Lab 01: CS631

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# Lab 01: CS631
## Working in R
### Alison Hill

---

## R Basics

* R is an interpreter (`>`)

* Name objects in R (`i_like_snake_case <- `)

* Know your object types (`typeof()`)

* Case matters (`my_names != My_names`)

* Use comments! (`# use the hashtag symbol`)

* Functions (`fun`!)

* Use packages (*"install once per machine, load once per R session"*)

* Use the `%>%` (*"dataframe first, dataframe once"*)

---

## R is an interpreter `>`

You enter commands line-by-line (as opposed to compiled languages).

* The `>` means R is a ready for a command

* The `+` means your last command isn't complete

- If you get stuck with a `+` use your escape key!

---
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# 🐍

## Name Objects in R

## `i_like_snake_case <-`

RStudio Keyboard Shortcuts:

OSX: `Option` + `-`

Else: `Alt` + `-`

*(the + means and, not the + key)*

---

## Name your own objects

```r
us <- c("Steven", "Jackie", "Alison") # combine strings
us
```

```
[1] "Steven" "Jackie" "Alison"
```

```r
num_labs <- c(1:10) # combine numbers
num_labs
```

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

```r
mood <- rep("yippee", length(num_labs)) # replicate 10 times
mood
```

```
 [1] "yippee" "yippee" "yippee" "yippee" "yippee" "yippee" "yippee" "yippee"
 [9] "yippee" "yippee"
```

---

## Re-name others' objects

```r
my_alpha <- letters # built-in, no package needed
my_alpha
```

```
 [1] "a" "b" "c" "d" "e" "f" "g" "h" "i" "j" "k" "l" "m" "n" "o" "p" "q" "r" "s"
[20] "t" "u" "v" "w" "x" "y" "z"
```

```r
my_names <- babynames # from the babynames package
my_names
```

```
# A tibble: 1,924,665 x 5
    year sex   name          n   prop
   <dbl> <chr> <chr>     <int>  <dbl>
 1  1880 F     Mary       7065 0.0724
 2  1880 F     Anna       2604 0.0267
 3  1880 F     Emma       2003 0.0205
 4  1880 F     Elizabeth  1939 0.0199
 5  1880 F     Minnie     1746 0.0179
 6  1880 F     Margaret   1578 0.0162
 7  1880 F     Ida        1472 0.0151
 8  1880 F     Alice      1414 0.0145
 9  1880 F     Bertha     1320 0.0135
10  1880 F     Sarah      1288 0.0132
# … with 1,924,655 more rows
```

---

## What to name objects?

```r
?make.names
```

.pull-left[
Object names cannot:

- Start with a number
- Contain a space
- Contain ["reserved" words](http://stat.ethz.ch/R-manual/R-devel/library/base/html/Reserved.html)
]

.pull-right[
Object names must:

- Start with a letter
- Contain letters, numbers, `_` and `.`
]

---

## Adopt a consistent naming style

```r
i_use_snake_case # recommended
otherPeopleUseCamelCase
some.people.use.periods
And_aFew.People_RENOUNCEconvention
```

From: http://r4ds.had.co.nz/workflow-basics.html#whats-in-a-name

Read more:
http://style.tidyverse.org/syntax.html#object-names

---
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# 🔦

## Know Your Data Types

## `typeof()`

---

## Know your data types

* Numeric (2 subtypes)
    - Integers (`1, 50`)
    - Double (`1.5, 50.25`, `?double`)
* Character (`"hello"`)
* Factor (`grade = "A" | grade = "B"`)
* Logical (`TRUE | FALSE`)

```r
typeof(num_labs) # numeric
```

```
[1] "integer"
```

```r
typeof(mood) # "yippee" is a character
```

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

```r
typeof(mood == "yippee") # is mood equal to "yippee"- T or F?
```

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

---

## Characters can be deceiving

```r
my_things <- c(num_labs, mood)
my_things
```

```
 [1] "1"      "2"      "3"      "4"      "5"      "6"      "7"      "8"     
 [9] "9"      "10"     "yippee" "yippee" "yippee" "yippee" "yippee" "yippee"
[17] "yippee" "yippee" "yippee" "yippee"
```

```r
typeof(my_things) 
```

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

---

## `NA` is special

```r
num_labs <- c(num_labs, NA) 
```

```r
typeof(num_labs)
```

```
[1] "integer"
```

```r
num_labs*3
```

```
 [1]  3  6  9 12 15 18 21 24 27 30 NA
```

```r
max(num_labs)
```

```
[1] NA
```

```r
max(num_labs, na.rm = TRUE)
```

```
[1] 10
```

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## Case matters

## `my_names != My_names`

---

## Case matters

This works:

```r
glimpse(babynames)
```

```
Observations: 1,924,665
Variables: 5
$ year <dbl> 1880, 1880, 1880, 1880, 1880, 1880, 1880, 1880, 1880, 1880, 1880…
$ sex  <chr> "F", "F", "F", "F", "F", "F", "F", "F", "F", "F", "F", "F", "F",…
$ name <chr> "Mary", "Anna", "Emma", "Elizabeth", "Minnie", "Margaret", "Ida"…
$ n    <int> 7065, 2604, 2003, 1939, 1746, 1578, 1472, 1414, 1320, 1288, 1258…
$ prop <dbl> 0.07238359, 0.02667896, 0.02052149, 0.01986579, 0.01788843, 0.01…
```

These do not:

```r
Glimpse(babynames) # no function
```

```
Error in Glimpse(babynames): could not find function "Glimpse"
```

```r
glimpse(Babynames) # no data 
```

```
Error in glimpse(Babynames): object 'Babynames' not found
```

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# 📢

## Comments

## `# go here`

---

## Text behind a `#` is a comment

```r
num_labs + 2 # add 2 here
```

```
 [1]  3  4  5  6  7  8  9 10 11 12 NA
```

```r
num_weeks <- num_labs + 2 # save as new object
```

```r
# I can say anything I want here...
num_weeks
```

```
 [1]  3  4  5  6  7  8  9 10 11 12 NA
```

```r
but not here
```

```
Error: <text>:1:5: unexpected symbol
1: but not
        ^
```

---
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# 🍰

## Functions

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## Functions

Sometimes abbreviated `funs` in documentation, which is a little ironic 😉.

Functions can come from:

- base R (these functions are "built in")
- packages
- you

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# Base R Functions

```r
seq(1, 12, 1) # base R
```

```
 [1]  1  2  3  4  5  6  7  8  9 10 11 12
```

---
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# Functions from Packages

```r
babynames %>% count(sex) # count is from dplyr
```

```
# A tibble: 2 x 2
  sex         n
  <chr>   <int>
1 F     1138293
2 M      786372
```

---
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# Roll Your Own Functions

```r
greet <- function(name) {
  glue::glue("Welcome to CS631, {name}!")
}
greet("Sloane")
```

```
Welcome to CS631, Sloane!
```

---

# Function help

```r
?seq
?count
```

Pay attention to:

* Usage *(recipe)*

* Arguments *(ingredients)*

* Examples

Read more: 
- http://r4ds.had.co.nz/workflow-basics.html#calling-functions
- http://socviz.co/appendix.html#a-little-more-about-r
- http://stat545.com/block011_write-your-own-function-01.html
- http://stat545.com/block011_write-your-own-function-02.html
- http://stat545.com/block011_write-your-own-function-03.html

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# 📦

## Packages

*"install once per machine, load once per R session"*

---

## Packages!

*Install once* per machine

```r
install.packages("dplyr")
```

*Load once* per R work session

```r
library(dplyr)
```

*also: quotes matter, sorry*

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## The `tidyverse` package ecosystem

https://www.tidyverse.org

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*"The tidyverse is an opinionated collection of R packages designed for data science. All packages share an underlying design philosophy, grammar, and data structures."*

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

See packages included here: https://www.tidyverse.org/packages/

---
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# `%>%`

## The pipe

*"dataframe first, dataframe once"*

```r
library(dplyr)
```

RStudio Keyboard Shortcuts:

OSX: `CMD` + `SHIFT` + `M`

Else: `CTRL` + `SHIFT` + `M`

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*Nesting* a dataframe inside a function is hard to read.

```r
slice(babynames, 1)
```

```
# A tibble: 1 x 5
   year sex   name      n   prop
  <dbl> <chr> <chr> <int>  <dbl>
1  1880 F     Mary   7065 0.0724
```

Here, the "sentence" starts with a <font color="#ED1941">verb</font>.

<hr>

*Piping* a dataframe into a function lets you read L to R

```r
babynames %>% slice(1)
```

```
# A tibble: 1 x 5
   year sex   name      n   prop
  <dbl> <chr> <chr> <int>  <dbl>
1  1880 F     Mary   7065 0.0724
```

Now, the "sentence" starts with a <font color="#ED1941">noun</font>.

---
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Sequences of functions make you read *inside out*

```r
slice(filter(babynames, sex == "M"), 1)
```

```
# A tibble: 1 x 5
   year sex   name      n   prop
  <dbl> <chr> <chr> <int>  <dbl>
1  1880 M     John   9655 0.0815
```

<hr>

Chaining functions together lets you read *L to R*

```r
babynames %>% filter(sex == "M") %>% slice(1)
```

```
# A tibble: 1 x 5
   year sex   name      n   prop
  <dbl> <chr> <chr> <int>  <dbl>
1  1880 M     John   9655 0.0815
```

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## "dataframe first, dataframe once"

---
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```r
babynames %>% filter(sex == "M") %>% slice(1)
```

```
# A tibble: 1 x 5
   year sex   name      n   prop
  <dbl> <chr> <chr> <int>  <dbl>
1  1880 M     John   9655 0.0815
```

<hr>

This does the same thing:

```r
babynames %>% filter(.data = ., sex == "M") %>% slice(.data = ., 1)
```

```
# A tibble: 1 x 5
   year sex   name      n   prop
  <dbl> <chr> <chr> <int>  <dbl>
1  1880 M     John   9655 0.0815
```

<hr>

So does this:

```r
babynames %>% filter(., sex == "M") %>% slice(., 1)
```

```
# A tibble: 1 x 5
   year sex   name      n   prop
  <dbl> <chr> <chr> <int>  <dbl>
1  1880 M     John   9655 0.0815
```

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## I know...

![](https://media.giphy.com/media/uEhLWy2eu87Ly/source.gif)

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## I promise, it gets better.

![](https://media.giphy.com/media/l0MYRzcWP7cjfNQ2I/giphy.gif)

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# 🏃🏽

## Resources for Working in R:

http://r4ds.had.co.nz/workflow-basics.html

http://moderndive.com/2-getting-started.html

https://bookdown.org/chesterismay/rbasics/3-rstudiobasics.html

https://github.com/rstudio/cheatsheets/blob/master/rstudio-ide.pdf

---

## Install & load multiple R packages

This can get to be a long list if we want to use a lot of new packages in our work session. We can make a function to load a *list* of packages, and install them if not already installed (more on functions later).

```r
pkgs <- c("readr", "dplyr", "tidyr") # list packages needed
```

```r
ipak <- function(pkg){
    new.pkg <- pkg[!(pkg %in% installed.packages()[, "Package"])]
    if (length(new.pkg)) 
        install.packages(new.pkg, dependencies = TRUE)
    sapply(pkg, require, character.only = TRUE)
}
```

```r
ipak(pkgs) # take function, and give it that list
```

Function from: https://gist.github.com/stevenworthington/3178163