Learning R

A collection of 53 posts

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R for Data Science Exercises: Numbers

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R for Data Science Exercises: Logical Vectors

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R for Data Science Exercises: Communication

Now that we understand your data, we need to communicate this understanding to others. To help others quickly build up a good mental model of the data, you will need to be as self-explanatory as possible. This chapter focuses on the tools you need to create good graphics.

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R for Data Science Exercises: Exploratory Data Analysis

The goal during EDA is to develop an understanding of your data using questions as tools to guide your exploration: 1. What type of variation occurs within my variables?; 2. What type of covariation occurs between my variables?

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R for Data Science Exercises: Visualise Layers

This section is based on the layered grammar of visualisations, facets, statistics, position adjustments, and coordinate systems to give you a fundamental understanding of plotting your data.

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R for Data Science Exercises: Workflow Scripts and Projects and Data Import

The final installment of exercises from the "Whole game" section of the R4DS 2nd edition. These exercises focus on becoming more familiar and efficient in R studio.

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R for Data Science Exercises: Data Transformation Case Study

In this case study, we will be comparing how often a player successfully hits the ball (H) to the total number of attempts they made to hit the ball (AB). Including a count ensures our analysis is based on a reasonable amount of data and not just a few instances.

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R for Data Science Exercises: Workflow Code Style and Data Tidying

These exercises are focussed on furthering your workflow coding style and tidying messy data to make analysis easier.

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R for Data Science Exercises: Data Transformation

These exercises are an introduction to the data transformation basics of R, including sorting, filtering, grouping, modifying, summarising, relocating and selecting data.

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R for Data Science Exercises: Workflow Basics

These exercises are an introduction to the workflow basics of R, including being mindful of typos in code and keyboard shortcuts for faster coding!

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R for Data Science Exercises: ggplot2

These exercises are an introduction to the ggplot2 package used to visualise data in R.

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R for Data Science Exercises: Introduction

The beginning of a series in which I'm working through the 'R for Data Science 2nd Edition' Exercises to learn R. Follow along if you're interested!

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Learning R: Base Graphics

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Learning R: Dates and Times

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Learning R: Simulation

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Learning R: Looking at Data

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Learning R: vapply and tapply

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Learning R: lapply and sapply

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Learning R: Functions

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Learning R: Logic

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Learning R: Matrices and Data Frames

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Learning R: Subsetting Vectors

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Learning R: Missing Values

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Learning R: Vectors

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Learning R: Sequences of Numbers