decisionmechanics

SELECT 
  acc.email,
  a1.program_user_id,
  a1.assessment_id AS initial_assessment_id,
  a2.assessment_id AS retake_assessment_id,
  s1.statement_id AS statement_id,
FROM assessments a1
JOIN assessments a2 ON a2.program_user_id = a1.program_user_id
JOIN scores s1 ON s1.assessment_id = a1.assessment_id
JOIN scores s2 ON s2.assessment_id = a2.assessment_id

decisionmechanics

get_recommendation_ratings <- function(rating_file_path) {
  # Read user ID, item ID, user preference CSV data
  ratings <- read.csv(file = rating_file_path, header = FALSE, col.names = c('user', 'item', 'preference'))
  
  # Create item co-occurrence matrix
  co_occurrence_matrix <- crossprod(table(ratings[, c('user', 'item')]))
  
  # Convert long format to wide format and replace NAs with 0s
  user_ratings <- tidyr::spread(ratings, user, preference, fill = 0)
  

decisionmechanics

library(readr)
library(dplyr)

url <- "https://archive.ics.uci.edu/ml/machine-learning-databases/wine-quality/winequality-white.csv"

df <- 
  read_delim(url, delim = ";") %>% 
  dplyr::mutate(taste = as.factor(ifelse(quality < 6, "bad", ifelse(quality > 6, "good", "average")))) %>% 
  dplyr::select(-quality)