I am trying to create similar_player_selected column. I have first 4 columns.
For row 1, player_id =1 and the most similar player to player 1 is player 3. But player 3 (row 3) isn't selected for campaign 1(player_selected=0) so I assign a value of 0 to similar_player_selected for row 1. For row 2, player_id=2 and the most similar player to player 2 is player 4. Player 4 is selected for the campaign 1(row 4) so I assign a value of 1 to similar_player_selected for row 2. Please note there are more than 1000 campaigns overall.
campaign_id
player_id
most_similar_player
player_selected
similar_player_selected
1
1
3
1
0
1
2
4
0
1
1
3
4
0
?
1
4
1
1
?
2
1
3
1
?
2
2
4
1
?
2
3
4
0
?
2
4
1
0
?
Using match we can subset player selected at matched locations
library(dplyr)
df |>
group_by(campaign_id) |>
mutate(
similar_player_selected = player_selected[match(most_similar_player, player_id)]
) |>
ungroup()
Faster base R alternative
df$similar_player_selected <- lapply(split(df, df$campaign_id), \(x)
with(x, player_selected[match(most_similar_player, player_id)])) |>
unlist()
campaign_id player_id most_similar_player player_selected similar_player_selected
<dbl> <dbl> <dbl> <dbl> <dbl>
1 1 1 3 1 0
2 1 2 4 0 1
3 1 3 4 0 1
4 1 4 1 1 1
5 2 1 3 1 0
6 2 2 4 1 0
7 2 3 4 0 0
8 2 4 1 0 1
Suppose there are two students - each student takes an exam multiple times (e.g.result_id = 1 is the first exam, result_id = 2 is the second exam, etc.). The student can either "pass" (1) or "fail" (0).
The data looks something like this:
library(data.table)
my_data = data.frame(id = c(1,1,1,1,1,1,2,2,2,2,2,2,2,2,2), results = c(0,1,0,1,0,0,1,1,1,0,1,1,0,1,0), result_id = c(1,2,3,4,5,6,1,2,3,4,5,6,7,8,9))
my_data = setDT(my_data)
id results result_id
1: 1 0 1
2: 1 1 2
3: 1 0 3
4: 1 1 4
5: 1 0 5
6: 1 0 6
7: 2 1 1
8: 2 1 2
9: 2 1 3
10: 2 0 4
11: 2 1 5
12: 2 1 6
13: 2 0 7
14: 2 1 8
15: 2 0 9
I am interested in counting the number of times that a student passes an exam, given that the student passed the previous two exams.
I tried to do this with the following code:
my_data$current_exam = shift(my_data$results, 0)
my_data$prev_exam = shift(my_data$results, 1)
my_data$prev_2_exam = shift(my_data$results, 2)
# Count the number of exam results for each record
out <- my_data[!is.na(prev_exam), .(tally = .N), by = .(id, current_exam, prev_exam, prev_2_exam)]
out = na.omit(out)
My code produces the following results:
> out
id current_exam prev_exam prev_2_exam tally
1: 1 0 1 0 2
2: 1 1 0 1 1
3: 1 0 0 1 1
4: 2 1 0 0 1
5: 2 1 1 0 2
6: 2 1 1 1 1
7: 2 0 1 1 2
8: 2 1 0 1 2
9: 2 0 1 0 1
However, I do not think that my code is correct.
For example, with Student_ID = 2 :
My code says that "Current_Exam = 1, Prev_Exam = 1, Prev_2_Exam = 0" happens 1 time, but looking at the actual data - this does not happen at all
Can someone please show me what I am doing wrong and how I can correct this?
Note: I think that this should be the expected output:
> expected_output
id current_exam prev_exam prev_2_exam tally
1: 1 0 1 0 2
2: 1 1 0 1 1
3: 1 0 0 1 1
4: 2 1 0 0 1
5: 2 1 1 0 1
6: 2 1 1 1 1
7: 2 0 1 1 2
8: 2 1 0 1 2
9: 2 0 1 0 0
You did not consider that you can not shift the results over id without placing NA.
. <- my_data[order(my_data$id, my_data$result_id),] #sort if needed
.$p1 <- ave(.$results, .$id, FUN = \(x) c(NA, x[-length(x)]))
.$p2 <- ave(.$p1, .$id, FUN = \(x) c(NA, x[-length(x)]))
aggregate(list(tally=.$p1), .[c("id","results", "p1", "p2")], length)
# id results p1 p2 tally
#1 1 0 1 0 2
#2 2 0 1 0 1
#3 2 1 1 0 1
#4 1 0 0 1 1
#5 1 1 0 1 1
#6 2 1 0 1 2
#7 2 0 1 1 2
#8 2 1 1 1 1
.
# id results result_id p1 p2
#1 1 0 1 NA NA
#2 1 1 2 0 NA
#3 1 0 3 1 0
#4 1 1 4 0 1
#5 1 0 5 1 0
#6 1 0 6 0 1
#7 2 1 1 NA NA
#8 2 1 2 1 NA
#9 2 1 3 1 1
#10 2 0 4 1 1
#11 2 1 5 0 1
#12 2 1 6 1 0
#13 2 0 7 1 1
#14 2 1 8 0 1
#15 2 0 9 1 0
An option would be to use filter to indicate those which had passed 3 times in a row.
cbind(., n=ave(.$results, .$id, FUN = \(x) filter(x, c(1,1,1), sides=1)))
# id results result_id n
#1 1 0 1 NA
#2 1 1 2 NA
#3 1 0 3 1
#4 1 1 4 2
#5 1 0 5 1
#6 1 0 6 1
#7 2 1 1 NA
#8 2 1 2 NA
#9 2 1 3 3
#10 2 0 4 2
#11 2 1 5 2
#12 2 1 6 2
#13 2 0 7 2
#14 2 1 8 2
#15 2 0 9 1
If olny the number of times that a student passes an exam, given that the student passed the previous two exams:
sum(ave(.$results, .$id, FUN = \(x) filter(x, c(1,1,1))==3), na.rm=TRUE)
#[1] 1
sum(ave(.$results, .$id, FUN = \(x)
x==1 & c(x[-1], 0) == 1 & c(x[-1:-2], 0, 0) == 1))
#[1] 1
When trying to count events that happen in series, cumsum() comes in quite handy. As opposed to creating multiple lagged variables, this scales well to counts across a larger number of events:
library(tidyverse)
d <- my_data |>
group_by(id) |> # group to cumulate within student only
mutate(
csum = cumsum(results), # cumulative sum of results
i = csum - lag(csum, 3, 0) # substract the cumulative sum from 3 observation before. This gives the number of exams passed in the current and previous 2 observations.
)
# Ungroup to get global count
d |>
ungroup() |>
count(i == 3) # Count the number of cases where the number of exams passes within 3 observations equals 3
#> # A tibble: 2 × 2
#> `i == 3` n
#> <lgl> <int>
#> 1 FALSE 14
#> 2 TRUE 1
# Retaining the group gives counts by student
d |>
count(i == 3) # Count the number of cases where the number of exams passes within 3 observations equals 3
#> # A tibble: 3 × 3
#> # Groups: id [2]
#> id `i == 3` n
#> <dbl> <lgl> <int>
#> 1 1 FALSE 6
#> 2 2 FALSE 8
#> 3 2 TRUE 1
Since you provided the data as data.table, here is how to do the same in that ecosystem:
my_data[ , csum := cumsum(results), .(id)]
my_data[ , i := csum - lag(csum, 3, 0), .(id)]
my_data[ , .(n_cases = sum(i ==3)), id]
#> id n_cases
#> 1: 1 0
#> 2: 2 1
Here's an approach using dplyr. It uses the lag function to look back 1 and 2 results. If the sum together with the current result is 3, then the condition is met. In the example you provided, the condition is only met once
my_data %>%
group_by(id) %>%
mutate(threex = ifelse(results + lag(results,1) + lag(results, 2) == 3, 1, 0)) %>%
filter(!is.na(threex))
id results result_id threex
<dbl> <dbl> <dbl> <dbl>
1 1 0 3 0
2 1 1 4 0
3 1 0 5 0
4 1 0 6 0
5 2 1 3 1
6 2 0 4 0
7 2 1 5 0
8 2 1 6 0
9 2 0 7 0
10 2 1 8 0
11 2 0 9 0
If you then just want to capture the cases when the condition is met, add a filter.
my_data %>%
group_by(id) %>%
mutate(threex = ifelse(results + lag(results,1) + lag(results, 2) == 3, 1, 0)) %>%
filter(threex == 1)
id results result_id threex
<dbl> <dbl> <dbl> <dbl>
1 2 1 3 1
If you are looking to understand how many times the condition is met per id, you can do this.
my_data %>%
group_by(id) %>%
mutate(threex = ifelse(results + lag(results,1) + lag(results, 2) == 3, 1, 0)) %>%
filter(threex == 1) %>%
select(id) %>%
summarize(count = n())
id count
<dbl> <int>
1 2 1
I have a panel data with the following structure:
ID Month Action
1 1 0
1 2 0
1 3 1
1 4 1
2 1 0
2 2 1
2 3 0
2 4 1
3 1 0
3 2 0
3 3 0
4 1 0
4 2 1
4 3 1
4 4 0
where each ID has one row for each month, action indicates if this ID did this action in this month or not, 0 is no, 1 is yes.
I need to find the ID that has continuously had action=1 once they started the action (it does not matter in which month they started, but once started, in the following months the action should always be 1). I also wish to record all the rows that belong to these IDs in a new data frame.
How can I do this in R?
In my example, ID=1 consistently had action=1 since Month 3, so the final data frame I'm looking for should only have the rows belong to ID=1.
ID Month Action
1 1 0
1 2 0
1 3 1
1 4 1
You could do something like:
library(dplyr)
df %>%
group_by(ID) %>%
filter(all(diff(Action)>=0) & max(Action)>0) -> newDF
This newDF includes only the IDs where (a) the Action is never decreasing (i.e., no 1=>0) and (b) there is at least one Action==1).
ID Month Action
<int> <int> <int>
1 1 1 0
2 1 2 0
3 1 3 1
4 1 4 1
A base R approach using ave where we check if all the numbers after first occurrence of 1 are all 1. The addition of any condition is to remove enteries with all 0's.
df[with(df, as.logical(ave(Action, ID, FUN = function(x) {
inds = cumsum(x)
any(inds > 0) & all(x[inds > 0] == 1)
}))), ]
# ID Month Action
#1 1 1 0
#2 1 2 0
#3 1 3 1
#4 1 4 1
Or another option with same logic but in a little concise way would be
df[with(df, ave(Action == 1, ID, FUN = function(x)
all(x[which.max(x):length(x)] == 1)
)), ]
# ID Month Action
#1 1 1 0
#2 1 2 0
#3 1 3 1
#4 1 4 1
I have a data frame as below. The Status of each ID recorded in different time points. 0 means the person is alive and 1 means dead.
ID Status
1 0
1 0
1 1
2 0
2 0
2 0
3 0
3 0
3 0
3 1
I want to shuffle the column Status and each ID can have a status of 1, just one time. After that, I want to have NA for other rows. For instance, I want my data frame to look like below after shuffling:
ID Status
1 0
1 0
1 0
2 0
2 1
2 NA
3 0
3 1
3 NA
3 NA
From the data you posted and your example output, it looks like you want to randomly sample df$Status and then do the replacement. To get what you want in one step you could do:
set.seed(3)
df$Status <- ave(sample(df$Status), df$ID, FUN = function(x) replace(x, which(cumsum(x)>=1)[-1], NA))
df
# ID Status
#1 1 0
#2 1 0
#3 1 0
#4 2 1
#5 2 NA
#6 2 NA
#7 3 0
#8 3 0
#9 3 1
#10 3 NA
One option to use cumsum of cumsum to decide first 1 appearing for an ID.
Note that I have modified OP's sample dataframe to represent logic of reshuffling.
library(dplyr)
df %>% group_by(ID) %>%
mutate(Sum = cumsum(cumsum(Status))) %>%
mutate(Status = ifelse(Sum > 1, NA, Status)) %>%
select(-Sum)
# # A tibble: 10 x 2
# # Groups: ID [3]
# ID Status
# <int> <int>
# 1 1 0
# 2 1 0
# 3 1 1
# 4 2 0
# 5 2 1
# 6 2 NA
# 7 3 0
# 8 3 1
# 9 3 NA
# 10 3 NA
Data
df <- read.table(text =
"ID Status
1 0
1 0
1 1
2 0
2 1
2 0
3 0
3 1
3 0
3 0", header = TRUE)
I work in the healthcare industry and I'm using machine learning algorithms to develop a model to predict when patients will not show up for their appointments. I'm trying to create a new feature that will be the sum of each patient's most recent consecutive no-shows. I've looked around a lot on stackoverflow and other resources, but cannot find exactly what I'm looking for. As an example, if a patient has no-showed her past two most recent appointments, then every row of the new feature's column with her ID will be filled in with 2's. If she no-showed three times, but showed up for her most recent appointment, then the new column will be filled in with 0's.
I tried using plyr's ddply with cumsum, but it did not give me the results I'm looking for. I used:
ddply(a, .(ID), transform, ConsecutiveNoshows = cumsum(Noshow))
Here is an example data set ('1' signifies a no-show):
ID Noshow
1 1
1 1
1 0
1 0
1 1
2 0
2 1
2 1
3 1
3 0
3 1
3 1
3 1
This is my desired outcome:
ID Noshow ConsecutiveNoshows
1 1 2
1 1 2
1 0 2
1 0 2
1 1 2
2 0 0
2 1 0
2 1 0
3 1 1
3 0 1
3 1 1
3 1 1
3 1 1
I'll be very grateful for any help. Thank you.
The idea is to sum() for each ID the number of Noshow before a 0 appears.
library(dplyr)
df %>%
group_by(ID) %>%
mutate(ConsecutiveNoshows = sum(!cumsum(Noshow == 0) >= 1))
Which gives:
#Source: local data frame [13 x 3]
#Groups: ID [3]
#
# ID Noshow ConsecutiveNoshows
# <int> <int> <int>
#1 1 1 2
#2 1 1 2
#3 1 0 2
#4 1 0 2
#5 1 1 2
#6 2 0 0
#7 2 1 0
#8 2 1 0
#9 3 1 1
#10 3 0 1
#11 3 1 1
#12 3 1 1
#13 3 1 1