r how to get total count of repeated values - r

Question

I have a dataframe with person_id, study_id columns like below:
person_id study_id
10 1
11 2
10 3
10 4
11 5
I want to get the count for number of persons (unique by person_id) with 1 study or 2 studies - so not those with particular value for study_id but:
2 persons with 1 study
3 persons with 2 studies
1 person with with 3 studies
etc
How can I do this? I think maybe a count through loop but I wonder if there is a package that makes it easier?

Answer 1

To get a sample data set that better matches your expected output, i'll use this
dd <- data.frame(
person_id = c(10, 11, 15, 12, 10, 13, 10, 11, 12, 14, 15),
study_id = 1:11
)
Now I can count the number of people with a given number of studies with.
table(rowSums(with(dd, table(person_id, study_id))>0))
# 1 2 3
# 2 3 1
Where the top line is the number of studies, and the bottom line it the number of people with that number of studies.
This works because
with(dd, table(person_id, study_id))
returns
study_id
person_id 1 2 3 4 5 6 7 8 9 10 11
10 1 0 0 0 1 0 1 0 0 0 0
11 0 1 0 0 0 0 0 1 0 0 0
12 0 0 0 1 0 0 0 0 1 0 0
13 0 0 0 0 0 1 0 0 0 0 0
14 0 0 0 0 0 0 0 0 0 1 0
15 0 0 1 0 0 0 0 0 0 0 1
and then we use >0 and rowSums to get a count of unique studies for each person. Then we use table again to summarize the results.
The creating the table for your data is taking up too much RAM, you can try
table(with(dd, tapply(study_id, person_id, function(x) length(unique(x)))))
which is a slightly different way to get at the same thing.

Answer 2

You can use the aggregate function to get counts per user.
Then use it again to get counts per counts
i.e. assume your data is called "test"
person_id study_id
10 1
11 2
10 3
10 4
11 5
12 NA
You can set your NA to be a number such as zero so they are not ignored i.e.
test$study_id[is.na(test$study_id)] = 0
Then you can run the same function but with a condition that the study_id has to be greater than zero
stg=setNames(
aggregate(
study_id~person_id,
data=test,function(x){sum(x>0)}),
c("person_id","num_studies"))
Output:
stg
person_id num_studies
10 3
11 2
12 0
Then do the same to get counts of counts
setNames(
aggregate(
person_id~num_studies,
data=stg,length),
c("num_studies","num_users"))
Output:
num_studies num_users
0 1
2 1
3 1

Answer 3

Here's a solution using dplyr
library(dplyr)
tmp <- df %>%
group_by(person_id) %>%
summarise(num.studies = n()) %>%
group_by(num.studies) %>%
summarise(num.persons = n())

Answer 4

> dat <- read.table(h=T, text = "person_id study_id
10 1
11 2
10 3
10 4
11 5
12 6")
I think you can just use xtabs for this. I may have misunderstood the question, but it seems like that's what you want.
> table(xtabs(dat))
# 10 11 12
# 3 2 1

Answer 5

df <- data.frame(
person_id = c(10,11,10,10,11,11,11),
study_id = c(1,2,3,4,5,5,1))
# remove replicated rows
df <- unique(df)
# number of studies each person has been in:
summary(as.factor(df$person_id))
#10 11
# 3 4
# number of people in each study
summary(as.factor(df$study_id))
# 1 2 3 4 5
# 2 1 1 1 2