Data visualization

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# Data visualization
### Abhijit Dasgupta
### March 25-27, 2019

---

layout: true

<div class="my-header">
PS 312, March 2019</div>

---
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# Starting with ggplot2
---
# Why ggplot2?

We're making the decision to use `ggplot2` for graphics

+ Makes pretty good formatting choices out of the box
+ Works like pipes!!
+ Is declarative (tell it what you want) without getting caught up in minutae
+ Strongly leverages data frames (good practice)
+ Fast enough
+ There are good templates if you want to change the look
---
# Introduction to ggplot2

The `ggplot2` package is a very flexible and (to me) intuitive way of visualizing data.
It is based on the concept of layering elements on a canvas.

> This idea of layering graphics on a canvas is, to me, a nice way of building graphs

---
# Introduction to ggplot2

You need:

+ A `data.frame` object
+ _Aesthetic mappings_ (aes) to say what data is used for what purpose in the viz
    + x- and y-direction
    + shapes, colors, lines
+ A _geometry object_ (geom) to say what to draw
    + You can "layer" geoms on each other to build plots
    
---
# Introduction to ggplot2

`ggplot` used pipes before pipes were a thing.

However, it uses the `+` symbol for
piping rather than the `%>%` operator, since it pre-dates the `tidyverse`

---
# Introduction to ggplot2

.pull-left[

```r
library(ggplot2)
theme_set(theme_bw())
ggplot(mtcars, aes(x = wt, y = mpg)) + geom_point()
```

+ A `data.frame` object: mtcars
+ Aesthetic mapping: 
    - x-axis: wt
    - y-axis: mpg
+ Geometry:
    + geom_point: draw points
]
.pull-right[

<img src="Day2_DataViz_files/figure-html/unnamed-chunk-2-1.png" height="400px" />
]

---
# Introduction to ggplot2

.pull-left[

```r
ggplot(mtcars, aes(x = wt, y = mpg)) + 
  geom_point()+ 
  geom_smooth()
```

+ A `data.frame` object: mtcars
+ Aesthetic mapping: 
 - x-axis: wt
 - y-axis: mpg
+ Geometry:
 + geom_point: draw points
 + geom_smooth: Add a layer which draws a best-fitting line
]
.pull-right[
<img src="Day2_DataViz_files/figure-html/unnamed-chunk-4-1.png" height="400px" />
]

---
# Introduction to ggplot2

.pull-left[

```r
ggplot(mtcars, aes(x = wt, y = mpg)) + 
  geom_point()+ 
  geom_smooth()+
  labs(x = 'Weight', 
       y = 'Miles per gallon', 
       title = 'Fuel efficiency by weight',
       caption = "Source: mtcars dataset")
```

]

---
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# Basic plots

---
# Histograms

```r
dos <- import('Data/FSI/Department of State.csv')
dos %>% 
 ggplot(aes(x = Award_Transaction_Value)) + geom_histogram()
```

---
# Histograms

```r
dos %>% 
  ggplot(aes(x = Award_Transaction_Value)) + geom_histogram()+
  scale_x_log10() # x-axis on log scale
```

---
# Density plots

```r
dos %>% 
  ggplot(aes(x = Award_Transaction_Value)) + geom_density()+
  scale_x_log10()
```

---
# Bar plots

```r
library(lubridate)
dos %>% 
  group_by(year = year(as_date(Award_Start_Date))) %>% 
  summarize(amount = sum(Award_Transaction_Value)) %>% 
  ggplot(aes(x = year, y = amount)) + # Note change in pipe operator
    geom_bar(stat='identity')
```

---
## Exercise

Using the `mtcars` dataset in R, create:

1. A histogram of the fuel efficiences (`mpg`) in the data set
2. A bar plot of frequencies of number of cylinders (`cyl`) in the car

---
## Solution

.pull-left[

```r
ggplot(mtcars, aes(x = mpg)) + geom_histogram(binwidth=3)
```

```r
# ggplot(mtcars) + geom_histogram(aes(x = mpg), binwidth = 3)
```
]
.pull-right[

```r
ggplot(mtcars, aes(x = factor(cyl))) + geom_bar()
```

<img src="Day2_DataViz_files/figure-html/unnamed-chunk-12-1.png" height="400px" />
]

---
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# Two continuous variables
---

# Scatter plot with best-fitting line

```r
ggplot(mtcars, aes(x = hp, y = mpg))+
  geom_point()+
  geom_smooth(method = 'lm')
```

---

## Line plot (for time series)

```r
library(forecast)
d <- data.frame(x = 1:length(gas), y = gas) # Australian monthly gas production
ggplot(d, aes(x, y)) + geom_line()
```

<img src="Day2_DataViz_files/figure-html/unnamed-chunk-14-1.png" height="400px" />
---
# Exercise

1. Create a scatter plot of sepal length and sepal width from the `iris` dataset, and 
add a smooth line through it

---

## Solution

```r
ggplot(iris, aes(Sepal.Length, Sepal.Width)) + geom_point() + geom_smooth()
```

---
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# Continuous variable with discrete variable

---

# Box plots

```r
dos %>% 
ggplot(aes(x = factor(year(as_date(Award_Start_Date))),
                y = Award_Transaction_Value))+
  geom_boxplot() +
  scale_y_log10()+
  labs(x = 'Year')
```

---
# Violin plots

```r
dos %>% 
ggplot(aes(x = factor(year(as_date(Award_Start_Date))),
                y = Award_Transaction_Value))+
  geom_violin() +
  scale_y_log10()+
  labs(x = 'Year')
```

---
## Exercise

1. Plot a boxplot of petal length by species using the `iris` dataset
---
## Solution

```r
ggplot(iris, aes(x = Species, y = Petal.Length))+geom_boxplot()
```

---
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# Grouped visualization

---

```r
grp_data <- dos %>% 
 group_by(Recipient_Location, year = year(as_date(Award_Start_Date))) %>% 
 summarize(amt = sum(Award_Transaction_Value)) %>% 
 filter(str_detect(Recipient_Location, '^C'))
ggplot(grp_data, aes(x = year, y = amt, color=Recipient_Location))+
 geom_line()+
 scale_y_log10()
```

---

```r
ggplot(grp_data, aes(x = year,  y = amt))+
  geom_line()+
  scale_y_log10()+
  facet_wrap(~Recipient_Location)
```

---
# Adding more strata

.pull-left[

```r
dos %>% filter(str_detect(Recipient_Location, '^C')) %>%
ggplot(aes(x = year(as_date(Award_Start_Date)), 
 y = Award_Transaction_Value, 
 color = Recipient_Location, 
 shape = Award_Transaction_Type))+
 geom_point()+ 
 labs(x = 'Year', color='Location')+
 scale_y_log10()
```
]
.pull-right[
<img src="Day2_DataViz_files/figure-html/unnamed-chunk-22-1.png" height="400px" />
]
---
# Adding more strata

.pull-left[

```r
dos %>% filter(str_detect(Recipient_Location, '^C')) %>%
ggplot(aes(x = year(as_date(Award_Start_Date)), 
 y = Award_Transaction_Value, 
 color = Recipient_Location, 
 shape = Award_Transaction_Type))+
 geom_jitter()+ 
 labs(x = 'Year', color='Location')+
 scale_y_log10()
```
]
.pull-right[
<img src="Day2_DataViz_files/figure-html/unnamed-chunk-24-1.png" height="400px" />
]

---

# Error bars

```r
schools <- rio::import('Data/FSI/schools.rds')
schools %>% filter(tophead=='Elementary schools', 
 head2=="Average hours in school day") %>% 
 filter(!is.na(State), State != 'United States') %>% 
 ggplot(aes(x = State, y = stats, ymin = stats - 2*se, 
 ymax = stats + 2*se)) +
 geom_pointrange()+
 labs(y = 'Avg hours in school day')+
 theme_bw()+
 theme(axis.text.x = element_text(angle=45, hjust = 1))
```

---
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# Maps

---

# Chloropleth

---

# Using shapefiles

```r
library(sf)
hrr_info <- st_read('~/Downloads/hrr_bdry-1/HRR_Bdry.SHP')
```

```
Reading layer `HRR_Bdry' from data source `/Users/abhijit/Downloads/hrr_bdry-1/HRR_Bdry.SHP' using driver `ESRI Shapefile'
Simple feature collection with 306 features and 3 fields
geometry type:  MULTIPOLYGON
dimension:      XY
bbox:           xmin: -124.7319 ymin: 24.54394 xmax: -66.95047 ymax: 49.38308
epsg (SRID):    NA
proj4string:    NA
```

```r
head(hrr_info)
```

```
Simple feature collection with 6 features and 3 fields
geometry type:  MULTIPOLYGON
dimension:      XY
bbox:           xmin: -88.885 ymin: 30.22065 xmax: -84.63694 ymax: 35.42359
epsg (SRID):    NA
proj4string:    NA
  HRR_BDRY_I HRRNUM        HRRCITY                       geometry
1          1      1 AL- BIRMINGHAM MULTIPOLYGON (((-85.89658 3...
2          2      2     AL- DOTHAN MULTIPOLYGON (((-86.194 31....
3          3      5 AL- HUNTSVILLE MULTIPOLYGON (((-86.69474 3...
4          4      6     AL- MOBILE MULTIPOLYGON (((-87.55554 3...
5          5      7 AL- MONTGOMERY MULTIPOLYGON (((-86.49925 3...
6          6      9 AL- TUSCALOOSA MULTIPOLYGON (((-87.31229 3...
```

```r
# ggplot(hrr_info)+geom_sf()
# ggsave('map.png')
```

---

# Putting together multiple graphs

.pull-left[

```r
# install.packages('cowplot')
library(cowplot)
p1 <- ggplot(iris, aes(Sepal.Length, Sepal.Width, color = Species)) +
 geom_point() + facet_grid(. ~ Species) + stat_smooth(method = "lm") +
 background_grid(major = 'y', minor = "none") +
 panel_border() + theme(legend.position = "none")

# plot B
p2 <- ggplot(iris, aes(Sepal.Length, fill = Species)) +
 geom_density(alpha = .7) + theme(legend.justification = "top")
p2a <- p2 + theme(legend.position = "none")

# plot C
p3 <- ggplot(iris, aes(Sepal.Width, fill = Species)) +
 geom_density(alpha = .7) + theme(legend.position = "none")

# legend
legend <- get_legend(p2)

# align all plots vertically
plots <- align_plots(p1, p2a, p3, align = 'v', axis = 'l')

# put together bottom row and then everything
bottom_row <- plot_grid(plots[[2]], plots[[3]], legend, labels = c("B", "C"), rel_widths = c(1, 1, .3), nrow = 1)
plot_grid(plots[[1]], bottom_row, labels = c("A"), ncol = 1)
```
]
.pull-right[
<img src="Day2_DataViz_files/figure-html/unnamed-chunk-29-1.png" height="400px" style="display: block; margin: auto;" />
]

---
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# Interactive graphs

---

# Interactive graphs

There are several packages available in R to do interactive graphs.

- plotly: A general plotting library from the folks at Plot.ly
- highchart: A port of the highcharter Javascript library
- dygraphs: A port of the dygraph Javascript library
- leaflet: Interactive maps

There are others as well. See [http://www.htmlwidgets.org](http://www.htmlwidgets.org])
for more details

---

# Plotly

.pull-left[

```r
library(ggplot2)
library(plotly)
p=ggplot(iris, aes(x=Sepal.Length, 
 y=Sepal.Width, 
 color=Species, 
 shape=Species)) +
 geom_point(size=6, alpha=0.6)
mytext=paste("Sepal Length = ", iris$Sepal.Length, 
 "\n" , "Sepal Width = ", iris$Sepal.Width, 
 "\n", "Row Number: ",rownames(iris), sep="")
pp=plotly::plotly_build(p)
style( pp, text=mytext, 
 hoverinfo = "text", 
 traces = c(1, 2, 3) )
```
]
.pull-right[
<div id="htmlwidget-32d82258f1cac79dc5cf" style="width:504px;height:500px;" class="plotly html-widget"></div>
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]
---

# Leaflet

.pull-left[

```r
library(leaflet)

m <- leaflet() %>%
 addTiles() %>% # Add default OpenStreetMap map tiles
 addMarkers(lng=174.768, lat=-36.852, popup="The birthplace of R")
m 
```
]
.pull-right[
<div id="htmlwidget-4c2a6f90d480ba2ae005" style="width:504px;height:400px;" class="leaflet html-widget"></div>
<script type="application/json" data-for="htmlwidget-4c2a6f90d480ba2ae005">{"x":{"options":{"crs":{"crsClass":"L.CRS.EPSG3857","code":null,"proj4def":null,"projectedBounds":null,"options":{}}},"calls":[{"method":"addTiles","args":["//{s}.tile.openstreetmap.org/{z}/{x}/{y}.png",null,null,{"minZoom":0,"maxZoom":18,"tileSize":256,"subdomains":"abc","errorTileUrl":"","tms":false,"noWrap":false,"zoomOffset":0,"zoomReverse":false,"opacity":1,"zIndex":1,"detectRetina":false,"attribution":"© <a href=\"http://openstreetmap.org\">OpenStreetMap<\/a> contributors, <a href=\"http://creativecommons.org/licenses/by-sa/2.0/\">CC-BY-SA<\/a>"}]},{"method":"addMarkers","args":[-36.852,174.768,null,null,null,{"interactive":true,"draggable":false,"keyboard":true,"title":"","alt":"","zIndexOffset":0,"opacity":1,"riseOnHover":false,"riseOffset":250},"The birthplace of R",null,null,null,null,{"interactive":false,"permanent":false,"direction":"auto","opacity":1,"offset":[0,0],"textsize":"10px","textOnly":false,"className":"","sticky":true},null]}],"limits":{"lat":[-36.852,-36.852],"lng":[174.768,174.768]}},"evals":[],"jsHooks":[]}</script>
]

---

# Dygraph

.pull-left[

```r
library(dygraphs)
library(xts)          # To make the convertion data-frame / xts format

# Create data + verify it is date format + change them to xts format:
data=data.frame(time=seq(from=Sys.Date()-40, 
                         to=Sys.Date(), by=1 ), 
                value=runif(41))
str(data$time)
data=xts(x = data$value, order.by = data$time)

# Add points
dygraph(data) %>%
  dyOptions( stemPlot = TRUE)
```
]
.pull-right[

```
 Date[1:41], format: "2019-02-20" "2019-02-21" "2019-02-22" "2019-02-23" "2019-02-24" ...
```

<div id="htmlwidget-67bbc72f22969d1add47" style="width:504px;height:400px;" class="dygraphs html-widget"></div>
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