Data Visualization 1

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# Data Visualization 1
### Bora Jin

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## Material

🎥 Watch [Data and Visualization](https://www.youtube.com/watch?v=FddF4b_GuTI)

- [Slides](https://rstudio-education.github.io/datascience-box/course-materials/slides/u2-d01-data-viz/u2-d01-data-viz.html#1)

🎥 Watch [Visualizing Data with ggplot2](https://www.youtube.com/watch?v=s2NF2J36ljE)

- [Slides](https://rstudio-education.github.io/datascience-box/course-materials/slides/u2-d02-ggplot2/u2-d02-ggplot2.html#1)

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## Today's Goal

- Build up an effective visualization systematically layer by layer with `ggplot2`

"The simple graph has brought more information to the data analyst's mind than any other device" - John Tukey

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## Quiz

**Q - What does each row of a dataset represent?**

Observation

**Q - What does each column of a dataset represent?**

Variable

**Q - All variables in a dataset must be in the same type (character, integer, etc.). (T/F)**

**Q - In this class, we mainly focus on variables having a single entry per observation. (T/F)**

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## Quiz

**Q - Which function spits out the dimension of a dataset?**

`dim()` (row, column)

**Q - How to check the number of rows of a dataset?**

`nrow()`

**Q - How to check the number of columns of a dataset?**

`ncol()`

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## Quiz

**Q - What is the information that you cannot get using `glimpse()`?**

a. number of rows

b. number of cols

c. list of variables

d. variable types

e. summaries of variables

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## Quiz

**Q - What is the information that you cannot get using `glimpse()`?**

a. number of rows

b. number of cols

c. list of variables

d. variable types

**e. summaries of variables**

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## Quiz

**Q - What is EDA?**

- Approach to analysing datasets to summarize and describe its main characteristics
- Often visual

**Q - Why is visualization important?**

It can reveal a new story that summary statistics could miss!

**Q - Before plotting, what package do we load?**

`library(tidyverse)` (`ggplot2` `\(\in\)` `tidyverse`)

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## Quiz

**Q - When plotting, what are the three fundamental things to define and the relevant code?**

- Data in `ggplot(data = )`
- Aesthetics in `ggplot(mapping = aes())`
- Geometries in `geom_xxxx()`

layers added by `+`

```r
ggplot(data = [dataset], 
       mapping = aes(x = [x-variable], y = [y-variable])) +
   geom_xxx() +
   other options
```

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## Quiz

**Q - What are *other options* include?**

- Labels and legends in `labs()`
- More aesthetics options (color, shape, size, alpha)
- Faceting
- Many more!

**Q - For aesthetics options, can you explain mapping vs. setting?**

- Mapping: map plotting characters to a specific variable in data through `aes()`
- Setting: determine them without regard to data values through `geom_xxxx()`

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## Quiz

**Q - Why faceting?**

- To explore conditional relationships 
- Multiple smaller plots in one plot

**Q - Two functions for faceting?**

--
- `facet_grid([row-variable] ~ [column-variable])`
- `facet_wrap(~ [variable])`

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# Questions?

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## Let's Practice Together!

- [AE 02: Part 1, Tour of RStudio + GitHub](https://sta199-summer22.netlify.app/appex/ae02_BJ.html)

- [AE 03: Data Visualization 1](https://sta199-summer22.netlify.app/appex/ae03_BJ.html)

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## Bulletin

- Watch videos for [Prepare: May 16](https://sta199-summer22.netlify.app/prepare/week02_may16_BJ.html)

- Submit your `ae02`

- Complete Part 1-3 in `ae03`