基于日期和其他条件的 R 中的累积总和使用 data.table

Question

I have some football data (2020/2021 Serie A), and I would like to compute the number of games each team played over the last n days (lets say to simplify over the last 30 days). Conditions are thus the team, the day the game is played (strictly smaller than) and that same day - 30 (greater or equal to).

I would like to know what is the best way to do that using data.table (alone), and more important, the logic behind the code. I woudl go for a loop over the teams and dates, but I think it is cumbersome and I am sure there is a way to have it done in one single row.

A sample is given below, with the result I would expect (day and date might seem misleading, because some games were postponed, but that is not important. Data is classified by date). Thank you very much.

Code	Team	Date	Day	Date - 30d	Games played over the last 30 days
TORATA	Atalanta	2020-09-26	2	2020-08-27	NA
LAZATA	Atalanta	2020-09-30	1	2020-08-31	1
ATACAG	Atalanta	2020-10-04	3	2020-09-04	2
NAPATA	Atalanta	2020-10-17	4	2020-09-17	3
ATASAM	Atalanta	2020-10-24	5	2020-09-24	4
CROATA	Atalanta	2020-10-31	6	2020-10-01	3
ATAINT	Atalanta	2020-11-08	7	2020-10-09	3

Answer 1

Here's one implementation, just data.table and base R:

dat[, z := sapply(Date, function(z) sum(between(z - Date, 0.1, 30)))]
dat
#      Code     Team       Date   Day Date...30d Games.played.over.the.last.30.days     z
#    <char>   <char>     <Date> <int>     <Date>                              <int> <int>
# 1: TORATA Atalanta 2020-09-26     2 2020-08-27                                 NA     0
# 2: LAZATA Atalanta 2020-09-30     1 2020-08-31                                  1     1
# 3: ATACAG Atalanta 2020-10-04     3 2020-09-04                                  2     2
# 4: NAPATA Atalanta 2020-10-17     4 2020-09-17                                  3     3
# 5: ATASAM Atalanta 2020-10-24     5 2020-09-24                                  4     4
# 6: CROATA Atalanta 2020-10-31     6 2020-10-01                                  3     3
# 7: ATAINT Atalanta 2020-11-08     7 2020-10-09                                  3     3

In this case, for each Date value, we count how many of the dates are within 30 days of it.

If you need the NA in place of a 0 , then you can add on dat[z < 1, z := NA] or similar.

---

Data:

library(data.table)
dat <- structure(list(Code = c("TORATA", "LAZATA", "ATACAG", "NAPATA", "ATASAM", "CROATA", "ATAINT"), Team = c("Atalanta", "Atalanta", "Atalanta", "Atalanta", "Atalanta", "Atalanta", "Atalanta"), Date = structure(c(18531, 18535, 18539, 18552, 18559, 18566, 18574), class = "Date"), Day = c(2L, 1L, 3L, 4L, 5L, 6L, 7L), Date...30d = structure(c(18501, 18505, 18509, 18522, 18529, 18536, 18544), class = "Date"), Games.played.over.the.last.30.days = c(NA, 1L, 2L, 3L, 4L, 3L, 3L)), class = c("data.table", "data.frame"), row.names = c(NA, -7L))
setDT(dat)

Answer 2

You could use runner in combination with data.table to calculate a running Date window count:

library(data.table)
library(runner)

setDT(data)

data[,Date:=as.Date(Date,'%Y-%m-%d')]

data[,N:=runner::runner(
                        x = Date, 
                        k = 30, # 30-days window
                        lag = 1,
                        idx = Date,
                        f = length)
    ,by=Team][]

     Code     Team       Date Day    Date30d Games30days N
1: TORATA Atalanta 2020-09-26   2 2020-08-27          NA 0
2: LAZATA Atalanta 2020-09-30   1 2020-08-31           1 1
3: ATACAG Atalanta 2020-10-04   3 2020-09-04           2 2
4: NAPATA Atalanta 2020-10-17   4 2020-09-17           3 3
5: ATASAM Atalanta 2020-10-24   5 2020-09-24           4 4
6: CROATA Atalanta 2020-10-31   6 2020-10-01           3 3
7: ATAINT Atalanta 2020-11-08   7 2020-10-09           3 3

Data:

data <- read.table(text='
Code    Team    Date    Day     Date30d     Games30days
TORATA  Atalanta    2020-09-26  2   2020-08-27  NA
LAZATA  Atalanta    2020-09-30  1   2020-08-31  1
ATACAG  Atalanta    2020-10-04  3   2020-09-04  2
NAPATA  Atalanta    2020-10-17  4   2020-09-17  3
ATASAM  Atalanta    2020-10-24  5   2020-09-24  4
CROATA  Atalanta    2020-10-31  6   2020-10-01  3
ATAINT  Atalanta    2020-11-08  7   2020-10-09  3',header=T)

Answer 3

You can get this with one line of code, using a non-equi join of the table onto itself.

Let's say fb is your input data (without the Games30days column). Like this:

     Code     Team       Date Day Date - 30d
1: TORATA Atalanta 2020-09-26   2 2020-08-27
2: LAZATA Atalanta 2020-09-30   1 2020-08-31
3: ATACAG Atalanta 2020-10-04   3 2020-09-04
4: NAPATA Atalanta 2020-10-17   4 2020-09-17
5: ATASAM Atalanta 2020-10-24   5 2020-09-24
6: CROATA Atalanta 2020-10-31   6 2020-10-01
7: ATAINT Atalanta 2020-11-08   7 2020-10-09

Then, just do a join on Team=Team , Date<Date , and Date>Date - 30d , like this:

games_played = fb[fb,on=.(Team = Team, Date<Date, Date>`Date - 30d`), nomatch=0][,.("Games30" =  .N), .(Date,Team)]

which returns

         Date     Team Games30
1: 2020-09-30 Atalanta       1
2: 2020-10-04 Atalanta       2
3: 2020-10-17 Atalanta       3
4: 2020-10-24 Atalanta       4
5: 2020-10-31 Atalanta       3
6: 2020-11-08 Atalanta       3

That result can easily be joined back to the original, to get all the columns, like this:

games_played[fb, on=.(Team, Date)]