create prop table using dplyr

#1

Hello,
I need to create a prop.table column in a tibble object using dplyr.
However, I 'm not sure. Here is my data:

responses=c("yes","no","yes","no","yes","no")
cases=c(100,500,200,600,400,1000)
zone=c(1,1,2,2,3,3)
data=data.frame(responses,zone,cases)
data=as_tibble(data)
data$responses=factor(data$responses)
data
data %>%group_by(zone) %>%  mutate(percentage=cases/sum(cases))

I need to compute a prop.table considering yes/no response on every zone.
I noticed I couldn't use prop.table (It was complex for me).
Also, if I try to perform the same operation but using cases as integers, I obtain just "1" as result.
I need to convert It to dbl (with hablar) in order to gain the decimal places.
Anyway, does my code look ok?
Thanks for your time and interest.

#2

your code looks fine to me, it works...
if you specifically want to use prop.table/proportions, then it doesnt really involve dplyr, it relies on using xtabs

(data <- tibble(
  zone = c(1, 1, 2, 2, 3, 3),
  cases = c(100L, 500L, 200L, 600L, 400L, 1000L),
  responses = as.factor(c("yes", "no", "yes", "no", "yes", "no"))
))

(myxt <- xtabs(cases ~ zone + responses, data))

(myprops <- proportions(myxt,"zone"))

(myprops_tbl <- as_tibble(myprops))

I didn't understand your issue with integers.
in my data i have the cases as integers, and your code works fine and gives decimal precision,...

data %>% 
group_by(zone) %>%  
mutate(percentage=cases/sum(cases))
#4

so is that occurring with this code ?

(data <- tibble(
  zone = c(1, 1, 2, 2, 3, 3),
  cases = c(100L, 500L, 200L, 600L, 400L, 1000L),
  responses = as.factor(c("yes", "no", "yes", "no", "yes", "no"))
))
data %>% 
group_by(zone) %>%  
mutate(percentage=cases/sum(cases))

I dont recognise / dont have context for any of the terms used by you here...

 convert(dbl(var_integer))

what libraries are the functions from, and what is var_integer in this context ? how would it fit with the example we are looking at?

1 Like
#3

I tried working with integers, always using dplyr, and if I don't do convert(dbl(var_integer)), it computes only 1.
I have no idea why that happens.