Intro to Statistical Computing

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## .base_color[Intro to Statistical]

## .base_color[Computing in R]

### .navy[Kelly McConville]

#### .navy[ Stat 108 | Week 1 | Spring 2023]

]

---

## Announcements

* Lecture slide decks will always be posted and linked to a Canvas Module the day before lecture.
    + These are HTML files but you can print them to PDF.
    + Will also bring printed versions for those who prefer paper copies.
* No section, no lecture quiz, no p-set this week.
* Only I will be running office hours this week at the following times:
    + Tuesday 10:30 am - noon in Science Center 316
    + Wednesday 1:30 - 3:00 pm in Science Center 316
    + Thursday 10:30 - 11:30 am in Science Center 316 (this week only)
* Note: HUIT is still setting up our RStudio Server.

***************************************

## Week 1 Goals

**Day 1 Lecture**

* Course overview

]

**Day 2 Lecture**

* Data Viz Principles

]

---

## But first, let me quickly introduce myself...

---

### Let's start with my path to Harvard...

---

## Research Interests

### Survey statistics and collaborate with

---

## Research Interests

### Where survey statistics meets data science

---

### Advising Undergraduate Forestry Data Science Research

---

## What is the point of this class?

### Let's talk learning goals!

---

## Learning Goals

### But first...

---

### Box Mac and Cheese

]

### versus Homemade Mac and Cheese?

]

---

### Goal: Learn to use several `R` packages.

]

]

---

### Goal: Learn to use several `R` packages.

* Focus on robust, well-documented packages.

* Understand their strengths and limitations.

* And if there isn't an existing `R` function that does what we want...

]

]

---

### Goal: Learn to create our own functions in `R`.

]

```r
magic_eight_ball <- function(
 question = "Will it rain today?"){
 answers <- c("Without a doubt",
 "Concentrate and ask again",
 "My sources say no")
 return(sample(x = answers, size = 1))
}

magic_eight_ball(question =
                   "Should I take Stat 108?")
```

```
## [1] "My sources say no"
```

]

---

### Goal: Learn to create our own functions in `R`.

* Learn the appropriate syntax and how to structure the arguments, body, and output.
* Create tests for our functions to make them more robust.
* Come full circle and bundle them up into their own `R` package in Project 2!

]

magic_eight_ball(question =
                   "Should I take Stat 108?")
```

```
## [1] "Concentrate and ask again"
```

]

---

### Goal: Develop our statistical programming skills.

* Implement code that reflects core ideas of statistical programming, including functions, iteration, control flow, vectorization, debugging, refactoring, and abstraction.

```r
for(i in 1:20){
  Iterate_over_something_awesome
}
```

]

```r
if(magic_eight_ball("Should I take Stat 108?")
  == "Without a doubt") {
    print("Stay seated")
  } else {
    print("Leave and buy a coffee.")
  }
```

]

* Apply coding habits and a coding style that align with best practices in the field.

```r
messy4324 <-select(dataset,thing1,thing2)%>%filter(thing1=="uncool")
```

]

```r
clean <- select(dataset, thing1, thing2) %>%
 filter(thing1 == "uncool")
```

]

---

## Learning Goals

---

### Goal: Wrangle and interact with a variety of data types.

#### Requires expanding our understanding of **data structures** in `R`.

* Most common data structure = `data.frame`/data set/spreadsheet

* Example: [Boston area Blue bike check-outs data](https://www.bluebikes.com/system-data)

```
##   bikeid tripduration   usertype           starttime            stoptime
## 1   3534         1322 Subscriber 2022-07-01 00:00:01 2022-07-01 00:22:03
## 2   5355          353   Customer 2022-07-01 00:00:02 2022-07-01 00:05:56
## 3   5834          413 Subscriber 2022-07-01 00:00:07 2022-07-01 00:07:01
## 4   5010          175 Subscriber 2022-07-01 00:00:13 2022-07-01 00:03:09
## 5   7398          751 Subscriber 2022-07-01 00:00:19 2022-07-01 00:12:50
## 6   7462          499   Customer 2022-07-01 00:00:19 2022-07-01 00:08:39
```

* Rows = observations
* Columns = variables
    * Two types: categorical and quantitative

* #### What variables **don't** fit neatly into these two types?

]
--

* #### What other **data structures** are there?

]

---

### Variable types: Dates and Times

* Are dates and times categorical or quantitative?

]

* What makes dates and times different?

]

```r
bluebikes %>%
  select(starttime, stoptime) %>%
  as.data.frame()
```

```
##             starttime            stoptime
## 1 2022-07-01 00:00:01 2022-07-01 00:22:03
## 2 2022-07-01 00:00:02 2022-07-01 00:05:56
## 3 2022-07-01 00:00:07 2022-07-01 00:07:01
## 4 2022-07-01 00:00:13 2022-07-01 00:03:09
## 5 2022-07-01 00:00:19 2022-07-01 00:12:50
## 6 2022-07-01 00:00:19 2022-07-01 00:08:39
```

* Will learn to use the `lubridate` package to wrangle dates and times.

---

## Variable types: Factors and Characters

* `R` typically stores categorical variables as one of two types: `factor` or `character`.

```r
bluebikes$usertype
```

```
## [1] "Subscriber" "Customer"   "Subscriber" "Subscriber" "Subscriber"
## [6] "Customer"
```

```r
class(bluebikes$usertype)
```

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

* Why do we need two different types??

* Will learn to use the `forcats` package to wrangle `factor`s.

]

* Will learn to use the `stringr` package to wrangle `character`s/strings/text.
    + Note: A character vector stores multiple strings/text.

]

---

## Wrangling text data

* Semi-structured text are data too!

* Example:  [Taylor Swift lyrics](https://github.com/shaynak/taylor-swift-lyrics)

```
## # A tibble: 6 × 3
## Song Album Lyric 
## <chr> <chr> <chr> 
## 1 22 (Taylor’s Version) Red (Taylor’s Version) It feels like a perfect night
## 2 22 (Taylor’s Version) Red (Taylor’s Version) To dress up like hipsters 
## 3 22 (Taylor’s Version) Red (Taylor’s Version) And make fun of our exes 
## 4 22 (Taylor’s Version) Red (Taylor’s Version) Uh-uh, uh-uh 
## 5 22 (Taylor’s Version) Red (Taylor’s Version) It feels like a perfect night
## 6 22 (Taylor’s Version) Red (Taylor’s Version) For breakfast at midnight
```

* But extracting useful information takes a lot of wrangling!  Will learn to use **regular expressions** to identify patterns in the text.

* Will also learn to analyze text with the `tidytext` package.

---

## What other data structures are there beyond `data.frames`?

---

## Data Structures: Spatial Data Frames

* [Example: American Community Survey Data](https://walker-data.com/tidycensus/) on median household income for counties in MA

```
## Simple feature collection with 6 features and 5 fields
## Geometry type: MULTIPOLYGON
## Dimension:     XY
## Bounding box:  xmin: -73.06851 ymin: 41.4811 xmax: -70.52557 ymax: 42.73664
## Geodetic CRS:  NAD83
##   GEOID                            NAME   variable estimate  moe
## 1 25017 Middlesex County, Massachusetts B19013_001   111790 1032
## 2 25005   Bristol County, Massachusetts B19013_001    74290 1371
## 3 25015 Hampshire County, Massachusetts B19013_001    76959 2504
## 4 25025   Suffolk County, Massachusetts B19013_001    80260 1586
## 5 25023  Plymouth County, Massachusetts B19013_001    98190 1711
## 6 25027 Worcester County, Massachusetts B19013_001    81660  987
##                         geometry
## 1 MULTIPOLYGON (((-71.89877 4...
## 2 MULTIPOLYGON (((-70.83595 4...
## 3 MULTIPOLYGON (((-73.06577 4...
## 4 MULTIPOLYGON (((-70.93091 4...
## 5 MULTIPOLYGON (((-70.88335 4...
## 6 MULTIPOLYGON (((-72.31363 4...
```

---

## Data Structures: Spatial Data Frames

* [Example: American Community Survey Data](https://walker-data.com/tidycensus/)

* Will learn to use the `sf` package for wrangling spatial data

---

## Data Structures: Lists

* `List`s are a more flexible structure for storing data!

```r
got
```

```
## [[1]]
## [[1]]$name
## [1] "Theon Greyjoy"
## 
## [[1]]$gender
## [1] "Male"
## 
## [[1]]$culture
## [1] "Ironborn"
## 
## 
## [[2]]
## [[2]]$name
## [1] "Tyrion Lannister"
## 
## [[2]]$gender
## [1] "Male"
## 
## [[2]]$culture
## [1] ""
```

]

* From a data analysis perspective, they are hard to work with!

* Will learn to use the `purrr` package to converting nested `list`s into `data.frame`s.

```
##                name gender  culture
## 1     Theon Greyjoy   Male Ironborn
## 2  Tyrion Lannister   Male         
## 3 Victarion Greyjoy   Male Ironborn
## 4              Will   Male         
## 5        Areo Hotah   Male Norvoshi
## 6             Chett   Male
```

]

---

## Learning Goals

---

### Goal: Create **static** data visualizations of multivariate data with `ggplot2`.

---

### Goal: Create **animated** data visualizations of multivariate data with `gganimate`.

---

### Goal: Create **interactive** data visualizations of multivariate data with `plotly` and `shiny`.

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#### Also follow **best practices** in data viz, which is Wednesday's lecture topic!

---

## Learning Goals

### What forms do you use to share the results of your data work?

---

###  Example: [US Forest Inventory and Analysis Program](https://www.fia.fs.fed.us/about/about_us/)

> Mission: "Make and keep current a comprehensive inventory and analysis of the present and prospective conditions of and requirements for the renewable resources of the forest and rangelands of the US."

* Every 10 years each state produces a book of estimates --lots of tables and some graphs.

* I wondered if dashboards would allow for more frequent updates and for more questions to be answered.

]

]

---

### And then the pandemic happened and Johns Hopkins made their [COVID dashboard](https://coronavirus.jhu.edu/map.html):

---

###  Example: [US Forest Inventory and Analysis Program](https://www.fia.fs.fed.us/about/about_us/)

* Very common now that any FIA work I do also includes a [corresponding dashboard](https://ncasi-shiny-tools.shinyapps.io/Counties/)

---

### Goal: Create interactive dashboards with `shinydashboard` and `flexdashboard`.

---

### Goal: Learn to create an `R` package with `devtools` for Project 2.

I have two `R` packages on the [Comprehensive R Archival Network](https://cran.r-project.org/):

* `mase`: Contains a collection of survey estimators that combine complex survey data with auxiliary data sources (e.g., satellite imagery, administrative records)
* `pdxTrees`: Contains tree data from Portland, OR's Parks and Rec Department

---

### Goal: Develop a collaborative, version controlled workflow using git and GitHub.

* Will use git and GitHub for both of the projects.
* Will also be given a personal GitHub repository for practice and storing course materials.
* GitHub is a great place to share code, R packages, and data work!

]

]

---

### Goal: Learn how to create a website with `distill`.

---

### Goal: Utilize a reproducible workflow for all `R` work!

* Store `R` work in `R` scripts and `RMarkdown` documents.

]

* Create reproducible coding examples so it is easy to ask and answer coding questions.

]

#### Now let's move onto the structure of the course!

---

## Stat 108 Structure

<img src="img/week_stat108s23.png" width="100%" style="display: block; margin: auto;" />
]

#### Meetings

* Lectures Mon and Wed at 9am.
* Starting in Week 2, optional Sections.
    + Mix of review, problem set work time, and project feedback activities.
* Lots of office hours.
    + Come and collaborate!

]

---

## Stat 108 Structure

<img src="img/week_stat108s23.png" width="100%" style="display: block; margin: auto;" />
]

#### Assessments

* **Weekly problem sets** 
    + Starts in Week 2.
    + Released on Thur and due the following Wed at 5:00pm.
    + Get 1 drop and 4 extension days
* **Weekly lecture quizzes**
    + Open notes
    + Starts in Week 2.
    + Released at noon on Wed and due 48 hours later.
    + Get 1 drop
* **Projects**
    + Project 1: Create an interactive dashboard.
    + Project 2: Create an `R` package.
* **Engagement**

]

---

###  Stat 108 Code of Conduct

I expect everyone in this class to strive to foster a learning environment that is equitable, inclusive, and welcoming.  If you experience any barriers to learning, please come to me or a college administrator with your concerns.

#### Code of Conduct

I expect all members of Stat 108 to make participation a harassment-free experience for everyone, regardless of age, body size, visible or invisible disability, ethnicity, sex characteristics, gender identity and expression, level of experience, education, socio-economic status, nationality, personal appearance, race, religion, or sexual identity and orientation.

I expect everyone to act and interact in ways that contribute to an open, welcoming, inclusive, and healthy community of learners.  You can contribute to a positive learning environment by demonstrating empathy and kindness, being respectful of differing viewpoints and experiences, and giving and gracefully accepting constructive feedback.

This Code of Conduct is adapted from the [Contributor Covenant](https://www.contributor-covenant.org/version/2/0/code_of_conduct.html), version 2.0.

****************************

Please read the syllabus for other policies (e.g, academic honesty, regrades...).

---

## Learning to be a Coder

Many of the assignments will provide opportunities to stretch yourself and learn code **not** covered directly in class.  Why?

### (Another) Goal: Develop our abilities to effectively search for and evaluate potential solutions and then adapt the code to our situation.

😾 **Potential Erroneous Side Effect:** Concluding that you are bad at coding because you can't solve the problem "on your own" or because you find the answers on StackOverflow confusing/unhelpful.

---

### (Another) Goal: Develop our abilities to effectively search for and evaluate potential solutions and then adapt the code to our situation.

I encourage employing the following strategy for solving coding questions:

&rarr; Try the problem.

&rarr; If and when you get stuck, look to the internet for potential solutions.

&rarr; If you find some promising ideas, try them out.

&rarr;  For the code that seems most helpful, spend time figuring out what each line does.

&rarr;  If the code doesn't exactly solve your problem, adapt it.  Even if it does, still consider whether or not modifications should be made.

&rarr; If still stuck, post your question on our class Slack and/or come to office hours.  **Don't just stay stuck.**

**Key:** Try to find the right balance between independent learning and supported learning.

+ And, get help **before** the frustration sets in!

---

### Reminders

* No section or no lecture quiz this week.
* Make sure to go through the syllabus, which can be found in the Getting Started Module on Canvas.
* Only I will be running office hours this week at the following times:
    + Tuesday 10:30 am - noon in Science Center 316
    + Wednesday 1:30 - 3:00 pm in Science Center 316
    + Thursday 10:30 - 11:30 am in Science Center 316 (this week only)
* Note: HUIT is still setting up our RStudio Server.