Recap II

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# Recap II
## First Weekend
### Basel R Bootcamp <a href='https://therbootcamp.github.io'>www.therbootcamp.com</a> <a href='https://twitter.com/therbootcamp'>@therbootcamp</a>
### July 2018

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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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# 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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# Wrangling

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1 - Start by assigning your result to a new object to save it!

2 - "Keep the pipe <high>%>%</high> going" to continue working with your data frame.

3 - The output of dplyr functions will (almost) always be a <high>tibble</high>.

4 - You can almost always include <high>multiple operations</high> within each function.

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```r
# Assign result to baslers_agg

baslers_agg <- baselers %>% 
 
 # Change column names with rename()
 rename(age_years = age,
 weight_kg = weight) %>% # PIPE!

# Select specific rows with filter()
 filter(age_years < 40) %>% # PIPE!
 
 # Create new columns witb mutate()
 mutate(debt_ratio = debt / income) %>% # PIPE!
 
 # Group data with group_by()
 group_by(sex) %>% # PIPE!
 
 # Calculate summary statistics with summarise()
 summarise(income_mean = mean(income),
 debt_mean = mean(debt),
 dr_mean = mean(dr))
```

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# Stats? There is a package for that!

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| Package| Models|
|------:|:----|
| `stats`|Generalized linear model|
|     `afex`|   Anovas|
|     `lme4`|   Mixed effects regression|
|     `rpart`|    Decision Trees|
|     `BayesFactor`| Bayesian statistics|
|     `igraph`| Network analysis|
|     `neuralnet`| Neural networks|
|     `MatchIt`| Matching and causal inference|
|     `survival`| Longitudinal survival analysis|
|     ...| Anything you can ever want!|

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# Basic structure of statistical functions

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Statistical functions always require a <high>data frame</high> called `data`, e.g.,...

|sex | age| height| weight| income|
|:------|---:|------:|------:|------:|
|male | 44| 174.3| 113.4| 6300|
|male | 65| 180.3| 75.2| 10900|
|female | 31| 168.3| 55.5| 5100|
 
They also require a <high>formula</high> that specifies a <high>dependent</high> variable (y) as a function of one or more <high>independent</high> variables (x1, x2, ...) in the form:

formula = y ~ x1 + x2 +...

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How to create a statistical object:

```r
# Example: Create regression object (my_glm)
my_glm <- glm(formula = income ~ age + height,
 data = baselers)
```

![](https://raw.githubusercontent.com/therbootcamp/Erfurt_2018June/master/_sessions/_image/formula_description.png)

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# What is machine learning?

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### Algorithms autonomously learning from data.

Given data, an algorithm tunes its <high>parameters</high> to match the data, understand how it works, and make predictions for what will occur in the future.
 

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

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# Today

<a href="https://therbootcamp.github.io/BaselRBootcamp_2018July">Schedule</a>