如何根据 R 中的列中的值聚合数据

Question

I am currently working on a project for work and I am struggling to summarize data correctly and I am worried that I am approaching this problem the wrong way. Basically, I have a dataset that looks like this:

Month.Year Code Count
8/2017     1    1 
2/2018     1    1
4/2018     2    1
4/2018     2    1
5/2020     3    1
5/2020     3    1
.
.
.

I need to summarize this data so that I can create grouped barplots with dates being the groups and the codes being the subgroups.

In this data set we have a date column by Month/Year, a Categorical Code (a value between 1 and 3), and a "Count" column that I created which is just the value 1 for each observation (I'm hoping this makes it easier to "sum" the number of obs).

The goal is to summarize this data at a Month and Code level for each year. In other words, I would like to have a different dataset for each year that looks something like this:

## Dataset for Year 2018
Month Code Value
1     1    24  
1     2    13  
1     3    0
2     1    0
2     2    5
2     3    22
.
.
.
## Dataset for Year 2019
Month Code Value
1     1    15  
1     2    2  
1     3    54
2     1    0
2     2    0
2     3    21
.
.
.

Answer 1

split the data set by year and then aggregate each sub-data.frame in a lapply loop.

Use sub to keep only the year to be used in the split instruction.

df1 <- read.table(text = "
Month.Year Code Count
8/2017     1    1 
2/2018     1    1
4/2018     2    1
4/2018     2    1
5/2020     3    1
5/2020     3    1
", header = TRUE)
df1
#>   Month.Year Code Count
#> 1     8/2017    1     1
#> 2     2/2018    1     1
#> 3     4/2018    2     1
#> 4     4/2018    2     1
#> 5     5/2020    3     1
#> 6     5/2020    3     1

sub(".*/", "", df1$Month.Year)
#> [1] "2017" "2018" "2018" "2018" "2020" "2020"

^{Created on 2022-03-07 by the reprex package (v2.0.1)}

Now save the split result and loop to compute the sums.

df1_year <- split(df1, sub(".*/", "", df1$Month.Year))
df1_year <- lapply(df1_year, \(x) {
  x$Month.Year <- sub("/\\d+$", "", x$Month.Year)
  names(x)[1] <- "Month"
  aggregate(Count ~ ., data = x, sum)
})

df1_year
#> $`2017`
#>   Month Code Count
#> 1     8    1     1
#> 
#> $`2018`
#>   Month Code Count
#> 1     2    1     1
#> 2     4    2     2
#> 
#> $`2020`
#>   Month Code Count
#> 1     5    3     2

^{Created on 2022-03-07 by the reprex package (v2.0.1)}

The result list members can be extracted with the standard extraction operators.

df_year[['2017']]   # by quoted name, '2017'
df_year[[1]]        # equivalent, 1st member