R: counting amount of patterns of numbers [closed] - r

Question

Closed. This question needs details or clarity. It is not currently accepting answers.
Want to improve this question? Add details and clarify the problem by editing this post.
Closed 5 years ago.
Improve this question
I'm fairly new here and also fairly new to R so apologies if anything is unclear.
Basically, I have a csv table of numbers for each person, 1 number for each week for 38 weeks.
For example, Anthony has number 6 in week 1, 12 in week 2 and so on, these numbers are fairly random and range from 1-20.
I have taken the numbers from the table and saved them into a string, hence Anthonys string when printed would look like
"6 12 18 7 17 4 16 11 20 15 3 5 19 10 8 9 1 14 13 19 11 16 18 4 17 7 6 12 14 1 10 13 20 15 3 5 8 9"
What I'm trying to do with this is find/count the amount of times a number between 1 and 10 occurs in groups of 3 consecutively and then groups of 4 consecutively and possibly 5.
For example, in this string 8, 9 and 1 occur consecutively and then 3, 5, 8 and 9 occur consecutively, meaning the amount of occurrences is 2.
I've tried using str_count from the stringr package and also tried a few different functions located here - Count the number of overlapping substrings within a string
I can't seem to find a method/function to get this to output what I want (a simple count of the number of occurrences).
If anyone could provide any insight/help it would be greatly appreciated.

Answer 1

It would be easier to keep these as numbers. Here I use scan() to turn your string into a vector of values indicating if each number is less than 10 or not then I call rle() on it to calculate run lenths
x <- "6 12 18 7 17 4 16 11 20 15 3 5 19 10 8 9 1 14 13 19 11 16 18 4 17 7 6 12 14 1 10 13 20 15 3 5 8 9"
rr <- rle(scan(text=x)<10)
Now I can mangle this into a data.frame and see which runs were longer than 2
subset(as.data.frame(unclass(rr)), values==T & lengths>2)
# lengths values
# 9 3 TRUE
# 17 4 TRUE
So we can see that we had a run of 3 and a run of 4.
I could clean this up by defining a function to turn the rle into a data.frame more easily and track the starting indexes
as.data.frame.rle <- function(x) {
data.frame(unclass(x), start=head(cumsum(c(0,rr$lengths))+1,-1))
}
and can then run
subset(as.data.frame(rle(scan(text=x)<10)), values==T & lengths>2)
# lengths values start
# 9 3 TRUE 15
# 17 4 TRUE 35
so we can see those runs start at positions 15 and 35.