在 R 的长时间序列数据集中保留前一年未发生的观察

Question

I have a df that looks like this:

ID Year
5  2010
5  2011
5  2014
3  2013
3  2014
10 2013
1  2010
1  2012
1  2014
...

The df contains the years 2009-2019, and is filtered on individuals living in a one particular town, that are 18-64 years old at that particular year.

For every year I need to keep only individuals that have moved into this town that particular year. So for example, I need to keep the difference between the population at year 2010 minus the population at year 2009. I also need to do this for every year (so for example, some people move out of town for a couple of years and then return - ID 5 is an example of this). In the end, I want one df for every year 2010-2019, so ten dfs that contain only individuals that moved into town that particular year.

I have played around with group_by() and left_join() , but haven't managed to succeed. There must be a simple solution, but I haven't been able to find one yet.

Answer 1

You can use the setdiff function to perform set(A) - set(B) operation. Split your data into dataframes by year, and then loop through them, finding the new joiners.

Example code:

library(dplyr)
set.seed(123)
df <- tibble(
    id = c(1, 2, 3, 4, 5,     # first year
           1, 2, 3, 5, 6, 7,  # 4 moves out, 6,7 move in
           2, 3, 4, 6, 7, 8), # 1,5 moves out, 4,8 move in
    year = c(rep(2009, 5), 
             rep(2010, 6), 
             rep(2011, 6)), 
    age = sample(18:64, size = 17) # extra column
)

# split into list of dataframes by year
df_by_year <- split(df, df$year)

# create a list to contain the 2 df (total years 3 - 1)
df_list <- vector("list", 2)

for(i in 1:length(df_list)){

    # determine incoming new people        
    new_joinees <- setdiff(df_by_year[[i+1]]$id, df_by_year[[i]]$id)

    # filter for above IDs
    df_list[[i]] <- dplyr::filter(df_by_year[[i+1]], id %in% new_joinees)
    
}