Finding the Sum and Count to make a percentage for groups

[Ctalley13]

Hello All,

I am in need of your help.

I cannot seem to find a direct scenario for what I am facing.

What I need to do is find the viral suppression rates by the sum and count to get a percentage and do a group_by with gender.

In this scenario, I have 3 columns: Gender, last quantitative lab value, and viral suppression numeric.

To explain further, the viral suppression numeric column was created with a case when based off of last quantitative lab value:

Demographic.Data <- Demographic.Data %>%
mutate(Viral_Suppression_Numeric = case_when(
Last.Quantitative.Lab.Value < 199 ~ "0",
Last.Quantitative.Lab.Value > 200 ~ "1",
T ~ NA_character_
))

that is how I have 3 columns.

The end result I would like is something along these lines, but by the viral suppression percentage and gender:

Male 34%
Female 57%
Transgender MTF 87%

I know to do this I would take the sum of viral suppression column and the count of viral suppression column and divide the two to get the percentage and then group by gender. However, I keep on having errors when I try.

one error I get is: x invalid 'type' (character) of argument

All the help is greatly appreciated.

below you will find a sample data set. I don't know how to make a reproduceable table yet. I apologize in advance as that is something I am learning.

gender last_quantitative_lab_value viral_suppression_numeric
Male 20 0
Male 20 0
Male 20 0
Male 22 0
Male 20 0
Female 20 0
Male 20 0
Male 20 0
Transgender MtF 1490 1
Female 1000 1
Male 20 0
Male 205 1
Male 20 0
Male 22 0
Male 208 1
Female 20 0
Female 201 1
Female 207 1
Transgender MtF 20 0
Female 1000 1

Thank you,

-Cody

[williaml]

With your data:

library(tidyverse)
df <- tibble::tribble(
              ~gender, ~last_quantitative_lab_value, ~viral_suppression_numeric,
               "Male",                          20L,                         0L,
               "Male",                          20L,                         0L,
               "Male",                          20L,                         0L,
               "Male",                          22L,                         0L,
               "Male",                          20L,                         0L,
             "Female",                          20L,                         0L,
               "Male",                          20L,                         0L,
               "Male",                          20L,                         0L,
        "Transgender",                        1490L,                         1L,
             "Female",                        1000L,                         1L,
               "Male",                          20L,                         0L,
               "Male",                         205L,                         1L,
               "Male",                          20L,                         0L,
               "Male",                          22L,                         0L,
               "Male",                         208L,                         1L,
             "Female",                          20L,                         0L,
             "Female",                         201L,                         1L,
             "Female",                         207L,                         1L,
        "Transgender",                          20L,                         0L,
             "Female",                        1000L,                         1L
        )

df %>% 
  group_by(gender) %>% 
  count(viral_suppression_numeric) %>% 
  mutate(prop = n/sum(n))

# A tibble: 6 × 4
# Groups:   gender [3]
  gender      viral_suppression_numeric     n  prop
  <chr>                           <int> <int> <dbl>
1 Female                              0     2 0.333
2 Female                              1     4 0.667
3 Male                                0    10 0.833
4 Male                                1     2 0.167
5 Transgender                         0     1 0.5  
6 Transgender                         1     1 0.5  

For making it reproducible next time:

FAQ: How to do a minimal reproducible example ( reprex ) for beginners

A minimal reproducible example consists of the following items: A minimal dataset, necessary to reproduce the issue The minimal runnable code necessary to reproduce the issue, which can be run on the given dataset, and including the necessary information on the used packages. Let's quickly go over each one of these with examples: Minimal Dataset (Sample Data) You need to provide a data frame that is small enough to be (reasonably) pasted on a post, but big enough to reproduce your issue. Let's say, as an example, that you are working with the iris data frame head(iris) #> Sepal.Length Sepal.Width Petal.Length Petal.Width Species #> 1 5.1 3.5 1.4 0.…