Exploratory data analysis

---

# Exploratory data analysis

1. Generate questions about your data
1. Search for answers by visualizing, transforming, and modeling your data
1. Use what you learn to refine your questions and or generate new questions
1. Rinse and repeat until you publish a paper

* Variation
* Covariation

---

---

count: false
 
## Characteristics of Exploratory Data Analysis
.panel1-penguins-eda-auto[

```r
*ggplot(
* data = penguins,
* mapping = aes(
*   x = body_mass_g,
*   y = flipper_length_mm
* )
*) 
```
]
 
.panel2-penguins-eda-auto[
<img src="index_files/figure-html/penguins-eda_auto_01_output-1.png" width="80%" style="display: block; margin: auto;" />
]

---
count: false
 
## Characteristics of Exploratory Data Analysis
.panel1-penguins-eda-auto[

```r
ggplot(
  data = penguins,
  mapping = aes(
    x = body_mass_g,
    y = flipper_length_mm
  )
) +
* geom_point()
```
]
 
.panel2-penguins-eda-auto[
<img src="index_files/figure-html/penguins-eda_auto_02_output-1.png" width="80%" style="display: block; margin: auto;" />
]

---
count: false
 
## Characteristics of Exploratory Data Analysis
.panel1-penguins-eda-auto[

```r
ggplot(
  data = penguins,
  mapping = aes(
    x = body_mass_g,
    y = flipper_length_mm
  )
) +
  geom_point() +
* geom_smooth()
```
]
 
.panel2-penguins-eda-auto[
<img src="index_files/figure-html/penguins-eda_auto_03_output-1.png" width="80%" style="display: block; margin: auto;" />
]

---

count: false
 
## Characteristics of Confirmatory Data Analysis
.panel1-penguins-final-auto[

```r
*ggplot(
* data = penguins,
* mapping = aes(
*   x = body_mass_g,
*   y = flipper_length_mm
* )
*) 
```
]
 
.panel2-penguins-final-auto[
<img src="index_files/figure-html/penguins-final_auto_01_output-1.png" width="80%" style="display: block; margin: auto;" />
]

---
count: false
 
## Characteristics of Confirmatory Data Analysis
.panel1-penguins-final-auto[

```r
ggplot(
  data = penguins,
  mapping = aes(
    x = body_mass_g,
    y = flipper_length_mm
  )
) +
* geom_point(alpha = .1)
```
]
 
.panel2-penguins-final-auto[
<img src="index_files/figure-html/penguins-final_auto_02_output-1.png" width="80%" style="display: block; margin: auto;" />
]

---
count: false
 
## Characteristics of Confirmatory Data Analysis
.panel1-penguins-final-auto[

```r
ggplot(
  data = penguins,
  mapping = aes(
    x = body_mass_g,
    y = flipper_length_mm
  )
) +
  geom_point(alpha = .1) +
* geom_smooth(se = FALSE)
```
]
 
.panel2-penguins-final-auto[
<img src="index_files/figure-html/penguins-final_auto_03_output-1.png" width="80%" style="display: block; margin: auto;" />
]

---
count: false
 
## Characteristics of Confirmatory Data Analysis
.panel1-penguins-final-auto[

```r
ggplot(
  data = penguins,
  mapping = aes(
    x = body_mass_g,
    y = flipper_length_mm
  )
) +
  geom_point(alpha = .1) +
  geom_smooth(se = FALSE) +
* labs(
*   title = "Relationship between body mass and\nflipper length of a penguin",
*   subtitle = "Sample of 344 penguins",
*   x = "Body mass(g)",
*   y = "Flipper length(mm)"
* )
```
]
 
.panel2-penguins-final-auto[
<img src="index_files/figure-html/penguins-final_auto_04_output-1.png" width="80%" style="display: block; margin: auto;" />
]

---
count: false
 
## Characteristics of Confirmatory Data Analysis
.panel1-penguins-final-auto[

```r
ggplot(
  data = penguins,
  mapping = aes(
    x = body_mass_g,
    y = flipper_length_mm
  )
) +
  geom_point(alpha = .1) +
  geom_smooth(se = FALSE) +
  labs(
    title = "Relationship between body mass and\nflipper length of a penguin",
    subtitle = "Sample of 344 penguins",
    x = "Body mass(g)",
    y = "Flipper length(mm)"
  ) +
* theme_xaringan(
*   title_font_size = 18,
*   text_font_size = 16
* )
```
]
 
.panel2-penguins-final-auto[
<img src="index_files/figure-html/penguins-final_auto_05_output-1.png" width="80%" style="display: block; margin: auto;" />
]

---

# `scorecard`

---

## `scorecard`

```r
glimpse(scorecard)
```

```
## Rows: 1,732
## Columns: 14
## $ unitid    <dbl> 100654, 100663, 100706, 100724, 100751, 100830, 100858, 1009…
## $ name      <chr> "Alabama A & M University", "University of Alabama at Birmin…
## $ state     <chr> "AL", "AL", "AL", "AL", "AL", "AL", "AL", "AL", "AL", "AL", …
## $ type      <fct> "Public", "Public", "Public", "Public", "Public", "Public", …
## $ admrate   <dbl> 0.9175, 0.7366, 0.8257, 0.9690, 0.8268, 0.9044, 0.8067, 0.53…
## $ satavg    <dbl> 939, 1234, 1319, 946, 1261, 1082, 1300, 1230, 1066, NA, 1076…
## $ cost      <dbl> 23053, 24495, 23917, 21866, 29872, 19849, 31590, 32095, 3431…
## $ netcost   <dbl> 14990, 16953, 15860, 13650, 22597, 13987, 24104, 22107, 2071…
## $ avgfacsal <dbl> 69381, 99441, 87192, 64989, 92619, 71343, 96642, 56646, 5400…
## $ pctpell   <dbl> 0.7019, 0.3512, 0.2536, 0.7627, 0.1772, 0.4644, 0.1455, 0.23…
## $ comprate  <dbl> 0.2974, 0.6340, 0.5768, 0.3276, 0.7110, 0.3401, 0.7911, 0.69…
## $ firstgen  <dbl> 0.3658281, 0.3412237, 0.3101322, 0.3434343, 0.2257127, 0.381…
## $ debt      <dbl> 15250, 15085, 14000, 17500, 17671, 12000, 17500, 16000, 1425…
## $ locale    <fct> City, City, City, City, City, City, City, City, City, Suburb…
```

---

# Univariate graphs

---

```r
*ggplot(
* data = scorecard,
* mapping = aes(x = cost)
*) 
```
]
 
.panel2-histogram-auto[
<img src="index_files/figure-html/histogram_auto_01_output-1.png" width="80%" style="display: block; margin: auto;" />
]

---
count: false
 
## Histogram
.panel1-histogram-auto[

```r
ggplot(
  data = scorecard,
  mapping = aes(x = cost)
) +
* geom_histogram()
```
]
 
.panel2-histogram-auto[
<img src="index_files/figure-html/histogram_auto_02_output-1.png" width="80%" style="display: block; margin: auto;" />
]

---

```r
ggplot(
  data = scorecard,
  mapping = aes(x = cost)
) +
* geom_histogram(bins = 50)
```
]
 
.panel2-histogram-bins-rotate[
<img src="index_files/figure-html/histogram-bins_rotate_01_output-1.png" width="80%" style="display: block; margin: auto;" />
]

---
count: false
 
## Histogram
.panel1-histogram-bins-rotate[

```r
ggplot(
  data = scorecard,
  mapping = aes(x = cost)
) +
* geom_histogram(bins = 30)
```
]
 
.panel2-histogram-bins-rotate[
<img src="index_files/figure-html/histogram-bins_rotate_02_output-1.png" width="80%" style="display: block; margin: auto;" />
]

---
count: false
 
## Histogram
.panel1-histogram-bins-rotate[

```r
ggplot(
  data = scorecard,
  mapping = aes(x = cost)
) +
* geom_histogram(bins = 10)
```
]
 
.panel2-histogram-bins-rotate[
<img src="index_files/figure-html/histogram-bins_rotate_03_output-1.png" width="80%" style="display: block; margin: auto;" />
]

---

```r
*ggplot(
* data = scorecard,
* mapping = aes(x = type)
*) 
```
]
 
.panel2-barplot-auto[
<img src="index_files/figure-html/barplot_auto_01_output-1.png" width="80%" style="display: block; margin: auto;" />
]

---
count: false
 
## Bar chart
.panel1-barplot-auto[

```r
ggplot(
  data = scorecard,
  mapping = aes(x = type)
) +
* geom_bar()
```
]
 
.panel2-barplot-auto[
<img src="index_files/figure-html/barplot_auto_02_output-1.png" width="80%" style="display: block; margin: auto;" />
]

---

## Covariation

1. Two-dimensional graphs
1. Multiple window plots
1. Utilizing additional channels

---

```r
*ggplot(
* data = scorecard,
* mapping = aes(
*   x = type,
*   y = cost
* )
*) 
```
]
 
.panel2-boxplot-auto[
<img src="index_files/figure-html/boxplot_auto_01_output-1.png" width="80%" style="display: block; margin: auto;" />
]

---
count: false
 
## Box plot
.panel1-boxplot-auto[

```r
ggplot(
  data = scorecard,
  mapping = aes(
    x = type,
    y = cost
  )
) +
* geom_boxplot()
```
]
 
.panel2-boxplot-auto[
<img src="index_files/figure-html/boxplot_auto_02_output-1.png" width="80%" style="display: block; margin: auto;" />
]

---

```r
*ggplot(
* data = scorecard,
* mapping = aes(
*   x = cost,
*   y = netcost
* )
*) 
```
]
 
.panel2-scatterplot-auto[
<img src="index_files/figure-html/scatterplot_auto_01_output-1.png" width="80%" style="display: block; margin: auto;" />
]

---
count: false
 
## Scatterplot
.panel1-scatterplot-auto[

```r
ggplot(
  data = scorecard,
  mapping = aes(
    x = cost,
    y = netcost
  )
) +
* geom_point()
```
]
 
.panel2-scatterplot-auto[
<img src="index_files/figure-html/scatterplot_auto_02_output-1.png" width="80%" style="display: block; margin: auto;" />
]

---

```r
*ggplot(
* data = scorecard,
* mapping = aes(x = cost)
*) +
* geom_histogram()
```
]
 
.panel2-histogram-facet-user[
<img src="index_files/figure-html/histogram-facet_user_01_output-1.png" width="80%" style="display: block; margin: auto;" />
]

---
count: false
 
## Multiple windows plot
.panel1-histogram-facet-user[

```r
ggplot(
  data = scorecard,
  mapping = aes(x = cost)
) +
  geom_histogram() +
* facet_wrap(facets = vars(type))
```
]
 
.panel2-histogram-facet-user[
<img src="index_files/figure-html/histogram-facet_user_02_output-1.png" width="80%" style="display: block; margin: auto;" />
]

---

```r
*ggplot(
* data = scorecard,
* mapping = aes(
*   x = cost,
*   y = netcost
* )
*) +
* geom_point()
```
]
 
.panel2-scatterplot-facet-user[
<img src="index_files/figure-html/scatterplot-facet_user_01_output-1.png" width="80%" style="display: block; margin: auto;" />
]

---
count: false
 
## Multiple windows plot
.panel1-scatterplot-facet-user[

```r
ggplot(
  data = scorecard,
  mapping = aes(
    x = cost,
    y = netcost
  )
) +
  geom_point() +
* facet_wrap(facets = vars(type))
```
]
 
.panel2-scatterplot-facet-user[
<img src="index_files/figure-html/scatterplot-facet_user_02_output-1.png" width="80%" style="display: block; margin: auto;" />
]

---

count: false
 
## Utilizing additional channels
.panel1-scatterplot-mult-channels-rotate[

```r
ggplot(
  data = scorecard,
  mapping = aes(
    x = cost,
    y = netcost,
*   color = type,
  )
) +
  geom_point()
```
]
 
.panel2-scatterplot-mult-channels-rotate[
<img src="index_files/figure-html/scatterplot-mult-channels_rotate_01_output-1.png" width="80%" style="display: block; margin: auto;" />
]

---
count: false
 
## Utilizing additional channels
.panel1-scatterplot-mult-channels-rotate[

```r
ggplot(
  data = scorecard,
  mapping = aes(
    x = cost,
    y = netcost,
*   color = type, size = debt
  )
) +
  geom_point()
```
]
 
.panel2-scatterplot-mult-channels-rotate[
<img src="index_files/figure-html/scatterplot-mult-channels_rotate_02_output-1.png" width="80%" style="display: block; margin: auto;" />
]

---

# Practice exploring data