Finding a Weighted Average Based on Years - r

Question

I want to create a weighted average of the baseball statistic WAR from 2017 to 2019.
The Averages would go as following:
2019: 57.14%
2018: 28.57%
2017: 14.29%
However some players only played in 2018 and 2019, some having played in 2019 and 2017.
If they've only played in two years it would be 67/33, and only one year would be 100% obviously.
I was wondering if there was an easy way to do this.
My data set looks like this
Name Season G PA HR BB_pct K_pct ISO wOBA wRC_plus Def WAR
337 A.J. Pollock 2017 112 466 14 7.5 15.2 0.205 0.340 103 2.6 2.2
357 A.J. Pollock 2018 113 460 21 6.7 21.7 0.228 0.338 111 0.9 2.6
191 Aaron Altherr 2017 107 412 19 7.8 25.2 0.245 0.359 120 -7.9 1.4
162 Aaron Hicks 2017 88 361 15 14.1 18.6 0.209 0.363 128 6.4 3.4
186 Aaron Hicks 2018 137 581 27 15.5 19.1 0.219 0.360 129 2.3 5.0
464 Aaron Hicks 2019 59 255 12 12.2 28.2 0.208 0.325 102 1.3 1.1
And the years vary from person to person, but was wondering if anyone had a way to do this weighted average dependent on the years they played. I also dont want any only 2017-ers if that make sense.

Answer 1

I guess, there is an easy way of doing your task. Unfortunately my approach is a little bit more complex. I'm using dplyr and purr.
First I put those weights into a list:
one_year <- 1
two_years <- c(2/3, 1/3)
three_years <- c(4/7, 3/7, 1/7)
weights <- list(one_year, two_years, three_years)
Next I split the datset into a list by the number of seasons each player took part:
df %>%
group_by(Name) %>%
mutate(n=n()) %>%
arrange(n) %>%
ungroup() %>%
group_split(n) -> my_list
Now I define a function that calculates the average using the weights:
WAR_average <- function(i) {my_list[[i]] %>%
group_by(Name) %>%
mutate(WAR_average = sum(WAR * weights[[i]]))}
And finally I apply the function WAR_average on my_list and filter/select the data:
my_list %>%
seq_along() %>%
lapply(WAR_average) %>% # apply function
reduce(rbind) %>% # bind the dataframes into one df
filter(Season != 2017 | n != 1) %>% # filter players only active in 2017
select(Name, WAR_average) %>% # select player and war_average
distinct() # remove duplicates
This whole process returns
# A tibble: 2 x 2
# Groups: Name [2]
Name WAR_average
<chr> <dbl>
1 A.J. Pollock 2.33
2 Aaron Hicks 4.24