This data set ranks the top 200 movies that were released in 2023 by their total gross or box office income. The data includes other variables such as the number of theaters that showed each movie and each movie’s release date and distributor. I intend on examining which distributors produced the highest-ranked movies of 2023 as well as if films released in certain months grossed more income than in other months. The source for this data set is Box Office Mojo.
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library(tidyverse)
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Rows: 200 Columns: 6
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chr (5): Title, Theaters, Total Gross, Release Date, Distributor
dbl (1): Rank
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ggplot(data = Top200Movies2023) +geom_bar(mapping =aes(x = Distributor, y =`Total Gross`), stat ="identity")
I cleaned the dataset by removing the commas (,) from the Total Gross and Theaters columns. This was accomplished via the gsub function after the Top200Movies2023 dataset was uploaded. I also attempted to remove the dollar sign ($) from the Total Gross column.
The first visualization simply outlines each film distributor that produced a film last year in a color-coded legend. The second visualization is meant to be a scatterplot where each dot represents a movie. The x-axis is the title of each film, while the y-axis is the total domestic gross. Lastly, the final visualization is supposed to portray a bar graph of the highest-grossing film distributors. Walt Disney films appears to have grossed the most money last year. Four Walt Disney films were in the top 10 highest-grossing films of 2023. Several distributors such as Oscilloscope and Quiver Distribution films made less than $700,000 at the box office, by comparison.
I wish all of my visualizations were clearer and focused. I wanted to show a comprehensive and detailed scatterplot where each film plotted was assigned a color that matched their film distributor in the legend. This would have made it much easier to compare which distributors had successful years at the box office. I also wish the x-axis on my bar graph was easier to read and that the y-axis had a reasonable scale. In both visualizations I wanted them to represent only the top 10 highest-grossing film distributors. This would have made the data appear much less cluttered, and it would have eliminated the majority of the distributors. I was also not able to construct a visualization that measured total gross against the date the film was released. It would have been interesting to analyze if most films made money during certain months and lived up to the term “Summer blockbuster.”
