Environment Setup

## -- Attaching packages ---------------------------------------------------------------------------------------------------------------------------------------------------------- tidyverse 1.2.1 --
## v ggplot2 2.2.1     v purrr   0.2.4
## v tibble  1.4.2     v dplyr   0.7.4
## v tidyr   0.8.0     v stringr 1.2.0
## v readr   1.1.1     v forcats 0.2.0
## -- Conflicts ------------------------------------------------------------------------------------------------------------------------------------------------------------- tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag()    masks stats::lag()
## Loading required package: bitops
## 
## Attaching package: 'RCurl'
## The following object is masked from 'package:tidyr':
## 
##     complete
## 
## Attaching package: 'magrittr'
## The following object is masked from 'package:purrr':
## 
##     set_names
## The following object is masked from 'package:tidyr':
## 
##     extract

Load CSV Data

data <- getURL("https://raw.githubusercontent.com/baroncurtin2/data607/master/ratings.csv") %>%
  read_csv() %>%
  data.frame(stringsAsFactors = FALSE)

Data Wrangling

# the data is currently very wide. one of the fundamentals of data is to arrange the data such that each variable is in a column, each observation is a row, and each value is a cell

tidydata <- data %>%
  gather(movie, rating, -name, na.rm = TRUE) %>%
  arrange(name, movie)

Data Visualization

# lets make some boxplots for fun
ggplot(data = tidydata, aes(x = movie, y = rating, col = movie)) +
  geom_boxplot()

Just for fun, we can conclude that Get Out was the highest rated movie amongst this sample. It also experienced the least deviation from the median/mean as evidenced by boxplot.