Creating a calculated Average / Ratio to plot on a Geom_line comparing yearly data

[Ctalley13]

Hello All,

In some cases I use Tableau to show some visual work, but I would like to learn how to do that in R.

What I would like to do is get a calculated average / ration of a positivity ratio seen like in the picture attached to this thread.

below I have began a code and it will depict a visual showing total positive cases each month in the year of 2019 and 2020.

Example: In January of 2019 there were 10 new cases.

However, as mentioned, I would like that to be converted into a ratio shown over the year.

Example: In the graph shared, there was a 0.53% Positivity ratio.

I think I am just stuck on the calculation part and would appreciate all the help.

Thank you so much!

Aphirm_2019_2020_HIV_Testing_Data %>%
mutate(Session.Date = as.Date(Session.Date, format = "%m/%d/%Y"),
session_date_ym = as.yearmon(Session.Date)) %>%
count(session_date_ym, final_test_result_coded) %>%
filter(final_test_result_coded == "Positive")%>%
mutate(year = as.factor(year(session_date_ym)))%>%
ggplot(aes(month(session_date_ym, label = TRUE), n, color = year, group = year, linetype = year)) +
geom_line()+
geom_text(aes(label = n), vjust = -0.1, color = "black")+
scale_y_continuous(limits = c(0,18), expand = c(0,0), breaks = seq(0,15,5))+
labs(x = "Months", y = "Frequency", title = "2019 / 2020 HIV Testing Positive Outcomes", subtitle = "Reactive Testing Results", caption = expression(paste("Data exported and provided by Aphirm")))+
theme(panel.grid = element_blank(),
panel.background = element_rect(fill = "white"),
panel.border = element_rect(color = "black", fill = NA))

[andresrcs]

Could you ask this with a minimal REPRoducible EXample (reprex)? A reprex makes it much easier for others to understand your issue and figure out how to help.

If you've never heard of a reprex before, you might want to start by reading this FAQ:

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.…

[technocrat]

suppressPackageStartupMessages({
  library(ggplot2)
})

# this is just to reduce one of the standard data sets to a convenient size
portion <- dplyr::filter(mtcars,mpg > 23)

# hjust and vjust default to zero; usually require manual adjustment
ggplot(portion,aes(mpg,drat)) + 
  geom_point() +
  geom_line() + 
  geom_text(aes(label=drat),hjust=-.5, vjust=0.5) +
  theme_minimal()