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README.Rmd
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README.Rmd
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---
title: "Introduction to R"
output: github_document
---
## What is R? What is RStudio?
The term “R” is used to refer to both the programming language and the software that interprets the scripts written using it.
[RStudio](https://rstudio.com/) is currently a very popular way to not only write your R scripts but also to interact with the R software. To function correctly, RStudio needs R and therefore both need to be installed on your computer.
## Why R?
### R does not involve lots of pointing and clicking, and that’s a good thing
The learning curve might be steeper than with other software, but with R, the results of your analysis do not rely on remembering a succession of pointing and clicking, but instead on a series of written commands, and that’s a good thing! So, if you want to redo your analysis because you collected more data, you don’t have to remember which button you clicked in which order to obtain your results; you just have to run your script again.
Working with scripts makes the steps you used in your analysis clear, and the code you write can be inspected by someone else who can give you feedback and spot mistakes.
Working with scripts forces you to have a deeper understanding of what you are doing, and facilitates your learning and comprehension of the methods you use.
### R code is great for reproducibility
Reproducibility is when someone else (including your future self) can obtain the same results from the same dataset when using the same analysis.
R integrates with other tools to generate manuscripts from your code. If you collect more data, or fix a mistake in your dataset, the figures and the statistical tests in your manuscript are updated automatically.
An increasing number of journals and funding agencies expect analyses to be reproducible, so knowing R will give you an edge with these requirements.
### R is interdisciplinary and extensible
With 10,000+ packages that can be installed to extend its capabilities, R provides a framework that allows you to combine statistical approaches from many scientific disciplines to best suit the analytical framework you need to analyze your data. For instance, R has packages for image analysis, GIS, time series, population genetics, and a lot more.
### R works on data of all shapes and sizes
The skills you learn with R scale easily with the size of your dataset. Whether your dataset has hundreds or millions of lines, it won’t make much difference to you.
R is designed for data analysis. It comes with special data structures and data types that make handling of missing data and statistical factors convenient.
R can connect to spreadsheets, databases, and many other data formats, on your computer or on the web.
### R produces high-quality graphics
The plotting functionalities in R are endless, and allow you to adjust any aspect of your graph to convey most effectively the message from your data.
### R has a large and welcoming community
Thousands of people use R daily. Many of them are willing to help you through mailing lists and websites such as Stack Overflow, or on the RStudio community.
### Not only is R free, but it is also open-source and cross-platform
Anyone can inspect the source code to see how R works. Because of this transparency, there is less chance for mistakes, and if you (or someone else) find some, you can report and fix bugs.
## Workshop objectives
We assume no previous knowledge of R or any other programming language with this workshop. We're starting from the beginning! The major objectives of this workshop are to
* Familiarize you with R and RStudio functionality,
* Help you understand the basics of R programming,
* Give you the tools to work with your own data, and
* Help you get help
If by the end of this you love R and want to learn more, consider joining the [R User Group!](https://commons.gc.cuny.edu/groups/rug-r-users-group/). RUG will meeting every other week to learn and work together on projects.
## Sections
[Introduction](sections/01-introduction.md)
[Objects and functions](sections/02-functions.md)
[Vectors and data types](sections/04-vectors.md)
[Data structure](sections/06-data-structure.md)
[Packages](sections/07-packages.md)
[Importing data](sections/08-importing-data.md)
-----
[Get Started >>>](sections/01-introduction.md)
-----
## Resources
[RStudio primers](https://rstudio.cloud/learn/primers)
[R for Data Science](https://r4ds.had.co.nz/)
[RStudio Education](https://education.rstudio.com/)
[tidytuesday github](https://github.com/rfordatascience/tidytuesday)
[tidytuesday youtube](https://www.youtube.com/user/safe4democracy/videos)
-----
**Note**: This material is copied and modified from [Data Carpentry](https://datacarpentry.org/R-ecology-lesson/00-before-we-start.html). [http://creativecommons.org/licenses/by/3.0/](http://creativecommons.org/licenses/by/3.0/).
"Copyright (c) Data Carpentry"