Introduction to Data Science

class: center, middle, inverse, title-slide

.title[
# Introduction to Data Science
]
.subtitle[
## Week 2: Toolkit
]
.author[
### Ugur Aytun
]
.institute[
### METU, Department of Economics | ECON 413
]

---

# Before starting

- In this week We learn the basics of R.

- Before starting, I should recommend to use copilot. It is a great tool to learn R. It completes your code and gives you hints about the functions and packages. You can find information in the syllabus.

- Second thing, creating a R project for this course makes your life easier. Do not forget to specify directory for your project.

- Firstly open a folder titled "ECON413". Then create a project for this directory. File -> New Project -> Existing Directory -> Browse -> Select the directory -> Create Project

--
- Organize the folders in your project. Create folders such as "R files", "Data", "Raw data", "Results". When beginning  to new project, I always create these folders.

---
# Basics -arithmetic operations

-- R is great calculator. You can use it for basic arithmetic operations.

``` r
# 1. Addition
2+3
```

```
## [1] 5
```

``` r
# 2. Subtraction
5-3
```

```
## [1] 2
```

``` r
# 3. Division
10/2
```

```
## [1] 5
```

``` r
# 4. Multiplication
3*4
```

```
## [1] 12
```

---
# Basics -arithmetic operations (cont.)

-- R is great calculator. You can use it for basic arithmetic operations.

``` r
# 5. Exponentiation
2^3
```

```
## [1] 8
```

``` r
# 6. Order of operations
(2+3)*4
```

```
## [1] 20
```

``` r
# 7. Modulo
100 %/% 60 ## How many whole hours in 100 minutes?
```

```
## [1] 1
```

---
# Basics -logical operations

-- R is great for logical operations.

``` r
# 1. Greater than
2>3
```

```
## [1] FALSE
```

``` r
# 
1 > 2 & 1 > 0.5 ## The "&" stands for "and"
```

```
## [1] FALSE
```

``` r
# 
1 > 2 | 1 > 0.5 ## The "|" stands for "or"
```

```
## [1] TRUE
```

---
# Basics -logical operations (cont.)

-- R is great for logical operations.

``` r
# 2. Negation (!) -this helps us to filter the data.

!(2>3)
```

```
## [1] TRUE
```

``` r
# 3. Value matching (==)
"apple" == "orange"
```

```
## [1] FALSE
```

``` r
# %in% is a great operator to check if a value is in a vector.
4 %in% c(1,2,3,4,5)
```

```
## [1] TRUE
```

``` r
# "Not-in" operator
`%ni%` = Negate(`%in%`)
4 %ni% 5:10
```

```
## [1] TRUE
```

---
# Basics -logical operations (cont.)

-- R is great for logical operations.

``` r
# 4. Floating-point numbers

0.1 + 0.2 == 0.3
```

```
## [1] FALSE
```

``` r
# all.equal() is a great function to compare floating-point numbers.

all.equal(0.1 + 0.2, 0.3)
```

```
## [1] TRUE
```

---
# Basics -assignment

-- Use <- or = to assign values to variables.

``` r
# 1. Assign a value to a variable
x <- 5

# 2. Print the value of x
x
```

```
## [1] 5
```

``` r
# We can also use -> to assign values to variables. But we do not recommend it.

4 -> y
```

- We can also use = but it it has special role in functions. So, it is better to use <- for assignment.

---
# Basics -help

-- Use ? to get help about a function.

``` r
# 1. Get help about a linear model function
?lm
```

```
## starting httpd help server ... done
```

``` r
# 2. typing "help(lm)" also gives the same result.
help(lm)
```

---
# Basics -vignettes

-- Use vignette() to get help about a package.

``` r
# 1. Get help about a ggplo2 package
vignette("ggplot2")
```

---
# Objects

- R is an object-oriented language.

- Vectors, matrices, data frames (like data.table), lists, functions, etc. are objects.

- Each objects has rules and properties. For example, a vector can only contain one type of data. A matrix can contain multiple types of data.

``` r
# Create a data frame called "d"
d <- data.frame(x = 1:2, y = 3:4)
d
```

```
##   x y
## 1 1 3
## 2 2 4
```

``` r
# Convert it to (i.e. create) a matrix call "m"
m <- as.matrix(d)
m
```

```
##      x y
## [1,] 1 3
## [2,] 2 4
```

---
# Object class, type and structure.

``` r
d <- data.frame(x = 1:2, y = 3:4)
class(d) # class of d
```

```
## [1] "data.frame"
```

``` r
typeof(d) # type of d
```

```
## [1] "list"
```

``` r
str(d) # structure of d
```

```
## 'data.frame':	2 obs. of  2 variables:
##  $ x: int  1 2
##  $ y: int  3 4
```

---
# Global environment

``` r
# Create a data frame called "d"
d <- data.frame(x = 1:2, y = 3:4)

# Let's try regress y on x

lm(y ~ x) # Error in eval(predvars, data, env): object 'y' not found
```

```
## 
## Call:
## lm(formula = y ~ x)
## 
## Coefficients:
## (Intercept)            x  
##           4           NA
```

``` r
lm(y ~ x, data = d)
```

```
## 
## Call:
## lm(formula = y ~ x, data = d)
## 
## Coefficients:
## (Intercept)            x  
##           2            1
```

---
# Reserved words

- if
- else
- repeat
- while
- function
- for
- in
- next
- break
- TRUE
- FALSE
- NULL
- Inf
- NaN
- NA

---
# Indexing

--
- We can use "[" to index a vector, matrix, or data frame.

``` r
a <- c(1:10)
a[4] # forth element of a
```

```
## [1] 4
```

``` r
my_list = list(a = "hello", b = c(1,2,3), c = data.frame(x = 1:5, y = 6:10))
my_list[[1]]
```

```
## [1] "hello"
```

``` r
my_list[[2]][3]
```

```
## [1] 3
```

---
# Indexing

--
- We can also use "$" to index a list or data frame.

``` r
my_list = list(a = "hello", b = c(1,2,3), c = data.frame(x = 1:5, y = 6:10))
my_list$a
```

```
## [1] "hello"
```

``` r
my_list$b
```

```
## [1] 1 2 3
```

``` r
my_list$c
```

```
##   x  y
## 1 1  6
## 2 2  7
## 3 3  8
## 4 4  9
## 5 5 10
```

``` r
my_list$b[3]
```

```
## [1] 3
```

---
# Indexing

--
- We can also use "$" to index a list or data frame.

``` r
my_list = list(a = "hello", b = c(1,2,3), c = data.frame(x = 1:5, y = 6:10))
my_list$b[3]
```

```
## [1] 3
```

``` r
my_list$c$x
```

```
## [1] 1 2 3 4 5
```

---
# Indexing

--
- We can also use "$" to index a list or data frame.

``` r
d <- data.frame(x = 1:2, y = 3:4)
lm(d$y ~ d$x)
```

```
## 
## Call:
## lm(formula = d$y ~ d$x)
## 
## Coefficients:
## (Intercept)          d$x  
##           2            1
```

---
# Functions

--
- Functions are the building blocks of R. Examples of functions are mean(), sd(), lm(), etc.

``` r
example_function = function (a, b) {
output = a + b
return (output)
}
example_function(1,2)
```

```
## [1] 3
```

---
# Libraries

--
- Functions are the building blocks of R. Examples of functions are mean(), sd(), lm(), etc.

``` r
library(data.table)
```

```
## Warning: package 'data.table' was built under R version 4.3.3
```