r ppt

1. R SCRIPTING BASICS
Ross Wickham
Senior Hydraulic Engineer
NWW, Hydrology Branch
Date: 17 March 2022

2. 2
OUTLINE
• R Background
• Basic Syntax and Scripting
• Key Packages
• Example Applications
• Demo
• Summary
• Resources

3. 3
R BACKGROUND
• Object oriented, like Python, C++, Java
• Interpreted language (you don’t need to pre-compile anything)
• Free, open-source
• Implementation of S programming, a statistical programming language
• Created for statistics, data mining, and data analysis

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Assignment operators are bidirectional, and can be chained
a = 3, b = 4, c = 4, d = 5, e = 6
Create sequences, or specified values; c = default concatenate function
BASIC SYNTAX

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Vectors are a key data type, like Python lists:
Standard (or base) R syntax is to wrap objects with a function for evaluation:
Subset vectors using brackets
BASIC SYNTAX

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Data.frames are an essential data storage type for any tabular/paired data (e.g., time series),
like pandas dataframe in Python:
Data.frames are easily plotted:
BASIC SYNTAX

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Code is streamlined for statistical testing
BASIC SYNTAX

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Loading and installing packages is very simple:
Add a “?” in front of any function to see its
help page in RStudio:
Examples and additional documentation
can be found in package vignettes:
INSTALLING AND HELP

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• An alternative to basic R syntax that has quickly gained popularity
• Tidyverse style has a core philosophy for data structure and analysis (“tidy data”):
• Every column is a variable
• Every row is an observation
• Every cell is a single value
• Rich library of functions to streamline data analysis using “pipelines”
• %>% operator indicates you are passing the object on the left to the function on the right.
• These can be chained, passing the object between multiple functions
TIDYVERSE SYNTAX

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My preferred method: use RStudio, an Integrated Developer Environment (IDE)
• See your code
• Test evaluations
• View errors
• See plots
• Get help
• See current objects being used (your “environment”)
• See history
• Customize the user interface
• Configure R version
• Available on App Portal
SCRIPTING IN R

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SCRIPTING WITH RSTUDIO
Source Code
Current Objects
Help, Plots,
Packages, and
File
management
Console for
testing code and
viewing output
Navigate through script tabs
Code
Outline

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Able to control which version of R is being used: Tools > Global Options
SCRIPTING WITH RSTUDIO – CONFIGURING R
*Use 64-bit unless
you have a good
reason to use 32-bit

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R VS PYTHON
• Use what you know and are comfortable with – both are great
• R is more portable
• Python is generally considered easier to learn
• R is better for statistics
• R considered easier for plotting
• Python typically faster, better for machine learning

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KEY PACKAGES (FOR H&H)
• tidyverse – set of packages designed to work together for data analysis under a core philosophy
– ggplot2 – plotting library
– dplyr – data manipulation
– purrr – create complex data pipelines
– readr – fast, user-friendly way to read rectangular data
– tidyr – consistently organize tabular data
– and others…
• reshape2 – data.frame manipulation
• lubridate – parse and manipulate dates
• rgdal, sp, raster – read, write, and manipulate geospatial objects
• plotly – interactive plots
• dataRetrieval - USGS data web retrieval
Specific to the Corps:
• cwms_read – read publicly available NWD CWMS data, Jeff Tilton (NWD)
• dssrip – read, manipulate, write DSS data, Evan Heisman (HEC)
Advanced Users:
• shiny, shinydashboard – develop web app user interfaces; dynamically interact with data
• leaflet – interactive maps, with interactive content

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QUESTIONS?

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RESOURCES
Simple R cheat sheet
Cheat sheets for multiple packages
Portable version of R and RStudio with example code:
<HEC share drive>
The R Manuals (written by R development core team)
The Little Book of R for Time Series
Code Academy tutorials
Translation between Python pandas and R data.frame
Stackoverflow: help forum
dssrip package: read and write DSS in R
cwms_read package: read NWW CWMS data in Python and R