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<!DOCTYPE html>
<html lang="" xml:lang="">
<head>
<title>Data Visualization</title>
<meta charset="utf-8" />
<meta name="author" content="Amy Tzu-Yu Chen" />
<meta name="date" content="2020-06-27" />
<link href="libs/remark-css-0.0.1/default.css" rel="stylesheet" />
<link href="libs/remark-css-0.0.1/default-fonts.css" rel="stylesheet" />
</head>
<body>
<textarea id="source">
class: center, middle, inverse, title-slide
# Data Visualization
## Lesser known intro
### Amy Tzu-Yu Chen
### 2020-06-27
---
# About Me
- UCLA'16, Statistics
- Data Scientist at System1 & Computational Linguistics MS Student at University of Washington
- Happy R user since STAT 20
- Find me at amy17519 @ Twitter, Github, and LinkedIn
---
# Why is this a "lesser known" intro
You can easily find tutorials if you google "how to use "blah blah" visualization library"
<br />
<br />
(🤭 and just copy and paste code! It works!)
--
You can even build amazing graphs *_without coding_* using Tableau etc
<br />
<br />
(🤭 and they look nice! Nicer than my ggplots sometimes!)
--
<br />
<br />
<br />
🤔... However, having a deeper understanding on visualization tools and process is a great asset for data practitioners
--
🤩 Know behind-the-library design philosophy ➡️ helps you understand a diverse range of graphics and powerful tools faster
--
🤩 Practice visualization process ➡️ inform yourself, then educate your audience
---
# Agenda
- Grammar of Graphics
- Strategy
- Visualization Process
- Making *_Exploratory_* Graphs
- Making *_Confirmatory_* Graphs
- Toolbox
- Resources
---
class: center, middle, inverse
## Grammar of Graphics
---
# The Beauty of Grammar of Graphics
In languages, grammar keeps things in order.
<br />
--
If you know some grammar, you don't need to know all the vocabularies to speak.
<br />
--
If you know some grammar of graphics, you don't need to know all coding syntax or graph types to make an informative graph
---
# History - Grammar of Graphics
.pull-left[
- Late 1990s: the concept was introduced by Leland Wilkinson. See [The Grammar of Graphics 2nd Edition, 2005](https://www.springer.com/gp/book/9780387245447).
- 2000s: Hadley Wickham built the R visualization library ggplot2 based on grammar of graphics with modifications. He also published [A Layered Grammar of Graphics, 2010](http://vita.had.co.nz/papers/layered-grammar.html).
- Many applications in visualization libraries/projects in different languages.
]
.pull-right[
<img src="img/01grammar-mapping2.png" width=60%>
]
---
# Grammar of Graphics - Components of Grammar
.center[
<img src="img/01grammar-of-graphics2.png" width = 60% height = 60%>
]
---
# Example Data
```r
# df_measles comes from dataset dslabs::us_contagious_diseases
str(df_measles)
```
```
## Classes 'data.table' and 'data.frame': 3825 obs. of 6 variables:
## $ disease : Factor w/ 7 levels "Hepatitis A",..: 2 2 2 2 2 2 2 2 2 2 ...
## $ state : Factor w/ 51 levels "Alabama","Alaska",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ year : num 1928 1929 1930 1931 1932 ...
## $ weeks_reporting: num 52 49 52 49 41 51 52 49 40 49 ...
## $ count : num 8843 2959 4156 8934 270 ...
## $ population : num 2589923 2619131 2646248 2670818 2693027 ...
## - attr(*, ".internal.selfref")=<externalptr>
```
```r
head(df_measles)
```
```
## disease state year weeks_reporting count population
## 1: Measles Alabama 1928 52 8843 2589923
## 2: Measles Alabama 1929 49 2959 2619131
## 3: Measles Alabama 1930 52 4156 2646248
## 4: Measles Alabama 1931 49 8934 2670818
## 5: Measles Alabama 1932 41 270 2693027
## 6: Measles Alabama 1933 51 1735 2713243
```
---
# Grammar of Graphics
.left-column[
- Data
- Aesthestics
- Geometry
- Stats
- Facets
- Coordinate
- Theme
]
```r
# Annual reported Measles cases in California
ggplot(data = CA_Measles, aes(x = year, y = count)) +
geom_line()
```
<img src="dataviz_files/figure-html/unnamed-chunk-4-1.png" width="400px" />
---
# Grammar of Graphics
.left-column[
- **Data**
- **Aesthestics**
- **Geometry**
- Stats
- Facets
- Coordinate
- Theme
]
```r
# Annual reported Measles cases in California
ggplot(data = CA_Measles, aes(x = year, y = count)) +
geom_line()
```
<img src="dataviz_files/figure-html/unnamed-chunk-5-1.png" width="400px" />
---
# Grammar of Graphics
.left-column[
- **Data**
- **Aesthestics**
- **Geometry**
- Stats
- Facets
- Coordinate
- Theme
]
.right-column[
#### Data, Aesthestics(for input data), and Geometry are required to make a minimal graph
```r
* ggplot(aes(x = year, y = count)) +
* geom_line()
* ## Error: `data` must be a data frame.... 🤔
ggplot(data = CA_Measles) +
geom_line()
## Error in order(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, :
## argument 3 is not a vector
ggplot(data = CA_Measles, aes(x = year, y = count))
# No error, but you will get an empty ggplot canvas
```
]
---
# Grammar of Graphics
.left-column[
- **Data**
- **Aesthestics**
- **Geometry**
- Stats
- Facets
- Coordinate
- Theme
]
.right-column[
#### Data, Aesthestics(for input data), and Geometry are required to make a minimal graph
```r
ggplot(aes(x = year, y = count)) +
geom_line()
## Error: `data` must be a data frame.... 🤔
* ggplot(data = CA_Measles) +
* geom_line()
* ## Error in order(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, :
* ## argument 3 is not a vector
ggplot(data = CA_Measles, aes(x = year, y = count))
# No error, but you will get an empty ggplot canvas
```
]
---
# Grammar of Graphics
.left-column[
- **Data**
- **Aesthestics**
- **Geometry**
- Stats
- Facets
- Coordinate
- Theme
]
.right-column[
#### Data, Aesthestics(for input data), and Geometry are required to make a minimal graph
```r
ggplot(aes(x = year, y = count)) +
geom_line()
## Error: `data` must be a data frame.... 🤔
ggplot(data = CA_Measles) +
geom_line()
## Error in order(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, :
## argument 3 is not a vector
* ggplot(data = CA_Measles, aes(x = year, y = count))
* # No error, but you will get an empty ggplot canvas
```
]
---
# Different Library, Similar Syntax, Same Basic Components
```r
library(plotly)
plot_ly(CA_Measles, x = ~year, y = ~count, type = 'scatter', mode = 'lines')
library(highcharter)
hchart(CA_Measles, 'line', hcaes(x = year, y = count)
```
---
class: center, middle, inverse
## Strategy
---
.center[
<img src="img/03choosing-a-good-chart-09.png", width = 80%, height = 80%>
]
---
## Bottomline
- Focus on showing data patterns using an appropriate ~~fancy~~ graph
- *_Informativeness_* >> Clarity >> Aesthestics
- Data visualization could be subjective, but
<blockquote>
The greatest value of a picture is when it forces us to notice what we never expected to see
.right[-- <cite>John W. Tukey</cite>]
</blockquote>
-- as opposed to what we wanted to confirm.
---
class: center, middle, inverse
## Visualization Process
---
# Making Exploratory Graphs
<blockquote>
to be able to say that we looked one layer deeper, and found nothing, is a definite step forward -- though not as far as to be able to say that we looked deeper and found thus-and-suck
.right[-- <cite>John W. Tukey</cite>]
</blockquote>
- Make LOTS of exploratory graphs, and only present those that can convince yourself and guide the audience through your data analysis
- In this stage, we only care about *_informativeness_*! We will worry about Clarity and Aesthestics in next stage.
---
# Making Exploratory Graphs - Measles
.left-column[
- **Data**
- **Aesthestics**
- **Geometry**
- Stats
- Facets
- Coordinate
- Theme
]
```r
ggplot(data = CA_Measles, aes(x = year, y = count)) +
geom_line()
```
<img src="dataviz_files/figure-html/unnamed-chunk-6-1.png" width="400px" />
---
# Making Exploratory Graphs - Measles
.left-column[
- **Data**
- **Aesthestics**
- **Geometry**
- Stats
- **Facets**
- Coordinate
- Theme
]
```r
ggplot(data = df_measles, aes(x = year, y = count)) +
geom_line() +
facet_wrap(~state)
```
<img src="dataviz_files/figure-html/unnamed-chunk-7-1.png" width="400px" />
---
# Making Exploratory Graphs - Measles
.left-column[
- **Data**
- **Aesthestics**
- **Geometry**
- Stats
- Facets
- Coordinate
- Theme
]
```r
df_avg_pop <- df_measles[, .(mean_pop = mean(population, na.rm = TRUE)), state]
ggplot(data = df_avg_pop, aes(x = state, y = mean_pop)) +
geom_col()
```
<img src="dataviz_files/figure-html/unnamed-chunk-8-1.png" width="400px" />
---
# Making Exploratory Graphs - Measles
.left-column[
- **Data**
- **Aesthestics**
- **Geometry**
- Stats
- **Facets**
- Coordinate
- Theme
]
```r
ca_wy_noweight <- ggplot(df_measles[state %in% c("California", "Wyoming")],
aes(x = year, y = count)) +
geom_line() +
facet_wrap(~state)
ca_wy_weighted <- ggplot(df_measles[state %in% c("California", "Wyoming")],
aes(x = year, y = count / (population / 1000000))) +
geom_line() +
facet_wrap(~state)
```
---
# Making Exploratory Graphs - Measles
.left-column[
- **Data**
- **Aesthestics**
- **Geometry**
- Stats
- **Facets**
- Coordinate
- Theme
]
```r
library(patchwork)
ca_wy_noweight / ca_wy_weighted
```
<img src="dataviz_files/figure-html/unnamed-chunk-10-1.png" width="400px" />
---
# Making Exploratory Graphs - Measles
.left-column[
- **Data**
- **Aesthestics**
- **Geometry**
- Stats
- **Facets**
- Coordinate
- Theme
]
```r
ggplot(data = df_measles, aes(x = year, y = count / (population / 1000000))) +
geom_line() +
facet_wrap(~state)
```
<img src="dataviz_files/figure-html/unnamed-chunk-11-1.png" width="400px" />
---
# Making Confirmatory Graphs
- Few exploratory graphs need to become confirmatory graphs
- Key findings or evidence in your data analysis that help draw conclusions or inform modeling decisions
- Now we have the information we want to share, we can work on Clarity and Aesthestics
---
# Making Confirmatory Graphs -- Measles
.left-column[
- **Data**
- **Aesthestics**
- **Geometry**
- Stats
- **Facets**
- **Coordinate**
- **Theme**
]
```r
ggplot(data = df_avg_pop, aes(x = reorder(state, mean_pop), y = mean_pop)) +
geom_col() + coord_flip() +
ggtitle("Average US State Population, 1928-2002") +
scale_y_continuous(labels = scales::comma) +
xlab("Avg Population") + ylab("State") +
theme_bw()
```
<img src="dataviz_files/figure-html/unnamed-chunk-12-1.png" width="350px" />
---
# Making Confirmatory Graphs -- Measles
.left-column[
- **Data**
- **Aesthestics**
- **Geometry**
- Stats
- **Facets**
- **Coordinate**
- **Theme**
]
```r
ggplot(data = df_measles, aes(x = year, y = count / (population / 1000000))) +
geom_line() + facet_wrap(~state) +
ggtitle("Measle Cases per Million People by State, 1928-2002") +
scale_y_continuous(labels = scales::comma) +
xlab("State") + ylab("Cases/1m Population") +
theme_bw()
```
<img src="dataviz_files/figure-html/unnamed-chunk-13-1.png" width="350px" />
---
class: center, middle, inverse
## Toolbox
---
# Visualization Toolbox
- Lots of [ggplot](https://ggplot2.tidyverse.org) extensions
- [patchwork](https://github.com/thomasp85/patchwork) - arrange and stitch graphs together
- [gganimate](https://gganimate.com/articles/gganimate.html) - make animated ggplots
- [ggdendro](https://cran.r-project.org/web/packages/ggdendro/vignettes/ggdendro.html) - make dendrogram in ggplot
- [ggrepel](https://cran.r-project.org/web/packages/ggrepel/vignettes/ggrepel.html) - display labels nicely
- [ggradar](https://github.com/ricardo-bion/ggradar) - radar chart
- [ggmap](https://github.com/dkahle/ggmap) - draw maps
- [cowplot](https://cran.r-project.org/web/packages/cowplot/vignettes/introduction.html) - arrange graphs to be publication ready
- [ggiraph](http://davidgohel.github.io/ggiraph/index.html) - make ggplot interactive
- [geofacet](https://hafen.github.io/geofacet/) - facet on a map
- Color Palettes
- [r-color-palettes](https://github.com/EmilHvitfeldt/r-color-palettes)
- [Wes Anderson Palettes](https://github.com/karthik/wesanderson)
- [html color codes](https://htmlcolorcodes.com/resources/best-color-palette-generators/) - if you really want to customize
---
# Visualization Toolbox
- [Highcharter](http://jkunst.com/highcharter/)
- [Dygraph](https://rstudio.github.io/dygraphs/)
- [Plotly](https://github.com/ropensci/plotly)
- [leaflet](https://rstudio.github.io/leaflet/) - Interactive maps
- [Altair](https://github.com/altair-viz/altair) - Can show distribution in the highlighted region - Python only
---
class: center, middle, inverse
## Resources
---
# Tutorials, Videos, Books, and Paper
- Liz Sander - [Telling stories with data using the grammar of graphics](https://codewords.recurse.com/issues/six/telling-stories-with-data-using-the-grammar-of-graphics)
- Hadley Wickham - [A Layered Grammar of Graphics](https://byrneslab.net/classes/biol607/readings/wickham_layered-grammar.pdf)
- Thomas Lin Pedersen - [ggplot2 Workshop](https://www.youtube.com/watch?v=h29g21z0a68) (video, 4.5hr tutorials with latest dev updates)
- John W. Tukey - [Exploratory Data Analysis, Preface](https://books.google.com/books/about/Exploratory_Data_Analysis.html?id=UT9dAAAAIAAJ&source=kp_book_description)
- Dipanjan (DJ) Sarkar - [A Comprehensive Guide to the Grammar of Graphics for Effective Visualization of Multi-dimensional Data](https://towardsdatascience.com/a-comprehensive-guide-to-the-grammar-of-graphics-for-effective-visualization-of-multi-dimensional-1f92b4ed4149)
---
class: center, middle, inverse
# Thanks!
Slides created using the R package [**xaringan**](https://github.com/yihui/xaringan).
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