Lecture 4

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# Lecture 4
----
## **DANL 200: Introduction to Data Analytics**
### Byeong-Hak Choe
### September 8, 2022

---
# Announcement

### <p style="color:#00449E">TA and Tutors</p>

- Teaching Assistant (TA): Emine Morris 
  - Major in Accounting
  - Minor in Data Analytics and Mathematics

- TA Hours: 
  - Mondays and Wednesdays: 5:00 PM-6:30 PM 
  - Tuesdays and Thursdays: 3:00 PM-4:45 PM

- Where: Data Analytics Lab (South Hall, 3rd Floor)

- Tutoring schedules are coming soon.

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# Workflow
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---
# Workflow
### <p style="color:#00449E"> Shortcuts for RStudio and RScript </p>

.pull-left[
**Mac**

- **command + shift + N** opens a new RScript.
- **command + return** runs a current line or selected lines.
- **command + shift + C** is the shortcut for # (commenting).
- **option + - ** is the shortcut for `<-`.
]

.pull-right[
**Windows**

- **Ctrl + Shift + N** opens a new RS-cript.
- **Ctrl + return** runs a current line or selected lines.
- **Ctrl + Shift + C** is the shortcut for # (commenting).
- **Alt + - ** is the shortcut for `<-`.
]

---
# Workflow

- **Home/End** moves the blinking cursor bar to the beginning/End of the line.
  - **Ctrl** (**command** for Mac Users) **+** <svg aria-hidden="true" role="img" viewBox="0 0 448 512" style="height:1em;width:0.88em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:currentColor;overflow:visible;position:relative;"><path d="M447.1 256C447.1 273.7 433.7 288 416 288H109.3l105.4 105.4c12.5 12.5 12.5 32.75 0 45.25C208.4 444.9 200.2 448 192 448s-16.38-3.125-22.62-9.375l-160-160c-12.5-12.5-12.5-32.75 0-45.25l160-160c12.5-12.5 32.75-12.5 45.25 0s12.5 32.75 0 45.25L109.3 224H416C433.7 224 447.1 238.3 447.1 256z"/></svg> / <svg aria-hidden="true" role="img" viewBox="0 0 448 512" style="height:1em;width:0.88em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:currentColor;overflow:visible;position:relative;"><path d="M438.6 278.6l-160 160C272.4 444.9 264.2 448 256 448s-16.38-3.125-22.62-9.375c-12.5-12.5-12.5-32.75 0-45.25L338.8 288H32C14.33 288 .0016 273.7 .0016 256S14.33 224 32 224h306.8l-105.4-105.4c-12.5-12.5-12.5-32.75 0-45.25s32.75-12.5 45.25 0l160 160C451.1 245.9 451.1 266.1 438.6 278.6z"/></svg> works too.

- **Ctrl** (**command** for Mac Users) **+ Z** undoes the previous action.
- **Ctrl** (**command** for Mac Users) **+ Shift + Z** redoes when undo is executed.

- **Ctrl** (**command** for Mac Users) **+ F** is useful when finding a phrase (and replace the phrase) in the RScript.

- **Ctrl** (**command** for Mac Users) **+ D** deletes a current line.

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# Management of Files, Directories, and Scripts
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# Management of Files and Directories
### <p style="color:#00449E"> Finding the path name of the file </p>

.panelset[

.panel[.panel-name[Windows 11]

- **Step 1.** Go to your folder using the File Explorer.
- **Step 2.** Right-click the file.
- **Step 3.** Click "Copy as path".
- **Step 4.** Paste the path name of the file to the R script (Ctrl+V).
- **Step 5.** 
  - *Option 1.* Replace backslash(`\`) with double-backslash(`\\`) in the path name.
  - *Option 2.* Replace backslash(`\`) with slash(`/`) in the path name.
]

.panel[.panel-name[Windows 10]

- **Step 1.** Go to your folder using the File Explorer.
- **Step 2.** Click the file.
- **Step 3.** Hold down the "Shift" key
- **Step 4.** Right-click the file.
- **Step 5.** Click "Copy as path".
- **Step 6.** Paste the path name of the file to the R script (Ctrl+V).
- **Step 7.** 
  - *Option 1.* Replace backslash(`\`) with double-backslash(`\\`) in the path name.
  - *Option 2.* Replace backslash(`\`) with slash(`/`) in the path name.
]

.panel[.panel-name[Mac]

- **Step 1.** Go to your folder using the Finder.
- **Step 2.** Right-click the file in the folder
- **Step 3.** Hold down the "option" key
- **Step 4.** Click "Copy 'PATH\_FOR\_YOUR\_FILE' as Pathname" from the menu.
- **Step 5.** Paste it to the R script (command+V).

]

]

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# Working with Data from Files
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---
# Working with Data from Files

- Step 1. Download the file, `tvshows.csv`, from the Files section in our Canvas.

- Step 2. In the code below, replace 'PATH_NAME_FOR_THE_FILE_tvshows.csv' with the path name for the file, `tvshows.csv`.

- Step 3. Run the following R code:

```r
tvshows <- read.table(
		'PATH_NAME_FOR_THE_FILE_tvshows.csv',
		sep = ',',
		header = TRUE,
		stringsAsFactor = TRUE )
View(uciCar)
```

---
# Working with Data from Files
### <p style="color:#00449E"> Examining data frame </p>

- `class()` tells you what kind of R object you have.

- `dim()` shows how many rows and columns are in the data for `data.frame`.

- `head()` shows the top few rows of the data.

- `help()` provides the documentation for a class. 
  - Try `help(class(uciCar))`.
  
  
- `str()` gives us the structure for an object.

---
# Working with Data from Files
### <p style="color:#00449E"> Examining data frame </p>

- `summary()` provides a summary of almost any R object.

- `skimr::skim()` provides a more detailed summary.
  - `skimr` is the package that provides the function `skim()`.
  
- `print()` prints all the data. 
  - Note: for large datasets, this can take a very long time and is something you want to avoid.
  
- `View()` displays the data in a simple spreadsheet-like grid viewer.

- `dplyr::glimpse()` displays brief information about the data.

---
# Working with Data from Files
### <p style="color:#00449E"> Examining data frame </p>

```r
print(tvshows)

class(tvshows)
dim(tvshows)
head(tvshows)
help(class(tvshows))
str(tvshows)

summary(tvshows)
library(tvshows)
skim(tvshows)

library(tidyverse)  # library(dplyr) if tidyverse is not available
glimpse(tvshows)
```

---
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# Data Visualization with `ggplot()`
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---
# Exploratory Data Analysis

- In data visualization, you'll learn the basic structure of a `ggplot` plot. It turns data into plots.
  
- In data transformation, you'll learn the key verbs that allow you to select important variables, filter out key observations, create new variables, and compute summaries.

- In **exploratory data analysis**, you'll combine visualization and transformation with your curiosity and skepticism to ask and answer interesting questions about data.

---
# Data Visualization - First Steps

```r
library(tidyverse)   # library(ggplot2) if tidyverse is not available
mpg
?mpg
```

- The `mpg` data frame, provided by `ggplot2`, contains observations collected by the US Environmental Protection Agency on 38 models of car.

- Q. Do cars with big engines use more fuel than cars with small engines?
  - `displ`: a car's engine size, in liters.
  - `hwy`: a car's fuel efficiency on the highway, in miles per gallon (mpg).

---
# Data Visualization - First Steps
### <p style="color:#00449E"> Creating a `ggplot` </p>

-  What does the relationship between engine size and fuel efficiency look like?

- To plot `mpg`, run the following code to put `displ` on the `x`-axis and `hwy` on the `y`-axis:

```r
ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy))
```

---
# Data Visualization - First Steps
### <p style="color:#00449E"> Graphing Template </p>
- To make a ggplot plot, replace the bracketed sections in the code below with a `data.frame`, a `geom` function, or a collection of mappings such as `x = VAR_1` and `y = VAR_2`.

```r
ggplot(data = <DATA>) + 
  <GEOM_FUNCTION>(mapping = aes(<MAPPINGS>))
```

---
# Data Visualization - First Steps
### <p style="color:#00449E"> Exercises </p>

- Run `ggplot(data = mpg)`. What do you see?
```r
ggplot(data = mpg)
```

- How many rows are in `mpg`? How many columns?

- What does the `drv` variable describe? Read the help for `?mpg` to find out.

- Make a scatterplot of `hwy` vs. `cyl`.

- What happens if you make a scatterplot of `class` vs. `drv`? Why is the plot not useful?

---
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# Aesthetic Mappings
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---
# Aesthetic Mappings

- In the plot below, one group of points (highlighted in red) seems to fall outside of the linear trend.

- How can you explain these cars? Are those hybrids?

---
# Aesthetic Mappings

- An aesthetic is a visual property (e.g., `size`, `shape`, `color`) of the objects (e.g., `class`) in your plot.

- You can display a point in different ways by changing the values of its aesthetic properties.

---
# Aesthetic Mappings
### <p style="color:#00449E"> Adding a `color` to the plot </p>

```r
ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy, 
*                          color = class) )
```

--
### <p style="color:#00449E"> Adding a `shape` to the plot </p>

```r
ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy, 
*                          shape = class) )
```

---
# Aesthetic Mappings
### <p style="color:#00449E"> Adding a `size` to the plot </p>

```r
ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy, 
*                          size = class) )
```

--
### <p style="color:#00449E"> Adding an `alpha` (transparency) to the plot </p>

```r
ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy, 
*                          alpha = class) )
```

---
# Aesthetic Mappings
### <p style="color:#00449E"> Discrete vs. Continuous Variables </p>

- A **discrete variable** is a variable whose value is obtained by *counting*.
  - Number of students present
  - Number of red marbles in a jar
  - Number of heads when flipping three coins 
  - Students’ grade level

- A **continuous variable** is a variable whose value is obtained by *measuring*.
  - Height of students in class
  - Weight of students in class
  - Time it takes to get to school
  - Distance traveled between classes

---
# Aesthetic Mappings
### <p style="color:#00449E"> Specifying a `color` to the plot </p>

```r
ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy), 
*            color = "blue")
```

---
# Aesthetic Mappings

- To set an aesthetic manually, set the aesthetic by name as an argument of your `geom_` function; i.e. it goes outside of `aes()`.

- You'll need to pick a level that makes sense for that aesthetic:

- The name of a `color` as a *character string*.
  
    - The `size` of a point in *mm*.
  
    - The `shape` of a point as a *number*, as shown below.
  
<img src="../lec_figs/r4s_330_3.png" width="60%" style="display: block; margin: auto;" />

---
# Aesthetic Mappings
### <p style="color:#00449E"> Specifying a `color` to the plot? </p>

```r
ggplot(data = mpg) + 
  geom_point( mapping = aes(x = displ, y = hwy, 
*                           color = "blue") )
```

---
# Common problems in ggplot()

- One common problem when creating `ggplot2` graphics is to put the `+` in the wrong place.

```r
ggplot(data = mpg) 
*+ geom_point( mapping = aes(x = displ, y = hwy) )
```

---
# Aesthetic Mappings
### <p style="color:#00449E"> Exercises </p>

- Which variables in mpg are categorical? Which variables are continuous? (Hint: type `?mpg` to read the documentation for the dataset). How can you see this information when you run `mpg`?

- Map a continuous variable to `color`, `size`, and `shape.` How do these aesthetics behave differently for categorical vs. continuous variables?

---
# Aesthetic Mappings
### <p style="color:#00449E"> Exercises </p>

- What happens if you map the same variable to multiple aesthetics?

- What does the stroke aesthetic do? What shapes does it work with? (Hint: use `?geom_point`)

- What happens if you map an aesthetic to something other than a variable name, like `aes(color = displ < 5)`? (Note, you'll also need to specify `x` and `y`).

---
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# Facets
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---
# Facets

- One way to add a variable, particularly useful for categorical variables, is to use **facets** to split your plot into facets, subplots that each display one subset of the data.

- To facet your plot by a single variable, use `facet_wrap()`.

```r
ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy)) + 
* facet_wrap(~ class, nrow = 2)
```

---
# Facets

- To facet your plot on the combination of two variables, add `facet_grid()` to your plot call.

- The first argument of `facet_grid()` is also a formula. This time the formula should contain two variable names separated by a `~`.

```r
ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy)) + 
* facet_grid(drv ~ cyl)
```

---
# Facets
### <p style="color:#00449E"> Exercises </p>

- What happens if you facet on a continuous variable?

- What do the empty cells in plot with `facet_grid(drv ~ cyl)` mean? How do they relate to this plot?

```r
ggplot(data = mpg) + 
  geom_point(mapping = aes(x = drv, y = cyl))
```

- What plots does the following code make? What does `.` do?

```r
ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy)) +
* facet_grid(drv ~ .)

ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy)) +
* facet_grid(drv ~ .)

ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy)) +
* facet_grid(. ~ cyl)
```

---
# Facets
### <p style="color:#00449E"> Exercises </p>

- Take the first faceted plot in this section:

```r
ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy)) + 
  facet_wrap(~ class, nrow = 2)
```
  - What are the advantages to using faceting instead of the color aesthetic? What are the disadvantages? How might the balance change if you had a larger dataset?

---
# Facets
### <p style="color:#00449E"> Exercises </p>

- Read `?facet_wrap`. What does `nrow` do? What does `ncol` do? What other options control the layout of the individual panels? Why doesn’t `facet_grid()` have `nrow` and `ncol` arguments?

- When using `facet_grid`, you should usually put the variable with more unique levels in the columns. Why?

---
# Aesthetic Mappings and Facets
### <p style="color:#00449E"> Exercises </p>

- Use the following data.frame.

```r
tvshows_web <- read.table(
		'https://bcdanl.github.io/data/tvshows.csv',
		sep = ',',
		header = TRUE,
		stringsAsFactor = TRUE)
```

- Describe the relationship between audience size (`GRP`) and audience engagement (`PE`) using `ggplot`. Explain the relationship in words.