fuzzyjoin with dates in R

Question

I am working on a project where I am analyzing individual-level survey data within countries based on outcomes of sports matches across countries and I am not sure what the most efficient way to produce the merge that I want is.

I am working on two separate datasets. One contains individual-level data nested within countries. The data might look something like this:

country <- c(rep("Country A", 4), rep("Country B", 6))
date <- c("2000-01-01", "2000-01-02", "2000-01-03", "2000-01-04", rep("2000-01-01", 2), "2000-01-02", rep("2000-01-03", 3))
outcome <- rnorm(10)
individual_data <- cbind.data.frame(country, date, outcome)
rm(country, date, outcome)

The other has country-match level data, which will look something like this:

date <- rep("2000-01-02", 2)
country <- c("Country A", "Country B")
opponent <- c("Country B", "Country A")
match_outcome <- c("L", "W")
match_data <- cbind.data.frame(date, country, opponent, match_outcome)
rm(date, country, opponent, match_outcome)

In this example, there's just one match, played on January 2nd, 2000, where country A lost to country B. I would like to perform a fuzzy_join so that as opposed to this left_join here, the match_data matches up with the individual_data even if the date isn't exact.

# incorrect
merged <- left_join(individual_data, match_data)

I would like to do this with a range of 3 days, and I would like an indicator of how many days it is before and after the match within this range. The final product would look something like this:

country <- c(rep("Country A", 4), rep("Country B", 6))
date <- c("2000-01-01", "2000-01-02", "2000-01-03", "2000-01-04", rep("2000-01-01", 2), "2000-01-02", rep("2000-01-03", 3))
outcome <- rnorm(10)
opponent <- c(rep("Country B", 4), rep("Country A", 6))
match_outcome <- c(rep("L", 4), rep("W", 6))
match_date <- rep("2000-01-02", 10)
difference <- c(-1, 0, 1, 2, -1, -1, 0, rep(1, 3))
desired_output <- cbind.data.frame(country, date, outcome, opponent, match_outcome, match_date, difference)
rm(country, date, outcome, opponent, match_outcome, match_date, difference)

Can anyone help me out? I've been really struggling with how to get this done. Here is what I've tried so far:

match_data$match_date_minus3 <- ymd(match_data$date) - days(3)
match_data$match_date_plus3 <- ymd(match_data$date) + days(3)

test_output <- fuzzy_left_join(individual_data, match_data,
                                by = c("country" = "country",
                                       "match_date_minus3" = "date",
                                       "match_date_plus3" = "date"),
                                match_fun = list("==", ">", "<"))

but I get the following error: Error in which(m) : argument to 'which' is not logical

For reference if anyone is aware, I'm trying to replicate the results of Depeteris-Chauvin et al. 2018.

match_fun = list(\`==\`, \`>\`, \`<\`)) like so? Now I'm getting: Error: All columns in a tibble must be 1d or 2d objects: * Column \`col\` is NULL — Julian, Nov 05 '19 at 19:54

akrun · Accepted Answer · 2019-11-05T20:04:07.550

There are three issues

Replace the double quote with backquote inside the match_fun
the by values should be reversed
'date' columns are changed to respective Date class

library(fuzzyjoin)
library(dplyr)
individual_data$date <- as.Date(individual_data$date)
match_data$match_date_minus3 <- as.Date(match_data$match_date_minus3)
match_data$match_date_plus3 <- as.Date(match_data$match_date_plus3)
fuzzy_left_join(individual_data, match_data,
                                 by = c("country" = "country",
                                        'date' = "match_date_minus3",
                                        'date' = "match_date_plus3"),
                                 match_fun = list(`==`, `>`, `<`)) %>%
  select(country = country.x, date = date.x, outcome, 
          opponent, match_outcome, match_date = date.y)
#     country       date    outcome  opponent match_outcome match_date
#1  Country A 2000-01-01  1.4003662 Country B             L 2000-01-02
#2  Country A 2000-01-02  0.5526607 Country B             L 2000-01-02
#3  Country A 2000-01-03  0.4316405 Country B             L 2000-01-02
#4  Country A 2000-01-04 -0.1171910 Country B             L 2000-01-02
#5  Country B 2000-01-01  1.3433921 Country A             W 2000-01-02
#6  Country B 2000-01-01 -1.1773011 Country A             W 2000-01-02
#7  Country B 2000-01-02 -0.6953120 Country A             W 2000-01-02
#8  Country B 2000-01-03  1.3484053 Country A             W 2000-01-02
#9  Country B 2000-01-03 -0.7266405 Country A             W 2000-01-02
#10 Country B 2000-01-03 -0.9139988 Country A             W 2000-01-02

fuzzyjoin with dates in R

1 Answers1

Linked

Related