How to easily format a frequency table in R?

The above is great (upvoted, but tidyverse is needed as well), or even simpler

library(tidyverse)
Sick%>%
pivot_longer(-age,names_to='symptom')%>%
count(age,symptom)%>%
pivot_wider(names_from='symptom',values_from='n')

I've found in learning R that a great many problems can be solved by pivoting long and then wide or vice versa with some transform or calculation in between :)

I hope this is right. If I understand your question you just want to count the yes's for each category. I've put it into a function so just change x = Sick to whatever your dataframe is called and run the function.

EDIT I forget which package the pipe and columns_to_rownames comes from, I've added dplyr as a require but it may come from magrittr. If in doubt just load the tidyverse.

       sick_tbl <- function(x = Sick){
    require(dplyr)
    sick_piv <- pivot_longer(x, names_to = "names", values_to = "values",
                 -c(age))
    
    count <- sick_piv%>% 
      count(values, names, age) %>% 
      filter(values == "Yes") %>% 
    select(!values)
    
    
    
    sick_out <- pivot_wider(count,
                            names_from = "age",
                            values_from = "n") %>% 
      column_to_rownames(var = "names")
    
    sick_out[is.na(sick_out)] <- 0
    
    sick_out <<- sick_out}

To run on your example data:

    sick_tbl(x = Sick)
    
    
        Adult Child Elderly Infant
    chills      1     2       4     NA
    cough       4     2       5      1
    fatigue     3     3       2      1
    fever       2     4       2      2

Here are three pretty concise options:

Base R

# Can skip the lapply if the Y/N columns were character to begin with
with(subset(cbind(Sick[1], stack(lapply(Sick[-1], as.character))), values == "Yes"), 
     table(ind, age))

data.table

library(data.table)
melt(as.data.table(Sick), id.vars="age")[value == "Yes", table(variable, age)]

questionr

library(questionr)
cross.multi.table(Sick[-1], Sick[[1]], true.codes = list("Yes"))

First reshape, then use the datasummary function from the modelsummary package (self-promotion alert). The benefit of this solution is that you can customize the look of your table and save it to many formats (html, latex, word, markdown, etc.):

library('modelsummary')
library('tidyverse')

dat = pivot_longer(Sick, -age, names_to = "Symptom") %>%
      filter(value == "Yes")

datasummary(Symptom ~ N * age, data = dat)