library(dplyr)
## 
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
## 
##     filter, lag
## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union
library(readr)

# Load the movies dataset
movies <- read_csv("https://gist.githubusercontent.com/tiangechen/b68782efa49a16edaf07dc2cdaa855ea/raw/0c794a9717f18b094eabab2cd6a6b9a226903577/movies.csv")
## Rows: 77 Columns: 8
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (4): Film, Genre, Lead Studio, Worldwide Gross
## dbl (4): Audience score %, Profitability, Rotten Tomatoes %, Year
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

1. rename

movies_renamed <- movies %>%
  rename(movie_title = Film, release_year = Year)

2 select

movies_selected <- movies_renamed %>%
  select(movie_title, release_year, Genre, Profitability, `Rotten Tomatoes %`)

3 filter

movies_filtered <- movies_selected %>%
  filter(release_year > 2000, `Rotten Tomatoes %` > 80)

4 mutate

movies_mutated <- movies_filtered %>%
  mutate(Profitability_millions = Profitability / 1e6)

5 arrange

movies_sorted <- movies_mutated %>%
  arrange(desc(`Rotten Tomatoes %`), desc(Profitability_millions))

6 combining functions

final_movies <- movies %>%
  rename(movie_title = Film, release_year = Year) %>%
  select(movie_title, release_year, Genre, Profitability, `Rotten Tomatoes %`) %>%
  filter(release_year > 2000, `Rotten Tomatoes %` > 80) %>%
  mutate(Profitability_millions = Profitability / 1e6) %>%
  arrange(desc(`Rotten Tomatoes %`), desc(Profitability_millions))

7 interpret question

No there is not an exact correlation in regards to rotten tomatoes to profitablity.