Module # 6 Visual Differences & Deviation Analysis via R

For this assignment, I decided to take a dataset from kaggle.com of the most common game types among novice chess players. The dataset contains the ratings of both sides, game length, time control, and the Portable Game Notation (pgn) of the entire game. When analyzing the raw data, it can be challenging to understand which type of game is most common. By doing a simple bar chart hundreds of rows of data is turned into a simple graphic that anyone can understand. My basic visualization does fit into Few and Yau’s discussion. My bar graph shows the frequency of types of chess games people can conclude that the most popular types of chess match for beginners is rapid games. For beginners, this is the best time control as it allows for them to have the time to understand why they are making the moves that they are making. Whereas blits and bullet are much more fast pace causing for players likelihood of making mistakes to be greater. 

dataset:

https://www.kaggle.com/datasets/tianmin/chesscom-games-of-novice-chess-player

Code:

setwd("C:/Users/13212/OneDrive/Documents/College/USF/Spring 23/Visual analytics/Module 6 - Visual differences")

novice <- read.csv("novice_players.csv")

library(ggplot2)

libra

#what is the most common time control

ggplot(novice, aes(x=time_class)) + geom_bar() + ggtitle("Most Common Time Control") + xlab("Time Control") + ylab("Frequency")

Output: