Recap

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# Recap
### Introduction to Data Science with R <a href='https://therbootcamp.github.io'>www.therbootcamp.com</a> <a href='https://twitter.com/therbootcamp'>@therbootcamp</a>
### October 2018

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<a href="https://therbootcamp.github.io/">Introduction to Data Science with R, October 2018</a>
&emsp;&emsp;&emsp;&emsp;&emsp;&emsp;&emsp;&emsp;&emsp;&emsp;&emsp;&emsp;&emsp;&emsp;&emsp;
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<a href="https://therbootcamp.github.io/">www.therbootcamp.com</a>
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# Essentials: The 24 Lessons of the R Bootcamp

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1. Everything is an object
2. Use `<-` to create/change objects
3. Name objects using `_`
4. Objects have classes
5. Everything happens through functions
6. Functions have (default) arguments
7. Functions expect certain object classes
8. View help files using `?`
9. Study errors and warnings
10. Data is stored in data frames
11. Select variables (vectors) using `$`
12. Use RStudio and projects
13. Use editor and shortcuts
14. First load packages and data
15. Use auto-complete
16. Comment and format for readability
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# 3 Object types for data

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R has 3 main data objects...

<high>`list`</high> - R's multi-purpose container
- Can carry any data, incl. lists
- Often used for function outputs

<high>`data_frame`</high> - R's spreadsheet
- Specific type of `list`
- Typical data format
- For multi-variable data sets

<high>`vectors`</high> - R's data container
- Actually carries the data
- Contain data of 1 of many types

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<img src="https://raw.githubusercontent.com/therbootcamp/therbootcamp.github.io/master/_sessions/_image/main_objects.png"></img>
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# Functions

Functions have 3 elements:

1 - <high>Name</high>: Used to refer to the function and call (execute) it.

2 - <high>Arguments</high>: Used to provide (data) inputs and to control what the function does. Arguments with default values (e.g., `use = "everything"`) need not be specified. Arguments without default values (e.g., `x`) need be specified. <high>Inputs must have the appropriate class!</high>

3 - <high>Body</high>: The code that uses the inputs (arguments) to produce the desired output. The code of the functions body is based <high>copies of the inputs</high>, which are named according to the arguments names.

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 <img src="https://raw.githubusercontent.com/therbootcamp/therbootcamp.github.io/master/_sessions/_image/function.png"></img>
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# Documentation

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R documentation (<high>help files</high> and <high>vignettes</high>) will become very to use once you are familiar with the basic R vocabulary.

Pay attention to...

<high>Usage</high> - shows how to use function, its arguments and their defaults. <high>Arguments</high> - describes arguments, and their `class`. <high>Value</high> - describes what the function returns. <high>Examples</high> - provide working R code.

```r
# To access help files
?name_of_function

# search help files
??name_of_function
```

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```r
?cor
```
<img src="https://raw.githubusercontent.com/therbootcamp/therbootcamp.github.io/master/_sessions/_image/help_cor.png" width="500">
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# Data Input/Output

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Raw data can come in many shapes and sizes, but <high>R's got you covered</high>.
 
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| Package     |   Description|
|:----------------|:-------------------------------------------------------|
| `readr`| `.csv`, `.txt`, etc. |
| `haven`| `.sav`, `.sas7bdat`, `.dta`          |
| `readxl`  | `.xls`, `.xlsx` |
| `R.matlab` | `.mat` |
| `jsonlite` | `.json` |
| `rvest` | `.html` |
| `XML`, `xml2` | `.xml` |

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# Finding the file path

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1 - Identify the file path using the <high>auto-complete</high>.

2 - Initiate auto-complete and browse through the folder structure by placing the cursor between two quotation marks and using the <high>tab key</high>.

3 - Auto-complete begins with the project folder - <high>place your data inside your project folder!</high>

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# What is wrangling?

<high>Transform</high>

Change column names

Add new columns

<high>Organise</high>

Sort data by columns

Merging data from two separate dataframes

Move data between columns and rows

<high>Aggregate</high>

Group data and summarise
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 <img src="https://raw.githubusercontent.com/therbootcamp/therbootcamp.github.io/master/_sessions/_image/organise_transform_aggregate.png" height = "550px">

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# The Pipe! <high>`%>%`</high>

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`dplyr` makes extensive use of a new operator called the "Pipe" <high>`%>%`</high>

Read the "Pipe" <high>`%>%`</high> as "And Then..."

```r
# Start with data
data %>% # AND THEN...
  
DO_SOMETHING %>% # AND THEN...
  
DO_SOMETHING %>% # AND THEN...
  
DO_SOMETHING %>% # AND THEN...
```

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<img src="https://upload.wikimedia.org/wikipedia/en/thumb/b/b9/MagrittePipe.jpg/300px-MagrittePipe.jpg" width = "450px"> 
 This is not a pipe (but %>% is!)

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<hfont><high>Questions?</high></hfont> 
 <a href = "https://therbootcamp.github.io/Intro2DataScience_2018Oct/">Link to schedule</a>

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