PART II: Import, manipulate and analyze your data

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## Right Start Programming with R language

### Part II: Import, manipulate and analyse

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Bilikisu Aderinto

Stephen Balogun

Sat, January 29 2022

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## Recap of Part I

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- Setting up your work environment is essential for good project experience.

- Your interactions with R will be largely through functions which are contained in packages

- Most times, you will work with tabular data (made up of rows and columns)

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<img src="./imgs/recap.jpg" width="704" />
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## Outline

1. Importing your data (web, computer, other sources)

3. Managing different file formats (*.csv*, *.xlsx*, *.txt* etc)

4. Data cleaning and transformation

5. Basic analysis

6. Data visualization: plotting charts

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## Our data at a glance: Africa COVID-19 dataset

- Country/Region: Country of reporting

- Date: date of reporting

- Confirmed: total confirmed cases of COVID-19 from the country

- Deaths: total confirmed deaths due to COVID-19 from the country

- Recovered: Cumulative number of persons who have recovered from COVID-19 from the country

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## Organizing the project directory

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- Put your data (inputs) in a different folder

- Keep your R scripts separate

- Keep your outputs in a different folders

- Keep your work reproducible, __*Avoid "point-and-click" as much as possible*__

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<img src="./imgs/organizing.jpg" width="3200" />
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## Managing your R scripts

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- Will you be needing more than a single script?

- Divide your scripts into sections and label appropriately. Use **cTRL + SHIFT + R**

- loading required packages
    
    - importing files
    
    - cleaning data
    
    - exploration
    
    - plotting charts
    
    - saving outputs
    
- Keep things simple

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## Loading required packages

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- **6 base** packages are loaded by default with their functions made available

- Generally, any other functions will require you to access the package first

- Consider all the additional packages you might need in addition to the base packages and load them

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<img src="./imgs/base_pkgs.png" width="381" />
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## Importing your file

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- Is your file on the local computer or online?

- If online, do you want to import directly from the internet or download a local copy?

- What type of file extension are you working with? Different file extension may require different packages

- Import your file to R

- Format your file appropriately (print and variable names)

- Assign your file a name

- Is your file tidy? What are the challenges?

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<img src="./imgs/import.jpg" width="3968" />
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## Data cleaning - {dplyr}, {tidyr} and other tidyverse packages

A few things identified with our data that we need to do

We need to:

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- Format column names

- Format the **date** for the Excel and the text documents

- Convert **sex** to factors (categorical variables)

- format **state** as factors

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- change **married** to a logical variable

- change **height** to metres

- split **BP** into **bp_systole** and **bp_diastole**

- Calculate new **BMI**
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## Practice

- Simple exploration

- Summary of your data

- Using the `pipe` operator

- Data wrangling

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# Plotting charts

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## Data visualization

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- R has several packages for visualizing charts.

- Charts can be static, embedded with html widgets or interactive

- {ggplot2} is one of the most common. Implemented based on the grammar of graphics

- The type of chart that you plot will depend on the type of data that you have

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## Common plots

- Histogram: for continuous variables

- Bar chart: for categorical variables

- Scatter plots: for continous variables

- Line plot: for trend analysis

- Box plot: for visualization of summaries

- Pie chart: avoid if possible

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## Layer options

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<img src="./imgs/grammar.png" width="1566" />
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<img src="./imgs/grammar2.png" width="2342" />
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## Themes

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## Practice - 2

- Using `{ggplot2}` package

- Mapping variables

- Selecting your type of chart

- Plotting bar chart

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## Summary

- R can import several file types from both local or online platforms

- You will often need to clean and transform your data before analysis

- Analysis will depend on the type of conclusion of interest

- There are several powerful packages for visualization.

- {ggplot2}, one of the most common, uses graphical layers.

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# Questions