Movies Dataset Analysis

```{r, echo=FALSE, message=FALSE, warning=FALSE} # Load necessary libraries library(dplyr) library(readr)

Load the movies dataset

movies <- read_csv(“https://gist.githubusercontent.com/tiangechen/b68782efa49a16edaf07dc2cdaa855ea/raw/0c794a9717f18b094eabab2cd6a6b9a226903577/movies.csv”)



```{r}
movies <- movies %>%
  rename(
    movie_title = Film,        # Renaming 
    release_year = Year        # Renaming 
  )

# Display the first few rows of the dataset to confirm changes
head(movies)

Rename columns

```{r} selected_columns_df <- movies %>% select(movie_title, release_year, Genre, Profitability)

Display the first few rows

head(selected_columns_df)


### Select specific columns
```{r}
selected_columns_df <- movies %>%
  select(movie_title, release_year, Genre, Profitability)

head(selected_columns_df)

Filter movies after 2000 with Rotten Tomatoes % higher than 80

``{r} filtered_movies <- movies %>% filter(release_year > 2000,Rotten Tomatoes %` > 80)

Display the first few rows of the filtered dataset

head(filtered_movies)


### Profitability Millions column
```{r}
movies <- movies %>%
  mutate(Profitability_millions = Profitability / 1e6)

head(movies)

Correlation between Rotten Tomatoes % and Profitability

``{r} correlation <- cor(sorted_movies$Rotten Tomatoes %`, sorted_movies$Profitability_millions, use = “complete.obs”)

#correlation result correlation ```