[Coursera] Course 7. Data Analysis with R programming

WEEK 1 - Programming and data analytics
WEEK 2 - Programming using RStudio
WEEK 3 - Working with data in R
WEEK 4 - More about visualizations, aesthetics, and annotations

[Definition]

-Programming languages: The words and symbols we use to write instructions for computers to follow

-R: A programming language frequently used for statistical analysis, visualization, and other data analysis

-Open source: Code that is freely available and may be modified and shared by the people who use it

-Integrated Development Environment (IDE): A software application that brings together all the tools you may want to use in a single place

-CRAN (Comprehensive R Archive Network): An online archive with R packages, source code, manuals, and documentation

-Packages (R): Units of reproducible R code

-Tidyverse (R): A system of packages in R with common design philosophy for data manipulation, exploration, and visualization

-ggplot2 (R): Create a variety of data viz by applying different visual properties to the data variables in R

-tidyr (R): A package used for data cleaning to make tidy data

-readr (R): Used for importing data

-dplyr (R): Offers a consistent set of functions that help you complete some common data manipulation tasks

-Mapping (R): Matching up a specific variable in your dataset with a specific aesthetic

[The basic concepts of R]

• Functions: A body of reusable code used to perform specific tasks in R
• Comments: Describe or explain what's going on in the code
• Variables: A representation of a value in R that can be stored for use later during programming
• Data types: Logical, integer, double, character, etc.
• Vectors: A group of data elements of the same type
• Pipes: A tool in R for expressing a sequence of multiple operations, represented with "%>%"

[The most common data structures in R]

• Vector: A one-dimensional sequence of data elements. Only a single data type. / List: Elements can be of any type.
• Array
• Data frame: A collection of columns
• Matrix: A two-dimensional (rows and columns) collection of data elements. Only contain a single data type.

[What Packages include]

• Reusable R functions
• Documentation about the functions
• Sample datasets
• Tests for checking your code

[To get summaries of a data fram]

• skim_without_charts()
• glimpse()
• head()
• select()

[Operators]

1. Arithmetic	+, -, *, /, %%, %/%, ^
2. Relational	<, >, <=, >=, ==, !=
3. Logical	&, &&, |, ||, !
4. Assignment	<-, <<-, =, ->, ->>

[Core concepts in ggplot2]

• Aesthetics: A visual property of an object in your plot
• Geoms: The geometric object used to represent your data
• Facets: Let you display smaller groups, or subsets of your data
• Labels and annotations: Let you customize your plot

ex) ggplot(data = penguins) + geom_point(mapping = aes(x = flipper_length_mm, y = body_mass_g))

[Facet functions]

• Facet_wrap()
• Facet_grid()

[Resources for R]

https://ourcodingclub.github.io/tutorials.html

Tutorials

 

https://ropensci.org/

rOpenSci - open tools for open science

 

https://cran.r-project.org/web/views/

CRAN Task Views

 

https://rstudio-education.github.io/tidyverse-cookbook/

The Tidyverse Cookbook

 

https://r4ds.had.co.nz/index.html

Welcome | R for Data Science

 

https://datacarpentry.org/dc_zurich/R-ecology/05-visualisation-ggplot2.html

Visualizing data with ggplot2

 

https://viz-ggplot2.rsquaredacademy.com/index.html

Data Visualization with ggplot2

 

https://github.com/yixuan/prettydoc/

GitHub - yixuan/prettydoc: Creating Pretty HTML From R Markdown