Lecture 2

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# Lecture 2
----
## **DANL 200: Introduction to Data Analytics**
### Byeong-Hak Choe
### Septermber 1, 2022

---
# Announcement

### Changes in Office Hours

- Office: South Hall 117B.
- Office Hours: 
  - Mondays 3:30 PM-5:30 PM 
  - Wednesdays 1:30 PM-3:30 PM.

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# Installing the Tools
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# Installing the Tools
### R programming

The R language is available as a free download from the R Project website at:

- Windows: [https://cran.r-project.org/bin/windows/base/](https://cran.r-project.org/bin/windows/base/)
- Mac: [https://cran.r-project.org/bin/macosx/](https://cran.r-project.org/bin/macosx/)
  -  Download the file of R that corresponds to your Mac OS (Big Sur, Apple silicon arm64, High Sierra, El Capitan, Mavericks, etc.)

---
# Installing the Tools
### RStudio

- **RStudio** offers a graphical interface to assist in creating R code:

- The RStudio Desktop is available as a free download from the following webpage:
    - [https://www.rstudio.com/products/rstudio/download/#download](https://www.rstudio.com/products/rstudio/download/#download)

---
# Installing the Tools
### RStudio Environment 
.pull-left[
<img src="../lec_figs/rstudio_env.png" width="100%" style="display: block; margin: auto;" />
]

.pull-right[
- **Script Pane** is where you write R commands in a script file that you can save. 
  - An R script is simply a text file containing R commands. 
  - RStudio will color-code different elements of your code to make it easier to read.

]
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# Installing the Tools
### RStudio Environment 
.pull-left[
<img src="../lec_figs/rstudio_env.png" width="100%" style="display: block; margin: auto;" />
]

.pull-right[
- **Console Pane** allows you to interact directly with the R interpreter and type commands where R will immediately execute them.

]
---
# Installing the Tools
### RStudio Environment 
.pull-left[
<img src="../lec_figs/rstudio_env.png" width="100%" style="display: block; margin: auto;" />
]

.pull-right[
- **Environment Pane** is where you can see the values of variables, data frames, and other objects that are currently stored in memory.

]
---
# Installing the Tools
### RStudio Environment 
.pull-left[
<img src="../lec_figs/rstudio_env.png" width="100%" style="display: block; margin: auto;" />
]

.pull-right[
- **Plots Pane** contains any graphics that you generate from your R code.

]

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# Installing the Tools
### RStudio Environment

```r
# Answer "no" to:
# Do you want to install from sources the packages which need compilation?

update.packages(ask = FALSE, checkBuilt = TRUE)

pkgs <- c("tidyverse", "nycflights13", "gapminder", "skimr")
install.packages(pkgs,
	dependencies = c("Depends", "Imports", "LinkingTo"))

```

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# Management of Files, Directories, and Scripts
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# Management of Files, Directories, and Scripts
### Materials for the book, Practical Data Science with R

- Click the green "Code" button and download the ZIP file from the following GitHub page: [https://github.com/WinVector/PDSwR2](https://github.com/WinVector/PDSwR2).

.panelset[

.panel[.panel-name[Windows]

- **Step 1.** Go to your Download folder (or the folder where the downloaded file is saved) using the File Explorer.
- **Step 2.** Click the ZIP file one time.
- **Step 3.** Cut the file by using the shortcut (**Ctrl+X**).  
- **Step 4.** Go to your working folder for the course using the File Explorer.
- **Step 5.** Paste the file to your working folder by using **Ctrl+V**.
- **Step 6.** Right-click the ZIP file and click "Extract ..."
]

.panel[.panel-name[Mac]

- **Step 1.** Go to your Download folder (or the folder where the downloaded file is saved) using the Finder.
- **Step 2.** Click the ZIP file (or the folder if the ZIP file is extracted) one time.
- **Step 3.** Copy the file (or the folder) by using the shortcut (**command+C**). 
- **Step 4.** Go to your working folder for the course using the Finder.
- **Step 5.** Paste the file to your working folder by using **command+option+V**.
- **Step 6.** Right-click the ZIP file and click "Extract ..."
]

]

---
# Management of Files and Directories
### Finding the path name of the file

.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.* Add `r ` at the beginning of the path name:
  - 'r PATH\_FOR\_YOUR\_FILE'.
]

.panel[.panel-name[Windows 10]

- **Step 1.** Go to your folder using the File Explorer.
- **Step 2.** Keep pressing the "Shift" key
- **Step 3.** Right-click the file.
- **Step 4.** Click "Copy as path".
- **Step 5.** Paste the path name of the file to the R script (Ctrl+V).
- **Step 6.** 
  - *Option 1.* Replace backslash(`\`) with double-backslash(`\\`) in the path name.
  - *Option 2.* Add `r ` at the beginning of the path name:
  - 'r PATH\_FOR\_YOUR\_FILE'.
]

.panel[.panel-name[Mac]

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

]

]

---
# Management of Files, Directories, and Scripts
### Code and comment style 
- The two main principles for coding and managing data are:
 - Make things easier for your future self.
 - Don't trust your future self.

- The `#` mark is R's comment character.
  - `#` indicates that the rest of the line is to be ignored.
  - Write comments before the line that you want the comment to apply to.

- Consider using block commenting for separating code sections.
  - `####` defines a coding block.

- Break down long lines and long algebraic expressions.

---
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# Starting with R
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# Starting with R
### RStudio Options Setting 
.pull-left[
<img src="../lec_figs/RStudio_options.png" width="100%" style="display: block; margin: auto;" />
]

.pull-right[
- This option menu is found by menus as follows:
  - *Mac*: RStudio `$>$` Preferences 
  - *Windows*: Tools `$>$` Global Options
]

---
# Starting with R
- Let's try a few commands to help you become familiar with R and its basic data types.

.pull-left[
```r
1
1/2
'Joe'
"Joe"
"Joe"=='Joe'

c()
is.null(c())
is.null(5)
```
]
.pull-right[

- In R, **vectors** are arrays of same-typed values.
 - They can be built with the `c()` notation.
 
```r
c(1)
c(1, 2)
c("Apple", 'Orange')
length(c(1, 2))
vec <- c(1, 2)
vec
```
]

---
# Starting with R
### Code and comment style 
- R has many assignment operators (e.g., `<-`, `=`, `->` ).
- The preferred one is `<-`.

```r
x <- 2
x < - 3
print(x)

x <- 5
x = 5
5 -> x
```

---
# Starting with R
### Shortcuts 
.pull-left[
### Mac 
- **command + return** runs a current line (where the blinking cursor is) or selected lines.
- **command + shift + C** is the shortcut for #.
- **option + -** is the shortcut for `<-`.
]

.pull-right[
### Windows 
- **Ctrl + Enter** runs a current line (where the blinking cursor is) or selected lines.
- **Ctrl + Shift + C** is the shortcut for #.
- **Alt + -** is the shortcut for `<-`.
]

---
# Starting with R
### R data types

- Primary data types in R are as follows:
  - **Logical**: A simple binary variable that may have only two values---TRUE or FALSE.
  - **Numeric**: Decimal numbers
  - **Integer**: Integers
  - **Character**: Text strings
  - **Factor**: Categorical values. Each possible value of a factor is known as a *level*.
  - **Ordered Factor**: A special factor data type where the order of the levels is significant. E.g., Low, Medium, and High

---
# Starting with R
### R data types

- Test the data types.
```r
x <- TRUE
y <- 1
z <- 'Data Analytics'
productCategory <- c('fruit', 'vegetable', 'dry goods', 'fruit',
 'vegetable', 'dry goods')
productCategoryFactor <- factor(productCategory)
```
- The `class()` function returns the data type of an object.
 - What are classes for `x`, `y`, `z`, `productCategory`, and `productCategoryFactor`?

---
# Starting with R
### R data types

- Most R data types are *mutable*, in that we're allowed to change them.

```r
a <- c(1, 2)
b <- a

print(b)

# Alters a
a[[1]] <- 5

print(a)
print(b)
```

---
# Starting with R
### Lists

- **Lists**, unlike *vectors*, can store more than one type of object.
 - The ways to access items in lists are the `$` operator and the `[[]]` operator.
 
```r
x <- list('a' = 6, b = 'fred')
names(x)

x$a
x$b

x[['a']]

x[c('a', 'a', 'b', 'b')]
```

---
# Starting with R
### R data types

- Here are examples of a vector and a list.

```r
example_vector <- c(10, 20, 30)
example_list <- list(a = 10, b = 20, c = 30)

example_vector[1]
example_list[1]

example_vector[[2]]
example_list[[2]]

example_vector[c(FALSE, TRUE, TRUE)]
example_list[c(FALSE, TRUE, TRUE)]

example_list$b
example_list[["b"]]
```

---
# Starting with R
### Errors

- Errors are just R's way of saying it safely refused to complete an ill-formed operation

- Fear of errors should not limit experiments.

```r
x <- 1:5
print(x)

x <- meanMISSPELLED(x) 
print(x)

x <- mean(x) 
print(x)
```

---
# Starting with R
### Data Frames

- R’s central data structure is the data frame. 
- A data frame is organized into rows and columns. 
- Data frames are essentially lists of columns.
- Data frames can have columns of different types.

.pull-left[
```r
d <- data.frame(x=c(1,2),
 y=c('a','b'))

d[['x']]
d$x
d[[1]]
```
]

.pull-right[
```r
d
d[1,]
d[,1]

d[1,1]
d[1, 'x']
```

]

---
# Starting with R
### Data Frames

- The R **data.frame** class is designed to store data in a very good "ready for analysis" format.

```r
d <- data.frame(col1 = c(1, 2, 3), col2 = c(-1, 0, 1))
print(d)
d$col3 <- d$col1 + d$col2
print(d)
```

---
# Starting with R
### NULL and NA values

- `NULL` is just an alias for `c()`, the empty vector.
- `NA` indicates missing or unavailable data.

```r
c(c(), 1, NULL)
c("a", NA, "c")
```

---
# Starting with R
### NULL and NA values

- `NULL` is just an alias for `c()`, the empty vector.
- `NA` indicates missing or unavailable data.

```r
c(c(), 1, NULL)
c("a", NA, "c")
```

---
# Starting with R
### NULL and NA values

- Most R data types are *mutable*, in that we're allowed to change them.

```r
d <- data.frame(x = 1, y = 2)
d2 <- d
d$x <- 5

print(d)
print(d2)
```

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

- Step 1. Find the path name for the file, `car.data.csv`, from the sub-folder, 'UCICar' in the folder, 'PDSwR2-main'.

- Step 2. In the code below, replace 'PATH_NAME_FOR_THE_FILE_car.data.csv' with the path name for the file, `car.data.csv`.

- Step 3. Run the following R code:

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

---
# Working with Data from Files
### Examining data frame

- `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
### Examining data frame

- `summary()` provides a summary of almost any R object. 
- `skimr::skim()` provides a more detailed summary.
- `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
### Examining data frame

```r
print(uciCar)
class(uciCar)
dim(uciCar)
head(uciCar)
help(class(uciCar))
str(uciCar)
summary(uciCar)

library(skimr)
skim(uciCar)
library(tidyverse)
glimpse(uciCar)
```

---
# Working with Data from Files
### Reading from an URL

- We can import the data file from the web.

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

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

- Let's try some data visualization using `ggplot()`:

```r
ggplot(tvshows) + 
  geom_point(aes(x=GRP, y=PE, color=Genre))
  
ggplot(tvshows) + 
  geom_point(aes(x=GRP, y=PE)) + 
  facet_wrap(~Genre)
```
-  What is the nature of the the relationship between audience size (GRP) and audience engagement (PE)?