The line chart helps show the cumulative gross earnings for major holidays across five years (2020-2024). Each of the lines represents a different year, each a different color to help identify trends in earnings.
The interactive line chart shows the lifetime gross earnings of the most popular Marvel movies, based on their release dates. Each point shows a film’s total earnings, and hovering over the point will allow youj to see the release date and corresponding earnings.
The bar chart shows general gross earnings by day of the week for the year of 2024 and the top Marvel movie lifetime earnings based on the day they were released. The lighter shade of red shows the Marvel movie earnings and the darker red shows the general movie earnings. For general movie earnings, Friday is the day of the week for the year with the highest cumulative gross value, with a gross value of 890069518 billion.
These line graphs compare Marvel and DC market share by year in a couple of categories. The top left compares Rating which fluctuates between the two. DC has the highest overall rating of 9 for its movie The Dark Knight. The bottom left compares Opening Weekend numbers in the USA which Marvel is pretty consistently on top. Marvel has the top Opening Weekend of 357.115007 million for its movie Avengers: Endgame. The top right compares their Gross Worldwide where Marvel is also consistently higher than DC. Marvel has the highest Gross Worldwide of 2.7978006 billion for its movie Avengers: Endgame.
Source: The FliteCast - Click the link to read an article about Marvel VS DC Films, comparing their strengths and weaknesses!
These scatter plots compare the budget versus box office sales for different Marvel and DC movies. The top left shows domestic, top right shows international, and bottom left shows worldwide box office sales. To view the data on Statista click here for Marvel, and click here for DC!
This analysis provides a comprehensive framework for Marvel to identify and prioritize international markets to maximize box office revenue. The three charts and graphs examine the performance of Marvel movies both domestically and internationally, alongside economic indicators like GDP, to identify countries with higher spending potential. By combining movie performance data with economic insights, this study highlights which international markets offer the greatest opportunities for growth and strategic investment.
| Movie | Average U.S. Revenue (USD) | Average International Revenue (USD) |
|---|---|---|
| Avengers: Endgame | 858,373,000 | 1,914,648,672 |
| Avengers: Infinity War | 678,815,482 | 1,369,544,272 |
| Avengers: Age of Ultron | 459,005,868 | 940,057,392 |
| Iron Man 3 | 409,003,133 | 805,717,971 |
| Captain America: Civil War | 408,084,349 | 744,646,268 |
| Captain Marvel | 426,829,839 | 702,177,135 |
| Black Panther | 700,243,066 | 640,634,844 |
| Thor: Ragnarok | 315,058,289 | 537,175,014 |
| Doctor Strange | 232,641,920 | 444,427,705 |
| Guardians of the Galaxy | 333,716,356 | 438,395,354 |
| Thor: The Dark World | 206,362,140 | 438,330,688 |
| Ant-Man and the Wasp | 216,648,740 | 406,260,660 |
| Ant-Man | 180,202,163 | 338,883,044 |
| Iron Man 2 | 312,433,331 | 310,111,529 |
| Thor | 181,030,624 | 268,295,994 |
| Iron Man | 318,819,126 | 266,517,911 |
| The Incredible Hulk | 134,806,913 | 130,365,514 |
These graphs show the differences in success/ popularity for Marvel Characters. Each movie is plotted based on the worldwide box office revenue, while the comics are from each superhero, adding up the different series and issues.
The Avengers movies are the most successful throughout the phases of Marvel, along with Spiderman: No Way Home. For the comics, Spiderman has the most issues published, followed by The Avengers.
This dashboard was created using Quarto in RStudio, and the R Language and Environment.
The dataset used to create this dashboard was downloaded from
Arnold J (2024). ggthemes: Extra Themes, Scales and Geoms for ‘ggplot2’. R package version 5.1.0, https://github.com/jrnold/ggthemes, https://jrnold.github.io/ggthemes/.
Bache S, Wickham H (2022). magrittr: A Forward-Pipe Operator for R. R package version 2.0.3, https://CRAN.R-project.org/package=magrittr.
C. Sievert. Interactive Web-Based Data Visualization with R, plotly, and shiny. Chapman and Hall/CRC Florida, 2020.
Dancho M, Vaughan D (2023). tidyquant: Tidy Quantitative Financial Analysis. R package version 1.0.7, https://github.com/business-science/tidyquant.
H. Wickham. ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag New York, 2016.
Kunst J (2022). highcharter: A Wrapper for the ‘Highcharts’ Library. R package version 0.9.4, https://CRAN.R-project.org/package=highcharter.
Neuwirth E (2022). RColorBrewer: ColorBrewer Palettes. R package version 1.1-3, https://CRAN.R-project.org/package=RColorBrewer.
Posit team (2024). RStudio: Integrated Development Environment for R. Posit Software, PBC, Boston, MA. URL http://www.posit.co/.
R Core Team (2024). R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/.
Rinker, T. W. & Kurkiewicz, D. (2017). pacman: Package Management for R. version 0.5.0. Buffalo, New York. http://github.com/trinker/pacman
Wickham H, Averick M, Bryan J, Chang W, McGowan LD, François R, Grolemund G, Hayes A, Henry L, Hester J, Kuhn M, Pedersen TL, Miller E, Bache SM, Müller K, Ooms J, Robinson D, Seidel DP, Spinu V, Takahashi K, Vaughan D, Wilke C, Woo K, Yutani H (2019). “Welcome to the tidyverse.” Journal of Open Source Software, 4(43), 1686. doi:10.21105/joss.01686 https://doi.org/10.21105/joss.01686.
Wickham H, François R, Henry L, Müller K, Vaughan D (2023). dplyr: A Grammar of Data Manipulation. R package version 1.1.4, https://github.com/tidyverse/dplyr, https://dplyr.tidyverse.org.
Wickham H (2023). stringr: Simple, Consistent Wrappers for Common String Operations. R package version 1.5.1, https://github.com/tidyverse/stringr, https://stringr.tidyverse.org.
Wickham H, Pedersen T, Seidel D (2023). scales: Scale Functions for Visualization. R package version 1.3.0, https://github.com/r-lib/scales, https://scales.r-lib.org.
Xie Y (2024). knitr: A General-Purpose Package for Dynamic Report Generation in R. R package version 1.48, https://yihui.org/knitr/.
Yihui Xie (2015) Dynamic Documents with R and knitr. 2nd edition. Chapman and Hall/CRC. ISBN 978-1498716963
Yihui Xie (2014) knitr: A Comprehensive Tool for Reproducible Research in R. In Victoria Stodden, Friedrich Leisch and Roger D. Peng, editors, Implementing Reproducible Computational Research. Chapman and Hall/CRC. ISBN 978-1466561595
Zhu H (2024). kableExtra: Construct Complex Table with ‘kable’ and Pipe Syntax. R package version 1.4.0, https://github.com/haozhu233/kableExtra, http://haozhu233.github.io/kableExtra/.