Getting Started

.leftcol30[
<center>
<img src="https://raw.githubusercontent.com/emse-eda-gwu/2021-Spring/master/images/eda_hex_sticker.png" width=250>
</center>
]
.rightcol70[
# Week 1: .fancy[Getting Started]

### <svg style="height:0.8em;top:.04em;position:relative;fill:white;" viewBox="0 0 512 512"><path d="M496 128v16a8 8 0 0 1-8 8h-24v12c0 6.627-5.373 12-12 12H60c-6.627 0-12-5.373-12-12v-12H24a8 8 0 0 1-8-8v-16a8 8 0 0 1 4.941-7.392l232-88a7.996 7.996 0 0 1 6.118 0l232 88A8 8 0 0 1 496 128zm-24 304H40c-13.255 0-24 10.745-24 24v16a8 8 0 0 0 8 8h464a8 8 0 0 0 8-8v-16c0-13.255-10.745-24-24-24zM96 192v192H60c-6.627 0-12 5.373-12 12v20h416v-20c0-6.627-5.373-12-12-12h-36V192h-64v192h-64V192h-64v192h-64V192H96z"/></svg> EMSE 4575: Exploratory Data Analysis
### <svg style="height:0.8em;top:.04em;position:relative;fill:white;" viewBox="0 0 448 512"><path d="M224 256c70.7 0 128-57.3 128-128S294.7 0 224 0 96 57.3 96 128s57.3 128 128 128zm89.6 32h-16.7c-22.2 10.2-46.9 16-72.9 16s-50.6-5.8-72.9-16h-16.7C60.2 288 0 348.2 0 422.4V464c0 26.5 21.5 48 48 48h352c26.5 0 48-21.5 48-48v-41.6c0-74.2-60.2-134.4-134.4-134.4z"/></svg> John Paul Helveston
### <svg style="height:0.8em;top:.04em;position:relative;fill:white;" viewBox="0 0 448 512"><path d="M0 464c0 26.5 21.5 48 48 48h352c26.5 0 48-21.5 48-48V192H0v272zm320-196c0-6.6 5.4-12 12-12h40c6.6 0 12 5.4 12 12v40c0 6.6-5.4 12-12 12h-40c-6.6 0-12-5.4-12-12v-40zm0 128c0-6.6 5.4-12 12-12h40c6.6 0 12 5.4 12 12v40c0 6.6-5.4 12-12 12h-40c-6.6 0-12-5.4-12-12v-40zM192 268c0-6.6 5.4-12 12-12h40c6.6 0 12 5.4 12 12v40c0 6.6-5.4 12-12 12h-40c-6.6 0-12-5.4-12-12v-40zm0 128c0-6.6 5.4-12 12-12h40c6.6 0 12 5.4 12 12v40c0 6.6-5.4 12-12 12h-40c-6.6 0-12-5.4-12-12v-40zM64 268c0-6.6 5.4-12 12-12h40c6.6 0 12 5.4 12 12v40c0 6.6-5.4 12-12 12H76c-6.6 0-12-5.4-12-12v-40zm0 128c0-6.6 5.4-12 12-12h40c6.6 0 12 5.4 12 12v40c0 6.6-5.4 12-12 12H76c-6.6 0-12-5.4-12-12v-40zM400 64h-48V16c0-8.8-7.2-16-16-16h-32c-8.8 0-16 7.2-16 16v48H160V16c0-8.8-7.2-16-16-16h-32c-8.8 0-16 7.2-16 16v48H48C21.5 64 0 85.5 0 112v48h448v-48c0-26.5-21.5-48-48-48z"/></svg> January 12, 2021
]

---
class: center
background-image: url("images/planning.jpg")
background-size: contain

---
class: center

# It's nice to see your faces 😄

.leftcol[
<center>
<img src="images/zoom_video.jpg" width="500px">
</center>
]
.rightcol[.left[
If you're okay with it, please turn on your camera - it creates a more engaging discussion environment and an opportunity for us to get to know each other better.

Fun Zoom backgrounds encouraged 😄

(Your privacy is important, and I understand if you wish to keep cameras off. No pressure.)
]]

---

# Week 1: .fancy[Getting Started]

## 1. Course Goal
## 2. Course Introduction
## 3. Break: Install Stuff
## 4. Workflow & Reading In Data
## 5. Data Provenance
## 6. Tidy Data

---

# Week 1: .fancy[Getting Started]

## 1. .orange[Course Goal]
## 2. Course Introduction
## 3. Break: Install Stuff
## 4. Workflow & Reading In Data
## 5. Data Provenance
## 6. Tidy Data

---

## Course 1: [Intro to Programming for Analytics](https://p4a.seas.gwu.edu/2020-Fall/)

**"Computational Literacy"**

- Programming: Conditionals (if/else), loops, functions, testing, data types.
- Analytics: Data structures, import / export, basic data manipulation & visualization.

## Course 2: [Exploratory Data Analysis](https://emse-eda-gwu.github.io/2021-Spring/)

**"Data Literacy"**

- Strategies for conducting an exploratory data analysis.
- Design principles for visualizing and communicating _information_ extracted from data.
- Reproducibility: Reports that contain code, equations, visualizations, and narrative text.

---
class: center, inverse, middle

# **Class goal**: translate _data_ into _information_

---
class: center

# **Class goal**: translate _data_ into _information_

Average student engagement scores

Class       | Type | City | County
 ------------|-------------|------|-------
 Special Ed. | Charter     | 643  | 793
 Special Ed. | Public      | 735  | 928
 General Ed. | Charter     | 590  | 724
 General Ed. | Public      | 863  | 662

]

<img src="figs/student-engagement-final-1.png" width="432" />
]

---

# Data exploration: an iterative process

```r
engagement_data <- data.frame(
    City   = c(643, 735, 590, 863),
    County = c(793, 928, 724, 662),
    School = c('Special Ed., Charter', 'Special Ed., Public',
               'General Ed., Charter', 'General Ed., Public'))
engagement_data
```

```
#>   City County               School
#> 1  643    793 Special Ed., Charter
#> 2  735    928  Special Ed., Public
#> 3  590    724 General Ed., Charter
#> 4  863    662  General Ed., Public
```
]]

```r
engagement_data <- engagement_data %>%
    gather(Location, Engagement, City:County) %>%
    mutate(Location = fct_relevel(
      Location, c('City', 'County')))
engagement_data
```

```
#>                 School Location Engagement
#> 1 Special Ed., Charter     City        643
#> 2  Special Ed., Public     City        735
#> 3 General Ed., Charter     City        590
#> 4  General Ed., Public     City        863
#> 5 Special Ed., Charter   County        793
#> 6  Special Ed., Public   County        928
#> 7 General Ed., Charter   County        724
#> 8  General Ed., Public   County        662
```
]]

---

# Data exploration: an iterative process

```r
engagement_data %>%
    ggplot() +
    geom_col(aes(x = Engagement, y = School,
                 fill = Location),
             position = 'dodge')
```

<img src="figs/student-engagement-bars1-1.png" width="432" />
]]

<img src="figs/student-engagement-bars2-1.png" width="432" />
]

---

# Data exploration: an iterative process

<img src="figs/student-engagement-bars3-1.png" width="432" />
]

Remove unnecessary axes, change colors, fix labels:

<img src="figs/unnamed-chunk-5-1.png" width="432" />
]

---

**A fully reproducible analysis**

```r
data <- data.frame(
    City   = c(643, 735, 590, 863),
    County = c(793, 928, 724, 662),
    School = c('Special Ed., Charter', 'Special Ed., Public',
               'General Ed., Charter', 'General Ed., Public'),
    Highlight = c(0, 0, 0, 1)) %>%
    gather(Location, Engagement, City:County) %>%
    mutate(
      Location = fct_relevel(Location, c('City', 'County')),
      Highlight = as.factor(Highlight),
      x = ifelse(Location == 'County', 1, 0))
```
]
.rightcol[

```r
plot <- ggplot(data, aes(x = x, y = Engagement, group = School, color = Highlight)) +
    geom_point() +
    geom_line() +
    scale_color_manual(values = c('#757575', '#ed573e')) +
    labs(x = 'Sex', y = 'Engagement',
         title = paste0('Students in public, general education classes\n',
                        'in county schools have surprisingly low engagement')) +
    scale_x_continuous(limits = c(-1.2, 1.2), labels = c('City', 'County'),
                       breaks = c(0, 1)) +
    geom_text_repel(aes(label = Engagement, color = as.factor(Highlight)),
                    data          = subset(engagement, Location == 'County'),
                    size          = 5,
                    nudge_x       = 0.1,
                    segment.color = NA) +
    geom_text_repel(aes(label = Engagement, color = as.factor(Highlight)),
                    data          = subset(engagement, Location == 'City'),
                    size          = 5,
                    nudge_x       = -0.1,
                    segment.color = NA) +
    geom_text_repel(aes(label = School, color = as.factor(Highlight)),
                    data          = subset(engagement, Location == 'City'),
                    size          = 5,
                    nudge_x       = -0.25,
                    hjust         = 1,
                    segment.color = NA) +
    theme_cowplot() +
    background_grid(major = 'x') +
    theme(axis.line = element_blank(),
          axis.title.x = element_blank(),
          axis.title.y = element_blank(),
          axis.text.y = element_blank(),
          axis.ticks = element_blank(),
          legend.position = 'none')
```
]]]

]]

---

# Week 1: .fancy[Getting Started]

## 1. Course Goal
## 2. .orange[Course Introduction]
## 3. Break: Install Stuff
## 4. Workflow & Reading In Data
## 5. Data Provenance
## 6. Tidy Data

---

# Meet your instructor!

### John Helveston, Ph.D.

.font80[
- 2018 - Present Assistant Professor, Engineering Management & Systems Engineering
- 2016-2018 Postdoc at [Institute for Sustainable Energy](https://www.bu.edu/ise/), Boston University
- 2016 PhD in Engineering & Public Policy at Carnegie Mellon University
- 2015 MS in Engineering & Public Policy at Carnegie Mellon University
- 2010 BS in Engineering Science & Mechanics at Virginia Tech
- Website: [www.jhelvy.com](http://www.jhelvy.com/)
]]

---
# Meet your tutors!

.leftcol30[.circle[
<img src="images/pantha.jpg" width="300">
]]
.rightcol70[
### **Saurav Pantha** (aka "The Firefighter")

- Graduate Assistant (GA)
- Masters student in EMSE
]

---

# Meet your tutors!

.leftcol30[.circle[
<img src="images/kim.png" width="300">
]]
.rightcol70[
### **Jennifer Kim** (aka "The Monitor")

- Learning Assistant (LA)
- EMSE Junior & P4A alumni
]

---

# Prerequisites

## [EMSE 4574: Intro to Programming for Analytics](https://p4a.seas.gwu.edu/2020-Fall/)

You should be able to:

- Use RStudio to write basic R commands.
- Know the distinctions between different R operators and data types, including numeric, string, and logical data.
- Use **tidyverse** functions to wrangle and manipulate data in R.
- Use the **ggplot2** library to create plots in R.

> [<svg style="height:0.8em;top:.04em;position:relative;" viewBox="0 0 512 512"><path d="M496 128v16a8 8 0 0 1-8 8h-24v12c0 6.627-5.373 12-12 12H60c-6.627 0-12-5.373-12-12v-12H24a8 8 0 0 1-8-8v-16a8 8 0 0 1 4.941-7.392l232-88a7.996 7.996 0 0 1 6.118 0l232 88A8 8 0 0 1 496 128zm-24 304H40c-13.255 0-24 10.745-24 24v16a8 8 0 0 0 8 8h464a8 8 0 0 0 8-8v-16c0-13.255-10.745-24-24-24zM96 192v192H60c-6.627 0-12 5.373-12 12v20h416v-20c0-6.627-5.373-12-12-12h-36V192h-64v192h-64V192h-64v192h-64V192H96z"/></svg> Check out R for Analytics Primer](http://jhelvy.github.io/r4aPrimer/)

---

# Course website

## <svg style="height:0.8em;top:.04em;position:relative;" viewBox="0 0 496 512"><path d="M336.5 160C322 70.7 287.8 8 248 8s-74 62.7-88.5 152h177zM152 256c0 22.2 1.2 43.5 3.3 64h185.3c2.1-20.5 3.3-41.8 3.3-64s-1.2-43.5-3.3-64H155.3c-2.1 20.5-3.3 41.8-3.3 64zm324.7-96c-28.6-67.9-86.5-120.4-158-141.6 24.4 33.8 41.2 84.7 50 141.6h108zM177.2 18.4C105.8 39.6 47.8 92.1 19.3 160h108c8.7-56.9 25.5-107.8 49.9-141.6zM487.4 192H372.7c2.1 21 3.3 42.5 3.3 64s-1.2 43-3.3 64h114.6c5.5-20.5 8.6-41.8 8.6-64s-3.1-43.5-8.5-64zM120 256c0-21.5 1.2-43 3.3-64H8.6C3.2 212.5 0 233.8 0 256s3.2 43.5 8.6 64h114.6c-2-21-3.2-42.5-3.2-64zm39.5 96c14.5 89.3 48.7 152 88.5 152s74-62.7 88.5-152h-177zm159.3 141.6c71.4-21.2 129.4-73.7 158-141.6h-108c-8.8 56.9-25.6 107.8-50 141.6zM19.3 352c28.6 67.9 86.5 120.4 158 141.6-24.4-33.8-41.2-84.7-50-141.6h-108z"/></svg> Everything you need will be on the course website:<br>https://eda.seas.gwu.edu/2021-Spring/

## <svg style="height:0.8em;top:.04em;position:relative;" viewBox="0 0 448 512"><path d="M0 464c0 26.5 21.5 48 48 48h352c26.5 0 48-21.5 48-48V192H0v272zm320-196c0-6.6 5.4-12 12-12h40c6.6 0 12 5.4 12 12v40c0 6.6-5.4 12-12 12h-40c-6.6 0-12-5.4-12-12v-40zm0 128c0-6.6 5.4-12 12-12h40c6.6 0 12 5.4 12 12v40c0 6.6-5.4 12-12 12h-40c-6.6 0-12-5.4-12-12v-40zM192 268c0-6.6 5.4-12 12-12h40c6.6 0 12 5.4 12 12v40c0 6.6-5.4 12-12 12h-40c-6.6 0-12-5.4-12-12v-40zm0 128c0-6.6 5.4-12 12-12h40c6.6 0 12 5.4 12 12v40c0 6.6-5.4 12-12 12h-40c-6.6 0-12-5.4-12-12v-40zM64 268c0-6.6 5.4-12 12-12h40c6.6 0 12 5.4 12 12v40c0 6.6-5.4 12-12 12H76c-6.6 0-12-5.4-12-12v-40zm0 128c0-6.6 5.4-12 12-12h40c6.6 0 12 5.4 12 12v40c0 6.6-5.4 12-12 12H76c-6.6 0-12-5.4-12-12v-40zM400 64h-48V16c0-8.8-7.2-16-16-16h-32c-8.8 0-16 7.2-16 16v48H160V16c0-8.8-7.2-16-16-16h-32c-8.8 0-16 7.2-16 16v48H48C21.5 64 0 85.5 0 112v48h448v-48c0-26.5-21.5-48-48-48z"/></svg> The [schedule](https://emse-eda-gwu.github.io/2021-Spring/schedule.html) is the best starting point

---

# Quizzes

## <svg style="height:0.8em;top:.04em;position:relative;" viewBox="0 0 448 512"><path d="M0 464c0 26.5 21.5 48 48 48h352c26.5 0 48-21.5 48-48V192H0v272zm320-196c0-6.6 5.4-12 12-12h40c6.6 0 12 5.4 12 12v40c0 6.6-5.4 12-12 12h-40c-6.6 0-12-5.4-12-12v-40zm0 128c0-6.6 5.4-12 12-12h40c6.6 0 12 5.4 12 12v40c0 6.6-5.4 12-12 12h-40c-6.6 0-12-5.4-12-12v-40zM192 268c0-6.6 5.4-12 12-12h40c6.6 0 12 5.4 12 12v40c0 6.6-5.4 12-12 12h-40c-6.6 0-12-5.4-12-12v-40zm0 128c0-6.6 5.4-12 12-12h40c6.6 0 12 5.4 12 12v40c0 6.6-5.4 12-12 12h-40c-6.6 0-12-5.4-12-12v-40zM64 268c0-6.6 5.4-12 12-12h40c6.6 0 12 5.4 12 12v40c0 6.6-5.4 12-12 12H76c-6.6 0-12-5.4-12-12v-40zm0 128c0-6.6 5.4-12 12-12h40c6.6 0 12 5.4 12 12v40c0 6.6-5.4 12-12 12H76c-6.6 0-12-5.4-12-12v-40zM400 64h-48V16c0-8.8-7.2-16-16-16h-32c-8.8 0-16 7.2-16 16v48H160V16c0-8.8-7.2-16-16-16h-32c-8.8 0-16 7.2-16 16v48H48C21.5 64 0 85.5 0 112v48h448v-48c0-26.5-21.5-48-48-48z"/></svg> In class every other week-ish (5 total, lowest dropped)

## <svg style="height:0.8em;top:.04em;position:relative;" viewBox="0 0 512 512"><path d="M256 8C119 8 8 119 8 256s111 248 248 248 248-111 248-248S393 8 256 8zm57.1 350.1L224.9 294c-3.1-2.3-4.9-5.9-4.9-9.7V116c0-6.6 5.4-12 12-12h48c6.6 0 12 5.4 12 12v137.7l63.5 46.2c5.4 3.9 6.5 11.4 2.6 16.8l-28.2 38.8c-3.9 5.3-11.4 6.5-16.8 2.6z"/></svg> ~5 minutes

## <svg style="height:0.8em;top:.04em;position:relative;" viewBox="0 0 512 512"><path d="M139.61 35.5a12 12 0 0 0-17 0L58.93 98.81l-22.7-22.12a12 12 0 0 0-17 0L3.53 92.41a12 12 0 0 0 0 17l47.59 47.4a12.78 12.78 0 0 0 17.61 0l15.59-15.62L156.52 69a12.09 12.09 0 0 0 .09-17zm0 159.19a12 12 0 0 0-17 0l-63.68 63.72-22.7-22.1a12 12 0 0 0-17 0L3.53 252a12 12 0 0 0 0 17L51 316.5a12.77 12.77 0 0 0 17.6 0l15.7-15.69 72.2-72.22a12 12 0 0 0 .09-16.9zM64 368c-26.49 0-48.59 21.5-48.59 48S37.53 464 64 464a48 48 0 0 0 0-96zm432 16H208a16 16 0 0 0-16 16v32a16 16 0 0 0 16 16h288a16 16 0 0 0 16-16v-32a16 16 0 0 0-16-16zm0-320H208a16 16 0 0 0-16 16v32a16 16 0 0 0 16 16h288a16 16 0 0 0 16-16V80a16 16 0 0 0-16-16zm0 160H208a16 16 0 0 0-16 16v32a16 16 0 0 0 16 16h288a16 16 0 0 0 16-16v-32a16 16 0 0 0-16-16z"/></svg> [Example quiz](https://p4aquizdemo.formr.org/)

> **Why quiz at all?** The "retrieval effect" - basically, you have to _practice_ remembering things, otherwise your brain won't remember them (see the book ["Make It Stick: The Science of Successful Learning"](https://www.hup.harvard.edu/catalog.php?isbn=9780674729018))

---

## Assignments

## 1) <svg style="height:0.8em;top:.04em;position:relative;" viewBox="0 0 448 512"><path d="M448 360V24c0-13.3-10.7-24-24-24H96C43 0 0 43 0 96v320c0 53 43 96 96 96h328c13.3 0 24-10.7 24-24v-16c0-7.5-3.5-14.3-8.9-18.7-4.2-15.4-4.2-59.3 0-74.7 5.4-4.3 8.9-11.1 8.9-18.6zM128 134c0-3.3 2.7-6 6-6h212c3.3 0 6 2.7 6 6v20c0 3.3-2.7 6-6 6H134c-3.3 0-6-2.7-6-6v-20zm0 64c0-3.3 2.7-6 6-6h212c3.3 0 6 2.7 6 6v20c0 3.3-2.7 6-6 6H134c-3.3 0-6-2.7-6-6v-20zm253.4 250H96c-17.7 0-32-14.3-32-32 0-17.6 14.4-32 32-32h285.4c-1.9 17.1-1.9 46.9 0 64z"/></svg> Weekly "reflections" on [readings](https://eda.seas.gwu.edu/2021-Spring/r1-exploring-data.html)

## 2) <svg style="height:0.8em;top:.04em;position:relative;" viewBox="0 0 512 512"><path d="M109.46 244.04l134.58-134.56-44.12-44.12-61.68 61.68a7.919 7.919 0 0 1-11.21 0l-11.21-11.21c-3.1-3.1-3.1-8.12 0-11.21l61.68-61.68-33.64-33.65C131.47-3.1 111.39-3.1 99 9.29L9.29 99c-12.38 12.39-12.39 32.47 0 44.86l100.17 100.18zm388.47-116.8c18.76-18.76 18.75-49.17 0-67.93l-45.25-45.25c-18.76-18.76-49.18-18.76-67.95 0l-46.02 46.01 113.2 113.2 46.02-46.03zM316.08 82.71l-297 296.96L.32 487.11c-2.53 14.49 10.09 27.11 24.59 24.56l107.45-18.84L429.28 195.9 316.08 82.71zm186.63 285.43l-33.64-33.64-61.68 61.68c-3.1 3.1-8.12 3.1-11.21 0l-11.21-11.21c-3.09-3.1-3.09-8.12 0-11.21l61.68-61.68-44.14-44.14L267.93 402.5l100.21 100.2c12.39 12.39 32.47 12.39 44.86 0l89.71-89.7c12.39-12.39 12.39-32.47 0-44.86z"/></svg> 3 Mini Projects (due 2 weeks from date assigned)

## 3) <svg style="height:0.8em;top:.04em;position:relative;" viewBox="0 0 512 512"><path d="M109.46 244.04l134.58-134.56-44.12-44.12-61.68 61.68a7.919 7.919 0 0 1-11.21 0l-11.21-11.21c-3.1-3.1-3.1-8.12 0-11.21l61.68-61.68-33.64-33.65C131.47-3.1 111.39-3.1 99 9.29L9.29 99c-12.38 12.39-12.39 32.47 0 44.86l100.17 100.18zm388.47-116.8c18.76-18.76 18.75-49.17 0-67.93l-45.25-45.25c-18.76-18.76-49.18-18.76-67.95 0l-46.02 46.01 113.2 113.2 46.02-46.03zM316.08 82.71l-297 296.96L.32 487.11c-2.53 14.49 10.09 27.11 24.59 24.56l107.45-18.84L429.28 195.9 316.08 82.71zm186.63 285.43l-33.64-33.64-61.68 61.68c-3.1 3.1-8.12 3.1-11.21 0l-11.21-11.21c-3.09-3.1-3.09-8.12 0-11.21l61.68-61.68-44.14-44.14L267.93 402.5l100.21 100.2c12.39 12.39 32.47 12.39 44.86 0l89.71-89.7c12.39-12.39 12.39-32.47 0-44.86z"/></svg> [Final Project](https://emse-eda-gwu.github.io/2021-Spring/a-project.html) (Teams of 2 - 3 students)

Item            | Due Date
----------------|---------------
Proposal        | March 12
Progress Report | April 16
Final Report    | April 30
Presentation    | May 03
Interview       | Exam week

---
background-color: #FFF

# .center[Grades]

---

# .center[Grades]

Item                          | Weight | Notes
------------------------------|--------|-------------------------------------
Reflections                   | 6 %    | Weekly assignment (12 x 0.5%)
Quizzes                       | 12 %   | 5 quizzes, lowest dropped
Mini Project 1                | 9 %    | Individual projects
Mini Project 2                | 9 %    |
Mini Project 3                | 9 %    |
Final Project Proposal        | 10 %   | Teams of 2-3 students
Final Project Progress Report | 10 %   |
Final Project Report          | 15 %   |
Final Project Presentation    | 10 %   |
Final Interview               | 10 %   | Individual interview about your project

---

# Course policies

> "Plagiarism is trying to pass someone else’s work off as your own. Copying is about reverse-engineering."
>
> .right[-- Austin Kleon, from [Steal Like An Artist](https://austinkleon.com/steal/)&ensp;]
]

---

# Late submissions

## - **5** late days - use them anytime, no questions asked
## - No more than **2** late days on any one assignment
## - Contact me for special cases

---

# How to succeed in this class

## <svg style="height:0.8em;top:.04em;position:relative;" viewBox="0 0 640 512"><path d="M96 224c35.3 0 64-28.7 64-64s-28.7-64-64-64-64 28.7-64 64 28.7 64 64 64zm448 0c35.3 0 64-28.7 64-64s-28.7-64-64-64-64 28.7-64 64 28.7 64 64 64zm32 32h-64c-17.6 0-33.5 7.1-45.1 18.6 40.3 22.1 68.9 62 75.1 109.4h66c17.7 0 32-14.3 32-32v-32c0-35.3-28.7-64-64-64zm-256 0c61.9 0 112-50.1 112-112S381.9 32 320 32 208 82.1 208 144s50.1 112 112 112zm76.8 32h-8.3c-20.8 10-43.9 16-68.5 16s-47.6-6-68.5-16h-8.3C179.6 288 128 339.6 128 403.2V432c0 26.5 21.5 48 48 48h288c26.5 0 48-21.5 48-48v-28.8c0-63.6-51.6-115.2-115.2-115.2zm-223.7-13.4C161.5 263.1 145.6 256 128 256H64c-35.3 0-64 28.7-64 64v32c0 17.7 14.3 32 32 32h65.9c6.3-47.4 34.9-87.3 75.2-109.4z"/></svg> Participate during class!

## <svg style="height:0.8em;top:.04em;position:relative;" viewBox="0 0 512 512"><path d="M109.46 244.04l134.58-134.56-44.12-44.12-61.68 61.68a7.919 7.919 0 0 1-11.21 0l-11.21-11.21c-3.1-3.1-3.1-8.12 0-11.21l61.68-61.68-33.64-33.65C131.47-3.1 111.39-3.1 99 9.29L9.29 99c-12.38 12.39-12.39 32.47 0 44.86l100.17 100.18zm388.47-116.8c18.76-18.76 18.75-49.17 0-67.93l-45.25-45.25c-18.76-18.76-49.18-18.76-67.95 0l-46.02 46.01 113.2 113.2 46.02-46.03zM316.08 82.71l-297 296.96L.32 487.11c-2.53 14.49 10.09 27.11 24.59 24.56l107.45-18.84L429.28 195.9 316.08 82.71zm186.63 285.43l-33.64-33.64-61.68 61.68c-3.1 3.1-8.12 3.1-11.21 0l-11.21-11.21c-3.09-3.1-3.09-8.12 0-11.21l61.68-61.68-44.14-44.14L267.93 402.5l100.21 100.2c12.39 12.39 32.47 12.39 44.86 0l89.71-89.7c12.39-12.39 12.39-32.47 0-44.86z"/></svg> Start assignments early and **read carefully**!

## <svg style="height:0.8em;top:.04em;position:relative;" viewBox="0 0 448 512"><path d="M448 360V24c0-13.3-10.7-24-24-24H96C43 0 0 43 0 96v320c0 53 43 96 96 96h328c13.3 0 24-10.7 24-24v-16c0-7.5-3.5-14.3-8.9-18.7-4.2-15.4-4.2-59.3 0-74.7 5.4-4.3 8.9-11.1 8.9-18.6zM128 134c0-3.3 2.7-6 6-6h212c3.3 0 6 2.7 6 6v20c0 3.3-2.7 6-6 6H134c-3.3 0-6-2.7-6-6v-20zm0 64c0-3.3 2.7-6 6-6h212c3.3 0 6 2.7 6 6v20c0 3.3-2.7 6-6 6H134c-3.3 0-6-2.7-6-6v-20zm253.4 250H96c-17.7 0-32-14.3-32-32 0-17.6 14.4-32 32-32h285.4c-1.9 17.1-1.9 46.9 0 64z"/></svg> Actually read (before class)!

## <svg style="height:0.8em;top:.04em;position:relative;" viewBox="0 0 640 512"><path d="M176 256c44.11 0 80-35.89 80-80s-35.89-80-80-80-80 35.89-80 80 35.89 80 80 80zm352-128H304c-8.84 0-16 7.16-16 16v144H64V80c0-8.84-7.16-16-16-16H16C7.16 64 0 71.16 0 80v352c0 8.84 7.16 16 16 16h32c8.84 0 16-7.16 16-16v-48h512v48c0 8.84 7.16 16 16 16h32c8.84 0 16-7.16 16-16V240c0-61.86-50.14-112-112-112z"/></svg> Get sleep and take breaks often!

## <svg style="height:0.8em;top:.04em;position:relative;" viewBox="0 0 640 512"><path d="M128 96c26.5 0 48-21.5 48-48S154.5 0 128 0 80 21.5 80 48s21.5 48 48 48zm384 0c26.5 0 48-21.5 48-48S538.5 0 512 0s-48 21.5-48 48 21.5 48 48 48zm125.7 372.1l-44-110-41.1 46.4-2 18.2 27.7 69.2c5 12.5 17 20.1 29.7 20.1 4 0 8-.7 11.9-2.3 16.4-6.6 24.4-25.2 17.8-41.6zm-34.2-209.8L585 178.1c-4.6-20-18.6-36.8-37.5-44.9-18.5-8-39-6.7-56.1 3.3-22.7 13.4-39.7 34.5-48.1 59.4L432 229.8 416 240v-96c0-8.8-7.2-16-16-16H240c-8.8 0-16 7.2-16 16v96l-16.1-10.2-11.3-33.9c-8.3-25-25.4-46-48.1-59.4-17.2-10-37.6-11.3-56.1-3.3-18.9 8.1-32.9 24.9-37.5 44.9l-18.4 80.2c-4.6 20 .7 41.2 14.4 56.7l67.2 75.9 10.1 92.6C130 499.8 143.8 512 160 512c1.2 0 2.3-.1 3.5-.2 17.6-1.9 30.2-17.7 28.3-35.3l-10.1-92.8c-1.5-13-6.9-25.1-15.6-35l-43.3-49 17.6-70.3 6.8 20.4c4.1 12.5 11.9 23.4 24.5 32.6l51.1 32.5c4.6 2.9 12.1 4.6 17.2 5h160c5.1-.4 12.6-2.1 17.2-5l51.1-32.5c12.6-9.2 20.4-20 24.5-32.6l6.8-20.4 17.6 70.3-43.3 49c-8.7 9.9-14.1 22-15.6 35l-10.1 92.8c-1.9 17.6 10.8 33.4 28.3 35.3 1.2.1 2.3.2 3.5.2 16.1 0 30-12.1 31.8-28.5l10.1-92.6 67.2-75.9c13.6-15.5 19-36.7 14.4-56.7zM46.3 358.1l-44 110c-6.6 16.4 1.4 35 17.8 41.6 16.8 6.6 35.1-1.7 41.6-17.8l27.7-69.2-2-18.2-41.1-46.4z"/></svg> Ask for help!

---

# [Getting Help](https://p4a.seas.gwu.edu/2020-Fall/ref-getting-help.html)

## <svg style="height:0.8em;top:.04em;position:relative;" viewBox="0 0 448 512"><path d="M94.12 315.1c0 25.9-21.16 47.06-47.06 47.06S0 341 0 315.1c0-25.9 21.16-47.06 47.06-47.06h47.06v47.06zm23.72 0c0-25.9 21.16-47.06 47.06-47.06s47.06 21.16 47.06 47.06v117.84c0 25.9-21.16 47.06-47.06 47.06s-47.06-21.16-47.06-47.06V315.1zm47.06-188.98c-25.9 0-47.06-21.16-47.06-47.06S139 32 164.9 32s47.06 21.16 47.06 47.06v47.06H164.9zm0 23.72c25.9 0 47.06 21.16 47.06 47.06s-21.16 47.06-47.06 47.06H47.06C21.16 243.96 0 222.8 0 196.9s21.16-47.06 47.06-47.06H164.9zm188.98 47.06c0-25.9 21.16-47.06 47.06-47.06 25.9 0 47.06 21.16 47.06 47.06s-21.16 47.06-47.06 47.06h-47.06V196.9zm-23.72 0c0 25.9-21.16 47.06-47.06 47.06-25.9 0-47.06-21.16-47.06-47.06V79.06c0-25.9 21.16-47.06 47.06-47.06 25.9 0 47.06 21.16 47.06 47.06V196.9zM283.1 385.88c25.9 0 47.06 21.16 47.06 47.06 0 25.9-21.16 47.06-47.06 47.06-25.9 0-47.06-21.16-47.06-47.06v-47.06h47.06zm0-23.72c-25.9 0-47.06-21.16-47.06-47.06 0-25.9 21.16-47.06 47.06-47.06h117.84c25.9 0 47.06 21.16 47.06 47.06 0 25.9-21.16 47.06-47.06 47.06H283.1z"/></svg> Use [Slack](https://emse-eda-s21.slack.com/) to ask questions.

## <svg style="height:0.8em;top:.04em;position:relative;" viewBox="0 0 640 512"><path d="M208 352c-2.39 0-4.78.35-7.06 1.09C187.98 357.3 174.35 360 160 360c-14.35 0-27.98-2.7-40.95-6.91-2.28-.74-4.66-1.09-7.05-1.09C49.94 352-.33 402.48 0 464.62.14 490.88 21.73 512 48 512h224c26.27 0 47.86-21.12 48-47.38.33-62.14-49.94-112.62-112-112.62zm-48-32c53.02 0 96-42.98 96-96s-42.98-96-96-96-96 42.98-96 96 42.98 96 96 96zM592 0H208c-26.47 0-48 22.25-48 49.59V96c23.42 0 45.1 6.78 64 17.8V64h352v288h-64v-64H384v64h-76.24c19.1 16.69 33.12 38.73 39.69 64H592c26.47 0 48-22.25 48-49.59V49.59C640 22.25 618.47 0 592 0z"/></svg> Meet with your tutors

## <svg style="height:0.8em;top:.04em;position:relative;" viewBox="0 0 640 512"><path d="M496 224c-79.6 0-144 64.4-144 144s64.4 144 144 144 144-64.4 144-144-64.4-144-144-144zm64 150.3c0 5.3-4.4 9.7-9.7 9.7h-60.6c-5.3 0-9.7-4.4-9.7-9.7v-76.6c0-5.3 4.4-9.7 9.7-9.7h12.6c5.3 0 9.7 4.4 9.7 9.7V352h38.3c5.3 0 9.7 4.4 9.7 9.7v12.6zM320 368c0-27.8 6.7-54.1 18.2-77.5-8-1.5-16.2-2.5-24.6-2.5h-16.7c-22.2 10.2-46.9 16-72.9 16s-50.6-5.8-72.9-16h-16.7C60.2 288 0 348.2 0 422.4V464c0 26.5 21.5 48 48 48h347.1c-45.3-31.9-75.1-84.5-75.1-144zm-96-112c70.7 0 128-57.3 128-128S294.7 0 224 0 96 57.3 96 128s57.3 128 128 128z"/></svg> [Schedule a meeting](https://jhelvy.appointlet.com/b/professor-helveston) w/Prof. Helveston:

- Mondays from 8:00-5:00pm
- Wednesdays from 3:20-5:00pm
- Thursdays from 12:00-5:00pm

## <svg style="height:0.8em;top:.04em;position:relative;" viewBox="0 0 640 512"><path d="M278.9 511.5l-61-17.7c-6.4-1.8-10-8.5-8.2-14.9L346.2 8.7c1.8-6.4 8.5-10 14.9-8.2l61 17.7c6.4 1.8 10 8.5 8.2 14.9L293.8 503.3c-1.9 6.4-8.5 10.1-14.9 8.2zm-114-112.2l43.5-46.4c4.6-4.9 4.3-12.7-.8-17.2L117 256l90.6-79.7c5.1-4.5 5.5-12.3.8-17.2l-43.5-46.4c-4.5-4.8-12.1-5.1-17-.5L3.8 247.2c-5.1 4.7-5.1 12.8 0 17.5l144.1 135.1c4.9 4.6 12.5 4.4 17-.5zm327.2.6l144.1-135.1c5.1-4.7 5.1-12.8 0-17.5L492.1 112.1c-4.8-4.5-12.4-4.3-17 .5L431.6 159c-4.6 4.9-4.3 12.7.8 17.2L523 256l-90.6 79.7c-5.1 4.5-5.5 12.3-.8 17.2l43.5 46.4c4.5 4.9 12.1 5.1 17 .6z"/></svg> [GW Coders](http://gwcoders.github.io/)

---

# <svg style="height:0.8em;top:.04em;position:relative;" viewBox="0 0 576 512"><path d="M576 304v96c0 26.51-21.49 48-48 48H48c-26.51 0-48-21.49-48-48v-96c0-26.51 21.49-48 48-48h480c26.51 0 48 21.49 48 48zm-48-80a79.557 79.557 0 0 1 30.777 6.165L462.25 85.374A48.003 48.003 0 0 0 422.311 64H153.689a48 48 0 0 0-39.938 21.374L17.223 230.165A79.557 79.557 0 0 1 48 224h480zm-48 96c-17.673 0-32 14.327-32 32s14.327 32 32 32 32-14.327 32-32-14.327-32-32-32zm-96 0c-17.673 0-32 14.327-32 32s14.327 32 32 32 32-14.327 32-32-14.327-32-32-32z"/></svg> [Course Software](https://eda.seas.gwu.edu/2021-Spring/ref-course-software.html)

## <svg style="height:0.8em;top:.04em;position:relative;" viewBox="0 0 448 512"><path d="M94.12 315.1c0 25.9-21.16 47.06-47.06 47.06S0 341 0 315.1c0-25.9 21.16-47.06 47.06-47.06h47.06v47.06zm23.72 0c0-25.9 21.16-47.06 47.06-47.06s47.06 21.16 47.06 47.06v117.84c0 25.9-21.16 47.06-47.06 47.06s-47.06-21.16-47.06-47.06V315.1zm47.06-188.98c-25.9 0-47.06-21.16-47.06-47.06S139 32 164.9 32s47.06 21.16 47.06 47.06v47.06H164.9zm0 23.72c25.9 0 47.06 21.16 47.06 47.06s-21.16 47.06-47.06 47.06H47.06C21.16 243.96 0 222.8 0 196.9s21.16-47.06 47.06-47.06H164.9zm188.98 47.06c0-25.9 21.16-47.06 47.06-47.06 25.9 0 47.06 21.16 47.06 47.06s-21.16 47.06-47.06 47.06h-47.06V196.9zm-23.72 0c0 25.9-21.16 47.06-47.06 47.06-25.9 0-47.06-21.16-47.06-47.06V79.06c0-25.9 21.16-47.06 47.06-47.06 25.9 0 47.06 21.16 47.06 47.06V196.9zM283.1 385.88c25.9 0 47.06 21.16 47.06 47.06 0 25.9-21.16 47.06-47.06 47.06-25.9 0-47.06-21.16-47.06-47.06v-47.06h47.06zm0-23.72c-25.9 0-47.06-21.16-47.06-47.06 0-25.9 21.16-47.06 47.06-47.06h117.84c25.9 0 47.06 21.16 47.06 47.06 0 25.9-21.16 47.06-47.06 47.06H283.1z"/></svg> [Slack](https://emse-eda-s21.slack.com/): See bb for link to join;<br>install on phone and **turn notifications on**!

## <svg style="height:0.8em;top:.04em;position:relative;" viewBox="0 0 581 512"><path d="M581 226.6C581 119.1 450.9 32 290.5 32S0 119.1 0 226.6C0 322.4 103.3 402 239.4 418.1V480h99.1v-61.5c24.3-2.7 47.6-7.4 69.4-13.9L448 480h112l-67.4-113.7c54.5-35.4 88.4-84.9 88.4-139.7zm-466.8 14.5c0-73.5 98.9-133 220.8-133s211.9 40.7 211.9 133c0 50.1-26.5 85-70.3 106.4-2.4-1.6-4.7-2.9-6.4-3.7-10.2-5.2-27.8-10.5-27.8-10.5s86.6-6.4 86.6-92.7-90.6-87.9-90.6-87.9h-199V361c-74.1-21.5-125.2-67.1-125.2-119.9zm225.1 38.3v-55.6c57.8 0 87.8-6.8 87.8 27.3 0 36.5-38.2 28.3-87.8 28.3zm-.9 72.5H365c10.8 0 18.9 11.7 24 19.2-16.1 1.9-33 2.8-50.6 2.9v-22.1z"/></svg> [R](https://cloud.r-project.org/) & [RStudio](https://rstudio.com/products/rstudio/download/) (Install both)

## <svg style="height:0.8em;top:.04em;position:relative;" viewBox="0 0 640 512"><path d="M634.91 154.88C457.74-8.99 182.19-8.93 5.09 154.88c-6.66 6.16-6.79 16.59-.35 22.98l34.24 33.97c6.14 6.1 16.02 6.23 22.4.38 145.92-133.68 371.3-133.71 517.25 0 6.38 5.85 16.26 5.71 22.4-.38l34.24-33.97c6.43-6.39 6.3-16.82-.36-22.98zM320 352c-35.35 0-64 28.65-64 64s28.65 64 64 64 64-28.65 64-64-28.65-64-64-64zm202.67-83.59c-115.26-101.93-290.21-101.82-405.34 0-6.9 6.1-7.12 16.69-.57 23.15l34.44 33.99c6 5.92 15.66 6.32 22.05.8 83.95-72.57 209.74-72.41 293.49 0 6.39 5.52 16.05 5.13 22.05-.8l34.44-33.99c6.56-6.46 6.33-17.06-.56-23.15z"/></svg> Install [Cisco AnyConnect VPN Client](https://seascf.seas.gwu.edu/vpn-access) to use RStudio in the cloud: https://rstudio.seas.gwu.edu/

## <svg style="height:0.8em;top:.04em;position:relative;" viewBox="0 0 448 512"><path d="M448 73.143v45.714C448 159.143 347.667 192 224 192S0 159.143 0 118.857V73.143C0 32.857 100.333 0 224 0s224 32.857 224 73.143zM448 176v102.857C448 319.143 347.667 352 224 352S0 319.143 0 278.857V176c48.125 33.143 136.208 48.572 224 48.572S399.874 209.143 448 176zm0 160v102.857C448 479.143 347.667 512 224 512S0 479.143 0 438.857V336c48.125 33.143 136.208 48.572 224 48.572S399.874 369.143 448 336z"/></svg> [DataCamp](https://www.datacamp.com/): sign up with your **@gwu.edu** email

---
class: inverse, center

<br>
# .fancy[Break]

# Install Stuff

---

# Week 1: .fancy[Getting Started]

## 1. Course Goal
## 2. Course Introduction
## 3. Break: Install Stuff
## 4. .orange[Workflow & Reading In Data]
## 5. Data Provenance
## 6. Tidy Data

---

## Workflow for reading in data

1) Use R Projects (.Rproj files) to organize your analysis - **don't double-click .R files**!

2) Use the `here` package to create file paths

```r
path <- here::here("folder", "file.csv")
```

3) Import data with these functions:

File type  | Function       | Library
-----------|----------------|----------
`.csv`     | `read_csv()`   | **readr**
`.txt`     | `read.table()` | **utils**
`.xlsx`    | `read_excel()` | **readxl**

---

# Importing Comma Separated Values (.csv)

Read in `.csv` files with `read_csv()`:

```r
library(tidyverse)
library(here)

csvPath <- here('data', 'milk_production.csv')
*milk_production <- read_csv(csvPath)

head(milk_production)
```

```
#> # A tibble: 6 x 4
#>   region    state          year milk_produced
#>   <chr>     <chr>         <dbl>         <dbl>
#> 1 Northeast Maine          1970     619000000
#> 2 Northeast New Hampshire  1970     356000000
#> 3 Northeast Vermont        1970    1970000000
#> 4 Northeast Massachusetts  1970     658000000
#> 5 Northeast Rhode Island   1970      75000000
#> 6 Northeast Connecticut    1970     661000000
```

---

# Importing Text Files (.txt)

Read in `.txt` files with `read.table()`:

```r
txtPath <- here('data', 'nasa_global_temps.txt')
*global_temps <- read.table(txtPath, skip = 5, header = FALSE)

head(global_temps)
```

```
#>     V1    V2    V3
#> 1 1880 -0.18 -0.11
#> 2 1881 -0.10 -0.14
#> 3 1882 -0.11 -0.17
#> 4 1883 -0.19 -0.21
#> 5 1884 -0.28 -0.24
#> 6 1885 -0.31 -0.26
```

---

# Importing Text Files (.txt)

Read in `.txt` files with `read.table()`:

```r
txtPath <- here('data', 'nasa_global_temps.txt')
global_temps <- read.table(txtPath, skip = 5, header = FALSE)
*names(global_temps) <- c('year', 'no_smoothing', 'loess') # Add header

head(global_temps)
```

```
#>   year no_smoothing loess
#> 1 1880        -0.18 -0.11
#> 2 1881        -0.10 -0.14
#> 3 1882        -0.11 -0.17
#> 4 1883        -0.19 -0.21
#> 5 1884        -0.28 -0.24
#> 6 1885        -0.31 -0.26
```

---

# Importing Excel Files (.xlsx)

Read in `.xlsx` files with `read_excel()`:

```r
library(readxl)

xlsxPath <- here('data', 'pv_cell_production.xlsx')
*pv_cells <- read_excel(xlsxPath, sheet = 'Cell Prod by Country', skip = 2)
```
.code70[

```r
glimpse(pv_cells)
```

```
#> Rows: 25
#> Columns: 10
#> $ Year            <chr> NA, NA, "1995", "1996", "1997", "1998", "1999", "2000", "2001", "2002", "2003", "2004", "2005", "2006", "2007", "2008", "2009", "2010", "2011", "2012", "2013", NA, "Note: NA = data not available.", NA, "Source: Compiled by …
#> $ China           <chr> "Megawatts", NA, "NA", "NA", "NA", "NA", "NA", "2.5", "3", "10", "13", "40", "128.30000000000001", "341.8", "1192.8735755126208", "2535.9804999999997", "5193.2335000000003", "12882.114299891044", "24338.646000000004", "2413…
#> $ Taiwan          <chr> NA, NA, "NA", "NA", "NA", "NA", "NA", "NA", "3.5", "8", "17", "39.299999999999997", "88", "169.5", "413.19362206495737", "871.4", "1573.2", "3755.9046488657718", "4773.1499999999996", "5270.1999999999989", "6338.56500000000…
#> $ Japan           <dbl> NA, NA, 16.4, 21.2, 35.0, 49.0, 80.0, 128.6, 171.2, 251.1, 363.9, 601.5, 833.0, 926.4, 937.5, 1268.0, 1503.0, 2169.0, 2707.0, 2641.8, 3679.0, NA, NA, NA, NA
#> $ Malaysia        <chr> NA, NA, "NA", "NA", "NA", "NA", "NA", "NA", "0", "0", "0", "0", "0", "0", "100.1", "397.9", "1228.0566037735848", "1919.0129442119946", "2684.5953947368421", "2597.365436241611", "3072.59", NA, NA, NA, NA
#> $ Germany         <chr> NA, NA, "NA", "NA", "NA", "NA", "NA", "22.5", "23.5", "55", "121.5", "193", "339", "469.1", "815.35421116529074", "1476.6923205919056", "1606.0497978436656", "2181.2726133183096", "2152.8626315789475", "1406.7827181208054",…
#> $ `South Korea`   <chr> NA, NA, "NA", "NA", "NA", "NA", "NA", "NA", "0", "0", "0", "0", "5.3", "13", "31.883935905674612", "70.848164851527258", "234", "886.29518449560589", "1227.3", "1107.0999999999999", "1127.0999999999999", NA, NA, NA, NA
#> $ `United States` <dbl> NA, NA, 34.7500, 38.8500, 51.0000, 53.7000, 60.8000, 75.0000, 100.3000, 120.6000, 103.0000, 138.7000, 153.1000, 177.6000, 261.9804, 403.1250, 594.7922, 1162.5177, 1044.1895, 886.4018, 868.4250, NA, NA, NA, NA
#> $ Others          <chr> NA, NA, "NA", "NA", "NA", "NA", "NA", "48.200000000000017", "69.800000000000011", "97.299999999999955", "131", "186.29999999999995", "235.70000000000027", "361.09999999999991", "410.97322650945807", "709.03112641453299", "6…
#> $ World           <dbl> NA, NA, 77.600, 88.600, 125.800, 154.900, 201.300, 276.800, 371.300, 542.000, 749.400, 1198.800, 1782.400, 2458.500, 4163.859, 7732.977, 12595.992, 26399.539, 40761.761, 39523.565, 44464.496, NA, NA, NA, NA
```
]

---

# Importing Excel Files (.xlsx)

Read in `.xlsx` files with `read_excel()`:

```r
library(readxl)

xlsxPath <- here('data', 'pv_cell_production.xlsx')
pv_cells <- read_excel(xlsxPath, sheet = 'Cell Prod by Country', skip = 2) %>%
* mutate(Year = as.numeric(Year)) %>% # Convert "non-years" to NA
* filter(!is.na(Year)) # Drop NA rows in Year
```
.code60[

```r
glimpse(pv_cells)
```

```
#> Rows: 19
#> Columns: 10
#> $ Year            <dbl> 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013
#> $ China           <chr> "NA", "NA", "NA", "NA", "NA", "2.5", "3", "10", "13", "40", "128.30000000000001", "341.8", "1192.8735755126208", "2535.9804999999997", "5193.2335000000003", "12882.114299891044", "24338.646000000004", "24139.014999999999", …
#> $ Taiwan          <chr> "NA", "NA", "NA", "NA", "NA", "NA", "3.5", "8", "17", "39.299999999999997", "88", "169.5", "413.19362206495737", "871.4", "1573.2", "3755.9046488657718", "4773.1499999999996", "5270.1999999999989", "6338.5650000000005"
#> $ Japan           <dbl> 16.4, 21.2, 35.0, 49.0, 80.0, 128.6, 171.2, 251.1, 363.9, 601.5, 833.0, 926.4, 937.5, 1268.0, 1503.0, 2169.0, 2707.0, 2641.8, 3679.0
#> $ Malaysia        <chr> "NA", "NA", "NA", "NA", "NA", "NA", "0", "0", "0", "0", "0", "0", "100.1", "397.9", "1228.0566037735848", "1919.0129442119946", "2684.5953947368421", "2597.365436241611", "3072.59"
#> $ Germany         <chr> "NA", "NA", "NA", "NA", "NA", "22.5", "23.5", "55", "121.5", "193", "339", "469.1", "815.35421116529074", "1476.6923205919056", "1606.0497978436656", "2181.2726133183096", "2152.8626315789475", "1406.7827181208054", "1054.8…
#> $ `South Korea`   <chr> "NA", "NA", "NA", "NA", "NA", "NA", "0", "0", "0", "0", "5.3", "13", "31.883935905674612", "70.848164851527258", "234", "886.29518449560589", "1227.3", "1107.0999999999999", "1127.0999999999999"
#> $ `United States` <dbl> 34.7500, 38.8500, 51.0000, 53.7000, 60.8000, 75.0000, 100.3000, 120.6000, 103.0000, 138.7000, 153.1000, 177.6000, 261.9804, 403.1250, 594.7922, 1162.5177, 1044.1895, 886.4018, 868.4250
#> $ Others          <chr> "NA", "NA", "NA", "NA", "NA", "48.200000000000017", "69.800000000000011", "97.299999999999955", "131", "186.29999999999995", "235.70000000000027", "361.09999999999991", "410.97322650945807", "709.03112641453299", "663.66000…
#> $ World           <dbl> 77.600, 88.600, 125.800, 154.900, 201.300, 276.800, 371.300, 542.000, 749.400, 1198.800, 1782.400, 2458.500, 4163.859, 7732.977, 12595.992, 26399.539, 40761.761, 39523.565, 44464.496
```
]

---
class: inverse

# Your turn

Download [today's class notes](https://eda.seas.gwu.edu/2021-Spring/class/1-getting-started/1-getting-started.zip)

Write code to import the following data files from the "data" folder:

- `lotr_words.csv`
- `north_america_bear_killings.txt`
- `uspto_clean_energy_patents.xlsx`

---

# Week 1: .fancy[Getting Started]

## 1. Course Goal
## 2. Course Introduction
## 3. Break: Install Stuff
## 4. Workflow & Reading In Data
## 5. .orange[Data Provenance]
## 6. Tidy Data

---

### Data provenance - It matters where you get your data

**Validity**:

- Is this data trustworthy? Is it authentic?
- Where did the data come from?
- How has the data been changed / managed over time?
- Is the data complete?

**Comprehension**:

- Is this data accurate?
- Can you explain your results?
- Is this the _right_ data to answer your question?

**Reproducibility**: The data source is the start of the reproducibility chain.

---

## <svg style="height:0.8em;top:.04em;position:relative;" viewBox="0 0 512 512"><path d="M505 442.7L405.3 343c-4.5-4.5-10.6-7-17-7H372c27.6-35.3 44-79.7 44-128C416 93.1 322.9 0 208 0S0 93.1 0 208s93.1 208 208 208c48.3 0 92.7-16.4 128-44v16.3c0 6.4 2.5 12.5 7 17l99.7 99.7c9.4 9.4 24.6 9.4 33.9 0l28.3-28.3c9.4-9.4 9.4-24.6.1-34zM208 336c-70.7 0-128-57.2-128-128 0-70.7 57.2-128 128-128 70.7 0 128 57.2 128 128 0 70.7-57.2 128-128 128z"/></svg> **Document your source like a museum curator**

**Example**: View `README.md` file in the `data` folder

Whenever you download data, you should **at a minimum** record the following:

- The name of the file you are describing.
  - The date you downloaded it.
  - The original name of the downloaded file (in case you renamed it).
  - The url to the site you downloaded it from.
  - The source of the _original_ data (sometimes different from the site you downloaded it from).
  - A short description of the data, maybe how they were collected (if available).
  - A dictionary for the data (e.g. a simple markdown table describing each variable).

---
class: inverse

# Your turn

Documentation in the "data/README.md" file is missing for the following data sets:

- wildlife_impacts.csv: [source](https://github.com/rfordatascience/tidytuesday/tree/master/data/2019/2019-07-23) (Breakout Rooms 1 & 2)
- north_america_bear_killings.txt: [source](https://data.world/makeovermonday/2019w21) (Breakout Rooms 3 & 4)
- uspto_clean_energy_patents.xlsx: [source](https://www.nsf.gov/statistics/2018/nsb20181/report/sections/industry-technology-and-the-global-marketplace/global-trends-in-sustainable-energy-research-and-technologies) (Breakout Rooms 5 & 6)

Go to the above sites and add the following information to the "data/README.md" file:

- The name of the downloaded file.
- The web address to the site you downloaded the data from.
- The source of the _original_ data (if different from the website).
- A short description of the data and how they were collected.
- A dictionary for the data (hint: the site might already have this!).

---

# Week 1: .fancy[Getting Started]

## 1. Course Goal
## 2. Course Introduction
## 3. Break: Install Stuff
## 4. Workflow & Reading In Data
## 5. Data Provenance
## 6. .orange[Tidy Data]

---

# Variables, values, and observations

- **Variable**: Something you can measure
- **Value**: The measurement of a variable
- **Observation**: A set of associated measurements across different variables

```r
head(fed_spend_long)
```

```
#> # A tibble: 6 x 3
#>   department  year rd_budget_mil
#>   <chr>      <dbl>         <dbl>
#> 1 DOD         1976         35696
#> 2 NASA        1976         12513
#> 3 DOE         1976         10882
#> 4 HHS         1976          9226
#> 5 NIH         1976          8025
#> 6 NSF         1976          2372
```
]

---

# Tidy data

Tidy data follows the following three rules:

- Each **variable** has its own **column**
- Each **observation** has its own **row**
- Each **value** has its own **cell**

---

## Tidy data

---

```r
head(fed_spend_long)
```

```r
head(fed_spend_wide)
```

```
#> # A tibble: 6 x 15
#>    year   DHS   DOC   DOD   DOE   DOT   EPA   HHS Interior  NASA   NIH   NSF Other  USDA    VA
#>   <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>    <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1  1976     0   819 35696 10882  1142   968  9226     1152 12513  8025  2372  1191  1837   404
#> 2  1977     0   837 37967 13741  1095   966  9507     1082 12553  8214  2395  1280  1796   374
#> 3  1978     0   871 37022 15663  1156  1175 10533     1125 12516  8802  2446  1237  1962   356
#> 4  1979     0   952 37174 15612  1004  1102 10127     1176 13079  9243  2404  2321  2054   353
#> 5  1980     0   945 37005 15226  1048   903 10045     1082 13837  9093  2407  2468  1887   359
#> 6  1981     0   829 41737 14798   978   901  9644      990 13276  8580  2300  1925  1964   382
```
]]

---

## Identifying tidy data

> 1. Pick a cell in a column
> 2. Ask "is **cell** a _value_ of **column**?"
> 3. Repeat for each column

```r
head(fed_spend_long)
```

```r
head(fed_spend_wide)
```

---

## Identifying tidy data

> Are the column names _values_ of a variable?

```r
head(fed_spend_long)
```

```r
head(fed_spend_wide)
```

---

# **Quick practice 1**: Is this data frame "tidy"?

> Decide [here](https://docs.google.com/presentation/d/1c66hRe_adYoWUPrhPf2A17pL4fmhAlCmNNqkSIloNnE/edit?usp=sharing) (link also in #classroom)

**Description**: Tuberculosis cases in various countries
.code90[

```
#> # A tibble: 6 x 4
#>   country      year  cases population
#>   <chr>       <dbl>  <dbl>      <dbl>
#> 1 Afghanistan  1999    745   19987071
#> 2 Afghanistan  2000   2666   20595360
#> 3 Brazil       1999  37737  172006362
#> 4 Brazil       2000  80488  174504898
#> 5 China        1999 212258 1272915272
#> 6 China        2000 213766 1280428583
```
]

---

# **Quick practice 2**: Is this data frame "tidy"?

> Decide [here](https://docs.google.com/presentation/d/1c66hRe_adYoWUPrhPf2A17pL4fmhAlCmNNqkSIloNnE/edit?usp=sharing) (link also in #classroom)

**Description**: Word counts by character type in "Lord of the Rings" trilogy
.code80[

```
#> # A tibble: 9 x 4
#>   Film                       Race   Female  Male
#>   <chr>                      <chr>   <dbl> <dbl>
#> 1 The Fellowship Of The Ring Elf      1229   971
#> 2 The Fellowship Of The Ring Hobbit     14  3644
#> 3 The Fellowship Of The Ring Man         0  1995
#> 4 The Return Of The King     Elf       183   510
#> 5 The Return Of The King     Hobbit      2  2673
#> 6 The Return Of The King     Man       268  2459
#> 7 The Two Towers             Elf       331   513
#> 8 The Two Towers             Hobbit      0  2463
#> 9 The Two Towers             Man       401  3589
```
]

---

# **Quick practice 3**: Is this data frame "tidy"?

> Decide [here](https://docs.google.com/presentation/d/1c66hRe_adYoWUPrhPf2A17pL4fmhAlCmNNqkSIloNnE/edit?usp=sharing) (link also in #classroom)

**Description**: Photovoltaic cell production by country
.code90[

```
#> # A tibble: 6 x 10
#>    Year China Taiwan Japan Malaysia Germany `South Korea` `United States` Others             World
#>   <dbl> <chr> <chr>  <dbl> <chr>    <chr>   <chr>                   <dbl> <chr>              <dbl>
#> 1  1995 NA    NA      16.4 NA       NA      NA                       34.8 NA                  77.6
#> 2  1996 NA    NA      21.2 NA       NA      NA                       38.8 NA                  88.6
#> 3  1997 NA    NA      35   NA       NA      NA                       51   NA                 126. 
#> 4  1998 NA    NA      49   NA       NA      NA                       53.7 NA                 155. 
#> 5  1999 NA    NA      80   NA       NA      NA                       60.8 NA                 201. 
#> 6  2000 2.5   NA     129.  NA       22.5    NA                       75   48.200000000000017 277.
```
]

---
class: center, middle, inverse

# Why do we need tidy data?

(a quick explanation with cute graphics, by [Allison Horst](https://github.com/allisonhorst/stats-illustrations))

---
class: center
background-image: url("images/horst_tidydata_1.jpg")
background-size: contain

---
class: center
background-image: url("images/horst_tidydata_2.jpg")
background-size: contain

---
class: center
background-image: url("images/horst_tidydata_3.jpg")
background-size: contain

---
class: center
background-image: url("images/horst_tidydata_4.jpg")
background-size: contain

---

# Some tidy examples: data wrangling

Compute the total R&D spending in each year

```r
head(fed_spend_long)
```

```r
fed_spend_long %>%
  group_by(year) %>%
  summarise(total = sum(rd_budget_mil))
```

```
#> # A tibble: 42 x 2
#>     year  total
#>    <dbl>  <dbl>
#>  1  1976  86227
#>  2  1977  91807
#>  3  1978  94864
#>  4  1979  96601
#>  5  1980  96305
#>  6  1981  98304
#>  7  1982  95448
#>  8  1983  95010
#>  9  1984 105371
#> 10  1985 114818
#> # … with 32 more rows
```
]

---

# Some tidy examples: data wrangling

Compute the total R&D spending in each year

```r
head(fed_spend_wide)
```

```r
fed_spend_wide %>%
  mutate(total = DHS + DOC + DOD + DOE + DOT + EPA + HHS + Interior + NASA + NIH + NSF + Other + USDA + VA) %>%
  select(year, total)
```

---

# Some tidy examples: plotting

Make a bar chart of total R&D spending by agency

```r
head(fed_spend_long)
```

```r
ggplot(fed_spend_long) +
* geom_col(aes(x = rd_budget_mil, y = reorder(department, rd_budget_mil)),
           width = 0.7, alpha = 0.8) +
  theme_bw(base_size = 15) +
  labs(x = "R&D Spending ($Millions)",
       y = "Federal Agency")
```

<img src="figs/fed-spend-bars-1.png" width="432" />
]

---
class: inverse, center, middle

# Tidying and Untidying your data with

# `spread()` and `gather()`

---

## `spread()`: from tidy ("long") to untidy ("wide")

### `key` = column names, `value` = cells

---

## `spread()`: from tidy ("long") to untidy ("wide")

### `key` = column names, `value` = cells

```r
head(fed_spend_long)
```

```r
fed_spend_wide <- fed_spend_long %>%
*   spread(key = department,
*          value = rd_budget_mil)

head(fed_spend_wide)
```

---

## `gather()`: from untidy ("wide") to tidy ("long")

### `key` = column names, `value` = cells

---

## `gather()`: from untidy ("wide") to tidy ("long")

### `key` = column names, `value` = cells

```r
fed_spend_long <- fed_spend_wide %>%
*   gather(key = "department",
*          value = "rd_budget_mil",
*          DHS:VA)

head(fed_spend_long)
```

```
#> # A tibble: 6 x 3
#>    year department rd_budget_mil
#>   <dbl> <chr>              <dbl>
#> 1  1976 DHS                    0
#> 2  1977 DHS                    0
#> 3  1978 DHS                    0
#> 4  1979 DHS                    0
#> 5  1980 DHS                    0
#> 6  1981 DHS                    0
```
]

---
class: inverse

# Your turn: Tidy <--> Untidy

We already read in the following two data frames:

- `pv_cells`
- `milk_production`

Now we'll modify the format of each:

1. Use `spread()` to "untidy" the `milk_production` data into a format where the columns are state names and the values are the milk produced in each state.

2. Use `gather()` to "tidy" the `pv_cells` data into a data frame with three names: `year`, `country`, `numCells`

---
class: center, middle, inverse

# Start thinking about research questions

---

# Writing a research question

Follow [these guidelines](https://writingcenter.gmu.edu/guides/how-to-write-a-research-question) - your question should be:

- **Clear**: your audience can easily understand its purpose without additional explanation.

- **Focused**: it is narrow enough that it can be addressed thoroughly with the data available and within the limits of the final project report.

- **Concise**: it is expressed in the fewest possible words.

- **Complex**: it is not answerable with a simple "yes" or "no," but rather requires synthesis and analysis of data.

- **Arguable**: its potential answers are open to debate rather than accepted facts (do others care about it?)

---

# Writing a research question

**Bad question: Why are social networking sites harmful?**

- Unclear: it does not specify _which_ social networking sites or state what harm is being caused; assumes that "harm" exists.

**Improved question: How are online users experiencing or addressing privacy issues on such social networking sites as Facebook and Twitter?**

- Specifies the sites (Facebook and Twitter), type of harm (privacy issues), and who is harmed (online users).

**Other good examples**: See the [Example Projects Page](https://eda.seas.gwu.edu/2021-Spring/ref-example-analyses.html) page