Counting the number of observations by groups with conditions in R - r

Question

I would like to count the number of observations within each group using conditions in R.
For example, I would like to count how many observations for ID "A" in every 10 days.
ID (A,A,A,A,A,A,A,A)
Day (7,14,17,25,35,37,42,57)
X (9,20,14,24,23,30,20,40)
Output Image
(In the first 10 days, we have one observation for ID "A". Days:7
In the next 10 days, we have two observations for ID "A". Days:14,17)
ID (A,A,A,A,A,A,A,A)
Day_10 (1,2,3,4,5,6)
Count_10 (1,2,1,2,1,1)
Also it would be great if I can calculate the number of observations before and after the certain values. For the given X value, I would like to know how many observation between [X-10, X+10] within ID "A".
The output image would be as follows:
ID (A,A,A,A,A,A,A,A)
X (9,20,14,24,23,30,40,50)
Count_X10 (3,3,3,3,3,3,2,1)
Count_X10: for a given X(=9) there are three observations within ID "A" [-1,19]

Answer 1

Here are the data loaded as a data.frame to keep the observations connected. Note that I added a second group to to show how to handle that
df <-
data.frame(
ID = rep(c("A","B"), each = 8)
, Day = c(7,14,17,25,35,37,42,57)
, X = c(9,20,14,24,23,30,20,40)
)
Then, I used dplyr to pass the data through a series of steps. First, I split by the ID column, then used lapply to run a function on each of those ID groups, including calculating two columns of interest (then returning the whole data.frame). Finally, I stitch the rows back together with bind_rows
df %>%
split(.$ID) %>%
lapply(function(x){
x$nextTen <- sapply(x$Day, function(thisDay){
sum(between(x$Day, thisDay, thisDay + 10))
})
x$plusMinusTen <- sapply(x$Day, function(thisDay){
sum(between(x$Day, thisDay - 10, thisDay + 10))
})
return(x)
}) %>%
bind_rows()
The result is
ID Day X nextTen plusMinusTen
1 A 7 9 3 3
2 A 14 20 2 3
3 A 17 14 2 4
4 A 25 24 2 3
5 A 35 23 3 4
6 A 37 30 2 3
7 A 42 20 1 3
8 A 57 40 1 1
9 B 7 9 3 3
10 B 14 20 2 3
11 B 17 14 2 4
12 B 25 24 2 3
13 B 35 23 3 4
14 B 37 30 2 3
15 B 42 20 1 3
16 B 57 40 1 1
But any condition you are interested good be added to that lapply step.

Answer 2

Your sample data :
df = data.frame(
ID = rep('A', 8),
Day = c(7, 14, 17, 25, 35, 37, 42, 57),
X = c(9, 20, 14, 24, 23, 30, 40, 50),
stringsAsFactors = FALSE)
Note: You give two different values for vector X. I suppose it is c(9, 20, 14, 24, 23, 30, 40, 50), and not c(9, 20, 14, 24, 23, 30, 20, 40).
First calculation:
library(dplyr)
output1 = df %>%
mutate(Day_10 = ceiling(Day/10)) %>%
group_by(ID, Day_10) %>%
summarise(Count_10 = n())
The mutate step creates the ranges of 10 days by rounding Day/10. Then we group by ID and Day_10 and we count the number of observations within each group.
> output1
ID Day_10 Count_10
<chr> <dbl> <int>
1 A 1 1
2 A 2 2
3 A 3 1
4 A 4 2
5 A 5 1
6 A 6 1
Second calculation:
output2 = df %>%
group_by(ID) %>%
mutate(Count_X10 = sapply(X, function(x){sum(Day >= x-10 & Day <= x+10)})) %>%
select(-Day)
We group by ID, and for each X we count the number of days with this ID that are between X-10 and X+10.
> output2
ID X Count_X10
<chr> <dbl> <int>
1 A 9 3
2 A 20 3
3 A 14 3
4 A 24 3
5 A 23 3
6 A 30 3
7 A 40 3
8 A 50 2
Note: I suppose there's a mistake in the desired output you give, because for instance, when X = 50, there are 2 observations within [40, 60] with ID "A": days 42 and 57.