Need Help Incorporating Tidyr's Spread into a Function that Outputs a List of Dataframes with Grouped Counts

Question

library(tidyverse)

Using the sample data at the bottom, I want to find counts of the Gender and FP variables, then spread these variables using tidyr::spread(). I'm attempting to do this by creating a list of dataframes, one for the Gender counts, and one for FP counts. The reason I'm doing this is to eventually cbind both dataframes. However, I'm having trouble incorporating the tidyr::spread into my function.

The function below creates a list of two dataframes with counts for Gender and FP, but the counts are not "spread."

  group_by_quo=quos(Gender,FP)

 DF2<-map(group_by_quo,~DF%>%
    group_by(Code,!!.x)%>%
    summarise(n=n()))  

If I add tidyr::spread, it doesn't work. I'm not sure how to incorporate this since each dataframe in the list has a different variable.

group_by_quo=quos(Gender,FP)

 DF2<-map(group_by_quo,~DF%>%
    group_by(Code,!!.x)%>%
    summarise(n=n()))%>%
    spread(!!.x,n) 

Any help would be appreciated!

Sample Code:

Subject<-c("Subject1","Subject2","Subject1","Subject3","Subject3","Subject4","Subject2","Subject1","Subject2","Subject4","Subject3","Subject4")
Code<-c("AAA","BBB","AAA","CCC","CCC","DDD","BBB","AAA","BBB","DDD","CCC","DDD")
Code2<-c("AAA2","BBB2","AAA2","CCC2","CCC2","DDD2","BBB2","AAA2","BBB2","DDD2","CCC2","DDD2")
Gender<-c("Male","Male","Female","Male","Female","Female","Female","Male","Male","Male","Male","Male")
FP<-c("F","P","P","P","F","F","F","F","F","F","F","F")
DF<-data_frame(Subject,Code,Code2,Gender,FP)

Answer 1

I think you misplaced the closing parenthesis. This code works for me:

library(tidyverse)

Subject<-c("Subject1","Subject2","Subject1","Subject3","Subject3","Subject4","Subject2","Subject1","Subject2","Subject4","Subject3","Subject4")
Code<-c("AAA","BBB","AAA","CCC","CCC","DDD","BBB","AAA","BBB","DDD","CCC","DDD")
Code2<-c("AAA2","BBB2","AAA2","CCC2","CCC2","DDD2","BBB2","AAA2","BBB2","DDD2","CCC2","DDD2")
Gender<-c("Male","Male","Female","Male","Female","Female","Female","Male","Male","Male","Male","Male")
FP<-c("F","P","P","P","F","F","F","F","F","F","F","F")
DF<-data_frame(Subject,Code,Code2,Gender,FP)

group_by_quo <- quos(Gender, FP)

DF2 <- map(group_by_quo,
  ~DF %>%
    group_by(Code,!!.x) %>%
    summarise(n=n()) %>%
    spread(!!.x,n))

This last part is a bit more concise using count:

DF2 <- map(group_by_quo,
  ~DF %>%
    count(Code,!!.x) %>%
    spread(!!.x,n))

And by using count the unnecessary grouping information is removed as well.