Intro to R.Rpres

Intro to R
========================================================
author: Jeho Park, Stephen Park  
date: August 4, 2018  
autosize: true  

Agenda
========================================================

- About R
- R Basics
- Working With Data
- Hands-On Project
- Further Learning

This R Workshop is...
========================================================
* for newbies.
  - If you already have some experience in R, please raise your hands.
* about R's programming aspects.
  - It's designed to help you start using and coding R.
* not about Statistics.
  - I assume that you already have some basic knowledge of Statistics
* for you to start learning R afterwards.
  - This workshop is designed to give you a nudge to learn more R down the road. There are many resources to learn R in depth. 
  
Learning Objectives:
========================================================
_By the end of this this workshop, you will be able to:_
* Install R libraries/packages.
* Import and export data from/to a simple CSV file in R.
* Distinguish the differences between R data objects and convert them.
* Use subsetting methods to create subsamples.
* Create basic plots using both base plot in R.
* Create R markdown documents and R slides.
* Create tidy data and manipulate those data using dplyr package.


What is R?
========================================================
* R is a statical programming language/environment.
* R is open source and free.
* R is widely used.
* R is cross-platform.
* R is hard to learn (really?).

What is not R?
========================================================
* S: R's ancestor
* S-Plus: Commercial; modern implementation of S
* SAS: Commercial; widely used in the commercial analytics.
* STATA: Commercial; widely used by economists.
* SPSS: Commercial; easy to use; widely used in Social Science.
* MATLAB: Commercial; can do some Stats. 
* Python: Also can do some Stats; one of the two mostly used programming languages in data science.

Then Why R?
========================================================
* __R community is active and constantly growing__
* R is one of the most popular stat programming lang
* R has tons of user generated libraries/packages
* R code is easily shared with others
* R is constantly improved


Then Why R?
========================================================
* R community is active and constantly growing
* __R is one of the most popular stat tools/programming languages__
* R has tons of user generated libraries/packages
* R code is easily shared with others
* R is constantly improved

###### Further reading about R's popularity in science and engineering:<br>
###### R moves up to 5th place in IEEE language rankings at  https://www.r-bloggers.com/r-moves-up-to-5th-place-in-ieee-language-rankings/
###### https://spectrum.ieee.org/static/interactive-the-top-programming-languages-2017 (6th in 2017)


Then Why R?
========================================================

IEEE Spectrum: Popular Programming Languages in 2017
![Poplar Programming Languages](The_Top_Programming_Languages_2017_-_IEEE_Spectrum.png)

Then Why R?
========================================================

* R community is active and constantly growing
* R is one of the most popular stat tools/programming languages
* __R has tons of user generated libraries/packages__
* R code is easily shared with others
* R is constantly improved

Getting help online and offline
===============================
### On the Internet:
* Google! (Now Google knows "r" means the R programming language)
* Stack Overflow: http://stackoverflow.com/questions/tagged/r
* Cross-Validated: the statistics Q&A site http://stats.stackexchange.com/
* Rseek meta search engine: http://rseek.org/ 
* R-help listserv: https://www.r-project.org/mail.html

R Basics
========================================================
Let's get ready for R-ing!

Open your RStudio app.
Open your browser.

Getting Started - RConsole
========================================================
- Upon opening RStudio, you will see the Console
  - ![RConsole](rconsole.png)

Getting Started - RConsole
========================================================
- You can perform some calculations by typing into it.
```{r}
2+2
```

Getting Started - Variables 
========================================================
- You can also use variables and assign them values.
```{r}
x = 2
x+2
```
```{r}
# The "<-" operator is the same as "="
y <- x+1 # "<-" is preferred
x+y
```

Getting Started - R Environment
========================================================
- When you create a variable it will show up on the environment pane in RStudio
![Environment Pane](renvironment.png)

R Basics - Arithmetic Operators
========================================================
- Operators are symbols that use some input and *operate* on them.
- Arithmetic operators are our common mathematical symbols.
```{r echo='false'}
x*x # Multiplication
x/y # Division
x^3 # Exponentiation
```

R Basics - Logical Operators
========================================================
- Logical operators output either True or False
```{r}
2 < 5  # Less Than
x >= 2 # Greater Than Or Equal To
```
R Basics - Logical Operators
========================================================
```{r}
x == 1 # Equal to. NOTE: This is different from "="
x != x # Not Equal to
```

R Basics - Data Types
========================================================
- R can work with more than just numbers.
```{r}
class("hello, world!") # The class function returns the data type of a variable or value.
class(x == y) 
```

R Basics - Vectors
========================================================
- Up until now, we have been working with single numbers and variables also known as scalars.
- In R, there are also vectors which are sequences of values.
```{r}
a <- c(1,2,3)
b <- c(3:1)

for(i in 1:3) {
  print(a[i]+b[i])
}
```
R Basics - Vectors - 2
========================================================
- R's basic data object is "vector"
- Operators are optimized for vector  (element-wise) operations
- Use vectorized operations than loops whenever possible
```{r}
a + b
a^2
```

R Basics - Built-In Functions
========================================================
- Functions take an input and produce an output. 
- R contains many functions that are built-in, some of which we have already seen such as class() and c().
```{r}
abs(-5) # Absolute Value
sqrt(4) # Square Root
```
R Basics - Built-In Functions - 2
========================================================
```{r}
sum(a)  # Sum
toupper("hello") # To Upper Case
```
R Basics - Built-In Functions - 3
========================================================
```{r}
sd(a) # Can you guess what this funtion calc?
cor(a,b) # How about this?
```


R Basics - Libraries and Packages
========================================================
- R is known for its community and its libraries/packages
- Packages are collections of R functions, data, and compiled code in a well-defined format. The directory where packages are stored is called the library.
- We will install the dplyr package, a popular package for data manipulation.
- ```
install.packages('dplyr')
```
```{r}
library('dplyr')
```

R Basics - Help
========================================================
- With all these packages and functions you may forget how to use one or need to remember which one to use.
```{r}
# This will search for usage of the hist function
?hist
```

```{r}
# This will search packages and functions about histograms
??histogram
```

Exercise 1
==============

- Review of some basic R features
- Open ./exercises/intro/exercise_1.Rmd (this is a R markdown file)


Working with Data
========================================================

Working with Data - DataFrames
========================================================
- A data frame is used for storing data tables. It is a list of vectors of equal length.
```{r}
names <- c("John", "Kim", "Terry")
ages  <- c(21, 34, 16)
male  <- c(TRUE, FALSE, TRUE)
data.frame(names, ages, male)
```
Working with Data - DataFrames - Datasets
========================================================
- R's datasets package has some built in datasets that we will be using.
- The CO2 data frame has 84 rows and 5 columns of data from an experiment on the cold tolerance of the grass species Echinochloa crus-galli.
```{r}
help(CO2)
```
- You can use '$' to access a vector (column/feature/etc) of a dataframe

Working with Data - Data Exploration - Head
========================================================
```{r}
# The head function shows the first few entries in a dataframe.
head(CO2)
```
Working with Data - Data Exploration - Summary
========================================================
```{r}
# The summary function shows summary statistics.
summary(CO2)
```

Working with Data - Data Visualization
========================================================
```{r, eval=FALSE}
hist(CO2$uptake) # Use help function for more plotting options
```
Working with Data - Data Visualization - Boxplot
========================================================
```{r, eval=FALSE}
boxplot(CO2$conc)
```
Working with Data - Data Visualization - Scatterplot
========================================================
```{r, eval=FALSE}
plot(CO2$uptake, CO2$conc)
```
Exercise 2
==============

- Review of datasets and data visualization
- Open ./exercises/intro/exercise_2.Rmd

Working with Data - Data Subsseting (classic) - 1
=========================
Operators that can be used to extract subsets of R objects.
* '[' and ']' always returns an object of the same class as the original; can be used to select more than one element.
* '[[' and ']]' is used to extract elements of a list or a data frame; it can only be used to extract a single element.
* $ is used to extract elements of a list or data frame by name.

Working with Data - Data Subsetting (classic) - 2
==========================
```{r, eval=FALSE}
x <- c("a", "b", "c", "c", "d", "a")
x[1]
x[1:4]
x[x > "a"] 
u <- x > "a" # what's u here?
u
x[u] # subsetting using a boolean vector
y <- list(foo=x, bar=x[u]) 
y
y[[1]]
y$bar
```

Working with Data - Data subsetting (classic) - 3
========================================================
- Create a dataframe containing the first 10 observations. 
- Create a dataframe where only the Plant identifier is Qn3.
- Create a dataframe only containing observations with the CO2 concentration level greater than 600.
```{r, eval=FALSE}
co2_10obs <- CO2[<row>, <col>]
co2_Qn3 <- CO2[?, ?]
co2_gt600conc <- subset(CO2, conc > 600)
```

Working with Data - Data Manipulation (dplyr)
========================================================
- Dplyr is the most common package used for data exploration and transformation
- Dplyr functions: filter, select, arrange, mutate, summarise (plus group_by)
```{r}
head(filter(CO2, Treatment=='nonchilled'))
```
Working with Data - Data Manipulation (dplyr) - Select
========================================================
- ```select``` will output only the columns that you choose
```{r}
head(select(CO2, Plant, conc, uptake), 3)
```
Working with Data - Data Manipulation (dplyr) - Chaining
========================================================
- You can chain/pipe dplyr functions together
    - The infix (or pipe) operator '%>%' will feed the resulting object into the 1st paramater of the next function
```{r}
x <- select(filter(CO2, Treatment=='nonchilled'), Plant, conc, uptake) # OR
y <- CO2 %>% filter(Treatment=='nonchilled') %>% select(Plant, conc, uptake)
x == y
```

Working with Data - Data Manipulation (dplyr) - Arrange
========================================================
- ```arrange()``` sorts rows by a column
```{r}
# Use desc() to sort descending
CO2 %>% arrange(desc(uptake)) %>% head(3)
```

Working with Data - Data Manipulation (dplyr) - Mutate
========================================================
- ```mutate()``` creates new variables
```{r}
CO2 %>% mutate(conc_L = conc / 1000) %>% head()
```

Working with Data - Data Manipulation - Summarise
========================================================
- ```group_by()``` and ```summarise()``` work together to aggregate variables
```{r}
CO2 %>% group_by(Type) %>% summarise(avg_uptake = mean(uptake))
```

Working with Data - Data Manipulation (dplyr) - Tips
========================================================
- In datasets with a lot of columns you can select all columns but selected ones by using the '-' operator ```select(CO2, -Plant)```
- When aggregating data you need an aggregate function such as mean, median, n (number of rows in a group), sum, etc.
- If your data has missing values, you need to add the parameter ```na.rm = TRUE```, this will skip any missing values.
- When working with data, the data manipulation process often takes more time than the analysis.

Exercise 3
==============

- Review of some data manipulation functions in dplyr 
- Open ./exercises/intro/exercise_3.Rmd

Tydyverse
===================



Further Learning - Beginner/Intermediate
========================================================
- Beginner/Intermediate Topics
    - User Defined Functions
    - Control Structures
    - Statistical Methods
    - Data Cleaning
    - Data Visualization Libraries (ggplot2, plotly)
        - https://plot.ly/r/dashboard/
Further Learning - Advanced
========================================================
- Advanced Topics
    - Deep Learning
    - Functional Programming
    - Object Oriented Programming
    - Debugging Tools
    - Performance

Hands-On Project
========================================================

Hands-On Project
========================================================
- In this workshop we have been using the console but when working on a project you will generally use the editor so you can save your scripts. This helps anyone reproduce your findings.
- When using a script, you must run your code either all at once (Run All) or Line-by-Line

Hands-On Project - Reading the Data
========================================================
- Download the data to your computer at http://bit.ly/r-hands-on-s2018
- You can import the data to R either by code or by clicking Import Dataset on the Environment Tab
```{r}
# You need to put the full path to the file in quotes or change the working directory [getwd(), setwd()]
Births <- read.csv("Births2015.csv")
Auto   <- read.csv("Auto.csv")
```

Hands-On Project - Analysis
========================================================
1. What is the name of the 1st car in the dataset?
2. What is the total number of babies born in 2015?
3. Make a boxplot of car mpg.
4. Make a histogram of number of births.
5. How many babies are born on Wednesdays?
6. Which date had the least amount of babies born?
7. Is there a relationship between the number of cylinders and mpg? 
8. Plot the average mpg for cars of each number of cylinders?

Hands-On Project - Answers
========================================================
1. ```head(Auto,1)```: chevrolet chevelle malibu
2. ```sum(Births$births)```: 3978497
3. ```boxplot(Auto$mpg)```
4. ```hist(Births$births)```
5. ```Births %>% group_by(wday) %>% summarise(sum=sum(births)) %>% arrange(sum)```: 638513
6. ```arrange(Births, births)```: 12/25/2015
7. ```cor(Auto$mpg, Auto$cylinders)``` or ```plot(Auto$cylinders, Auto$mpg)```

Hands-On Project - Answer #8
========================================================
```{r}
x <- Auto %>% group_by(cylinders) %>% summarise(mean(mpg))
plot(x$cylinders, x$`mean(mpg)`)
```

Hands-On Project - Answer #8 - 2
========================================================
```{r}
plot(x$cylinders, x$`mean(mpg)`, type='o', xlab="Cylinders", ylab="Average MPG", main="Cylinders vs. Average MPG") # Cleaner Version
```

Resources
========================================================
- Swirl (http://swirlstats.com/)
- DataCamp (https://www.datacamp.com/courses/tech:r)
- R for Data Science by Hadley Wickham (http://r4ds.had.co.nz/) [FREE]
- Statistics.com (https://www.statistics.com/landing-page/r-courses/)