Final Project for STAT 302: Data Visualization

For my final project, I cleaned and analyzed the “All Playstation 4 Games” dataset.

Amber Chiodini
2022-09-14

Introduction

The Playstation 4 (PS4) is a home video game console that was developed by Sony Computer Entertainment. It is the successor to the PS3 and launched on November 15th, 2013 in North America. The following dataset contains a wide-range of information such as the names of the games along with their publishers and developers.

Objective

I chose this dataset because the PS4, which has been available to the public for almost a decade, has led to the production of many award-winning games such as “God of War (2018)”, “Spider-Man (2018)” and “Horizon Zero Dawn (2017)”.

The goal of this project is to analyze the “All Playstation 4 Games” dataset and answer the following three questions:

  1. Which year were the most games released?
  2. What is the relationship between average completion time and games?
  3. Who are the most popular publishers and developers & how many action games did they produce?
Show code 
# Load package(s)
library(tidyverse)
library(dplyr)
library(patchwork)

# Read in the data 
play_data <- readr::read_csv("data/playstation_4_games.csv") %>% 
  filter(between(ReleaseYear, as.numeric('2013'), as.numeric('2022')))

Graphic 1

Show code 
# Build plot
ggplot(data = play_data, mapping = aes(x = ReleaseYear)) +
  geom_bar(fill = "#2E6DB4") + 
  geom_text(stat='count', aes(label = ..count..), vjust = -1) +
  scale_x_continuous(
    name = "Release Year", 
    breaks = 2013:2022
  ) + 
  scale_y_continuous(
    name = "Number of Games", 
    limits = c(0, 2000),
    breaks = seq(0, 2000, 500),
    labels = scales::label_comma(), 
    expand = c(0, 0) 
  ) + 
  theme_classic() +
  ggtitle("Number of games released each year") +
  theme (
    plot.title = ggtext::element_markdown(hjust = 0.5))

Graphic 2

Show code 
# Data 
labels_hours = c("0-1", "1-2", "3-4", "5-6", "6-8", "8-10", "10-12", "12-15", "15-20", "20-25", "25-30", "30-35", "35-40", "40-50", "50-60", "60-80", "80-100", "100-120", "120-150", "150-200", "200+")

CompletionTime <- subset(play_data, `CompletionTime(Hours)`!="nan")

# Build plot 
ggplot(data = CompletionTime, mapping = aes(x = `CompletionTime(Hours)`, fill = `CompletionTime(Hours)`)) + 
  geom_bar() + 
  # geom_text(stat='count', aes(label = ..count..), vjust = -1) + 
  scale_x_discrete(
    name = "Average Completion Time (Hours)", 
    limits = factor(labels_hours, level = c("0-1", "1-2", "3-4", "5-6", "6-8", "8-10", "10-12", "12-15", "15-20", "20-25", "25-30", "30-35", "35-40", "40-50", "50-60", "60-80", "80-100", "100-120", "120-150", "150-200", "200+"))
  ) + 
  scale_y_continuous(
    name = "Number of Games",
    limits = c(0, 850),
    breaks = seq(0, 850, 100),
    expand = c(0, 0)
  ) +
  theme_classic() +
  ggtitle("Number of games at different durations") + 
  theme (
    plot.title = ggtext::element_markdown(hjust = 0.5), 
    axis.text.x=element_text(angle = 90, hjust = 0.5), 
    legend.position = "none"
  )

Graphic 3

Show code 
# Data
labels_dev <- c("Capcom Entertainment", "Arc System Works", "Omega Force", "Square Enix", "Breakthrough Gaming", "Konami Digital Entertainment", "Nippon Ichi Software", "Smobile", "Lightwood Games", "Hamster")

genre <- play_data$Genre == "Action"

# Build plot
pop_dev <- ggplot(data = play_data, mapping = aes(x = Developer, fill = genre))+
  geom_bar() + 
  coord_flip() +
  scale_x_discrete(
    name = "Developers",
    limits = factor(labels_dev, level = c("Capcom Entertainment", "Arc System Works", "Omega Force", "Square Enix", "Breakthrough Gaming", "Konami Digital Entertainment", "Nippon Ichi Software", "Smobile", "Lightwood Games", "Hamster")), 
    ) +
  scale_y_continuous(
    name = "Number of Games",
    limits = c(0, 130),
    breaks = seq(0, 130, 20),
    expand = c(0, 0)
  ) +
  scale_fill_manual(
    name = "Genre", 
    labels = c("Not Action", "Action"), 
    values = c("#2E6DB4", "#00AC9F")
  ) + 
  theme_classic() +
  ggtitle("Top 10 Most Popular Developers") +
  theme(
    plot.title = ggtext::element_markdown(hjust = 0.5))

pop_dev
Show code 
# Data 
labels_pub <- c("Sony Computer Entertainment", "Ubisoft", "KOEI TECMO", "SEGA", "Sometimes You", "Square Enix", "Bandai Namco Entertainment", "Hamster", "Ratalaika Games", "eastasiasoft")

genre <- play_data$Genre == "Action"

# Build plot 
pop_pub <- ggplot(data = play_data, mapping = aes(x = Publisher, fill = genre)) +
  geom_bar() + 
  coord_flip() +
  scale_x_discrete(
    name = "Publishers", 
    limits = factor(labels_pub, level = c("Sony Computer Entertainment", "Ubisoft", "KOEI TECMO", "SEGA", "Sometimes You", "Square Enix", "Bandai Namco Entertainment", "Hamster", "Ratalaika Games", "eastasiasoft"))) + 
  scale_y_continuous(
    name = "Number of Games", 
    limits = c(0, 510), 
    breaks = seq(0, 510, 50), 
    expand = c(0, 0)
  ) +
  scale_fill_manual(
    name = "Genre", 
    labels = c("Not Action", "Action"), 
    values = c("#2E6DB4", "#00AC9F")
  ) + 
  theme_classic() +
  ggtitle("Top 10 Most Popular Publishers") + 
  theme(
    plot.title = ggtext::element_markdown(hjust = 0.5))

pop_pub
Show code 
pop_dev / pop_pub

What do the visualizations communicate?

Citation