# Modeling no-show rates of attendees in R

I'm trying to determine to what extent the no-show rate of registered candidates to a (past) event is dependent on the weekday that the event was hosted on.

Given a number of past events that have taken place on a weekday (Monday til Friday) I have a matrix that is structured as follows:

• 5 columns: Monday, ..., Friday
• Each entry in a row represents the no-show rate of a past event that has taken place on that weekday

As an example:

#data
df1 <- data.frame(Monday   = runif(10,0,0.2),
Tuesday  = runif(10,0.2,0.4),
Wednesday= runif(10,0.4,0.6),
Thursday = runif(10,0.6,0.8),
Friday   = runif(10,0.8,1))

Given this information, I've been trying to understand what function in R would allow me to:

1. Understand to what extent the day that the event is hosted on has a statistically significant effect on the no-show rate
2. If there's a significant effect, how can I predict the no-show rate of a future event based on the weekday

So far I've been trying log-regressions with functions such as glm in R with little success, as my inputs are not 0 or 1, but rather % in between.

Any suggestions whether this is the right approach or what else I might try?

• Welcome to SO! I think the first step would be to pivot your data to long format - that is, each row is a record and has two variables - day and no-show rate.
– PGSA
Commented Apr 5 at 12:41
• Thanks a lot for the quick answer! So I have done the following to convert this to the format you suggested: library(tidyr) df_long <- gather(df1, day, no_show_rate) What would you do next?
– Marco W.
Commented Apr 5 at 12:55
• If you want "statistically significant" then you could need numbers rather than rates. Commented Apr 5 at 16:46
• Does your data literally have 10 observations per day? Or are there more observations? Commented Apr 5 at 19:17
• Hi @Henry - thanks for your input. I do have the numbers (people registered, people not attending). Are you saying that it would be easier to model based on these numbers rather than the no-show rates? If yes, how? Commented Apr 8 at 6:51