I have data which is grouped by 'student_id':
my_data = data.frame(student_id = c(1,1,1,1,1,2,2,2,2,2,3,3,3,3,3),
exam_no = c(1,2,3,4,5,1,2,3,4,5,1,2,3,4,5),
result = rnorm(15,60,10))
my_data
student_id exam_no result
1 1 1 56.60374
2 1 2 55.76655
3 1 3 53.81728
4 1 4 74.82202
5 1 5 34.91834
6 2 1 58.32422
7 2 2 60.38213
8 2 3 49.40390
9 2 4 63.85426
10 2 5 40.32912
11 3 1 69.54969
12 3 2 43.36639
13 3 3 37.97265
14 3 4 52.36436
15 3 5 61.62080
My Question:
For each student, I want to select a set of consecutive rows, with random start and end rows.
For example, keep exams 2-4 for student 1, keep exams 2-5 for student 2, etc.
I thought of the following way to do this:
Create a data frame that contains the max number of exams each student takes (in my problem, each student takes the same number of exams, but in the future this could be different)
library(dplyr)
counts = my_data %>% group_by(student_id) %>% summarise(counts = n())
# create variables that indicate where to start ("min") and where to end ("max") for each student
counts$min = sample(1:counts$counts, 1)
counts$max = sample(counts$min:counts$counts,1)
From here, I was then going to write a loop that would select rows between "min" and "max" index for each student (e.g. my_data[min:max]), but the results from the previous code are giving me warnings and illogical results:
Warning message:
In 1:counts$counts :
numerical expression has 3 elements: only the first used
Warning messages:
1: In counts$min:counts$counts :
numerical expression has 3 elements: only the first used
2: In counts$min:counts$counts :
numerical expression has 3 elements: only the first used
# A tibble: 3 x 4
student_id counts min max
<dbl> <int> <int> <int>
1 1 5 4 5
2 2 5 4 5
3 3 5 4 5
I am not sure how to continue this - can someone please show me how to continue?
Thanks!
A base R option using cumsum to label the in-between consecutive rows
subset(
my_data,
ave(
exam_no,
student_id,
FUN = function(x) cumsum(seq_along(x) %in% sample.int(length(x), 2))
) == 1
)
which gives, for example
student_id exam_no result
2 1 2 61.83643
3 1 3 51.64371
4 1 4 75.95281
6 2 1 51.79532
7 2 2 64.87429
8 2 3 67.38325
11 3 1 75.11781
12 3 2 63.89843
13 3 3 53.78759
A more compact version by data.table with a similar idea as above is
library(data.table)
setDT(my_data)[, .SD[cumsum((1:.N) %in% sample.int(.N, 2)) == 1], student_id]
Using data.table, within each group, sample two values from .I (without replacement), and create a sequence of indices.
library(data.table)
setDT(my_data)
set.seed(3)
my_data[my_data[ , {ix = sample(.I, 2); ix[1]:ix[2]}, by = student_id]$V1]
# student_id exam_no result
# <num> <num> <num>
# 1: 1 5 74.05672
# 2: 1 4 49.37525
# 3: 1 3 67.41662
# 4: 1 2 67.64935
# 5: 2 4 55.15337
# 6: 2 3 58.95694
# 7: 3 4 50.79859
# 8: 3 3 53.66886
# 9: 3 2 47.01089
Related
I have the following data frame, that shows which cases are interconnected:
DebtorId DupDebtorId
1: 1 2
2: 1 3
3: 1 4
4: 5 1
5: 5 2
6: 5 3
7: 6 7
8: 7 6
My goal is to assign a unique group ID to each group of cases. The desired output is:
DebtorId group
1: 1 1
2: 2 1
3: 3 1
4: 4 1
5: 5 1
6: 6 2
7: 7 2
My train of thought:
library(data.table)
example <- data.table(
DebtorId = c(1,1,1,5,5,5,6,7),
DupDebtorId = c(2,3,4,1,2,3,7,6)
)
unique_pairs <- example[!duplicated(t(apply(example, 1, sort))),] #get unique pairs of DebtorID and DupDebtorID
unique_pairs[, group := .GRP, by=.(DebtorId)] #assign a group ID for each DebtorId
unique_pairs[, num := rowid(group)]
groups <- dcast(unique_pairs, group + DebtorId ~ num, value.var = 'DupDebtorId') #format data to wide for each group ID
#create new data table with unique cases to assign group ID
newdt <- data.table(DebtorId = sort(unique(c(example$DebtorId, example$DupDebtorId))), group = NA)
newdt$group <- as.numeric(newdt$group)
#loop through the mapped groups, selecting the first instance of group ID for the case
for (i in 1:nrow(newdt)) {
a <- newdt[i]$DebtorId
b <- min(which(groups[,-1] == a, arr.ind=TRUE)[,1])
newdt[i]$group <- b
}
Output:
DebtorId group
1: 1 1
2: 2 1
3: 3 1
4: 4 1
5: 5 2
6: 6 3
7: 7 3
There are 2 problems in my approach:
From the output, you can see that it fails to recognize that case 5
belongs to group 1;
The final loop is agonizingly slow, which would
render it useless for my use case of 1M rows in my original data, and going the traditional := way does not work with which()
I'm not sure whether my approach could be optimized, or there is a better way of doing this altogether.
This functionality already exists in igraph, so if you don't need to do it yourself, we can build a graph from your data frame and then extract cluster membership. stack() is just an easy way to convert a named vector to data frame.
library(igraph)
g <- graph.data.frame(df)
df_membership <- clusters(g)$membership
stack(df_membership)
#> values ind
#> 1 1 1
#> 2 1 5
#> 3 2 6
#> 4 2 7
#> 5 1 2
#> 6 1 3
#> 7 1 4
Above, values corresponds to group and ind to DebtorId.
Consider the following dt:
dt <- data.table(a=c(1,1,2,3),b=c(4,5,6,4))
That looks like that:
> dt
a b
1: 1 4
2: 1 5
3: 2 6
4: 3 4
I'm here aggregating each column by it's unique values and then counting how many uniquye values each column has:
> dt[,lapply(.SD,function(agg) dt[,.N,by=agg])]
a.agg a.N b.agg b.N
1: 1 2 4 2
2: 2 1 5 1
3: 3 1 6 1
So 1 appears twice in dt and thus a.N is 2, the same logic goes on for the other values.
But the problem is if this transformations of the original datatable have different dimensions at the end, things will get recycled.
For example this dt:
dt <- data.table(a=c(1,1,2,3,7),b=c(4,5,6,4,4))
> dt[,lapply(.SD,function(agg) dt[,.N,by=agg])]
a.agg a.N b.agg b.N
1: 1 2 4 3
2: 2 1 5 1
3: 3 1 6 1
4: 7 1 4 3
Warning message:
In as.data.table.list(jval, .named = NULL) :
Item 2 has 3 rows but longest item has 4; recycled with remainder.
That is no longer the right answer because b.N should have now only 3 rows and things(vector) got recycled.
This is why I would like to transform the expression dt[,lapply(.SD,function(agg) dt[,.N,by=agg])] in a list with different dimensions, with the name of items in the list being the name of the columns in the new transformed dt.
A sketch of what I mean is:
newlist
$a.agg
1 2 3 7
$a.N
2 1 1 1
$b.agg
4 5 6 4
$b.N
3 1 1
Or even better solution would be to get a datatable with a track of the columns on another column:
dt_final
agg N column
1 2 a
2 1 a
3 1 a
7 1 a
4 3 b
5 1 b
6 1 b
Get the data in long format and then aggregate by group.
library(data.table)
dt_long <- melt(dt, measure.vars = c('a', 'b'))
dt_long[, .N, .(variable, value)]
# variable value N
#1: a 1 2
#2: a 2 1
#3: a 3 1
#4: a 7 1
#5: b 4 3
#6: b 5 1
#7: b 6 1
In tidyverse -
library(dplyr)
library(tidyr)
dt %>%
pivot_longer(cols = everything()) %>%
count(name, value)
I have the following df
df<-data.frame(value = c(1,1,1,2,1,1,2,2,1,2),
group = c(5,5,5,6,7,7,8,8,9,10),
no_rows = c(3,3,3,1,2,2,2,2,1,1))
where identical consecutive values form a group, i.e., values in rows 1:3 fall under group 5. Column "no_rows" tells us how many rows/entries each group has, i.e., group 5 has 3 rows/entries.
I am trying to substitute all values, where no_rows < 2, with the value from a previous group. I expect my end df to look like this:
df_end<-data.frame(value = c(1,1,1,1,1,1,2,2,2,2),
group = c(5,5,5,6,7,7,8,8,9,10),
no_rows = c(3,3,3,1,2,2,2,2,1,1))
I came up with this combination of if...else in a for loop, which gives me the desired output, however it is very slow and I am looking for a way to optimise it.
for (i in 2:length(df$group)){
if (df$no_rows[i] < 2){
df$value[i] <- df$value[i-1]
}
}
I have also tried with dplyr::mutate and lag() but it does not give me the desired output (it only removes the first value per group instead of taking the value of a previous group).
df<-df%>%
group_by(group) %>%
mutate(value = ifelse(no_rows < 2, lag(value), value))
I looked for a solution now for a few days but I could not find anything that fit my problem completly. Any ideas?
a data.table approach...
first, get the values of groups with length >=2, then fill in missing values (NA) by last-observation-carried-forward.
library(data.table)
# make it a data.table
setDT(df, key = "group")
# get values for groups of no_rows >= 2
df[no_rows >= 2, new_value := value][]
# value group no_rows new_value
# 1: 1 5 3 1
# 2: 1 5 3 1
# 3: 1 5 3 1
# 4: 2 6 1 NA
# 5: 1 7 2 1
# 6: 1 7 2 1
# 7: 2 8 2 2
# 8: 2 8 2 2
# 9: 1 9 1 NA
#10: 2 10 1 NA
# fill down missing values in new_value
setnafill(df, "locf", cols = c("new_value"))
# value group no_rows new_value
# 1: 1 5 3 1
# 2: 1 5 3 1
# 3: 1 5 3 1
# 4: 2 6 1 1
# 5: 1 7 2 1
# 6: 1 7 2 1
# 7: 2 8 2 2
# 8: 2 8 2 2
# 9: 1 9 1 2
#10: 2 10 1 2
i want to subset a data frame and take all observations for each id until the first observation that didn't meet my condition. Something like this:
goodDaysAfterTreatMent <- subset(Patientdays, treatmentDate < date & goodThings > badThings)
Except that this returns all observations that meet the condition. I want something that stops with the first observation that didn't meet the condition, moves on to the next id, and returns all observations for this id that meets the condition, and so on.
the only way i can see is to use a lot of loops but loops and that's usually not a god thing.
Hope you guys have an idea
Assume that your condition is to return rows where v < 5 :
# example dataset
df = data.frame(id = c(1,1,1,1,2,2,2,2,3,3,3),
v = c(2,4,3,5,4,5,6,7,5,4,1))
df
# id v
# 1 1 2
# 2 1 4
# 3 1 3
# 4 1 5
# 5 2 4
# 6 2 5
# 7 2 6
# 8 2 7
# 9 3 5
# 10 3 4
# 11 3 1
library(tidyverse)
df %>%
group_by(id) %>% # for each id
mutate(flag = cumsum(ifelse(v < 5, 1, NA))) %>% # check if v < 5 and fill with NA all rows when condition is FALSE and after that
filter(!is.na(flag)) %>% # keep only rows with no NA flags
ungroup() %>% # forget the grouping
select(-flag) # remove flag column
# # A tibble: 4 x 2
# id v
# <dbl> <dbl>
# 1 1 2
# 2 1 4
# 3 1 3
# 4 2 4
Easy way:
Find First FALSE by (min(which(condition == F)):
Patientdays<-cbind.data.frame(treatmentDate=c(1:5,4,6:10),date=c(2:5,3,6:10,10),goodThings=c(1:11),badThings=c(0:10))
attach(Patientdays)# Just due to ease of use (optional)
condition<-treatmentDate < date & goodThings > badThings
Patientdays[1:(min(which(condition == F))-1),]
Edit: Adding result.
treatmentDate date goodThings badThings
1 1 2 1 0
2 2 3 2 1
3 3 4 3 2
4 4 5 4 3
I have a list of events and sequences. I would like to print the sequences in a separate table if event = x is included somewhere in the sequence. See table below:
Event Sequence
1 a 1
2 a 1
3 x 1
4 a 2
5 a 2
6 a 3
7 a 3
8 x 3
9 a 4
10 a 4
In this case I would like a new table that includes only the sequences where Event=x was included:
Event Sequence
1 a 1
2 a 1
3 x 1
4 a 3
5 a 3
6 x 3
Base R solution:
d[d$Sequence %in% d$Sequence[d$Event == "x"], ]
Event Sequence
1: a 1
2: a 1
3: x 1
4: a 3
5: a 3
6: x 3
data.table solution:
library(data.table)
setDT(d)[Sequence %in% Sequence[Event == "x"]]
As you can see syntax/logic is quite similar between these two solutions:
Find event's that are equal to x
Extract their Sequence
Subset table according to specified Sequence
We can use dplyr to group the data and filter the sequence with any "x" in it.
library(dplyr)
df2 <- df %>%
group_by(Sequence) %>%
filter(any(Event %in% "x")) %>%
ungroup()
df2
# A tibble: 6 x 2
Event Sequence
<chr> <int>
1 a 1
2 a 1
3 x 1
4 a 3
5 a 3
6 x 3
DATA
df <- read.table(text = " Event Sequence
1 a 1
2 a 1
3 x 1
4 a 2
5 a 2
6 a 3
7 a 3
8 x 3
9 a 4
10 a 4",
header = TRUE, stringsAsFactors = FALSE)