creating an 'ever event' variable from an 'incident event' variable - r

In R, in a repeated measures dataset, how can I create a variable that is the same for each measurement on an individual based upon an incident variable? For instance if I have:
id incident_MI
1 0
1 0
1 1
2 0
2 0
2 0
3 0
3 0
3 0
3 1
And I want to use the incident_MI to create an ever_MI variable like this:
id incident_MI Ever_MI
1 0 1
1 0 1
1 1 1
2 0 0
2 0 0
2 0 0
3 0 1
3 0 1
3 0 1
3 1 1
Any ideas on how I might code that in R?


We can check for any 1's in the 'incident_MI' after grouping by 'id' and convert it to 'numeric' with as.integer to create the 'Ever_MI'
library(dplyr)
df1 %>%
group_by(id) %>%
mutate(Ever_MI = as.integer(any(incident_MI==1)))
# A tibble: 10 x 3
# Groups: id [3]
# id incident_MI Ever_MI
# <int> <int> <int>
# 1 1 0 1
# 2 1 0 1
# 3 1 1 1
# 4 2 0 0
# 5 2 0 0
# 6 2 0 0
# 7 3 0 1
# 8 3 0 1
# 9 3 0 1
#10 3 1 1
Or as #lmo commented, the data.table option would be
library(data.table)
setDT(df1)[, Ever_MI := any(incident_MI), by=.(id)][]
Or using base R
df1$Ever_MI <- with(df1, ave(incident_MI, id, FUN = any))

Related

Is there a R function for preparing datasets for survival analysis like stset in Stata?

Datasets look like this
id start end failure x1
1 0 1 0 0
1 1 3 0 0
1 3 6 1 0
2 0 1 1 1
2 1 3 1 1
2 3 4 0 1
2 4 6 0 1
2 6 7 1 1
As you see, when id = 1, it's just the data input to coxph in survival package. However, when id = 2, at the beginning and end, failure occurs, but in the middle, failure disappears.
Is there a general function to extract data from id = 2 and get the result like id = 1?
I think when id = 2, the result should look like below.
id start end failure x1
1 0 1 0 0
1 1 3 0 0
1 3 6 1 0
2 3 4 0 1
2 4 6 0 1
2 6 7 1 1

A bit hacky, but should get the job done.
Data:
# Load data
library(tidyverse)
df <- read_table("
id start end failure x1
1 0 1 0 0
1 1 3 0 0
1 3 6 1 0
2 0 1 1 1
2 1 3 1 1
2 3 4 0 1
2 4 6 0 1
2 6 7 1 1
")
Data wrangling:
# Check for sub-groups within IDs and remove all but the last one
df <- df %>%
# Group by ID
group_by(
id
) %>%
mutate(
# Check if a new sub-group is starting (after a failure)
new_group = case_when(
# First row is always group 0
row_number() == 1 ~ 0,
# If previous row was a failure, then a new sub-group starts here
lag(failure) == 1 ~ 1,
# Otherwise not
TRUE ~ 0
),
# Assign sub-group number by calculating cumulative sums
group = cumsum(new_group)
) %>%
# Keep only last sub-group for each ID
filter(
group == max(group)
) %>%
ungroup() %>%
# Remove working columns
select(
-new_group, -group
)
Result:
> df
# A tibble: 6 × 5
id start end failure x1
<dbl> <dbl> <dbl> <dbl> <dbl>
1 1 0 1 0 0
2 1 1 3 0 0
3 1 3 6 1 0
4 2 3 4 0 1
5 2 4 6 0 1
6 2 6 7 1 1

How to count number of columns that have a value by a grouping variable in R?

I have data like this:
repetition Ob1 Ob2 Ob3 Ob4
1 0 0 0 1
1 0 0 3 0
1 1 3 3 0
1 2 3 3 0
2 4 0 2 2
2 4 0 3 0
2 0 0 0 0
3 0 0 0 0
3 4 0 4 0
3 0 0 0 0
I want to count the number of columns per repetition that have a certain value e.g. 1.
So in this case repetition 1 should return a 2 because Ob1 and Ob4 have a value of 1. Everything else gets a 0 because there are no other repetitions with a 1.

you can get count using dplyr package below code:
df$count <- rowSums(df[,2:5] == df$repetition)
df %>% select(repetition, count) %>% group_by(repetition) %>% summarise(count = sum(count))
# A tibble: 3 x 2
repetition count
<int> <dbl>
1 1 2
2 2 2
3 3 0

You can use by like:
by(x[-1]==1, x$repetition, function(y) sum(colSums(y) > 0))
#INDICES: 1
#[1] 2
#------------------------------------------------------------
#INDICES: 2
#[1] 0
#------------------------------------------------------------
#INDICES: 3
#[1] 0
or to return a named vector
c(by(x[-1]==1, x$repetition, function(y) sum(colSums(y) > 0)))
#1 2 3
#2 0 0

R: Long-data: how to remove all following obs within same ID once condition is met?

I have long data looking like this for example:
ID time condition
1 1 0
1 2 0
1 3 0
1 4 1
2 1 0
2 2 1
2 3 1
2 4 0
3 1 1
3 2 1
3 3 0
3 4 0
4 1 0
4 2 1
4 3 NA
4 4 NA
I want to only keep those rows before condition is met once so I want:
ID time condition
1 1 0
1 2 0
1 3 0
1 4 1
2 1 0
2 2 1
3 1 1
4 1 0
4 2 1
I tried to loop but a) it said looping is not good coding style in R and b) it won't work.
Sidenote: just if you are wondering, it does make sense that IDs have condition and then lose it again in my example, but I am only interested in when they first had it.
Thank you.

Here's an easy way with dplyr:
library(dplyr)
df %>% group_by(ID) %>%
filter(row_number() <= which.max(condition) | sum(condition) == 0)
# # A tibble: 7 x 3
# # Groups: ID [3]
# ID time condition
# <int> <int> <int>
# 1 1 1 0
# 2 1 2 0
# 3 1 3 0
# 4 1 4 1
# 5 2 1 0
# 6 2 2 1
# 7 3 1 1
It relies on which.max which returns the index of the first maximum value in vector. The | sum(condition) == 0 takes care to keep censored cases (where condition is always 0).
Using this data:
1 1 0
1 2 0
1 3 0
1 4 1
2 1 0
2 2 1
2 3 1
2 4 0
3 1 1
3 2 1
3 3 0
3 4 0')

Conditional running count (cumulative sum) with reset in R (dplyr)

I'm trying to calculate a running count (i.e., cumulative sum) that is conditional on other variables and that can reset for particular values on another variable. I'm working in R and would prefer a dplyr-based solution, if possible.
I'd like to create a variable for the running count, cumulative, based on the following algorithm:
Calculate the running count (cumulative) within combinations of id and age
Increment running count (cumulative) by 1 for every subsequent trial where accuracy = 0, block = 2, and condition = 1
Reset running count (cumulative) to 0 for each trial where accuracy = 1, block = 2, and condition = 1, and the next increment resumes at 1 (not the previous number)
For each trial where block != 2, or condition != 1, leave the running count (cumulative) as NA
Here's a minimal working example:
mydata <- data.frame(id = c(1,1,1,1,1,1,1,1,1,1,1),
age = c(1,1,1,1,1,1,1,1,1,1,2),
block = c(1,1,2,2,2,2,2,2,2,2,2),
trial = c(1,2,1,2,3,4,5,6,7,8,1),
condition = c(1,1,1,1,1,2,1,1,1,1,1),
accuracy = c(0,0,0,0,0,0,0,1,0,0,0)
)
id age block trial condition accuracy
1 1 1 1 1 0
1 1 1 2 1 0
1 1 2 1 1 0
1 1 2 2 1 0
1 1 2 3 1 0
1 1 2 4 2 0
1 1 2 5 1 0
1 1 2 6 1 1
1 1 2 7 1 0
1 1 2 8 1 0
1 2 2 1 1 0
The expected output is:
id age block trial condition accuracy cumulative
1 1 1 1 1 0 NA
1 1 1 2 1 0 NA
1 1 2 1 1 0 1
1 1 2 2 1 0 2
1 1 2 3 1 0 3
1 1 2 4 2 0 NA
1 1 2 5 1 0 4
1 1 2 6 1 1 0
1 1 2 7 1 0 1
1 1 2 8 1 0 2
1 2 2 1 1 0 1

Here is an option using data.table. Create a binary column based on matching the pasted values of 'accuracy', 'block', 'condition' with that of the custom values, grouped by run-length-id of the binary column ('ind'), 'id' and 'age', get the cumulative sum of 'ind' and assign (:=) it to a new column ('Cumulative')
library(data.table)
setDT(mydata)[, ind := match(do.call(paste0, .SD), c("121", "021")) - 1,
.SDcols = c("accuracy", "block", "condition")
][, Cumulative := cumsum(ind), .(rleid(ind), id, age)
][, ind := NULL][]
# id age block trial condition accuracy Cumulative
# 1: 1 1 1 1 1 0 NA
# 2: 1 1 1 2 1 0 NA
# 3: 1 1 2 1 1 0 1
# 4: 1 1 2 2 1 0 2
# 5: 1 1 2 3 1 0 3
# 6: 1 1 2 4 2 0 NA
# 7: 1 1 2 5 1 1 0
# 8: 1 1 2 6 1 0 1
# 9: 1 1 2 7 1 0 2
#10: 1 2 2 1 1 0 1

We can use case_when to assign the value which we need based on our conditions. We then add an additional group_by condition using cumsum to switch values when the temp column 0. In the final mutate step we temporarily replace NA values in temp to 0, then take cumsum over it and put back the NA values again to it's place to get the final output.
library(dplyr)
mydata %>%
group_by(id, age) %>%
mutate(temp = case_when(accuracy == 0 & block == 2 & condition == 1 ~ 1,
accuracy == 1 & block == 2 & condition == 1 ~ 0,
TRUE ~ NA_real_)) %>%
ungroup() %>%
group_by(id, age, group = cumsum(replace(temp == 0, is.na(temp), 0))) %>%
mutate(cumulative = replace(cumsum(replace(temp, is.na(temp), 0)),
is.na(temp), NA)) %>%
select(-temp, -group)
# group id age block trial condition accuracy cumulative
# <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
# 1 0 1 1 1 1 1 0 NA
# 2 0 1 1 1 2 1 0 NA
# 3 0 1 1 2 1 1 0 1
# 4 0 1 1 2 2 1 0 2
# 5 0 1 1 2 3 1 0 3
# 6 0 1 1 2 4 2 0 NA
# 7 0 1 1 2 5 1 0 4
# 8 1 1 1 2 6 1 1 0
# 9 1 1 1 2 7 1 0 1
#10 1 1 1 2 8 1 0 2
#11 1 1 2 2 1 1 0 1

Resetting TIME column when AMT > 0

I have a data frame that looks like this:
ID TIME AMT
1 0 50
1 1 0
1 2 0
1 3 0
1 4 0
1 4 50
1 5 0
1 7 0
1 9 0
1 10 0
1 10 50
The TIME column in the above data frame is continuous. I want to add another time column that resets time from zero when AMT>0. So, my output data frame should look like this:
ID TIME AMT TIME2
1 0 50 0
1 1 0 1
1 2 0 2
1 3 0 3
1 4 0 4
1 4 50 0
1 5 0 1
1 7 0 3
1 9 0 5
1 10 0 6
1 10 50 0
This is basically achieved by subtracting the TIME from a "fixed" reference TIME when AMT>0 (For example; the reference time for the second AMT>0 is 4. So, the TIME2 is calculated by subtracting 5-4=1 ;7-4=3; 9-4=5 etc. How can I do this automatically in R.

A data.table solution :
library(data.table)
setDT(DT)[,TIME2 := TIME-TIME[1],cumsum(AMT>0)]
# ID TIME AMT TIME2
# 1: 1 0 50 0
# 2: 1 1 0 1
# 3: 1 2 0 2
# 4: 1 3 0 3
# 5: 1 4 0 4
# 6: 1 4 50 0
# 7: 1 5 0 1
# 8: 1 7 0 3
# 9: 1 9 0 5
# 10: 1 10 0 6
# 11: 1 10 50 0

Was originally posting the same answer as #agstudy, so here's alternatively a possible base R solution
with(df, ave(TIME, cumsum(AMT > 0L), ID, FUN = function(x) x - x[1L]))
## [1] 0 1 2 3 4 0 1 3 5 6 0
Or
library(dplyr)
df %>%
group_by(cumsum(AMT > 0), ID) %>%
mutate(TIME2 = TIME - first(TIME))

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