R Basics

class: center, middle, inverse, title-slide

# R Basics
## <br> Getting up and Running
### Christopher Callaghan - CORE Lab
### 2019-08-07

---

# Motivation

--
R is HOT! 🔥

--
.pull-left[
The community

- Among one of the fastest growing programming languages (behind heavy weights such as Python and C)
 - 450+ user group world wide
 - 300,000 questions on Stackoverflow
 - Social media community under #rstats
]

--
.pull-right[
Extensibility

- 14,500 packages on CRAN
  - Thriving community on Github | Gitlab
  - RStudio
]

---
# The R Environment

.center[
![](img/rconsole.png)
]
--
In R, all data types and functions are considered objects.

---
# Quirks to Watch Out For

1. Capitalization

```r
Sys.time()
```

```
## [1] "2019-08-07 11:11:06 PDT"
```

vs.

```r
sys.time()
```

`Error in sys.time() : could not find function "sys.time"`

---
# Quirks to Watch Out For

2. Spacing

```r
x<-1
x
```

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

vs.

```r
x < -1
```

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

---
# Data types

```r
character_type <- "three"
logical_type   <- FALSE
numeric_type   <- 3
```

```r
typeof(character_type)
```

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

```r
typeof(logical_type)
```

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

```r
typeof(numeric_type)
```

```
## [1] "double"
```

---
# Structures

|    | Homogeneous   | Heterogeneous |
|----|---------------|---------------|
| 1D | Atomic Vector | List          |
| 2D | Matrix        | Data Frame    |

One-dimensional:
  - *Atomic vectors* - All observations are the same type
  - *Lists* - All observations can be a different type

Two-dimensional:
  - *Matrix* - All variables are the same type
  - *Data Frame* - All variables can be a different type

---
# Understanding Atomic Vectors

.pull-left[
Four (relevant) flavors:

- `logical`
  - `double`
  - `integer`
  - `character`
]

.pull-right[
<br>
<center>
![](img/vector.png)

]

```r
lgl_vector <- c(TRUE, FALSE, TRUE)
dbl_vector <- c(0x1, 2.0 , 3e0)
int_vector <- c(1L, 2L, 1:3L)
chr_vector <- c("Hello", "world", "!")
```

---
# Undestanding Lists

.pull-left[
A named (or not) sequence of objects.
]

.pull-right[
<center>
![](img/lists.png)

]

```r
my_list <- list(lgl = lgl_vector,
                dbl = dbl_vector,
                int = int_vector,
                chr = chr_vector)
my_list
```

```
## $lgl
## [1]  TRUE FALSE  TRUE
## 
## $dbl
## [1] 1 2 3
## 
## $int
## [1] 1 2 1 2 3
## 
## $chr
## [1] "Hello" "world" "!"
```

---
# Understanding Matrices

.pull-left[
A homogeneous two-dimensional structure.
]

.pull-right[
<center>
![](img/matrix.png)

]

```r
my_matrix <- matrix(dbl_vector,
                    nrow = length(dbl_vector),
                    ncol = length(dbl_vector))
my_matrix
```

```
##      [,1] [,2] [,3]
## [1,]    1    1    1
## [2,]    2    2    2
## [3,]    3    3    3
```

---
# Understanding Data frames

.pull-left[
A heterogeneous two-dimensional structure.
]

.pull-right[
<center>
![](img/dataframe.png)

]

```r
my_df <- data.frame(lgl_vector,
                    dbl_vector,
                    chr_vector)
my_df
```

```
##   lgl_vector dbl_vector chr_vector
## 1       TRUE          1      Hello
## 2      FALSE          2      world
## 3       TRUE          3          !
```

---
# Using Data Frames

```r
url <- "https://raw.githubusercontent.com/fivethirtyeight/russian-troll-tweets/master/IRAhandle_tweets_1.csv"

map_dfr(url, read_csv) %>%
  as_tibble()
```

```
## # A tibble: 243,891 x 21
##    external_author… author content region language publish_date
##               <dbl> <chr>  <chr>   <chr>  <chr>    <chr>       
##  1          9.06e17 10_GOP "\"We … Unkno… English  10/1/2017 1…
##  2          9.06e17 10_GOP Marsha… Unkno… English  10/1/2017 2…
##  3          9.06e17 10_GOP Daught… Unkno… English  10/1/2017 2…
##  4          9.06e17 10_GOP JUST I… Unkno… English  10/1/2017 2…
##  5          9.06e17 10_GOP 19,000… Unkno… English  10/1/2017 2…
##  6          9.06e17 10_GOP "Dan B… Unkno… English  10/1/2017 2…
##  7          9.06e17 10_GOP 🐝🐝🐝 ht… Unkno… English  10/1/2017 2…
##  8          9.06e17 10_GOP '@Sena… Unkno… English  10/1/2017 2…
##  9          9.06e17 10_GOP As muc… Unkno… English  10/1/2017 3…
## 10          9.06e17 10_GOP After … Unkno… English  10/1/2017 3…
## # … with 243,881 more rows, and 15 more variables: harvested_date <chr>,
## #   following <dbl>, followers <dbl>, updates <dbl>, post_type <chr>,
## #   account_type <chr>, retweet <dbl>, account_category <chr>,
## #   new_june_2018 <dbl>, alt_external_id <dbl>, tweet_id <dbl>,
## #   article_url <chr>, tco1_step1 <chr>, tco2_step1 <chr>,
## #   tco3_step1 <lgl>
```

---
# Functions

Repeatable instructions for the program to execute.

name <- function(args_list){
    
          body

}
      
For instance:

```r
purrr::is_empty
```

```
## function (x) 
## length(x) == 0
## <bytecode: 0x7fe978c3ec38>
## <environment: namespace:rlang>
```

---
# Functions Example

```r
1 + 2
```

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

```r
sum(1,2)
```

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

```r
sum
```

```
## function (..., na.rm = FALSE)  .Primitive("sum")
```

```r
my_sum <- function(x, y) {
  return(x + y)
}
my_sum(1, 2)
```

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

---
# Parting Thoughts

- [Data Camp](https://www.datacamp.com/), an online platform for learning data science. The early modules on R are free.
  - [R for Data Science](https://r4ds.had.co.nz/) from Hadley Wickham. An essential read for those invested in learning data science with R. The author is the Chief Scientist at RStudio and principal author of many R packages.
  - [Stat 545 course](http://stat545.com/) from the University of British Columbia, which is an online course on data wrangling, exploration and analysis with R.
  
Happy R learning! 😄