Level Difficulty in Candy Crush Saga

Description

Candy Crush Saga is a hit mobile game developed by King (part of Activision|Blizzard) that is played by millions of people all around the world.

In this Project, we will get to work with a real Candy Crush dataset and use this data to estimate level difficulty. This project involves manipulating data frames using dplyr and make plots using ggplot2.

Usage

Clone this repository and open the Jupyter notebook file (*.ipynb) in a Jupyter environment with R kernel support. Make sure to install the required packages such as tidyverse. You can do this by running the following commands in a code cell within the notebook:

install.packages("tidyverse")

Once the packages are installed, run the code cells in the notebook to generate the plots and analyses.

If you don't have a Jupyter environment set up, you can install Jupyter Notebook and the R kernel using the following steps:

1. Install Jupyter Notebook by following the instructions on the official Jupyter website.

2. Install the R kernel for Jupyter Notebook by running the following commands in your R console:

install.packages("IRkernel")
IRkernel::installspec()

After completing the installation, launch Jupyter Notebook, navigate to the folder containing the notebook file, and open it to begin running the analysis.

Contents

1. Candy Crush Saga: Load in the packages we're going to need for the project.
2. The data set: Load in the dataset and display the first couple of rows.
3. Checking the data set: Count how many players are in the dataset and how many days it spans.
4. Computing level difficulty: Calculate the probability of winning a level in a single attempt for each level.
5. Plotting difficulty profile: Plot a line graph with the difficulty for each level.
6. Spotting hard levels: Add points to the plot and a horizontal dashed line at the 10% value.
7. Computing uncertainty: Compute the standard error of the difficulty for each level using the given formula.
8. Showing uncertainty: Add error bars to the difficulty profile plot.
9. A final metric: Calculate how likely is it that a player will complete all the levels in the first attempt.
10. Should our level designer worry?: Should our level designer worry that a lot of players will complete the episode in one attempt?