Tidy Projects

class: center, middle, inverse, title-slide

# Tidy Projects
### The R Bootcamp Twitter: <a href='https://twitter.com/therbootcamp'>@therbootcamp</a>
### April 2018

---

# Tidying

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In this introduction you will learn...

>...more about R projects.
><br2>
>...how to write clean, documented code.
><br2>
>...to understand errors (and warnings).
><br2>
>...how to deal with missing values.
]

.pull-right5[
<img src="https://build2be.com/sites/build2be.com/files/shutterstock_232639537.jpg" width="500">
source<a href="https://build2be.com/"> https://build2be.com/</a>
]

---

.pull-left4[
# Projects

### Help access files (paths)

### Set options
- "Workspace, history saving
- Text encoding
- Tab or space
- Code wrapping
- etc.

### Support version control
  - [**Git**](https://git-scm.com/) & [**GitHub**](https://github.com/)

### Facilitate package building, shiny, markdown, and C++

And more...

]
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<img src="https://raw.githubusercontent.com/therbootcamp/therbootcamp.github.io/master/_sessions/_image/projects_1.png" height="280" vspace="10">
<img src="https://raw.githubusercontent.com/therbootcamp/therbootcamp.github.io/master/_sessions/_image/projects_2.png" height="280" vspace="10">
]

---

# [**Git**](https://git-scm.com/) & [**GitHub**](https://github.com/)

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### Git

Highly efficient system for **version control** and **collaboration**.

- `pull`, `commit`, `push`

### GitHub

Server-space + GUI + Facebook for git.

Check out: [**GitHub pages**](https://pages.github.com/)

]
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<img src="https://raw.githubusercontent.com/therbootcamp/therbootcamp.github.io/master/_sessions/_image/my_github.png" height="450" vspace="10">

]

---

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# Folder structure

Good, clean, well-documented projects begins with a **project** and a **folder structure**.
]
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<img src="https://raw.githubusercontent.com/therbootcamp/therbootcamp.github.io/master/_sessions/_image/folder_structure.png" height="400" vspace="25">
]

---

# Style

Check out the [**Google's R style guide**](https://google.github.io/styleguide/Rguide.xml) & [**Tidyverse style guide**](http://style.tidyverse.org/index.html)

### Bad

```r
mean(subset((data.frame(c('a','b'),runif(1000,0,1))),c..a....b..=='a')[,'runif.1000..0..1.'])
```

### Good

```r
# create my data.frame
my_data <- data.frame('group' = c('a','b'), 
 'value' = runif(1000,0,1))

# subset data
my_data %>% 
  filter(group == 'a') %>%
  summarize(mean(value))
```

---

# Naming files & objects

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### Files
- **Filenames** should be **meaningful**. 
- If you like, **order them by prefixing** them with numbers.

### Objects
- **Object names** should be lowercase.
<br2>
- Use `_` rather than `.` or 'camelCase' (using capitalization) for multi-word names. 
<br2>
- Use **nouns** for variables and **verbs** for functions.
<br2>
- Use **meaningful** names.
<br2>
- Avoid using names of **existing objects**
]

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```r
# ---- Files

# Good
analyze_my_data.R
0_read_my_data.R
1_analyze_my_data.R

# Bad
stuff.r
code.r

# ---- Objects

# Good
trial_id
trial_1

# Bad
nameOFtrial
trial.object
t
ekfjw
```

]

---

# Spaces

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+ Place **spaces** around all operators, e.g., `=`, `+`, `-`, `<-`, etc. Also applies for defining arguments in functions.
<br2>
+ Always put a space **after a comma**, never before.
<br2>
+ Extra **spacing** may be used to align assignments.
]

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```r
# Good
var_rt <- var(rt, na.rm = TRUE)

# Bad
var_rt<-var(rt, na.rm = TRUE)
```

```r
# Good
list(
  var_rt  = var(rt)
  mean_rt = mean(rt)
)
```

]
---

# Curly brackets
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+ An opening **curly bracket** should never be on its own line.
<br2>
+ **Always indent** within curly brackets. 
<br2>
+ To **indent** code, use two spaces. Don't use tabs.
]
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```r
# Good
if (my_dbl < 2){
 mean(c(1, 2, 3, 4, 5))
} else {
 median(c(1, 2, 3, 4, 5))
}

# Bad
if (my_dbl < 2)
{
mean(c(1, 2, 3, 4, 5))
} else {
median(c(1, 2, 3, 4, 5))
}
```
]

---

# Assignments & Comments

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For **assignments** use `<-`, not `=`, unless when specifying arguments in functions.

```r
# Good
x <- 24324

# Bad
x = 24324
```
]

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**Comment each line** of your code. To break up your code in chunks use `-` or `=`

```r
# Plot data ----------------------------

this_is <- "pseduo_code"
my_function(arg1 = x,
 arg2 = y)

# Plot data ============================

this_is <- "pseduo_code"
```
]
---

# Errors, Warnings, Messages

R has different categories for telling you something has happened depending on the severity of the event.

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**Errors** indicate that **something bad** has happened. Errors always stop the code.
 
**Warnings** indicate that **something potentially worrying** has happened. Warnings do not stop the code.
 
**Messages** indicate that **something noteworthy** has happened, e.g., completion of an analysis step. 
]

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```r
# Error
men(c(1, 2, 3))
```

```
## Error in men(c(1, 2, 3)): could not find function "men"
```

```r
# Warning
c(1, 2) + c(2, 3, 4)
```

```
## Warning in c(1, 2) + c(2, 3, 4): longer object length is not a multiple of shorter object length
```

```
## [1] 3 5 5
```

```r
# Message
message('This is a message')
```

```
## This is a message
```
]

---

# 7 most frequent errors

According to [stackoverflow.com](http://www.stackoverflow.com)

| Error| Example| Description|
|:------|:------------|:--------------------------------------------|
| `'could not find function'`|legth(my_vec)| There is a typo in the function name or that a package has not been loaded.|
| `'error in if'`|if(NA == 2) 2 + 2| The object in the `if`-clause is non-logical or NA.|
| `'error in eval'`|lm(fefq~wzfe)| An object is used that does not exist.|
| `'cannot open()'`|read_csv('hjht.txt')| The file does not exist. Could be a typo or a missing file path.|
| `'no applicable method'`|predict('efwe')| A 'generic function' has not been defined for this type/class |
| `'subsscript out of bounds'`|a <- matrix(c(1,2)); a[2,2]| R tried to access an element (or variable) that does not exist |
| package errors|| Occur when R is unable to install, compile, or load a package. Often this means that some software in the background is missing. |

---

# Missing data

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A pervasive problem in working with data is missing values. 
Only to become more important in the days of Big Data. 
In R, there are **two kinds of missing values**: the more general and frequent `NA`, and the more specific `NaN`. 
]

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```r
# NA and NaN
my_vec <- c(1,2) ; my_vec[5]
```

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

```r
0/0
```

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

```r
# Tests
is.na(my_vec[5]) ; is.na(0/0)
```

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

```r
is.nan(my_vec[5]) ; is.nan(0/0)
```

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

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

---

# Handling missing data

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Many functions have inbuilt **handlers** for missing data.

In most cases, however, missing values have to and should be dealt with **before the analysis**. 
]

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```r
# Example
my_vec_1 <- c(1, 2, 3, 4, NA)
my_vec_2 <- c(4, 2, NA, 3, 5)

# Functions examples that include handlers
mean(my_vec_1) ; cor(my_vec_1, my_vec_2)
```

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

```r
# Actually using the handlers
mean(my_vec_1, na.rm = TRUE)
```

```
## [1] 2.5
```

```r
cor(my_vec_1, my_vec_2, use = 'complete.obs')
```

```
## [1] -0.3273
```
]

---

# Impute missing data

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Missing data can be **imputed**.

How missing data should be imputed depends on whether the **data is missing at random** or not.

**Packages**: `Hmisc`, `DMwR`, `mice`, etc. 
]

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```r
# Example
my_df <- data.frame('x' = c(1, 2, 3, 4, NA),
 'y' = c(4, 2, NA, 3, 5)
 )

# Impute using mean
my_df$x[is.na(my_df$x)] <- mean(my_df$x, na.rm = TRUE)

# Impute using regression (package: mice)
model <- mice(my_df, method = 'norm', printFlag = FALSE)
my_df <- mice::complete(model)

# print
my_df
```

```
##     x     y
## 1 1.0 4.000
## 2 2.0 2.000
## 3 3.0 6.902
## 4 4.0 3.000
## 5 2.5 5.000
```
]

---

# Practical

<a href="https://therbootcamp.github.io/_sessions/D3S1_Tidying/Tidying_practical.html">Link to practical</a>