Open taliafabs opened 9 months ago
This code looks good, and makes use of the tidyverse and janitor R packages introduced in the first week of STA302.
However, it is lacking a written part about the data that was used. I can see that it your data was gathered from the Dinesafe Inspection Data in OpenDataTO. Some things I would suggest writing about include: the source of your data, the extent to which your dataset is accurate, trustworthy, and representative of DineSafe Toronto, and any possible biases.
The graph produced (it was sent to me via email since the qmd file isn't loaded on GitHub) clearly shows a distinction between "conditional pass" and "pass" status, however I would recommend explaining that more, as well as why it is important for us to know whether an establishment received a "Conditional Pass" or "Pass" in DineSafe Toronto.
PDF render as emailed to me: Workflow Tutorial STA302[6].pdf
I would have mentioned why there is a large difference between the number of "Pass" and "Conditional Pass"
I also think that a brief discussion of "Pass" and "Conditional Pass" DineSafe Toronto standards would have been beneficial here, specifically highlighting the importance of meeting the "Pass" standard and what that means for the health and safety of members of the general public who dine at the establishments included in the data set.
The graph clearly shows that the overwhelming majority of establishments in the database "Pass," so it might be a good idea to expand on what "Conditional Pass" means and what risks that might pose to the health and safety of customers.
this is where the peer reviews would go
code (messaged to me) install.packages("tidyverse") install.packages("janitor") install.packages("ggplot2") install.packages("usethis") install.packages("gitcreds")
library(tidyverse) library(janitor) library(ggplot2) library(usethis) library(gitcreds)
create_github_token()
gitcreds_set()
ds_raw <- read.csv("Dinesafe.csv")
ds_clean <- ds_raw |> clean_names() |>
filter(!is.na(establishment_name)) |>
select(establishment_name, establishment_address, establishment_status)
ds_clean |> ggplot(data = ds_clean, mapping = aes(x = establishment_status, fill = establishment_status)) + geom_bar() + labs(title = "Dinesafe Inspection Status Data", x = "Status", y = "Count", fill = "Status") + theme_minimal()