Introduction to R

# Introduction to R
## IPSA-Flacso Summer School
### Tiago Ventura

---

# Plans for Today

1. Workflow: R and Rstudio.

2. How to Interact with R.

3. Packages and Asking for Help in R.

4. Objects e Classes.

5. Boolean Operators.

5. Data Structure.

6. Data Manipulation with Base R.

6. Importing and Exporting Data in R.

---

# What's R?

R is a versatile, open source programming/scripting language that's useful both for statistics but also data science.

- Open source software under **GPL**.

- Superior (if not just comparable) to commercial alternatives.

- Not just for statistics, but also **general purpose** programming.

- Is **object oriented** (= R has objects) and **functional** (= You can write functions).

- Large and growing community of peers.

---

## RStudio.

RStudio is the premier R graphical user interface (GUI) and integrated development environment (IDE) that makes R easier to use.

.footnote[Source: [Rochele Terman's Intro to CSS Book](https://plsc-31101.github.io/course/r-basics.html)]

---
## Understanding the R Studio

---
##  How to Interact with R ?

`Open RStudio!`

---

### Command Line (bottom left panel).

You can interact with R directly using the Command Line. The symbol `>` indicates that R is ready to work! Copy and paste the command below to your command line, and click enter.

```r
2+2
```

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

When you see the symbol `+` it means your code was not completed. Then, you should press esc to move back to `>`.

```r
# Incomplete Code

incomplete<- "I am going to give you an incomplete
```

---

## Using Scripts.

Open a new script: `File` -> `New File` -> `R Script`

The script is just a plain text file. But you can send command from the script to your command line directly.

--
#### `command + enter` (Mac) ou `Ctrl + enter` (PC).  
--

```r
# Rode estas operação em um script. 
# Hashtags permite você comentar seu código. 
2^2
2*2
2/2
```

---

### Why Should I go with scripts?

- More appropriate when working with long codes.

- Show others all your steps.

- Allow your future self to return to your code.

---

## Other Important Tips.

#### Comments.

Use # signs to add comments within your code chunks. Help yourself in the future and make as much comments as you can in your code!

```r
# Hello all!
```

#### Errors

When the text is a legitimate error, it will be prefaced with “Error:”, and R will try to explain what went wrong.

```r
plot("hello")
```

```
## Error in plot.window(...): need finite 'ylim' values
```

![](intro_r_flacso_files/figure-html/unnamed-chunk-7-1.png)
---

## R Packages.

--
.pull-left[

#### What the Packages are?

- A set of thematic functions that someone put together for you.

- Sometimes packages also bring data

- In the end, just a folder saved downloaded to your computer.

]

There are a number of `packages` that are supplied with the R distribution. These are known as ``[base packages](https://stat.ethz.ch/R-manual/R-devel/library/base/html/00Index.html)".

Other we need to install.

- Packages should be installed only once.

`install.packages()`

- Should be loaded every R session.

`library()`

]

---
class: center, middle

### Installing Packages Via Cran.

```r
install.packages("devtools", force=TRUE)
```

### Load a Package.

```r
library(devtools)
```

### Install Packages Via Github

```r
devtools::install_github("electorArg/polAr")
```

---

## Asking for Help in  R

```r
# Specific to a function
?mean  # Help para a função mean.
help(mean)

# More General
??mean
```

---

## Asking for Help on Google.

- **Google**: name of the function + the error message you are getting.

- Remove all the information about you and your local path

- Try to understand the solution. Copy and Paste will rarely solve your problems.

---
class: center, middle

## 15 minutes Rule

Taken from [Rochele Terman plsc-31101](https://plsc-31101.github.io/course/introduction.html).

---

# Objects in R

---

# Objects: Definition?

In simple terms, an `object` is a bit of text that represents a specific value.

Objects names can only contain letters, numbers, the underscore character, and (unlike Python) the period character.

Assigning values to new objects is how you create things in R.

---
class:center, middle

### `Everything that exists in R is an object.`

---

## Creating Objects

*(assignment operator)* `<-`

```r
# Numeric Objects
x <- 5
y <- 7

# Character Objects
nome <- "Tiago Ventura"
```

All R statements where you create objects, assignment statements, have the same form:

```r
object_name <- value
```

---

## Can I use `=` to create objects?

Yes. But...

--
.center[
![](https://media.giphy.com/media/JYZ397GsFrFtu/giphy.gif)
]
--

### Best Practice

Use "<-" to create objects. And use "=" inside of functions.

```r
x<-6 
mean(x=c(5, 7))
```

---
## Whats is an object name?

An object name is just a piece of text.

Object names must start with a letter, and can only contain letters, numbers, _ and ..

You want your object names to be descriptive, so you’ll need a convention for multiple words.

Best Practice:

- **snake_case:** style of writing in which each space is replaced by an underscore (__) character, and the first letter of each word written in lowercase.

---
class: center, middle

## Other important commands

#### Checking my environment.

```r
ls()
```

```
## [1] "nome" "x"    "y"
```

#### Remove Objects

```r
rm(y)
```

#### Visualizing Objects

```r
print(nome)
```

```
## [1] "Tiago Ventura"
```

#### Changing Objects

```r
nome <- "Tiago Augusto Ventura"
```

---

## Objects Classes

Every object in R has a **class**. The class describes what the object is.

Main classes are:

<table class="table table-striped table-hover table-condensed" style="margin-left: auto; margin-right: auto;">
 <thead>
  <tr>
   <th style="text-align:left;"> Example </th>
   <th style="text-align:left;"> Tipo </th>
  </tr>
 </thead>
<tbody>
  <tr>
   <td style="text-align:left;"> a  , swc </td>
   <td style="text-align:left;"> Character </td>
  </tr>
  <tr>
   <td style="text-align:left;"> 2, 3, 15 </td>
   <td style="text-align:left;"> Numeric </td>
  </tr>
  <tr>
   <td style="text-align:left;"> 1, 2 </td>
   <td style="text-align:left;"> Interger </td>
  </tr>
  <tr>
   <td style="text-align:left;"> FALSE, TRUE </td>
   <td style="text-align:left;"> Logical </td>
  </tr>
</tbody>
</table>

---

## Checking the Class of my Objects.

`class()`

```r
class(3)
```

```
## [1] "numeric"
```

```r
class(TRUE)
```

```
## [1] "logical"
```

```r
meu_numero_da_sorte= "13"

class(meu_numero_da_sorte)
```

```
## [1] "character"
```

```r
class(meu_numero_da_sorte==13)
```

```
## [1] "logical"
```

---

## Another Way to Go: is.class?

```r
is.numeric(2)
```

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

```r
is.logical(TRUE)
```

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

```r
is.character("2")
```

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

```r
is.integer(1L)
```

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

---

## Coercing Objects.

R allows you to easily change the class of your objects using the functions: `as.class()`

```r
# Cria Objeto
num_1_5 <- c(1, 2, 3, 4, 5)

# Altera a classe
char_1_5 <- as.character(num_1_5)

# Checando
class(char_1_5)
```

```
## [1] "character"
```

```r
class(num_1_5)
```

```
## [1] "numeric"
```

```r
# Ou
as.numeric("25")
```

```
## [1] 25
```

---

# Boolean Operators in R

---
# Boolean ("logical") Operators

To complement our discussion on objects in R, let's briefly see how boolean operators work.

**Important Note**: These operators are logical. Always return TRUE or FALSE.

```r
        x == y      # equals to
        x != y      # does not equal
        x >= y      # greater than or equal to
        x <= y      # less than or equal to
        x > y       # greater than
        x < y       # less than
        x==1 & y==5 # "and" conditional statements
        x==1 | y==5 # "or" conditional statements
```

Boolean operators will be important in the future for you on tasks of data manipulations, creating new variables, writing functions and loops.

---

# Challenge 1.

Let's practice a bit  What do the commands below return? Try to guess!

#### Question 1:

```r
install.packages(tidyverse)
```

#### Question 2:

```r
false <- "FALSE"
false <- as.logical(false)
class(false)
```

#### Question 3

```r
mean(x = sample(1:50, 5)) == mean(x) # TRUE ou FALSE
```

---
class: middle, center, inverse

# Data Structure

---

##  Vector

```r
# Numeric Vector
X <- c(1, 2.3, 4, 5, 6.78, 6:10)
X
```

```
##  [1]  1.00  2.30  4.00  5.00  6.78  6.00  7.00  8.00  9.00 10.00
```

```r
# Class
class(X)
```

```
## [1] "numeric"
```

```r
# Length
length(X)
```

```
## [1] 10
```

---

## Matrix

**Main Feature:** Rectangular and Same type.

```r
# Coerce to a matrix
x_matrix <- as.matrix(X)

# Build a Matrix.
m <- matrix(1:10, nrow=5, ncol=2)
m
```

```
##      [,1] [,2]
## [1,]    1    6
## [2,]    2    7
## [3,]    3    8
## [4,]    4    9
## [5,]    5   10
```

```r
# Acessing a values
m[1, 1] # [rows, columns]
```

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

---

## List

**Main Feature**: Flexible, huge drawer where you can put anything you want.

```r
# coerce to a list

x_list<- as.list(X)

# or

lista_1 <- list(X, as.matrix(X), as.character(X))

# Visualize a lista.

str(lista_1)
```

```
## List of 3
##  $ : num [1:10] 1 2.3 4 5 6.78 6 7 8 9 10
##  $ : num [1:10, 1] 1 2.3 4 5 6.78 6 7 8 9 10
##  $ : chr [1:10] "1" "2.3" "4" "5" ...
```

```r
# Acessing a value: double [[]]
lista_1[[1]]
```

```
##  [1]  1.00  2.30  4.00  5.00  6.78  6.00  7.00  8.00  9.00 10.00
```

---

# Data Frame.

1. Classic Database.

2. Rectangular.

3. Works with columns with different classes.

4. Like a excel spreadsheet .

---

## Creating a Data Frame.

```r
# Coercing
as.data.frame(X)
```

```
##        X
## 1   1.00
## 2   2.30
## 3   4.00
## 4   5.00
## 5   6.78
## 6   6.00
## 7   7.00
## 8   8.00
## 9   9.00
## 10 10.00
```

```r
# Criando Manualmente

data <- data.frame(name=c("Tiago", "Tiago"), 
                   last_name=c("Ventura", "Ventura") ,
                   school=c("UMD", "FGV"), age=c(30,32))
data
```

```
##    name last_name school age
## 1 Tiago   Ventura    UMD  30
## 2 Tiago   Ventura    FGV  32
```

---

## Pre-Built Data Frames

```r
mtcars # already loaded in your R environment
```

```
##                      mpg cyl  disp  hp drat    wt  qsec vs am gear carb
## Mazda RX4           21.0   6 160.0 110 3.90 2.620 16.46  0  1    4    4
## Mazda RX4 Wag       21.0   6 160.0 110 3.90 2.875 17.02  0  1    4    4
## Datsun 710          22.8   4 108.0  93 3.85 2.320 18.61  1  1    4    1
## Hornet 4 Drive      21.4   6 258.0 110 3.08 3.215 19.44  1  0    3    1
## Hornet Sportabout   18.7   8 360.0 175 3.15 3.440 17.02  0  0    3    2
## Valiant             18.1   6 225.0 105 2.76 3.460 20.22  1  0    3    1
## Duster 360          14.3   8 360.0 245 3.21 3.570 15.84  0  0    3    4
## Merc 240D           24.4   4 146.7  62 3.69 3.190 20.00  1  0    4    2
## Merc 230            22.8   4 140.8  95 3.92 3.150 22.90  1  0    4    2
## Merc 280            19.2   6 167.6 123 3.92 3.440 18.30  1  0    4    4
## Merc 280C           17.8   6 167.6 123 3.92 3.440 18.90  1  0    4    4
## Merc 450SE          16.4   8 275.8 180 3.07 4.070 17.40  0  0    3    3
## Merc 450SL          17.3   8 275.8 180 3.07 3.730 17.60  0  0    3    3
## Merc 450SLC         15.2   8 275.8 180 3.07 3.780 18.00  0  0    3    3
## Cadillac Fleetwood  10.4   8 472.0 205 2.93 5.250 17.98  0  0    3    4
## Lincoln Continental 10.4   8 460.0 215 3.00 5.424 17.82  0  0    3    4
## Chrysler Imperial   14.7   8 440.0 230 3.23 5.345 17.42  0  0    3    4
## Fiat 128            32.4   4  78.7  66 4.08 2.200 19.47  1  1    4    1
## Honda Civic         30.4   4  75.7  52 4.93 1.615 18.52  1  1    4    2
## Toyota Corolla      33.9   4  71.1  65 4.22 1.835 19.90  1  1    4    1
## Toyota Corona       21.5   4 120.1  97 3.70 2.465 20.01  1  0    3    1
## Dodge Challenger    15.5   8 318.0 150 2.76 3.520 16.87  0  0    3    2
## AMC Javelin         15.2   8 304.0 150 3.15 3.435 17.30  0  0    3    2
## Camaro Z28          13.3   8 350.0 245 3.73 3.840 15.41  0  0    3    4
## Pontiac Firebird    19.2   8 400.0 175 3.08 3.845 17.05  0  0    3    2
## Fiat X1-9           27.3   4  79.0  66 4.08 1.935 18.90  1  1    4    1
## Porsche 914-2       26.0   4 120.3  91 4.43 2.140 16.70  0  1    5    2
## Lotus Europa        30.4   4  95.1 113 3.77 1.513 16.90  1  1    5    2
## Ford Pantera L      15.8   8 351.0 264 4.22 3.170 14.50  0  1    5    4
## Ferrari Dino        19.7   6 145.0 175 3.62 2.770 15.50  0  1    5    6
## Maserati Bora       15.0   8 301.0 335 3.54 3.570 14.60  0  1    5    8
## Volvo 142E          21.4   4 121.0 109 4.11 2.780 18.60  1  1    4    2
```

---
## Pre-Built Data Frames from Packages

```r
## Bancos de Dados Pré-Construídos.

devtools::install_github("apreshill/bakeoff")
library(bakeoff) # Chamando o pacote

# O que tenho no meu ambiente?
ls()
```

```
##  [1] "char_1_5"            "data"                "lista_1"            
##  [4] "m"                   "meu_numero_da_sorte" "nome"               
##  [7] "num_1_5"             "x"                   "X"                  
## [10] "x_list"              "x_matrix"
```

```r
# ativa o banco de dados
data("bakers")
ls()
```

```
##  [1] "bakers"              "char_1_5"            "data"               
##  [4] "lista_1"             "m"                   "meu_numero_da_sorte"
##  [7] "nome"                "num_1_5"             "x"                  
## [10] "X"                   "x_list"              "x_matrix"
```

```r
# Examine o objeto.
class(bakers)
```

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

```r
str(bakers)
```

```
## tibble[,8] [120 × 8] (S3: tbl_df/tbl/data.frame)
##  $ series     : Factor w/ 10 levels "1","2","3","4",..: 1 1 1 1 1 1 1 1 1 1 ...
##  $ baker_full : chr [1:120] "Annetha Mills" "David Chambers" "Edward \"Edd\" Kimber" "Jasminder Randhawa" ...
##  $ baker      : chr [1:120] "Annetha" "David" "Edd" "Jasminder" ...
##  $ age        : num [1:120] 30 31 24 45 25 51 44 48 37 31 ...
##  $ occupation : chr [1:120] "Midwife" "Entrepreneur" "Debt collector for Yorkshire Bank" "Assistant Credit Control Manager" ...
##  $ hometown   : chr [1:120] "Essex" "Milton Keynes" "Bradford" "Birmingham" ...
##  $ baker_last : chr [1:120] "Mills" "Chambers" "Kimber" "Randhawa" ...
##  $ baker_first: chr [1:120] "Annetha" "David" "Edward" "Jasminder" ...
```

---

## Acessing Information from your Data Frame

`data[rows, columns]`

**Rows**: Only using Numeric Index.

**Columns**: Using both Numeric Index or Textual Keys.

---

**Using Numeric Index**

```r
bakers[,1] # primeira coluna
```

```
## # A tibble: 120 x 1
##    series
##    <fct> 
##  1 1     
##  2 1     
##  3 1     
##  4 1     
##  5 1     
##  6 1     
##  7 1     
##  8 1     
##  9 1     
## 10 1     
## # … with 110 more rows
```

---

**Textual Key**

```r
bakers$hometown
```

```
##   [1] "Essex"                                    
##   [2] "Milton Keynes"                            
##   [3] "Bradford"                                 
##   [4] "Birmingham"                               
##   [5] "St Albans"                                
##   [6] "Midlothian, Scotland"                     
##   [7] "Manchester"                               
##   [8] "South Wales"                              
##   [9] "Midhurst, West Sussex"                    
##  [10] "Poynton, Cheshire"                        
##  [11] "Northampton"                              
##  [12] "Leicester"                                
##  [13] "Dunstable, Bedfordshire"                  
##  [14] "Formby, Liverpool"                        
##  [15] "Croydon"                                  
##  [16] "Ongar, Essex"                             
##  [17] "Arlesey, Bedfordshire"                    
##  [18] "Kidderminster, Worcestershire"            
##  [19] "London"                                   
##  [20] "Norfolk"                                  
##  [21] "Enfield, London"                          
##  [22] "West Kirby, The Wirral"                   
##  [23] "Sutton Coldfield"                         
##  [24] "Pease Pottage, West Sussex"               
##  [25] "Sheffield"                                
##  [26] "Hillswick, Shetland Islands"              
##  [27] "Wigan"                                    
##  [28] "Leicester"                                
##  [29] "Tamworth, Staffordshire"                  
##  [30] "Windsor, Berkshire"                       
##  [31] "Bristol"                                  
##  [32] "Bewbush, West Sussex"                     
##  [33] "Lichfield, Staffordshire"                 
##  [34] "Somerset"                                 
##  [35] "Saltley, Birmingham"                      
##  [36] "Aldershot, Hampshire"                     
##  [37] "Didcot, Oxfordshire"                      
##  [38] "Peterborough"                             
##  [39] "Market Harborough, Leicestershire"        
##  [40] "Teignmouth, Devon"                        
##  [41] "Sheffield"                                
##  [42] "London"                                   
##  [43] "Grimsby, Lincolnshire"                    
##  [44] "Milton Keynes"                            
##  [45] "Melbourn, Cambridgeshire"                 
##  [46] "Southend, Essex"                          
##  [47] "Reading, Berkshire"                       
##  [48] "Broadstairs, Kent"                        
##  [49] "Ashton upon Mersey, Trafford"             
##  [50] "Alkington, Shropshire"                    
##  [51] "Portsmouth, Hampshire"                    
##  [52] "London/Belfast"                           
##  [53] "Sneinton, Nottingham"                     
##  [54] "Brighton, East Sussex"                    
##  [55] "Poynton, Cheshire"                        
##  [56] "Ascot, Berkshire"                         
##  [57] "Barton-upon-Humber, Lincolnshire"         
##  [58] "Portknockie, Moray"                       
##  [59] "Mill Hill, London"                        
##  [60] "Bracknell, Berkshire"                     
##  [61] "Penwortham, Lancashire"                   
##  [62] "Dunkeld, Perth and Kinross"               
##  [63] "Great Wilbraham, Cambridgeshire"          
##  [64] "Auchterarder, Perthshire"                 
##  [65] "London"                                   
##  [66] "Leeds / Luton"                            
##  [67] "Swansea, Wales"                           
##  [68] "Yeadon, West Yorkshire"                   
##  [69] "Guildford, Surrey"                        
##  [70] "Manchester"                               
##  [71] "Woodford, London / Vilkaviškis, Lithuania"
##  [72] "Derby / Holywood, County Down"            
##  [73] "South London"                             
##  [74] "Barton-Le-Clay, Bedfordshire"             
##  [75] "Beckenham"                                
##  [76] "Brooke, Norfolk"                          
##  [77] "Bolton"                                   
##  [78] "Cardiff"                                  
##  [79] "Durham"                                   
##  [80] "Erith"                                    
##  [81] "London"                                   
##  [82] "Rochdale"                                 
##  [83] "Yeovil"                                   
##  [84] "Bristol"                                  
##  [85] "Merseyside"                               
##  [86] "Brentwood, Essex"                         
##  [87] "Crawley, West Sussex"                     
##  [88] "Merseyside"                               
##  [89] "North London"                             
##  [90] "Southend, Essex"                          
##  [91] "West Molesey, Surrey"                     
##  [92] "Radlett, Hertfordshire"                   
##  [93] "Watford, Hertfordshire"                   
##  [94] "Edinburgh"                                
##  [95] "North London"                             
##  [96] "London"                                   
##  [97] "Bristol"                                  
##  [98] "London"                                   
##  [99] "County Tyrone"                            
## [100] "Newport"                                  
## [101] "Wakefield"                                
## [102] "Leeds"                                    
## [103] "Sheffield"                                
## [104] "London"                                   
## [105] "Rotherham"                                
## [106] "London"                                   
## [107] "West Midlands"                            
## [108] "Essex"                                    
## [109] "Halifax"                                  
## [110] "Rotherham"                                
## [111] "Whitby"                                   
## [112] "Leeds"                                    
## [113] "Durham"                                   
## [114] "Surrey"                                   
## [115] "Stratford-upon-Avon"                      
## [116] "Tenby, Wales"                             
## [117] "Rainham"                                  
## [118] "Leicester"                                
## [119] "Somerset"                                 
## [120] "Chester"
```

```r
# Ou

bakers[,"hometown"]
```

```
## # A tibble: 120 x 1
##    hometown             
##    <chr>                
##  1 Essex                
##  2 Milton Keynes        
##  3 Bradford             
##  4 Birmingham           
##  5 St Albans            
##  6 Midlothian, Scotland 
##  7 Manchester           
##  8 South Wales          
##  9 Midhurst, West Sussex
## 10 Poynton, Cheshire    
## # … with 110 more rows
```

---

**Row with Numeric Index**

```r
bakers[1:5, ]
```

```
## # A tibble: 5 x 8
##   series baker_full   baker    age occupation    hometown baker_last baker_first
##   <fct>  <chr>        <chr>  <dbl> <chr>         <chr>    <chr>      <chr>      
## 1 1      "Annetha Mi… Annet…    30 Midwife       Essex    Mills      Annetha    
## 2 1      "David Cham… David     31 Entrepreneur  Milton … Chambers   David      
## 3 1      "Edward \"E… Edd       24 Debt collect… Bradford Kimber     Edward     
## 4 1      "Jasminder … Jasmi…    45 Assistant Cr… Birming… Randhawa   Jasminder  
## 5 1      "Jonathan S… Jonat…    25 Research Ana… St Alba… Shepherd   Jonathan
```

---
### Boolean Operators... back, back, back again.

```r
# Bakers com mais de 60 anos
bakers[bakers$age>60,]
```

```
## # A tibble: 10 x 8
##    series baker_full   baker    age occupation  hometown  baker_last baker_first
##    <fct>  <chr>        <chr>  <dbl> <chr>       <chr>     <chr>      <chr>      
##  1 2      "Janet Basu" Janet     63 Teacher of… Formby, … Basu       Janet      
##  2 3      "Brendan Ly… Brend…    63 Recruitmen… Sutton C… Lynch      Brendan    
##  3 4      "Christine … Chris…    66 Director o… Didcot, … Wallace    Christine  
##  4 5      "Diana Bear… Diana     69 Women's In… Alkingto… Beard      Diana      
##  5 5      "Norman Cal… Norman    66 Retired Na… Portknoc… Calder     Norman     
##  6 6      "Marie Camp… Marie     66 Retired     Auchtera… Campbell   Marie      
##  7 7      "Jane Beedl… Jane      61 Garden des… Beckenham Beedle     Jane       
##  8 7      "Lee Banfie… Lee       67 Pastor      Bolton    Banfield   Lee        
##  9 7      "Valerie \"… Val       66 Semi-retir… Yeovil    Stones     Valerie    
## 10 8      "Flo Atkins" Flo       71 Retired     Merseysi… Atkins     Flo
```

```r
#Bakers de Londers
bakers[bakers$hometown=="London",]
```

```
## # A tibble: 8 x 8
##   series baker_full   baker    age occupation    hometown baker_last baker_first
##   <fct>  <chr>        <chr>  <dbl> <chr>         <chr>    <chr>      <chr>      
## 1 2      Robert Bill… Robert    25 Photographer  London   Billington Robert     
## 2 4      Kimberley W… Kimbe…    30 Psychologist  London   Wilson     Kimberley  
## 3 6      Mat Riley    Mat       37 Fire fighter  London   Riley      Mat        
## 4 7      Selasi Gbor… Selasi    30 Client servi… London   Gbormittah Selasi     
## 5 9      Antony Amou… Antony    30 Banker        London   Amourdoux  Antony     
## 6 9      Dan Beasley… Dan       36 Full-time pa… London   Beasley-H… Dan        
## 7 9      Manon Lagrè… Manon     26 Software pro… London   Lagrève    Manon      
## 8 9      Ruby Bhogal  Ruby      29 Project mana… London   Bhogal     Ruby
```

---

### Useful Functions to Understand your Data Frames.

```r
head(bakers) # First lines

tail(bakers) # last lines linhas

summary(bakers) # classes

dim(bakers) # dimensions

glimpse(bakers) # summary
```

---

## Exporting Data Frames.

An crucial task you will perform in R relates to  exporting your results, including a new dataframe.

There are several functions to do this, depending on the format of the output you want. Some examples:

- `write.table()` for txt
- `write.csv()` for csv
- `write.xlsx` for xlsx
- `save()` to export as a RData

---

# Exporting as a  csv.

```r
# make a fake data set

dfake <- data.frame(normal=rnorm(100, 0, 1), 
                    uniform=runif(100, 0, 1), 
                    pois=rpois(100, 10))
# write.function(data, name_to_be_saved)
write.csv(dfake, "dfake.csv")
```

---

### But.. wait.. where is my data? Where R is looking at ?

`R` does not intuitively know where your data is. If the data is in a special folder called "super secret search", we have to tell `R` how to get there.

We do this in two ways:

1. Learn where your files are.

2. Define your **working directory** for your preferred folder: all your outputs and inputs will be there.

---

## Paths

Every time `R` is started, it looks at the same place (called _global path_), unless it is asked to go elsewhere.

```r
# Where is my R looking at?
getwd()
```

```
## [1] "/home/venturat/Dropbox/Workshops/flacso_workshop_/day_1"
```

```r
# Where should R be looking instead?
setwd("/home/venturat/Downloads")
```
---
## R Projects

A super useful way to organize your files in R is through the use of R projects.

We are not going to cover this feature here in the workshop.

But, I strongly recommend you to read about it [here](https://www.tidyverse.org/blog/2017/12/workflow-vs-script/)

---

## Importing data

```r
# Check Dir
getwd()
```

```
## [1] "/home/venturat/Dropbox/Workshops/flacso_workshop_/day_1"
```

```r
# See what we have here.
list.files()
```

```
##  [1] "ajuda.png"              "auth_bolsonaro.png"     "bail_design.jpg"       
##  [4] "bail.png"               "Bolsonarobasicmap.png"  "camara.png"            
##  [7] "data_store.png"         "data.jpeg"              "dfake.csv"             
## [10] "eleicoes.png"           "F1.large.jpg"           "intro_css_flacso_cache"
## [13] "intro_css_flacso.html"  "intro_css_flacso.Rmd"   "intro_r_flacso_files"  
## [16] "intro_r_flacso.html"    "intro_r_flacso.R"       "intro_r_flacso.Rmd"    
## [19] "libs"                   "mob_01.png"             "news_.png"             
## [22] "parameters_ideo.png"    "portunol"               "portunol.png"          
## [25] "R_vs_RStudio_1.png"     "redes_sociais.png"      "redes.png"             
## [28] "rstudio.png"            "scott.gif"              "survey.png"            
## [31] "toystore.jpg"           "tweets.png"             "twitter-api.jpg"       
## [34] "waiting.gif"            "xaringan-themer.css"
```

```r
# import
dados <- read.csv("dfake.csv")

head(dados)
```

```
##   X      normal   uniform pois
## 1 1  0.61264269 0.8716483   15
## 2 2  0.96576466 0.7061860   11
## 3 3 -0.03444607 0.7349059   14
## 4 4 -0.74612240 0.1884793    4
## 5 5  0.23555598 0.7643842    6
## 6 6  0.27551826 0.2249295   16
```

---

## Descriptive Statistics

Now that we can get data into R, we want to explore and summarize what's going on.

`summary()` allows for one to quickly summarize the distributions across a set of variables

```r
summary(mtcars)
```

```
##       mpg             cyl             disp             hp       
##  Min.   :10.40   Min.   :4.000   Min.   : 71.1   Min.   : 52.0  
##  1st Qu.:15.43   1st Qu.:4.000   1st Qu.:120.8   1st Qu.: 96.5  
##  Median :19.20   Median :6.000   Median :196.3   Median :123.0  
##  Mean   :20.09   Mean   :6.188   Mean   :230.7   Mean   :146.7  
##  3rd Qu.:22.80   3rd Qu.:8.000   3rd Qu.:326.0   3rd Qu.:180.0  
##  Max.   :33.90   Max.   :8.000   Max.   :472.0   Max.   :335.0  
##       drat             wt             qsec             vs        
##  Min.   :2.760   Min.   :1.513   Min.   :14.50   Min.   :0.0000  
##  1st Qu.:3.080   1st Qu.:2.581   1st Qu.:16.89   1st Qu.:0.0000  
##  Median :3.695   Median :3.325   Median :17.71   Median :0.0000  
##  Mean   :3.597   Mean   :3.217   Mean   :17.85   Mean   :0.4375  
##  3rd Qu.:3.920   3rd Qu.:3.610   3rd Qu.:18.90   3rd Qu.:1.0000  
##  Max.   :4.930   Max.   :5.424   Max.   :22.90   Max.   :1.0000  
##        am              gear            carb      
##  Min.   :0.0000   Min.   :3.000   Min.   :1.000  
##  1st Qu.:0.0000   1st Qu.:3.000   1st Qu.:2.000  
##  Median :0.0000   Median :4.000   Median :2.000  
##  Mean   :0.4062   Mean   :3.688   Mean   :2.812  
##  3rd Qu.:1.0000   3rd Qu.:4.000   3rd Qu.:4.000  
##  Max.   :1.0000   Max.   :5.000   Max.   :8.000
```

---

There are a wealth of useful summary operators that are built into `R`.

```r
mean()
sd()
var()
range()
min()
max()
median()
quantile()
fivenum()
colMeans()
rowMeans()
table()
```

...to name a few!

---

## I know!!!

---
class: center, middle, inverse

## Tommorrow

### Tidyverse: and you will fell better! I promise!