Create Global Baseline Functions
Import Libraries
# import tidyverse
# install.packages("readxl")
library(readxl)
## Warning: package 'readxl' was built under R version 4.4.3
library(tidyverse)
## Warning: package 'tidyverse' was built under R version 4.4.3
## Warning: package 'ggplot2' was built under R version 4.4.3
## Warning: package 'tibble' was built under R version 4.4.3
## Warning: package 'readr' was built under R version 4.4.3
## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.1.4 ✔ readr 2.1.5
## ✔ forcats 1.0.0 ✔ stringr 1.5.1
## ✔ ggplot2 3.5.2 ✔ tibble 3.3.0
## ✔ lubridate 1.9.3 ✔ tidyr 1.3.1
## ✔ purrr 1.0.2
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
library(glue)
## Warning: package 'glue' was built under R version 4.4.3
Import Data
# Import data provided in class
MovieRatings <- read_excel("MovieRatings.xlsx")
MovieRatings <- data.frame(select(MovieRatings, -1), row.names=MovieRatings$Critic)
# Import personal collected data
movie_reviews <- read.csv("movie_reviews.csv", row.names=1)
Define Global Base Function
globalBase <- function(data, name, movie){
mean_movie <- sum(data, na.rm=TRUE) / sum(!is.na(data))
movie_relative <- mean(data[[movie]], na.rm = TRUE) - mean_movie
person_relative <- rowMeans(data, na.rm=TRUE)[[name]] - mean_movie
result <- (mean_movie + movie_relative + person_relative) %>% round(2)
return(result)
}
Testing
# Testing with class example, Param and Pitch Perfect, should return 2.28
test <- globalBase(MovieRatings, "Param", "PitchPerfect2")
glue("Param Expected: 2.28 \n Param Actual: {test}")
## Param Expected: 2.28
## Param Actual: 2.28
# Using algorithm on my collected data
test2 <- globalBase(movie_reviews, "Dave", "Elvis")
glue("My Expected: 5.95 \n My Actual: {test2}")
## My Expected: 5.95
## My Actual: 5.95