I'm working on an unbalanced panel dataset. Data came from a game and for every user ( user_id
) in the record I have data for every level ( level
) of the game. As recording data started some time after introduction of the game, for some users I don't have data regarding the first levels, that's why I want to throw them out in a first step.
I've tried the complete.cases-function, but it only excludes the rows with the missing values (NAs), but not data for the whole user with missing values in level 1.
panel <- panel[complete.cases(panel), ]
That's why I need a code that excludes every user who has no record in level 1 (which in my dataset means he has an "NA" at one of the dependent variables, ie number of activities).
Update #1: Data looks like this (thanks to thc):
> game_data <- data.frame(player = c(1,1,1,2,2,2,3,3,3), level = c(1,2,3,1,2,3,1,2,3), score=c(0,150,170,80,100,110,75,100,0))
> game_data
player level score
1 1 1 0
2 1 2 150
3 1 3 170
4 2 1 80
5 2 2 100
6 2 3 110
7 3 1 75
8 3 2 100
9 3 3 0
I now want to exclude data from player 1, because he has a score of 0 in level 1.
Here is one approach
Example data:
game_data <- data.frame(player = c(1,1,2,2,2,3,3,3), level = c(2,3,1,2,3,1,2,3), score=sample(100, 8))
> game_data
player level score
1 1 2 19
2 1 3 13
3 2 1 65
4 2 2 32
5 2 3 22
6 3 1 98
7 3 2 58
8 3 3 84
library(dplyr)
game_data %>% group_by(player) %>% filter(any(level == 1)) %>% as.data.frame
player level score
1 2 1 65
2 2 2 32
3 2 3 22
4 3 1 98
5 3 2 58
6 3 3 84
I think I now find a solution with your help:
game_data %>% group_by(player) %>% filter(any(level == 1 & score > 0)) %>% as.data.frame
This seems to work and I just needed a little adjustment from your code thc, thank you very much for your help!
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