Using R Programming, answer the below

For the data file, look under "Data" download the movies_merged file, and look at pr1.Rmd (https://piazza.com/gatech/spring2017/cse6242/resources)

To unzip the file, download the movies_merged file!

Answer the following:

## 1. Remove non-movie rows

The variable `Type` captures whether the row is a movie, a TV series, or a game. Remove all rows from `df` that do not correspond to movies.

# TODO: Remove all rows from df that do not correspond to movies

## 2. Process `Runtime` column

The variable `Runtime` represents the length of the title as a string. Write R code to convert it to a numeric value (in minutes) and replace `df$Runtime` with the new numeric column.

Now investigate the distribution of `Runtime` values and how it changes over years (variable `Year`, which you can bucket into decades) and in relation to the budget (variable `Budget`). Include any plots that illustrate.

# TODO: Investigate the distribution of Runtime values and how it varies by Year and Budget

## 3. Encode `Genre` column

The column `Genre` represents a list of genres associated with the movie in a string format. Write code to parse each text string into a binary vector with 1s representing the presence of a genre and 0s the absence, and add it to the dataframe as additional columns. Then remove the original `Genre` column.

For example, if there are a total of 3 genres: Drama, Comedy, and Action, a movie that is both Action and Comedy should be represented by a binary vector <0, 1, 1>. Note that you need to first compile a dictionary of all possible genres and then figure out which movie has which genres (you can use the R `tm` package to create the dictionary).

# TODO: Investigate if Gross Revenue is related to Budget, Runtime or Genre

Plot the relative proportions of movies having the top 10 most common genres.

## 4. Eliminate mismatched rows

The dataframe was put together by merging two different sources of data and it is possible that the merging process was inaccurate in some cases (the merge was done based on movie title, but there are cases of different movies with the same title). The first sources release time was represented by the column `Year` (numeric representation of the year) and the second by the column `Released` (string representation of release date).

Find and remove all rows where you suspect a merge error occurred based on a mismatch between these two variables. To make sure subsequent analysis and modeling work well, avoid removing more than 10% of the rows that have a `Gross` value present.

```{r} # TODO: Remove rows with Released-Year mismatch ```

**Q**: What is your precise removal logic and how many rows did you end up removing?

**A**:

## 5. Explore `Gross` revenue

For the commercial success of a movie, production houses want to maximize Gross revenue. Investigate if Gross revenue is related to Budget, Runtime or Genre in any way.

Note: To get a meaningful relationship, you may have to partition the movies into subsets such as short vs. long duration, or by genre, etc.

```{r} # TODO: Investigate if Gross Revenue is related to Budget, Runtime or Genre ```

**Q**: Did you find any observable relationships or combinations of Budget/Runtime/Genre that result in high Gross revenue? If you divided the movies into different subsets, you may get different answers for them - point out interesting ones.

**A**:

```{r} # TODO: Investigate if Gross Revenue is related to Release Month ```

## 6. Process `Awards` column

The variable `Awards` describes nominations and awards in text format. Convert it to 2 numeric columns, the first capturing the number of wins, and the second capturing nominations. Replace the `Awards` column with these new columns, and then study the relationship of `Gross` revenue with respect to them.

Note that the format of the `Awards` column is not standard; you may have to use regular expressions to find the relevant values. Try your best to process them, and you may leave the ones that don't have enough information as NAs or set them to 0s.

```{r} # TODO: Convert Awards to 2 numeric columns: wins and nominations ```

**Q**: How did you construct your conversion mechanism? How many rows had valid/non-zero wins or nominations?

**A**:

```{r} # TODO: Plot Gross revenue against wins and nominations ```

**Q**: How does the gross revenue vary by number of awards won and nominations received?

**A**:

## 7. Movie ratings from IMDb and Rotten Tomatoes

There are several variables that describe ratings, including IMDb ratings (`imdbRating` represents average user ratings and `imdbVotes` represents the number of user ratings), and multiple Rotten Tomatoes ratings (represented by several variables pre-fixed by `tomato`). Read up on such ratings on the web (for example [rottentomatoes.com/about](https://www.rottentomatoes.com/about) and [ www.imdb.com/help/show_leaf?votestopfaq](http:// www.imdb.com/help/show_leaf?votestopfaq)).

Investigate the pairwise relationships between these different descriptors using graphs.

```{r} # TODO: Illustrate how ratings from IMDb and Rotten Tomatoes are related ```

**Q**: Comment on the similarities and differences between the user ratings of IMDb and the critics ratings of Rotten Tomatoes.

**A**:

## 8. Ratings and awards

These ratings typically reflect the general appeal of the movie to the public or gather opinions from a larger body of critics. Whereas awards are given by professional societies that may evaluate a movie on specific attributes, such as artistic performance, screenplay, sound design, etc.

Study the relationship between ratings and awards using graphs (awards here refers to wins and/or nominations).

```{r} # TODO: Show how ratings and awards are related ```

**Q**: How good are these ratings in terms of predicting the success of a movie in winning awards or nominations? Is there a high correlation between two variables?

**A**:

## 9. Expected insights

Come up with two new insights (backed up by data and graphs) that is expected. Here new means insights that are not an immediate consequence of one of the above tasks. You may use any of the columns already explored above or a different one in the dataset, such as `Title`, `Actors`, etc.

```{r} # TODO: Find and illustrate two expected insights ```

**Q**: Expected insight #1.

**A**:

**Q**: Expected insight #2.

**A**:

## 10. Unexpected insight

Come up with one new insight (backed up by data and graphs) that is unexpected at first glance and do your best to motivate it. Same instructions apply as the previous task.

```{r} # TODO: Find and illustrate one unexpected insight ```

**Q**: Unexpected insight.

**A**: