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Numbering rows within groups in a data frame
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Closed 1 year ago.
I am trying to create an additional variable (new variable-> flag) that will number the repetition of observation in my variable starting from 0.
dataset <- data.frame(id = c(1,1,1,2,2,4,6,6,6,7,7,7,7,8))
intended results will look like:
id flag
1 0
1 1
1 2
2 0
2 1
4 0
6 0
6 1
6 2
7 0
7 1
7 2
7 3
8 0
Thank You!
You may try
dataset$flag <- unlist(sapply(rle(dataset$id)$length, function(x) seq(1,x)-1))
id flag
1 1 0
2 1 1
3 1 2
4 2 0
5 2 1
6 4 0
7 6 0
8 6 1
9 6 2
10 7 0
11 7 1
12 7 2
13 7 3
14 8 0
data.table:
library(data.table)
setDT(dataset)[, flag := rowid(id) - 1]
dataset
id flag
1: 1 0
2: 1 1
3: 1 2
4: 2 0
5: 2 1
6: 4 0
7: 6 0
8: 6 1
9: 6 2
10: 7 0
11: 7 1
12: 7 2
13: 7 3
14: 8 0
Base R:
dataset$flag = sequence(rle(dataset$id)$lengths) - 1
dataset
id flag
1 1 0
2 1 1
3 1 2
4 2 0
5 2 1
6 4 0
7 6 0
8 6 1
9 6 2
10 7 0
11 7 1
12 7 2
13 7 3
14 8 0
Another base option:
transform(dataset,
flag = Reduce(function(x, y) y * x + y, duplicated(id), accumulate = TRUE))
id flag
1 1 0
2 1 1
3 1 2
4 2 0
5 2 1
6 4 0
7 6 0
8 6 1
9 6 2
10 7 0
11 7 1
12 7 2
13 7 3
14 8 0
dplyr -
library(dplyr)
dataset %>% group_by(id) %>% mutate(flag = row_number() - 1)
# id flag
# <dbl> <dbl>
# 1 1 0
# 2 1 1
# 3 1 2
# 4 2 0
# 5 2 1
# 6 4 0
# 7 6 0
# 8 6 1
# 9 6 2
#10 7 0
#11 7 1
#12 7 2
#13 7 3
#14 8 0
Base R with similar logic
transform(dataset, flag = ave(id, id, FUN = seq_along) - 1)
another way to reach what you expect but writing a little more
x <- dataset %>%
group_by(id) %>%
summarise(nreg=n())
df <- data.frame()
for(i in 1:nrow(x)){
flag <- data.frame(id = rep( x$id[i], x$nreg[i] ),
flag = seq(0, x$nreg [i] -1 )
)
df <- rbind(df, flag)
}
Related
I create a random dataset via:
#create dataset
first_column <- c(1:10) #random column
second_column <- c(1:10) #random column
third_column <- c(1:10) #random column
group <- c(1,1,1,2,2,1,2,1,1,1) #column used for selection
#merge columns
df <- data.frame(first_column, second_column, third_column, group)
#examine
print(df)
which outputs:
first_column second_column third_column group
1 1 1 1 1
2 2 2 2 1
3 3 3 3 1
4 4 4 4 2
5 5 5 5 2
6 6 6 6 1
7 7 7 7 2
8 8 8 8 1
9 9 9 9 1
10 10 10 10 1
I would like to set the values of columns first_column, second_column, and third_column to 0 if the value of group is equal to 2 (while retaining the values if the value of group equals 1), which should result in:
first_column second_column third_column group
1 1 1 1 1
2 2 2 2 1
3 3 3 3 1
4 0 0 0 2
5 0 0 0 2
6 6 6 6 1
7 0 0 0 2
8 8 8 8 1
9 9 9 9 1
10 10 10 10 1
What is the most convenient way to reach this?
We could create the logical index with group as i and assign the columns 1 to 3 as 0
df[df$group == 2, 1:3] <- 0
-output
> df
first_column second_column third_column group
1 1 1 1 1
2 2 2 2 1
3 3 3 3 1
4 0 0 0 2
5 0 0 0 2
6 6 6 6 1
7 0 0 0 2
8 8 8 8 1
9 9 9 9 1
10 10 10 10 1
I have the following data set:
time <- c(0,1,2,3,4,5,0,1,2,3,4,5,0,1,2,3,4,5)
value <- c(10,8,6,5,3,2,12,10,6,5,4,2,20,15,16,9,2,2)
group <- c(1,1,1,1,1,1,2,2,2,2,2,2,3,3,3,3,3,3)
data <- data.frame(time, value, group)
I want to create a new column called data$diff that is equal to data$value minus the value of data$value when data$time == 0 within each group.
I am beginning with the following code
for(i in 1:nrow(data)){
for(n in 1:max(data$group)){
if(data$group[i] == n) {
data$diff[i] <- ???????
}
}
}
But cannot figure out what to put in place of the question marks. The desired output would be this table: https://i.stack.imgur.com/1bAKj.png
Any thoughts are appreciated.
Since in your example data$time == 0 is always the first element of the group, you can use this data.table approach.
library(data.table)
setDT(data)
data[, diff := value[1] - value, by = group]
In case that data$time == 0 is not the first element in each group you can use this:
data[, diff := value[time==0] - value, by = group]
Output:
> data
time value group diff
1: 0 10 1 0
2: 1 8 1 2
3: 2 6 1 4
4: 3 5 1 5
5: 4 3 1 7
6: 5 2 1 8
7: 0 12 2 0
8: 1 10 2 2
9: 2 6 2 6
10: 3 5 2 7
11: 4 4 2 8
12: 5 2 2 10
13: 0 20 3 0
14: 1 15 3 5
15: 2 16 3 4
16: 3 9 3 11
17: 4 2 3 18
18: 5 2 3 18
Here is a base R approach.
within(data, diff <- ave(
seq_along(value), group,
FUN = \(i) value[i][time[i] == 0] - value[i]
))
Output
time value group diff
1 0 10 1 0
2 1 8 1 2
3 2 6 1 4
4 3 5 1 5
5 4 3 1 7
6 5 2 1 8
7 0 12 2 0
8 1 10 2 2
9 2 6 2 6
10 3 5 2 7
11 4 4 2 8
12 5 2 2 10
13 0 20 3 0
14 1 15 3 5
15 2 16 3 4
16 3 9 3 11
17 4 2 3 18
18 5 2 3 18
Here is a short way to do it with dplyr.
library(dplyr)
data %>%
group_by(group) %>%
mutate(diff = value[which(time == 0)] - value)
Which gives
# Groups: group [3]
time value group diff
<dbl> <dbl> <dbl> <dbl>
1 0 10 1 0
2 1 8 1 2
3 2 6 1 4
4 3 5 1 5
5 4 3 1 7
6 5 2 1 8
7 0 12 2 0
8 1 10 2 2
9 2 6 2 6
10 3 5 2 7
11 4 4 2 8
12 5 2 2 10
13 0 20 3 0
14 1 15 3 5
15 2 16 3 4
16 3 9 3 11
17 4 2 3 18
18 5 2 3 18
library(dplyr)
vals2use <- data %>%
group_by(group) %>%
filter(time==0) %>%
select(c(2,3)) %>%
rename(value4diff=value)
dataNew <- merge(data, vals2use, all=T)
dataNew$diff <- dataNew$value4diff-dataNew$value
dataNew <- dataNew[,c(1,2,3,5)]
dataNew
group time value diff
1 1 0 10 0
2 1 1 8 2
3 1 2 6 4
4 1 3 5 5
5 1 4 3 7
6 1 5 2 8
7 2 0 12 0
8 2 1 10 2
9 2 2 6 6
10 2 3 5 7
11 2 4 4 8
12 2 5 2 10
13 3 0 20 0
14 3 1 15 5
15 3 2 16 4
16 3 3 9 11
17 3 4 2 18
18 3 5 2 18
data=data.frame("Student"=c(1,1,1,2,2,2,3,3,3,4,4,4,5,5,5),
"Grade"=c(5,6,7,3,4,5,4,5,6,8,9,10,2,3,4),
"Pass"=c(NA,0,1,0,1,1,0,1,0,0,NA,NA,0,0,0),
"NEWPass"=c(0,0,1,0,1,1,0,1,1,0,0,0,0,0,0),
"GradeNEWPass"=c(7,7,7,4,4,4,5,5,5,10,10,10,4,4,4),
"GradeBeforeNEWPass"=c(6,6,6,3,3,3,4,4,4,10,10,10,4,4,4))
I have a data.frame called data. It has column names Student, Grade and Pass. I wish to do this:
NEWPass: Take Pass and for every Student fill in NA values with the previous value. If the first value is 'NA' than put a zero. Then this should be a running maximum.
GradeNEWPass: Take the lowest value of Grade that a Student got a one in NEWPass. If a Student did not get a one in NEWPass, this equals to the maximum grade.
GradeBeforeNEWPass: Take the value of Grade BEFORE a Student got a one in NEWPass. If a Student did not get a one in NEWPass, this equals to the maximum grade.
__
Attempts:
setDT(data)[, NEWPassTry := cummax(Pass), by = Student]
data$GradeNEWPass = data$NEWPassTry * data$Grade
data[, GradeNEWPass := min(GradeNEWPass), by = Student]
Not pretty, admittedly, but your logic includes words like "if any ... for a student", so it's a group-wise conditional, not a row-wise conditional.
library(magrittr) # just for %>% for breakout, not required
mydata %>%
.[, NEWPass2 :=
cummax(fifelse(seq_len(.N) == 1 & is.na(Pass), 0,
zoo::na.locf(Pass, na.rm = FALSE))), by = .(Student) ] %>%
.[, GradeNEWPass2 :=
if (any(NEWPass2 > 0)) min(Grade[ NEWPass2 > 0 ]) else max(Grade),
by = .(Student) ] %>%
.[, GradeBeforeNEWPass2 :=
if (NEWPass2[1] == 0 && any(NEWPass2 > 0)) Grade[ which(NEWPass2 > 0)[1] - 1 ] else max(Grade),
by = .(Student) ]
# Student Grade Pass NEWPass GradeNEWPass GradeBeforeNEWPass NEWPass2 GradeNEWPass2 GradeBeforeNEWPass2
# 1: 1 5 NA 0 7 6 0 7 6
# 2: 1 6 0 0 7 6 0 7 6
# 3: 1 7 1 1 7 6 1 7 6
# 4: 2 3 0 0 4 3 0 4 3
# 5: 2 4 1 1 4 3 1 4 3
# 6: 2 5 1 1 4 3 1 4 3
# 7: 3 4 0 0 5 4 0 5 4
# 8: 3 5 1 1 5 4 1 5 4
# 9: 3 6 0 1 5 4 1 5 4
# 10: 4 8 0 0 10 10 0 10 10
# 11: 4 9 NA 0 10 10 0 10 10
# 12: 4 10 NA 0 10 10 0 10 10
# 13: 5 2 0 0 4 4 0 4 4
# 14: 5 3 0 0 4 4 0 4 4
# 15: 5 4 0 0 4 4 0 4 4
I'm using magrittr::%>% solely to break it out into stages of computation, it is not required.
We can use data.table methods. Grouped by 'Student', create an index ('i1') where the 'Pass' is 1 and not an NA, then get the first position of 1 with which and head ('i2'), while calculating the max of 'Grade' ('mx'), then create the three columns based on the indexes ('v1' - get the cumulative maximum of the binary, 'v2' - if there are any 1s, then subset the 'Grade' with the index 'i2' or else return 'mx', similarly 'v3'- the index is subtracted 1 to get the 'Grade' value
library(data.table)
setDT(data)[, c('NEWPass1', 'GradeNEWPass1', 'GradeBeforeNEWPass1') :={
i1 <- Pass == 1 & !is.na(Pass)
i2 <- head(which(i1), 1)
mx <- max(Grade, na.rm = TRUE)
v1 <- cummax(+(i1))
v2 <- if(any(i1)) Grade[i2] else mx
v3 <- if(any(i1)) Grade[max(1, i2-1)] else mx
.(v1, v2, v3)}, Student]
data
# Student Grade Pass NEWPass GradeNEWPass GradeBeforeNEWPass NEWPass1 GradeNEWPass1 GradeBeforeNEWPass1
# 1: 1 5 NA 0 7 6 0 7 6
# 2: 1 6 0 0 7 6 0 7 6
# 3: 1 7 1 1 7 6 1 7 6
# 4: 2 3 0 0 4 3 0 4 3
# 5: 2 4 1 1 4 3 1 4 3
# 6: 2 5 1 1 4 3 1 4 3
# 7: 3 4 0 0 5 4 0 5 4
# 8: 3 5 1 1 5 4 1 5 4
# 9: 3 6 0 1 5 4 1 5 4
#10: 4 8 0 0 10 10 0 10 10
#11: 4 9 NA 0 10 10 0 10 10
#12: 4 10 NA 0 10 10 0 10 10
#13: 5 2 0 0 4 4 0 4 4
#14: 5 3 0 0 4 4 0 4 4
#15: 5 4 0 0 4 4 0 4 4
I'd like to count the rows in the column input if the values are smaller than the current row (Please see the results wanted below). The issue to me is that the condition is based on current row value, so it is very different from general case where the condition is a fixed number.
data <- data.frame(input = c(1,1,1,1,2,2,3,5,5,5,5,6))
input
1 1
2 1
3 1
4 1
5 2
6 2
7 3
8 5
9 5
10 5
11 5
12 6
The results I expect to get are like this. For example, for observations 5 and 6 (with value 2), there are 4 observations with value 1 less than their value 2. Hence count is given value 4.
input count
1 1 0
2 1 0
3 1 0
4 1 0
5 2 4
6 2 4
7 3 6
8 5 7
9 5 7
10 5 7
11 5 7
12 6 11
Edit: as I am dealing with grouped data with dplyr, the ultimate results I wish to get is like below, that is, I am wishing the conditions could be dynamic within each group.
data <- data.frame(id = c(1,1,2,2,2,3,3,4,4,4,4,4),
input = c(1,1,1,1,2,2,3,5,5,5,5,6),
count=c(0,0,0,0,2,0,1,0,0,0,0,4))
id input count
1 1 1 0
2 1 1 0
3 2 1 0
4 2 1 0
5 2 2 2
6 3 2 0
7 3 3 1
8 4 5 0
9 4 5 0
10 4 5 0
11 4 5 0
12 4 6 4
Here is an option with tidyverse
library(tidyverse)
data %>%
mutate(count = map_int(input, ~ sum(.x > input)))
# input count
#1 1 0
#2 1 0
#3 1 0
#4 1 0
#5 2 4
#6 2 4
#7 3 6
#8 5 7
#9 5 7
#10 5 7
#11 5 7
#12 6 11
Update
With the updated data, add the group by 'id' in the above code
data %>%
group_by(id) %>%
mutate(count1 = map_int(input, ~ sum(.x > input)))
# A tibble: 12 x 4
# Groups: id [4]
# id input count count1
# <dbl> <dbl> <dbl> <int>
# 1 1 1 0 0
# 2 1 1 0 0
# 3 2 1 0 0
# 4 2 1 0 0
# 5 2 2 2 2
# 6 3 2 0 0
# 7 3 3 1 1
# 8 4 5 0 0
# 9 4 5 0 0
#10 4 5 0 0
#11 4 5 0 0
#12 4 6 4 4
In base R, we can use sapply and for each input count how many values are greater than itself.
data$count <- sapply(data$input, function(x) sum(x > data$input))
data
# input count
#1 1 0
#2 1 0
#3 1 0
#4 1 0
#5 2 4
#6 2 4
#7 3 6
#8 5 7
#9 5 7
#10 5 7
#11 5 7
#12 6 11
With dplyr one way would be using rowwise function and following the same logic.
library(dplyr)
data %>%
rowwise() %>%
mutate(count = sum(input > data$input))
1. outer and rowSums
data$count <- with(data, rowSums(outer(input, input, `>`)))
2. table and cumsum
tt <- cumsum(table(data$input))
v <- setNames(c(0, head(tt, -1)), c(head(names(tt), -1), tail(names(tt), 1)))
data$count <- v[match(data$input, names(v))]
3. data.table non-equi join
Perhaps more efficient with a non-equi join in data.table. Count number of rows (.N) for each match (by = .EACHI).
library(data.table)
setDT(data)
data[data, on = .(input < input), .N, by = .EACHI]
If your data is grouped by 'id', as in your update, join on that variable as well:
data[data, on = .(id, input < input), .N, by = .EACHI]
# id input N
# 1: 1 1 0
# 2: 1 1 0
# 3: 2 1 0
# 4: 2 1 0
# 5: 2 2 2
# 6: 3 2 0
# 7: 3 3 1
# 8: 4 5 0
# 9: 4 5 0
# 10: 4 5 0
# 11: 4 5 0
# 12: 4 6 4
I'm just starting to learn R and I'm already facing the first bigger problem.
Let's take the following panel dataset as an example:
N=5
T=3
time<-rep(1:T, times=N)
id<- rep(1:N,each=T)
dummy<- c(0,0,1,1,0,0,0,1,0,0,0,1,0,1,0)
df<-as.data.frame(cbind(id, time,dummy))
id time dummy
1 1 1 0
2 1 2 0
3 1 3 1
4 2 1 1
5 2 2 0
6 2 3 0
7 3 1 0
8 3 2 1
9 3 3 0
10 4 1 0
11 4 2 0
12 4 3 1
13 5 1 0
14 5 2 1
15 5 3 0
I now want the dummy variable for all rows of a cross section to take the value 1 after the 1 for this cross section appears for the first time. So, what I want is:
id time dummy
1 1 1 0
2 1 2 0
3 1 3 1
4 2 1 1
5 2 2 1
6 2 3 1
7 3 1 0
8 3 2 1
9 3 3 1
10 4 1 0
11 4 2 0
12 4 3 1
13 5 1 0
14 5 2 1
15 5 3 1
So I guess I need something like:
df_new<-df %>%
group_by(id) %>%
???
I already tried to set all zeros to NA and use the na.locf function, but it didn't really work.
Anybody got an idea?
Thanks!
Use cummax
df %>%
group_by(id) %>%
mutate(dummy = cummax(dummy))
# A tibble: 15 x 3
# Groups: id [5]
# id time dummy
# <dbl> <dbl> <dbl>
# 1 1 1 0
# 2 1 2 0
# 3 1 3 1
# 4 2 1 1
# 5 2 2 1
# 6 2 3 1
# 7 3 1 0
# 8 3 2 1
# 9 3 3 1
#10 4 1 0
#11 4 2 0
#12 4 3 1
#13 5 1 0
#14 5 2 1
#15 5 3 1
Without additional packages you could do
transform(df, dummy = ave(dummy, id, FUN = cummax))