Introduction to R

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# Introduction to R
## Week 1: The basics
### Louisa Smith
### July 13 - July 17

---

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Let's start with...
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.larger[
The basics
]

---
# About this class

- Non-credit

- 6 weeks

- Watch the videos and do the exercises on your own (or with friends/classmates), come together for lab

- Practice by yourself in between classes

- Everything you need is at http://intro-to-r-2020.louisahsmith.com

.go[
You are not going to break anything!
]

---
# About me

.pull-left[
- Rising 5th-year PhD candidate in Epidemiology

- Started using R during my master's (so 6 years of experience); learned mostly by doing

- Problem sets, manuscripts, slides, website all in R

- Almost 100 R projects on my computer, over 1000 R scripts

]
.pull-right[
![](../img/apppic.png)
]

--
.bottom[
.go[
I have to Google things literally every time I use R!
]
]

---
# Plan

## **Week 1: The basics**

## **Week 2: Figures**

## **Week 3: Selecting, filtering, and mutating**

## **Week 4: Grouping and tables**

## **Week 5: Functions**

## **Week 6: Analyze your data**

---

.pull-left[
![](https://www.rstudio.com/wp-content/uploads/2019/02/rstudio-og.png)

]
.pull-right[.middle[

# An IDE for R

An *integrated development environment* is software that makes coding easier

- see objects you've imported and created
- autocomplete
- syntax highlighting
- run part or all of your code

]

.right[
.demo[Setup...]]
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1
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Your turn...
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.exercise[
- Install R
- Install R Studio
]
]

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# Packages

- Some functions are built into R
  - `mean()`, `lm()`, `table()`, etc.
- They actually come from built-in packages
  - `base`, `stats`, `graphics`, etc.
- Anyone (yes, *anyone*) build their own package to add to the functionality of R
  - `ggplot2`, `dplyr`, `data.table`, `survival`, etc.

.center[![:scale 70%](../img/stickers.png)]

.footnote[
Image from [Zhi Yang](https://zhiyang.netlify.app/post/hexwall/)
]

---
# Packages

.large[
- You have to **install** a package once*

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

- You then have to **load** the package every time you want to use it

```r
library(survival)
```
]
.footnote[*Actually, with every new major R release, but we won't worry about that.]

---
# Packages

"You only have to buy the book once, but you have to go get it out of the bookshelf every time you want to read it."

```r
install.packages("survival")
library(survival)
survfit(...)
```

.center[.hand[Several days later...]]

```r
library(survival)
coxph(...)
```

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.demo[Demonstration...]
]

---
# Package details

- When you use `install.packages`, packages are downloaded from [CRAN](https://cran.r-project.org) (The Comprehensive R Archive Network)
  - This is also where you downloaded R
- Packages can be hosted lots of other places, such as [Bioconductor](https://www.bioconductor.org) (for bioinformatics), and [Github](https://www.github.com) (for personal projects or while still developing)
- The folks at CRAN check to make things "work" in some sense, but don't check on the statistical methods...
  - But because R is open-source, you can always read the code yourself
- Two functions from different packages can have the same name... if you load them both, you may have some trouble

---
# tidyverse

- The same people who make RStudio also are responsible for a set of packages called the `tidyverse`

![](../img/tidy-data.png)

---
# tidyverse

.large[
.pull-left-wide[
- Running `install.packages(tidyverse)` actually downloads more than a dozen packages*
- Running `library(tidyverse)` loads:
.center[`ggplot2`, `dplyr`, `tidyr`, `readr`, `purrr`, `tibble`, `stringr`, `forcats`]
- This is by no means the only way to manage your data, but I find that a lot of the time, it's the easiest and simplest way to get things done.
]
]

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![](../img/tidyverse.png)
]

.footnote[*See which ones at [https://tidyverse.tidyverse.org](https://tidyverse.tidyverse.org)]

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.pull-right[
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Your turn...
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.exercise[
- Install the `tidyverse` "package"
- Load *one* of the `tidyverse` packages
]
]

---
name: r-projects
# R projects

.pull-left-narrow[
```
my-project/
 ├─ my-project.Rproj
 ├─ README
 ├─ data/
 │   ├── raw/
 │   └── processed/
 ├─ code/
 ├─ results/
 │   ├── tables/
 │   ├── figures/
 │   └── output/
 └─ docs/
```
]
.pull-right-wide[
- An `.Rproj` file is mostly just a placeholder. It remembers various options, and makes it easy to open a new RStudio session that starts up in the correct working directory. You never need to edit it directly.

- A README file can just be a text file that includes notes for yourself or future users.

- I like to have a folder for raw data -- which I never touch -- and a folder(s) for datasets that I create along the way.
]

---

# This course

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```
R-course/
 ├─ 01-week/
 │   ├── 01-week.Rproj
 │   ├── 01-exercises.R
 │   ├── 01-lab.Rmd
 │   ├── 01-slides.pdf
 │   └── data/
 │        └── nlsy.csv 
 ├─ 02-week/
 │   ├── 02-week.Rproj
 │   ├── 02-exercises.R
 │   ├── 02-lab.Rmd
 │   ├── 02-slides.pdf
 │   └── data/
 │        └── nhanes.xlsx 
 ├── 03-week/
```
]
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- Each week you'll download a zip file of some or all of the things you need for the week
 - You may be adding more later!
- Open the week's work by opening the `.Rproj` file
 - This will ensure you're in the right working directory to easily access the data, etc.
 
 
 
 
 
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.demo[Demonstration...]
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3
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Your turn...
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.exercise[
- Download the `01-week.zip` file [here](http://intro-to-r-2020.louisahsmith.com/exercises/01-week.zip)
- Open up the `01-week.Rproj` file
]
]

---
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# R uses `<-` for assignment
.midi[
Create an object `vals` that contains and sequence of numbers:

```r
# create values
vals <- c(1, 645, 329)
```

Put your cursor at the end of the line and hit ctrl/cmd + enter.

Now `vals` holds those values.

We can see them again by running just the name (put your cursor after the name and press ctrl/cmd + enter again).

```r
vals
```

```
## [1]   1 645 329
```

.go[No assignment arrow means that the object will be printed to the console.]
]

---
# Types of data (*classes*)
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We could also create a character *vector*:

```r
chars <- c("dog", "cat", "rhino")
chars
```

```
## [1] "dog"   "cat"   "rhino"
```

Or a *logical* vector:

```r
logs <- c(TRUE, FALSE, FALSE)
logs
```

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

.go[We'll see more options as we go along!]
]

---

# Types of objects
.midi[
We created *vectors* with the `c()` function (`c` stands for concatenate)

We could also create a *matrix* of values with the `matrix()` function:

```r
# turn the vector of numbers into a 2-row matrix
mat <- matrix(c(234, 7456, 12, 654, 183, 753), nrow = 2)
mat
```

```
##      [,1] [,2] [,3]
## [1,]  234   12  183
## [2,] 7456  654  753
```

The numbers in square brackets are *indices*, which we can use to pull out values:

```r
# extract second row
mat[2, ]
```

```
## [1] 7456  654  753
```
]

---
# Dataframes

We usually do analysis in R with dataframes (or some variant).

Dataframes are basically like spreadsheets: columns are variables, and rows are observations.

```r
gss_cat
```

```
## # A tibble: 21,483 x 9
## year marital age race rincome partyid relig denom tvhours
## <int> <fct> <int> <fct> <fct> <fct> <fct> <fct> <int>
## 1 2000 Never marr… 26 White $8000 to 99… Ind,near rep Protestant Southern ba… 12
## 2 2000 Divorced 48 White $8000 to 99… Not str repu… Protestant Baptist-dk … NA
## 3 2000 Widowed 67 White Not applica… Independent Protestant No denomina… 2
## 4 2000 Never marr… 39 White Not applica… Ind,near rep Orthodox-ch… Not applica… 4
## 5 2000 Divorced 25 White Not applica… Not str demo… None Not applica… 1
## 6 2000 Married 25 White $20000 - 24… Strong democ… Protestant Southern ba… NA
## 7 2000 Never marr… 36 White $25000 or m… Not str repu… Christian Not applica… 3
## 8 2000 Divorced 44 White $7000 to 79… Ind,near dem Protestant Lutheran-mo… NA
## 9 2000 Married 44 White $25000 or m… Not str demo… Protestant Other 0
## 10 2000 Married 47 White $25000 or m… Strong repub… Protestant Southern ba… 3
## # … with 21,473 more rows
```

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# *tibble*???

---
# tibbles are basically just pretty dataframes

.pull-left[

```r
as_tibble(gss_cat)[, 1:4]
```

```
# A tibble: 21,483 x 4
 year marital age race 
 <int> <fct> <int> <fct>
 1 2000 Never married 26 White
 2 2000 Divorced 48 White
 3 2000 Widowed 67 White
 4 2000 Never married 39 White
 5 2000 Divorced 25 White
 6 2000 Married 25 White
 7 2000 Never married 36 White
 8 2000 Divorced 44 White
 9 2000 Married 44 White
10 2000 Married 47 White
# … with 21,473 more rows
```
]

.pull-right[

```r
as.data.frame(gss_cat)[, 1:4]
```

```
   year       marital age  race
1  2000 Never married  26 White
2  2000      Divorced  48 White
3  2000       Widowed  67 White
4  2000 Never married  39 White
5  2000      Divorced  25 White
6  2000       Married  25 White
7  2000 Never married  36 White
8  2000      Divorced  44 White
9  2000       Married  44 White
10 2000       Married  47 White
11 2000       Married  53 White
12 2000       Married  52 White
13 2000       Married  52 White
14 2000       Married  51 White
15 2000      Divorced  52 White
16 2000       Married  40 Black
17 2000       Widowed  77 White
18 2000 Never married  44 White
19 2000       Married  40 White
20 2000       Married  45 Black
```
]

---

# and tibbles are the quickest and most intuitive way to make and read a dataset
.pull-left[

```r
dat1 <- tibble(
 age = c(24, 76, 38),
 height_in = c(70, 64, 68),
 height_cm = height_in * 2.54
)
dat1
```

```
## # A tibble: 3 x 3
## age height_in height_cm
## <dbl> <dbl> <dbl>
## 1 24 70 178.
## 2 76 64 163.
## 3 38 68 173.
```
]
.pull-right[

```r
dat2 <- tribble(
 ~n, ~food, ~animal,
 39, "banana", "monkey",
 21, "milk", "cat",
 18, "bone", "dog"
)
dat2
```

```
## # A tibble: 3 x 3
## n food animal
## <dbl> <chr> <chr> 
## 1 39 banana monkey
## 2 21 milk cat 
## 3 18 bone dog
```
]

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Your turn...
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- Work through the code in `01-week/01-todo.R`
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