assignment 2

install.packages("tidyverse", repos = "https://cloud.r-project.org/")
Installing package into 'C:/Users/Colton DeLeo/AppData/Local/R/win-library/4.4'
(as 'lib' is unspecified)
package 'tidyverse' successfully unpacked and MD5 sums checked

The downloaded binary packages are in
    C:\Users\Colton DeLeo\AppData\Local\Temp\RtmpKkz7wR\downloaded_packages

Introduction

My report analyzes movie attendance in Iceland using distribution and temporal visualizations.

Distribution Analysis

Visualization

b_distribution <- ice_movies %>%
  group_by(film.title) %>%
  summarise(total_admissions = sum(adm.weekend, na.rm = TRUE)) %>%
  arrange(desc(total_admissions)) %>%
  slice_head(n = 20) # Top 20 movies by admissions

b_distribution %>%
  ggplot(aes(x = reorder(film.title, total_admissions), y = total_admissions)) +
  geom_col(fill = "steelblue") +
  coord_flip() +
  labs(title = "Top 20 Movies by Total Admissions", x = "Film Title", y = "Total Admissions")

Analysis & Reflection

  • Distribution Patterns: We can observe a right skewed distrubution.The data shows that a small number of films dominate attendance figures.
  • Visualization Choices: A bar chart was chosen to clearly illustrate the distribution of admissions among the most popular films
  • Critical Evaluation: The visualization effectively highlights the disparity in movie attendance.

Self-rating: 9/10

Temporal Analysis

Visualization

b_temporal <- ice_movies %>%
  group_by(weekend.start) %>%
  summarise(total_admissions = sum(adm.weekend, na.rm = TRUE))

b_temporal %>%
  ggplot(aes(x = weekend.start, y = total_admissions)) +
  geom_line(color = "darkred", size = 1) +
  labs(title = "Total Admissions Over Time", x = "Date", y = "Total Admissions")
Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
ℹ Please use `linewidth` instead.

Analysis & Reflection

  • Key Temporal Patterns: significant fluctuation, noticeable peaks. Huge dip around 2020
  • Visualization Choices: A line graph was chosen to best illustrate change and significant changes as well
  • Critical Evaluation: Some context might help the graph and show why there was that dip

Self-rating: 8.5/10

Conclusion

The two visualizations show that while there is a few movies that dominate the top there is a lot of fluctuation. If we analyzed it further we could see why this is