video-games-and-sliced.Rmd

---
title: "Video Games and Sliced"
description: |
  Graphs and analysis using the #TidyTuesday data set for week 12 of 2021
    (16/3/2021): "Video Games and Sliced"
author:
  - name: Ronan Harrington 
    url: https://github.com/rnnh/
date: 03-23-2021
repository_url: https://github.com/rnnh/TidyTuesday/
preview: video-games-and-sliced_files/figure-html5/figure1-1.png
output:
  distill::distill_article:
    self_contained: false
    toc: true
---

## Setup

Loading the `R` libraries and [data set](https://github.com/rfordatascience/tidytuesday/blob/master/data/2021/2021-03-16/readme.md).

```{r, code_folding=TRUE, include=TRUE}
# Loading libraries
library(tidyverse)

# Reading in the raw data from GitHub (I would use "tt_load", but I hit an API
# rate limt)
games <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2021/2021-03-16/games.csv')
```

## Plotting Peak vs. Average number of players using 200 observations

For this plot, the top 200 observations for average number of players at the
same time are selected using `slice_max(order_by = avg, n = 200)`. The peak and
average number of players for these observations are plotted on a scatter plot.
The colour of the points indicates the game used for each observation. Models are
fit to illustrate trends in the data; these trends follow the featured three
games. The game "Cyberpunk 2077" was filtered out before creating this plot,
as it occurred in only one of the 200 observations.

```{r figure1, code_folding=TRUE, include=TRUE, fig.height=5, fig.width=6}
games %>% 
  select(gamename, avg, peak) %>%
  # Filtering out Cyberpunk 2077 as it has only a single observation
  filter(gamename != "Cyberpunk 2077") %>%
  slice_max(order_by = avg, n = 200) %>%
  ggplot(aes(x = avg, y = peak, colour = gamename)) + 
  geom_point() + 
  geom_smooth(formula = y ~ x) + 
  theme_bw() +
  theme(legend.position = "bottom") +
  labs(title = "Peak vs. Average number of players online simultaneously",
       subtitle = "Top 200 observations for Average used",
       y = "Highest number of players at the same time",
       x = "Average number of players at the same time",
       colour = "Game")
```

## Combining "year" and "month" into a new variable

Combining the `year` and `month` variables makes it easier to track when each
observation was recorded. Creating this new `year_month` variable using the
`as.date()` function from `{lubridate}` ensures that it will be interpreted as a
date.

```{r, include=TRUE, code_folding=TRUE}
# Creating a "year_month" variable with the year and month of each observation
# using the lubridate "as_date()" function, by pasting together...
games$year_month <- lubridate::as_date(paste(
  # ...the "year" variable...
  games$year,
  # ...the number of each month, obtained by matching the month names to the
  # "month.name" built-in constant...
  match(games$month, month.name),
  # ...the number "1", as a dummy "day" value...
  1,
  # ...separated by a "-".
  sep = "-"))

# Printing the start of the "games" object with the new variable...
games
```

## Plotting the monthly player gains and losses for three of the most popular games

This plot uses the `year_month` variable on the x-axis and the `gain` variable
on the y-axis to illustrate month-to-month gains and losses in average players.
The three games used share the majority of the highest `avg` (average number of
simultaenous players) values in the data set. This graph is faceted for each
game. To put these gains and losses into perspective, dashed lines are added at
plus and minus 100,000 players in each facet.

```{r figure2, code_folding=TRUE, include=TRUE, fig.height=4, fig.width=9}
games %>% 
  # Selecting the variables
  select(gamename, year_month, gain) %>%
  # Filtering the data set for three of the most popular games
  filter(gamename == "Dota 2" |
           gamename == "PLAYERUNKNOWN'S BATTLEGROUNDS" |
           gamename == "Counter-Strike: Global Offensive") %>%
  ggplot(aes(year_month, gain, fill = gamename)) +
  geom_col() +
  theme_classic() +
  theme(legend.position = "none") +
  # Adding dashed lines to put facets into perspective
  geom_hline(yintercept = 100000, linetype = "dashed") +
  geom_hline(yintercept = -100000, linetype = "dashed") +
  # Faceting the plot for each game
  facet_wrap(~gamename, scales = "free") +
  labs(
    title = "Gains/Losses in average number of players online for three games",
    subtitle = "Dashed lines added at +/-100,000 players for each game",
    x = "Time",
    y = "Gains/Losses in average number of players"
  )
```

## Discussion

The two plots in this post illustrate peak number of players online, average
number of players online, and changes in that average for three games. Of these
games, PLAYERUNKNOWN'S BATTLEGROUNDS (PUBG) has the highest values for peak and
average players by far. However, these high points were not sustained, with
dramatic losses in average number of players per month in 2018. By contrast,
Dota 2 has maintained a relatively steady player base, without dramatic gains or
losses. Counter-Strike: Global Offensive's spike in popularity around April 2020
coincides with the introduction of lockdown measures.