022-01-13 by the reprex package (v2.0.1)