R0.Rmd

---
title: "R0"
author: "Tyler Richards"
output:
  prettydoc::html_pretty:
    theme: tactile
    highlight: github
---


# Basic Operations and Functions in R 

## Why Learn R?

R is a data science specific langauge with a huge community of dedicated and helpful users in every field imaginable. Not only do Google, Microsoft, Uber, TechCrunch, P&G, the New York Times, and Bank of America  use R, but also the best academic institutions in the world in every deparment from journalism to finance to political science and biology. 

The goal of this workshop, and the next 2 R workshops, is to get you all from 'I have no idea how to do that' to 'yeah I think I could figure out how to do this.'

Before we get to all that, we need to set a good foundation for how R works. 

We'll begin by covering data types and classes, and then look into how to create vectors and dataframes, and after that we'll work on how to manipulate your data by filtering and indexing. 

## What is R?

R is an open source programming language and software environment for statistical computing and graphics.

R vs RStudio
R is the langauge while RStudio is the integrated development environment(IDE) analagous to Eclipse for python. 

Overview of RStudio  

* console  
* Scripts  
* environment   
* files, plots, packages  

Arguments for an R function come in an order that you must implicitley accept or explicitly define. 

R is keeps all your information in objects.  

## Common data/object types

```{r}
#Numeric
x <- 4.8
print(class(x))

```

```{r}
#Integer
y <- as.integer(x)
print(y)
```

```{r}
#Character
z <- "dog"
print(class(z))
```

```{r}
#Logical (also called Boolean)
a <- TRUE
print(class(a))
```

```{r}
#factor
heat_setting <- factor(c("Low", "Medium", "High"))
print(class(heat_setting))
```

## Data Structure Types

Major examples include Vectors, Matrices, Arrays, Data Frames, and Lists

### Vectors
```{r}
#Numerical vector
a<-c(1,2,3,4,5,6,7)   
print(a)
```

```{r}
#character vector
b<-c("one","two","three")   
print(b)

```

```{r}
#Logical vector
c<-c(TRUE,FALSE,FALSE,TRUE) 
print(c)
```


```{r}
#Subsetting vectors
a
a[1]
a[2:5]
a[-1]
a[-(1:3)]
a[-length(a)]

```

### Matrix

```{r}
# Matrix with data in it
new_matrix<-matrix(1:20,
                    nrow=5,
                    ncol=4) ##byrow=TRUE allows to fill by row
new_matrix
```


```{r}
#Subsetting Matrices
new_matrix[1,4]
new_matrix[5,2]
```

### Data Frames

```{r}
#first create vectors
studentID <- c(1, 2, 3, 4)
age <- c(25, 19, 22, 21)
university <- c("UF", "FSU", "UF", "UGA")
hired <- c(TRUE, FALSE, TRUE, FALSE)

#then combine them into a dataframe!
studentdata <- data.frame(studentID, age, university, hired)
studentdata
```

```{r}
#Subsetting dataframes
studentdata[1,3]
studentdata[1,"hired"]
studentdata$age
```

### Logicals

Logicals in R deal with ``TRUE`` and ``FALSE`` operations. These are also known as boolean operaters in many programming languages. 

We obtain logical expressions by comparing values using logical operaters. 

Logical operaters in R include the following:

* equal: ``==``
* not equal: ``!=``
* greater/less than: ``> <``
* greater/less than or equal: ``>= <=``

Examples:
```{r}
3<4
4>3*2
3==3*1
```


### Introduction to Functions 

```{r}
sum.of.squares <- function(x,y) {
  x^2 + y^2
}
sum.of.squares(2,3)

fahrenheit_to_kelvin <- function(temp_F) {
  temp_K <- ((temp_F - 32) * (5 / 9)) + 273.15
  return(temp_K)
}
fahrenheit_to_kelvin(32)
```


## Basic Functions for working with data
We will use the "mtcars" dataset to apply some of these ideas. This dataset is preinstalled with R to be used pretty much for the purpose of trying things out.  

The **head()** function shows you the top six rows while **tail()** shows the last six rows. 
Another useful function is the **str()** which gives a description of the structure of the data.

```{r}
class(airquality)
str(airquality)
head(airquality)
tail(airquality)
```

## Dplyr 

when you get new data, about 0% of the time it will be in the right format and immediately useful for modeling or analysis.
Dplyr is the grammar of data manipulation. Let's check out how it works. 


```{r}
#install.packages("dplyr")
library(dplyr)
```

Dplyr runs off of a series of verbs. We'll go through a few now. 

### Filter
```{r}
head(filter(airquality, Wind > 5))
```

```{r}
head(filter(airquality, Temp > 80 & Month > 5))
```

### Mutate

```{r}
head(mutate(airquality, TempInC = (Temp - 32) * 5 / 9))
```

### Summarise

```{r}
summarise(airquality, mean(Temp, na.rm = TRUE))
```

What if we want to perform multiple 'verbs' on our data at once? 

### Piping

Piping allows us to do multiple things to data without saving each thing

```{r}
airquality %>% 
  filter(Month != 5) %>% 
  mutate(TempInc = (Temp - 32) * 5 / 9)
```

### Group By
```{r}
airquality %>% 
    filter(Month != 5) %>% 
    group_by(Month) %>% 
    summarise(mean(Temp, na.rm = TRUE))
```