How to count cases at intervarls with conditions? for a tibble - r

Question

So I hope I can express my question, here I have the following example that I made up:
result <- c(1,1,1,1,1,1,1,1,1,1)
con1 <- c(1,2,2,2,1,1,2,2,2,2)
con2 <- c(2,1,2,2,1,1,2,2,2,1)
con3 <- c(2,2,1,1,1,2,2,2,2,1)
con4 <- c(2,1,2,2,1,1,2,1,1,2)
con5 <- c(1,2,2,2,1,2,2,2,2,1)
a <- tibble(Result=result,Con1=con1,Con2=con2,Con3=con3,Con4=con4,Con5=con5)
The above code gives me the following tibble, where each row is a patient:
> a
# A tibble: 10 x 6
Result Con1 Con2 Con3 Con4 Con5
<dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 1 1 2 2 2 1
2 1 2 1 2 1 2
3 1 2 2 1 2 2
4 1 2 2 1 2 2
5 1 1 1 1 1 1
6 1 1 1 2 1 2
7 1 2 2 2 2 2
8 1 2 2 2 1 2
9 1 2 2 2 1 2
10 1 2 1 1 2 1
The Result are cases that are positive for a mayor illnes (thats why all are 1's) while the Con_i are yes or no question for the patient where 1=yes and 2=no, I want to get the number of patients that said yes to: 0 questions, 1 questions, 2-3 questions and 4 or more questions.
So far I've tried to do this:
a1 <-a %>% add_column(X=1)
a1$X <- case_when(a$Con1==2 & a$Con2==2 & a$Con3==2 & a$Con4==2 & a$Con5==2 ~ 0,
a$Con1==1 & a$Con2==2 & a$Con3==2 & a$Con4==2 & a$Con5==2 |
a$Con1==2 & a$Con2==1 & a$Con3==2 & a$Con4==2 & a$Con5==2|
a$Con1==2 & a$Con2==2 & a$Con3==1 & a$Con4==2 & a$Con5==2|
a$Con1==2 & a$Con2==2 & a$Con3==2 & a$Con4==1 & a$Con5==2|
a$Con1==2 & a$Con2==2 & a$Con3==2 & a$Con4==2 & a$Con5==1 ~ 1)
table <- a1 %>% group_by(X) %>% count(X,Result)
table
> table
# A tibble: 3 x 3
# Groups: X [3]
X Result n
<dbl> <dbl> <int>
1 0 1 1
2 1 1 4
3 NA 1 5
But I know is not the most efficient way, plus i would need to make all the combinations for 2-3 cases and 4+ cases and is not scalable, so I'm looking for a much easier way to do it and scale it, hope I can get your help and thanks in advance!

Answer 1

Perhaps the simplest:
table(rowSums(a[,-1] < 2))
# 0 1 2 3 5 <--- counts of "1" in each row
# 1 4 2 2 1 <--- number of patients with that count
Since you need to group 2-3 and 4+, then
table(cut(rowSums(a[,-1] < 2), c(0, 1, 2, 4, Inf), include.lowest = TRUE))
# [0,1] (1,2] (2,4] (4,Inf]
# 5 2 2 1
While the logic is using < 2, it's just as easy to check for == 1L or similar equality.
Tracing this, step by step:
a[,-1] == 1
# Con1 Con2 Con3 Con4 Con5
# [1,] TRUE FALSE FALSE FALSE TRUE
# [2,] FALSE TRUE FALSE TRUE FALSE
# [3,] FALSE FALSE TRUE FALSE FALSE
# [4,] FALSE FALSE TRUE FALSE FALSE
# [5,] TRUE TRUE TRUE TRUE TRUE
# [6,] TRUE TRUE FALSE TRUE FALSE
# [7,] FALSE FALSE FALSE FALSE FALSE
# [8,] FALSE FALSE FALSE TRUE FALSE
# [9,] FALSE FALSE FALSE TRUE FALSE
# [10,] FALSE TRUE TRUE FALSE TRUE
rowSums(a[,-1] == 1)
# [1] 2 2 1 1 5 3 0 1 1 3
That last is the number of 1s for each "patient" (row).
From this, I count one 0, four 1s, two 2s plus two 3s, zero 4s plus one 5. This should total 5, 2, 2, 1 ... so #andrew_reece is correct, let's use cut(...,right=FALSE):
table(cut(rowSums(a[,-1] < 2), c(0, 1, 2, 4, Inf), right = FALSE))
# [0,1) [1,2) [2,4) [4,Inf)
# 1 4 4 1
I should have caught earlier the [0,1] (previous answer), indicating 0 and 1 are close-ended, meaning both 0 and 1 are included in the same bin.

Answer 2

An option with Reduce and table
table(Reduce(`+`, lapply(a[-1], `<`, 2)))
# 0 1 2 3 5
#1 4 2 2 1

Answer 3

Pivot your data so all the Con vars are a column, and the yes/no values for each Con sit in a separate column. Then you can use group_by and summarise operations to get your grouping:
a %>%
mutate(patient = letters[row_number()]) %>%
pivot_longer(starts_with("Con")) %>%
group_by(patient) %>%
summarise(yes = sum(value == 1),
no = sum(value == 2)) %>%
group_by(yes) %>%
summarise(yes_ct = n()) %>%
mutate(yes_grp = case_when(
yes %in% 2:3 ~ "2-3",
yes >= 4 ~ "ge4",
TRUE ~ as.character(yes)
)) %>%
group_by(yes_grp) %>%
summarise(ct = sum(yes_ct))
# A tibble: 4 x 2
yes_grp ct
<chr> <int>
1 0 1
2 1 4
3 2-3 4
4 ge4 1
I made an explicit patient variable (just row numbers, basically) to make pivot and group operations easier.

Answer 4

Try this:
library(data.table)
df <- setDT(a) - 1
df$sum <- 5 - rowSums( df[,2:6] )
freq <- data.table(table(df$sum))
names(freq) <- c('Questions_Yes', 'Patients')
freq <- freq[,`:=`(
Questions_Yes = case_when(
Questions_Yes %in% c(2:3) ~ "2-3",
Questions_Yes >= 4 ~ "4+",
TRUE ~ as.character(Questions_Yes)
))
][, .(Patients = sum(Patients)), by = Questions_Yes]
Questions_Yes Patients
1: 0 1
2: 1 4
3: 2-3 4
4: 4+ 1