如何从多个（3）数据帧中提取行，并在其中一列中有匹配的数字的情况下将它们输出到第四位？

Question

I'm trying to extract rows from three data frames batters_16, batters_17, and batters_18 -- which look like below

 player_id       player_name launch_speed launch_angle
1    443558       Nelson Cruz         94.4         11.1
2    519317 Giancarlo Stanton         93.8         14.0
3    408234    Miguel Cabrera         93.6         12.3
4    452095     Tyler Flowers         93.2         12.9
5    407812     Matt Holliday         93.0          8.3
6    120074       David Ortiz         92.8         16.6

and I want to sort them into separate data frames depending on whether or not their player_id shows up in all 3 years (frames), in exactly two of the frames (batters_18 and batters_16 but not batters_17), and a final one if they only appear in one of the three frames. Which should give me 7 total data frames. How can I get this done? I've written a function that tries to separate them using %in% and then runs a calculation but have just had no luck getting it to work -- the output is just 3 columns with almost random numbers and I'll regularly get errors like the one below.

Warning message:
In if (playerid %in% b18$player_id == FALSE & playerid %in% 
b17$player_id ==  : the condition has length > 1 and only the first 
element will be used

This is the function I wrote for reference.

# to combine batting stats from the 3 seasons in the appropriate categories
# but with a weighting of 45% in 2018, 35% in 2017, and 20% in 2016 for sake
# of favoring recent form and performance, but in each seasons all players have
# at least 50 events

 combine.batting.stats <- function(b18, b17, b16, playerID_map){

  #using the stats for each year along with the player ID map

  b18 = read.csv("~/HITS/batters_18.csv")
  b17 = read.csv("~/HITS/batters_17.csv")
  b16 = read.csv("~/HITS/batters_17.csv")
  playerID_map = read.csv("~/HITS/playerID_map.csv")
  playerid = playerID_map$MLBID
  average_launch_speed = 0
  average_launch_angle = 0

  # so first my weights with the scenarios being 
  # exists in all 3 years, exits in exactly two, and finally exists exactly one



  # the check for whether something is in a data frame is as below
  # SOMETHING %in% DATAFRAME$COLUMN
  # this should be used to code three different scenarios where I weight 
  # the value of season stats depending on how may seasons they qualify in

  if(playerid %in% b18$player_id == TRUE & playerid %in% b17$player_id == TRUE
     & playerid %in% b16$player_id == TRUE) {

    #calculation for case of 3 year player
    # 18 is 45%, 17 is 35%, and 16 is 20%

    average_launch_speed = (((b18$launch_speed * 0.45) + (b17$launch_speed * 0.35)
                             + (b16$launch_speed * 0.2)) / 3)

    average_launch_angle = (((b18$launch_angle * 0.45) + (b17$launch_angle * 0.35)
                             + (b16$launch_angle * 0.2)) / 3)

  }

  if(playerid %in% b18$player_id == TRUE & playerid %in% b17$player_id == TRUE
     & playerid %in% b16$player_id == FALSE) {

    #calculation for player in b18 and b17 but not b16....should be extended to
    #other 2 year player situations that is b17 and b16 but not b18 as well as
    #b18 and b16 but not b17 (which I would like to skew even more to b18 stats)
    #than players who have played the most recent 2 years to reflect potential 
    #post injury change

    average_launch_speed = (((b18$launch_speed * 0.6) + (b17$launch_speed * 0.4)) 
                            / 2)

    average_launch_angle = (((b18$launch_angle * 0.6) + (b17$launch_angle * 0.4)) 
                            / 2)

  }

  if(playerid %in% b18$player_id == TRUE & playerid %in% b17$player_id == FALSE & playerid %in% b16$player_id == TRUE) {

    #in b18 and b16 but not b17


    average_launch_speed = (((b18$launch_speed * 0.6) + (b16$launch_speed * 0.4)) 
                            / 2)

    average_launch_angle = (((b18$launch_angle * 0.6) + (b16$launch_angle * 0.4)) 
                            / 2)
    }

  if(playerid %in% b18$player_id == FALSE & playerid %in% b17$player_id == TRUE
     & playerid %in% b16$player_id == TRUE) {

    #in b17 and b16 but not b18


    average_launch_speed = (((b17$launch_speed * 0.6) + (b16$launch_speed * 0.4)) 
                            / 2)

    average_launch_angle = (((b17$launch_angle * 0.6) + (b16$launch_angle * 0.4)) 
                            / 2)

  }

  # next are those in only one single frame/year
  # this one is only in 18

  if(playerid %in% b18$player_id == TRUE & playerid %in% b17$player_id == FALSE
     & playerid %in% b16$player_id == FALSE){

    average_launch_speed = b18$launch_speed

    average_launch_angle = b18$launch_angle 

  }

  # only in b17

  if(playerid %in% b18$player_id == FALSE & playerid %in% b17$player_id == TRUE
     & playerid %in% b16$player_id == FALSE){

    average_launch_speed = b17$launch_speed

    average_launch_angle = b17$launch_angle 

  }

  #only in b16

  if(playerid %in% b18$player_id == FALSE & playerid %in% b17$player_id == FALSE
     & playerid %in% b16$player_id == TRUE){

    average_launch_speed = b16$launch_speed

    average_launch_angle = b16$launch_angle 

  }

  combined_stats = list(playerid, average_launch_speed, average_launch_angle)

  # returning a data frame from the function
  write.csv(combined_stats, "combined_stats_1.csv", col.names = TRUE, row.names = FALSE)

 }

Answer 1

Let's start by combining all your datasets into one tidy one:

batters_16$year<-2016
batters_17$year<-2017
batters_18$year<-2018
batters<-rbind(batters_16,batters_17,batters_18)

Now it's easy to do what you wanted using `dplyr':

batters<- batters %>% group_by(player_id)
filter(batters,any(year==2016) & all(year!=2017 & year!=2018)) # only 2016
filter(batters,any(year==2016) & any(year==2017) & all(year!=2018)) # only 2016 and 2017
etc...