NYC Restaurant Inspections
Multiple t-test models (broom package), Principal Component Analysis (PCA)
Notable topics: Multiple t-test models (broom package), Principal Component Analysis (PCA)
Recorded on: 2018-12-10
Timestamps by: Alex Cookson
Screencast
Timestamps
Separating column using separate function
Taking distinct observation, but keeping the remaining variables using distinct function with .keep_all argument
Using broom package and nest function to perform multiple t-tests at the same time
Tidying nested t-test models using broom package
Creating TIE fighter plot of estimates of means and their confidence intervals
Recode long description using regex to remove everything after a parenthesis
Using cut function to manually bin data along user-specified intervals
Asking, "What type of violations tend to occur more in some cuisines than others?"
Using semi_join function to get the most recent inspection of all the restaurants
Asking, "What violations tend to occur together?"
Using widyr package function pairwise_cor (pairwise correlation) to find co-occurrence of violation types
Beginning of PCA (Principal Component Analysis) using widely_svd function
Actually typing in the widely_svd function
Reviewing and explaining output of widely_svd function
Creating graph of opposing elements of a PCA dimension
Shortening string using str_sub function
Reference to Julia Silge's PCA walkthrough using StackOverflow data: https://juliasilge.com/blog/stack-overflow-pca/