CODEWITHSUNDEEP
rdplyrtidyverse
Jack
Jack

Reputation: 857

How to create a new column conditioned on the occurrences of two other columns in R using dplyr?

I want to create a country_year variable that is conditioned on the occurrence of countries and years as shown below in this small subsample that i have created. This means that if i have 2 countries with 3 different years, a new country_year variable will have the values of country1_year1, country1_year2, etc..

It seems so simple, but i am new to R and tried to look for different questions that target it with no success. Could someone guide me a bit please?

structure(list(id = structure(c(1, 1, 1, 2, 2, 2), format.stata = "%9.0g"), 
    country = structure(c("US", "US", "US", "UK", "UK", "UK"), format.stata = "%9s"), 
    year = structure(c(2003, 2004, 2005, 2003, 2004, 2005), format.stata = "%9.0g"), 
    country_year = structure(c(1, 2, 3, 4, 5, 6), format.stata = "%9.0g")), row.names = c(NA, 
-6L), class = c("tbl_df", "tbl", "data.frame"))

Upvotes: 0

Views: 43

Answers (2)

akrun
akrun

Reputation: 887741

An option with tidyverse would be

library(dplyr)
library(tidyr)
df %>%
     unite(country_year, country, year, sep="_", remove = FALSE)

-output

# A tibble: 6 x 4
#     id country_year country  year
#  <dbl> <chr>        <chr>   <dbl>
#1     1 US_2003      US       2003
#2     1 US_2004      US       2004
#3     1 US_2005      US       2005
#4     2 UK_2003      UK       2003
#5     2 UK_2004      UK       2004
#6     2 UK_2005      UK       2005

Upvotes: 1

Matt
Matt

Reputation: 7413

It seems like you are wanting to make a new variable country_year:

Using base R:

df$country_year <- paste0(df$country, "_", df$year)

Using dplyr:

library(dplyr)

df %>% 
  mutate(country_year = paste0(country,"_",year))

This gives us:

     id country  year country_year
  <dbl> <chr>   <dbl> <chr>       
1     1 US       2003 US_2003     
2     1 US       2004 US_2004     
3     1 US       2005 US_2005     
4     2 UK       2003 UK_2003     
5     2 UK       2004 UK_2004     
6     2 UK       2005 UK_2005

Upvotes: 1

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