Reputation: 561
New variable that indicates the first occurrence of a specific value
I want to create a new variable that indicates the first specific observation of a value for a variable.
In the following example dataset I want to have a new variable "firstna" that is "1" for the first observation of "NA" for this player.
game_data <- data.frame(player = c(1,1,1,1,2,2,2,2), level = c(1,2,3,4,1,2,3,4), points = c(20,NA,NA,NA,20,40,NA,NA))
game_data
player level points
1 1 1 20
2 1 2 NA
3 1 3 NA
4 1 4 NA
5 2 1 20
6 2 2 40
7 2 3 NA
8 2 4 NA
The resulting dataframe should look like this:
game_data_new <- data.frame(player = c(1,1,1,1,2,2,2,2), level = c(1,2,3,4,1,2,3,4), points = c(20,NA,NA,NA,20,40,NA,NA), firstna = c(0,1,0,0,0,0,1,0))
game_data_new
player level points firstna
1 1 1 20 0
2 1 2 NA 1
3 1 3 NA 0
4 1 4 NA 0
5 2 1 20 0
6 2 2 40 0
7 2 3 NA 1
8 2 4 NA 0
To be honest i don't know how to do this. It would be perfect if there is a dplyr option to do so.
Upvotes: 5
Views: 713
Answers (8)
Reputation: 47340
An option using diff
transform(game_data, firstna = ave(is.na(points), player, FUN = function(x) c(0,diff(x))))
# player level points firstna
# 1 1 1 20 0
# 2 1 2 NA 1
# 3 1 3 NA 0
# 4 1 4 NA 0
# 5 2 1 20 0
# 6 2 2 40 0
# 7 2 3 NA 1
# 8 2 4 NA 0
And its dplyr equivalent:
library(dplyr)
game_data %>% group_by(player) %>% mutate(firstna = c(0,diff(is.na(points))))
# # A tibble: 8 x 4
# # Groups: player [2]
# player level points firstna
# <dbl> <dbl> <dbl> <dbl>
# 1 1 1 20 0
# 2 1 2 NA 1
# 3 1 3 NA 0
# 4 1 4 NA 0
# 5 2 1 20 0
# 6 2 2 40 0
# 7 2 3 NA 1
# 8 2 4 NA 0
Upvotes: 0
Reputation: 7592
game_data %>%
group_by(player) %>%
mutate(firstna=as.numeric(is.na(points) & !duplicated(points)))
Group by player, then create a boolean vector for cases that are both NA and not duplicates for previous rows.
# A tibble: 8 x 4
# Groups: player [2]
player level points firstna
<dbl> <dbl> <dbl> <dbl>
1 1 1 20 0
2 1 2 NA 1
3 1 3 NA 0
4 1 4 NA 0
5 2 1 20 0
6 2 2 40 0
7 2 3 NA 1
8 2 4 NA 0
If you want the 1s on the last non-NA line before an NA, replace the mutate line with this:
mutate(lastnonNA=as.numeric(!is.na(points) & is.na(lead(points))))
First row of a block of NAs that runs all the way to the end of the player's group:
game_data %>%
group_by(player) %>%
mutate(firstna=as.numeric(is.na(points) & !duplicated(cbind(points,cumsum(!is.na(points))))))
Upvotes: 1
Reputation: 78832
library(tidyverse)
library(data.table)
data.frame(
player = c(1,1,1,1,2,2,2,2),
level = c(1,2,3,4,1,2,3,4),
points = c(20,NA,NA,NA,20,40,NA,NA)
) -> game_data
game_data_base1 <- game_data
game_data_dt <- data.table(game_data)
microbenchmark::microbenchmark(
better_base = game_data$first_na <- ave(
game_data$points,
game_data$player,
FUN=function(x) seq_along(x)==match(NA,x,nomatch=0)
),
brute_base = do.call(
rbind.data.frame,
lapply(
split(game_data, game_data$player),
function(x) {
x$firstna <- 0
na_loc <- which(is.na(x$points))
if (length(na_loc) > 0) x$firstna[na_loc[1]] <- 1
x
}
)
),
tidy = game_data %>%
group_by(player) %>%
mutate(firstna=as.numeric(is.na(points) & !duplicated(points))) %>%
ungroup(),
dt = game_data_dt[, firstna:=as.integer(is.na(points) & !is.na(shift(points))), player]
)
## Unit: microseconds
## expr min lq mean median uq max neval
## better_base 125.188 156.861 362.9829 191.6385 355.6675 3095.958 100
## brute_base 366.642 450.002 2782.6621 658.0380 1072.6475 174373.974 100
## tidy 998.924 1119.022 2528.3687 1509.0705 2516.9350 42406.778 100
## dt 330.428 421.211 1031.9978 535.8415 1042.1240 9671.991 100
Upvotes: 2
Reputation: 389175
Another ave option to find out first NA by group (player).
game_data$firstna <- ave(game_data$points, game_data$player,
FUN = function(x) cumsum(is.na(x)) == 1)
game_data
# player level points firstna
#1 1 1 20 0
#2 1 2 NA 1
#3 1 3 NA 0
#4 1 4 NA 0
#5 2 1 20 0
#6 2 2 40 0
#7 2 3 NA 1
#8 2 4 NA 0
Upvotes: 5
Reputation: 11500
Another way using base:
game_data$firstna <-
unlist(
tapply(game_data$points, game_data$player, function(x) {i<-which(is.na(x))[1];x[]<-0;x[i]<-1;x})
)
or as another ?ave clone:
ave(game_data$points, game_data$player, FUN = function(x) {
i<-which(is.na(x))[1];x[]<-0;x[i]<-1;x
})
Upvotes: 0
Reputation: 24500
A base R solution:
ave(game_data$points, game_data$player,
FUN = function(x) seq_along(x) == match(NA, x, nomatch = 0))
Upvotes: 8
Reputation: 859
You can do this by grouping by player and then mutating to check if a row has an NA value and the previous row doesn't
game_data %>%
group_by(player) %>%
mutate(firstna = ifelse(is.na(points) & lag(!is.na(points)),1,0)) %>%
ungroup()
Result:
# A tibble: 8 x 4
# Groups: player [2]
player level points firstna
<dbl> <dbl> <dbl> <dbl>
1 1 1 20 0
2 1 2 NA 1
3 1 3 NA 0
4 1 4 NA 0
5 2 1 20 0
6 2 2 40 0
7 2 3 NA 1
8 2 4 NA 0
Upvotes: 2
Reputation: 12569
Here is a solution with data.table:
library("data.table")
game_data <- data.table(player = c(1,1,1,1,2,2,2,2), level = c(1,2,3,4,1,2,3,4), points = c(20,NA,NA,NA,20,40,NA,NA))
game_data[, firstna:=is.na(points) & !is.na(shift(points)), player][]
# > game_data[, firstna:=is.na(points) & !is.na(shift(points)), player][]
# player level points firstna
# 1: 1 1 20 FALSE
# 2: 1 2 NA TRUE
# 3: 1 3 NA FALSE
# 4: 1 4 NA FALSE
# 5: 2 1 20 FALSE
# 6: 2 2 40 FALSE
# 7: 2 3 NA TRUE
# 8: 2 4 NA FALSE
Upvotes: 3
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