In R, how do I filter whole groups conditional on a value in the first row of the group?-CodePudding

I have a data frame in which I want to filter out whole groups if the top row of that group does not contain a particular condition in one column.

An example using the following dataset:

df <- data.frame(team=c('A', 'A', 'A', 'A', 'B', 'B', 'B', 'C', 'D', 'D', 'D', 'E', 'E'), gameplayed=c('Yes', 'No', 'Yes', 'Yes', 'No', 'Yes', 'No', 'Yes', 'Yes', 'No', 'Yes', 'No', 'Yes'))

I want to group these by 'team' first. Then, I want to remove the entire group if the first row contains a 'No' in the 'gameplayed' column.

This would be the desired output:

df2 <- data.frame(team=c('A', 'A', 'A', 'A', 'C', 'D', 'D', 'D'), gameplayed=c('Yes', 'No', 'Yes', 'Yes', 'Yes', 'Yes', 'No', 'Yes'))

I've played around with various options, such as the following, but can't get it to work for me:

> df %>% group_by(team) %>%   filter("Yes" == first(gameplayed))

CodePudding user response：

You can use ave like.

df[ave(df$gameplayed != "No", df$team, FUN=\(x) x[1]),]
#df[ave(df$gameplayed, df$team, FUN=\(x) x[1]) != "No",] #Alternative
#   team gameplayed
#1     A        Yes
#2     A         No
#3     A        Yes
#4     A        Yes
#8     C        Yes
#9     D        Yes
#10    D         No
#11    D        Yes

Benchmark

library(tidyverse)
bench::mark(check=FALSE,
  GKi = df[ave(df$gameplayed != "No", df$team, FUN=\(x) x[1]),],
  Limney = df %>% group_by(team) %>% filter(first(gameplayed) == "Yes") %>% ungroup(),
  Elias = df %>% group_by(team) %>% mutate(id = row_number()) %>% filter(!any(gameplayed == "No" && id == 1)) #With Warings
)
#  expression      min  median itr/s…¹ mem_a…² gc/se…³ n_itr  n_gc total…⁴ result
#  <bch:expr> <bch:tm> <bch:t>   <dbl> <bch:b>   <dbl> <int> <dbl> <bch:t> <list>
#1 GKi         75.74µs 82.05µs  11936.  4.52KB    16.8  5685     8   476ms <NULL>
#2 Limney       4.52ms   4.6ms    214.  7.92KB    15.6    96     7   448ms <NULL>
#3 Elias        3.36ms  3.42ms    291.  9.53KB    13.0   134     6   461ms <NULL>

GKi is in this case about 40 times faster than Elias and 50 times faster than Limney and uses less memory.

CodePudding user response：

You can first creat an id for your groups and then filter on gameplayed and id. You have to use any() to filter out the whole group.

library(tidyverse)
df <- df %>% group_by(team) %>% mutate(id = row_number())
df_final <- df %>% filter(!any(gameplayed == "No" && id == 1))

Or as pointed out in the comment you do not have to create an id and just use first():

df_final <- df %>% filter(!any(first(gameplayed) == "No"))