5_ggplot2_no_code.Rmd

---
title: "ggplot2"
author: "Gilad Ravid"
institute: "BGU"
date: "`r Sys.Date()`"
output:
  xaringan::moon_reader:
    lib_dir: libs
    css: ["default", "sup/gilad.css"]
    nature:
      highlightStyle: github
      highlightLines: true
      countIncrementalSlides: false
      beforeInit: "sup/macros.js"
---
.scroll-output-100[
```{r echo=FALSE, warning=FALSE}
library(linguisticsdown)
library(ggplot2)
library(knitr)
library(dplyr)
library(readxl)
library(httr)
library(tidyverse)
library(lubridate)
library(ggmap)
library(ggrepel)
library(gridExtra)
library(pander)
library(RCurl)
source("sup/common.r")
```
]
---
### Grammar of Graphics
statistical graphic is a mapping from data to aesthetic attributes (colour, shape, size) of geometric objects (points, lines, bars). The plot may also contain statistical transformations of the data and is drawn on a specific coordinate system. Facetting can be used to generate the same plot for different subsets of the dataset.

All plots are composed of:
- **Data** that you want to visualise and a set of aesthetic **mapping**s describing how variables in the data are mapped to aesthetic attributes that you
can perceive.
- **Layers** made up of geometric elements and statistical transformation. Geometric objects, **geom**s for short, represent what you actually see on the plot: points, lines, polygons, etc. Statistical transformations, **stat**s for short, summarise data in many useful ways. For example, binning and counting observations to create a histogram, or summarising a 2d relationship with a linear model.
- The **scale**s map values in the data space to values in an aesthetic space, whether it be colour, or size, or shape. Scales draw a legend or axes, which provide an inverse mapping to make it possible to read the original data values from the plot.
---
- A coordinate system, **coord** for short, describes how data coordinates are mapped to the plane of the graphic. It also provides axes and gridlines to make it possible to read the graph. We normally use a Cartesian coordinate system, but a number of others are available, including polar coordinates and map projections.
- A **face**ting specification describes how to break up the data into subsets and how to display those subsets as small multiples. This is also known as conditioning or latticing/trellising.
- A **theme** which controls the finer points of display, like the font size and background colour. While the defaults in ggplot2 have been chosen with care, you may need to consult other references to create an attractive plot.
---
.scroll-output-100[
```{r warning=FALSE,echo=FALSE}
#GET("https://query.data.world/s/oxy7ahmu5tekxkccbjvcmlg5nhgjhs", write_disk(tf <- tempfile(fileext = ".xlsx")))
#isr17 <- read_excel(tf)

isr17 <- read_excel("datasets/24_19_057t1.xlsx",sheet = 2,range = "a4:jx260",col_names = TRUE)
names(isr17)[6]<-"year"
isr17$year<-as.numeric(isr17$year)
isr17$age<-as.integer((2021-isr17$year)/10)
names(isr17)[11]<-"area"
isr17$area<-as.numeric(isr17$area)
names(isr17)[13]<-"population"
isr17$population<-as.numeric(isr17$population)
names(isr17)[3]<-"district"
isr17$district<-as.factor(isr17$district)
names(isr17)[4]<-"status"
isr17$status<-as.factor(isr17$status)
names(isr17)[212]<-"cluster"
names(isr17)[1]<-"c_name"
isr17$cluster<-as.numeric(isr17$cluster)
names(isr17)[39]<-"percImmigrants"
isr17$percImmigrants<-as.numeric(isr17$percImmigrants)
names(isr17)[89]<-"incomeSecurity"
isr17$incomeSecurity<-as.numeric(isr17$incomeSecurity)
isr17$percIncomeSecurity<-isr17$incomeSecurity/(isr17$population*1000)*100
names(isr17)[145]<-"percMaturityTest"
isr17$percMaturityTest<-as.numeric(isr17$percMaturityTest)

#xx<-names(isr17)
isr17_head<-isr17[1:100,]
ggplot(isr17_head,aes(x=area, y=population))+
  geom_point()

ggplot(isr17_head,aes(x=area, y=population,colour=district))+
  geom_point()

ggplot(isr17_head,aes(x=area, y=population,shape=status))+
  geom_point()

ggplot(isr17_head,aes(x=area, y=population,size=cluster))+
  geom_point()
```
]
---
scatterplot defined by:
1. Data: isr17
2. Aesthetic mapping: area mapped to x position, population economy to y position.
3. Layer: points
---
.scroll-output-100[
```{r warning=FALSE,echo=FALSE}
ggplot(isr17_head,aes(x=area, y=population))+
  geom_point(aes(colour="blue"))
ggplot(isr17_head,aes(x=area, y=population))+
  geom_point(colour="blue")
```
]
---
### Facetting
.scroll-output-100[
```{r warning=FALSE,echo=FALSE}
ggplot(isr17,aes(x=area, y=population))+
  geom_point()+
  facet_wrap(~cluster)
```
]
---
### Geoms
- geom_smooth()
- geom_boxplot()
- geom_histogram() and geom_freqpoly()
- geom_bar()
- geom_path() and geom_line()
---
.scroll-output-100[
```{r warning=FALSE,echo=FALSE}
ggplot(isr17,aes(x=area, y=population))+
  geom_point()+
  geom_smooth()

ggplot(isr17,aes(x=area, y=population))+
  geom_point()+
  geom_smooth(method="lm")
```
]
---
### Boxplot and Jittered Pointsr
.scroll-output-80[
```{r warning=FALSE,echo=FALSE}
ggplot(isr17,aes(x=status, y=population))+
  geom_point()

ggplot(isr17,aes(x=status, y=population))+
  geom_jitter()
  
ggplot(isr17,aes(x=status, y=population))+
  geom_boxplot()

ggplot(isr17,aes(x=status, y=population))+
  geom_violin()
```
]
---
### Histograms and Frequency Polygons
.scroll-output-80[
```{r warning=FALSE,echo=FALSE}
ggplot(isr17,aes(x=population))+
  geom_histogram()

ggplot(isr17,aes(x=population))+
  geom_freqpoly()

ggplot(isr17,aes(x=population,colour=district))+
  geom_freqpoly()

ggplot(isr17,aes(x=population,fill=district))+
  geom_histogram()+
  facet_wrap(~district)
```
]
---
### Bar charts
.scroll-output-80[
```{r warning=FALSE,echo=FALSE}
ggplot(isr17,aes(x=district))+
  geom_bar()

drugs <- data.frame(
drug = c("a", "b", "c"),
effect = c(4.2, 9.7, 6.1)
)

ggplot(drugs,aes(x=drug,y=effect))+geom_bar(stat="identity")
ggplot(drugs,aes(x=drug,y=effect))+geom_point()
```
]
---
### Time Series with line and path plots
.scroll-output-80[
```{r warning=FALSE,echo=FALSE}
ggplot(economics, aes(date, unemploy / pop)) +
geom_line()
ggplot(economics, aes(date, uempmed)) +
geom_line()

ggplot(economics, aes(unemploy / pop, uempmed)) +
geom_path() +
geom_point()
year <- function(x) as.POSIXlt(x)$year + 1900
ggplot(economics, aes(unemploy / pop, uempmed)) +
geom_path(colour = "grey50") +
geom_point(aes(colour = year(date)))
```
]
---
### Modifying the Axes
.scroll-output-80[
```{r warning=FALSE,echo=FALSE}
ggplot(isr17,aes(x=area, y=population))+
  geom_point(alpha=0.3)

ggplot(isr17,aes(x=area, y=population))+
  geom_point(alpha=0.3)+
  xlab("settlement area km^2")+
  ylab("settlement population in 10^3 people")

ggplot(isr17,aes(x=area, y=population))+
  geom_point(alpha=0.3)+
  xlab(NULL)+
  ylab(NULL)

ggplot(isr17,aes(x=status, y=population))+
  geom_jitter()

a<-"עירייה"
b<-"מועצה מקומית"
ggplot(isr17,aes(x=status, y=population))+
  geom_jitter()+
  xlim(a,b)+
  ylim(0,200)
```
]
---
### Output
.scroll-output-80[
```{r warning=FALSE,echo=FALSE}
p<-ggplot(isr17,aes(x=area, y=population))+
  geom_point(alpha=0.3)
print(p)
summary(p)

```
]
---
### Toolbox
Layers
  - data
  - summary
  - metadata
Plot types
  - geom_area() - area plot
  - geom_bar(stat="identity"), geom_col() - bar chart
  - geom_line geom_path() - line plot
  - geom_point() - scatterplot
  - geom_polygon()
  - geom_rect(), geom_tile(), geom_raster()

https://ggplot2.tidyverse.org/reference/

---

`r columns(4, 1)`
.scroll-output-100[
```{r warning=FALSE,echo=FALSE}
td <- data.frame(
x = c(3, 1, 5),
y = c(2, 4, 6),
label = c("a","b","c")
)
p <- ggplot(td, aes(x, y, label = label)) +
labs(x = NULL, y = NULL) + # Hide axis label
theme(plot.title = element_text(size = 12)) # Shrink plot title
p + geom_point() + ggtitle("point")
p + geom_text() + ggtitle("text")
p + geom_bar(stat = "identity") + ggtitle("bar")
p + geom_tile() + ggtitle("raster")

p + geom_line() + ggtitle("line")
p + geom_area() + ggtitle("area")
p + geom_path() + ggtitle("path")
p + geom_polygon() + ggtitle("polygon")

```
]
---
### Labels
`r columns(1, 1)`
.scroll-output-80[
```{r warning=FALSE,echo=FALSE}
td <- data.frame(x = 1, y = 3:1, family = c("sans", "serif", "mono"))
ggplot(td, aes(x, y)) +
geom_text(aes(label = family, family = family))

td <- data.frame(x = 1, y = 3:1, face = c("plain", "bold", "italic"))
ggplot(td, aes(x, y)) +
geom_text(aes(label = face, fontface = face))

td <- data.frame(
x = c(1, 1, 2, 2, 1.5),
y = c(1, 2, 1, 2, 1.5),
text = c(
"bottom-left", "bottom-right",
"top-left", "top-right", "center"
)
)
ggplot(td, aes(x, y)) +
geom_text(aes(label = text))
ggplot(td, aes(x, y)) +
geom_text(aes(label = text), vjust = "inward", hjust = "inward")

td <- data.frame(trt = c("a", "b", "c"), resp = c(1.2, 3.4, 2.5))
ggplot(td, aes(resp, trt)) +
geom_point() +
geom_text(aes(label = paste0("(", resp, ")")), nudge_y = -0.25) +
xlim(1, 3.6)

ggplot(isr17, aes(x=area, y=population)) +
geom_text(aes(label = c_name)) 

ggplot(isr17, aes(x=area, y=population)) +
geom_text(aes(label = c_name), check_overlap = TRUE)

ggplot(isr17, aes(x=area, y=population)) +
geom_label(aes(label = c_name), check_overlap = TRUE) 
```
]
---
### Annotations
geom_text()
geom_rect()
geom_line(), geom_path(), geom_segment()
geom_vline(), geom_hline(), geom_abline()

`r columns(1, 1/4,1)`

.scroll-output-80[
```{r warning=FALSE,echo=FALSE}
ggplot(economics, aes(date, unemploy)) +
geom_line()

presidential <- subset(presidential, start > economics$date[1])
ggplot(economics) +
geom_rect(aes(xmin = start, xmax = end,ymin = -Inf, ymax = Inf, fill = party), alpha = 0.2,data = presidential) +
geom_vline(aes(xintercept = as.numeric(start)),data = presidential,colour = "grey50", alpha = 0.5) +
geom_text(aes(x = start, y = 2500, label = name),data = presidential,size = 3, vjust = 0, hjust = 0, nudge_x = 50) +
geom_line(aes(date, unemploy)) +
scale_fill_manual(values = c("blue", "red"))

yrng <- range(economics$unemploy)
xrng <- range(economics$date)
caption <- paste(strwrap("Unemployment rates in the US have \n varied a lot over the years", 40), collapse = "\n")
ggplot(economics, aes(date, unemploy)) +
geom_line() +
annotate("text", x = xrng[1], y = yrng[2], label = caption,hjust = 0, vjust = 1, size = 4)
         
ggplot(diamonds, aes(log10(carat), log10(price))) +
geom_bin2d() +
facet_wrap(~cut, nrow = 1)

mod_coef <- coef(lm(log10(price) ~ log10(carat), data = diamonds))
ggplot(diamonds, aes(log10(carat), log10(price))) +
geom_bin2d() +
geom_abline(intercept = mod_coef[1], slope = mod_coef[2],colour = "white", size = 1) +
facet_wrap(~cut, nrow = 1)
```
]
---
### Collective Geoms

- individual geom - draws a distinct graphical object for each observation
- A collective geom - displays multiple observations with one geometric object

group aesthetic
---
`r columns(1, 2 / 3, 0.6)`
.scroll-output-100[
```{r warning=FALSE,echo=FALSE}
data(Oxboys,package = "nlme")
head(Oxboys)

# spaghetti plot

ggplot(Oxboys, aes(age, height, group = Subject)) +
geom_point() +
geom_line()

# interaction() for multiple grouping variables

ggplot(Oxboys, aes(age, height, group = Subject)) +
geom_line() +
geom_smooth(method = "lm", se = FALSE)

ggplot(Oxboys, aes(age, height)) +
geom_line(aes(group = Subject)) +
geom_smooth(method = "lm", size = 2, se = FALSE)

ggplot(Oxboys, aes(Occasion, height)) +
geom_boxplot()

ggplot(Oxboys, aes(Occasion, height)) +
geom_boxplot() +
geom_line(colour = "#3366FF", alpha = 0.5)

ggplot(Oxboys, aes(Occasion, height)) +
geom_boxplot() +
geom_line(aes(group = Subject), colour = "#3366FF", alpha = 0.5)
```
]
---
### Surface Plots

.scroll-output-80[
```{r warning=FALSE,echo=FALSE}
ggplot(faithfuld, aes(eruptions, waiting)) +
geom_contour(aes(z = density, colour = ..level..))

ggplot(faithfuld, aes(eruptions, waiting)) +
geom_raster(aes(fill = density))

small <- faithfuld[seq(1, nrow(faithfuld), by = 10), ]
ggplot(small, aes(eruptions, waiting)) +
geom_point(aes(size = density), alpha = 1/3) +
scale_size_area()
```
]
---
### Revealing Uncertainty

* Discrete x, range: `geom_errorbar()`, `geom_linerange()`
* Discrete x, range & center: `geom_crossbar()`, `geom_pointrange()`
* Continuous x, range: `geom_ribbon()`
* Continuous x, range & center: `geom_smooth(stat = "identity")`
`r columns(3, 1)`
.scroll-output[
```{r warning=FALSE,echo=FALSE}
y <- c(18, 11, 16)
df <- data.frame(x = 1:3, y = y, se = c(1.2, 0.5, 1.0))

base <- ggplot(df, aes(x, y, ymin = y - se, ymax = y + se))
base + geom_crossbar()
base + geom_pointrange()
base + geom_smooth(stat = "identity")

base + geom_errorbar()
base + geom_linerange()
base + geom_ribbon()
```
]
---
### Weighted Data
`r columns(2, 2/3)`
.scroll-output-80[
```{r warning=FALSE,echo=FALSE}
ggplot(isr17, aes(percIncomeSecurity,percMaturityTest)) + 
  geom_point()

# Weight by population
ggplot(isr17, aes(percIncomeSecurity,percMaturityTest)) + 
  geom_point(aes(size = population)) + 
  scale_size_area("Population\n(thusands)")

# Unweighted
ggplot(isr17, aes(percIncomeSecurity,percMaturityTest)) + 
  geom_point() + 
  geom_smooth(method = lm, size = 1)

# Weighted by population
ggplot(isr17, aes(percIncomeSecurity,percMaturityTest)) + 
  geom_point(aes(size = population)) + 
  geom_smooth(aes(weight = population), method = lm, size = 1) +
  scale_size_area(guide = "none")

ggplot(isr17, aes(percIncomeSecurity)) +
  geom_histogram(binwidth = 1) + 
  ylab("settlements")

ggplot(isr17, aes(percIncomeSecurity)) +
  geom_histogram(aes(weight = population), binwidth = 1) +
  ylab("Population (1000s)")
```
]
---
### Exploring Minard's 1812 plot with ggplot2
adapted from https://www.andrewheiss.com
http://www.datavis.ca/gallery/re-minard.php
`r columns(1, 1)`
.scroll-output[
```{r warning=FALSE, fig.width=10}
cities <- read.table("https://raw.githubusercontent.com/andrewheiss/fancy-minard/master/input/minard/cities.txt",header = TRUE, stringsAsFactors = FALSE)
troops <- read.table("https://raw.githubusercontent.com/andrewheiss/fancy-minard/master/input/minard/troops.txt",header = TRUE, stringsAsFactors = FALSE)
temps <- read.table("https://raw.githubusercontent.com/andrewheiss/fancy-minard/master/input/minard/temps.txt",header = TRUE, stringsAsFactors = FALSE) %>%
  mutate(date = dmy(date))  # Convert string to actual date

march.1812.plot.simple <- ggplot() +
  geom_path(data = troops, aes(x = long, y = lat, group = group,
                               color = direction, size = survivors),
            lineend = "round") +
  geom_point(data = cities, aes(x = long, y = lat),
             color = "#DC5B44") +
  geom_text_repel(data = cities, aes(x = long, y = lat, label = city),
                  color = "#DC5B44", family = "Open Sans Condensed Bold") +
  scale_size(range = c(0.5, 10)) +
  scale_colour_manual(values = c("#DFC17E", "#252523")) +
  guides(color = "none", size = "none") +
  theme_nothing()

# Change the x-axis limits to match the simple map
temps.nice <- temps %>%
  mutate(nice.label = paste0(temp, "°, ", month, ". ", day))

temps.1812.plot <- ggplot(data = temps.nice, aes(x = long, y = temp)) +
  geom_line() +
  geom_label(aes(label = nice.label),
            family = "Open Sans Condensed Bold", size = 2.5) +
  labs(x = NULL, y = "° Celsius") +
  scale_x_continuous(limits = ggplot_build(march.1812.plot.simple)$layout$panel_ranges[[1]]$x.range) +
  scale_y_continuous(position = "right") +
  coord_cartesian(ylim = c(-35, 5)) +  # Add some space above/below
  theme_bw(base_family = "Open Sans Condensed Light") +
  theme(panel.grid.major.x = element_blank(),
        panel.grid.minor.x = element_blank(),
        panel.grid.minor.y = element_blank(),
        axis.text.x = element_blank(), axis.ticks = element_blank(),
        panel.border = element_blank())

# Combine the two plots
both.1812.plot.simple <- rbind(ggplotGrob(march.1812.plot.simple),
                               ggplotGrob(temps.1812.plot))

# Adjust panels
panels <- both.1812.plot.simple$layout$t[grep("panel", both.1812.plot.simple$layout$name)]

# Because this plot doesn't use coord_equal, since it's not a map, we can use
# whatever relative numbers we want, like a 3:1 ratio
both.1812.plot.simple$heights[panels] <- unit(c(3, 1), "null")

grid::grid.newpage()
grid::grid.draw(both.1812.plot.simple)

```
]
---
### Diamonds Data
.scroll-output[
```{r warning=T,echo=FALSE}
diamonds

# Displaying Distributions
ggplot(diamonds, aes(depth))+geom_histogram()
ggplot(diamonds, aes(depth))+geom_histogram(binwidth=0.1)+xlim(55,70)
ggplot(diamonds, aes(depth)) +geom_freqpoly(aes(colour = cut), binwidth = 0.1, na.rm = TRUE) + xlim(58, 68) + theme(legend.position = "none")
ggplot(diamonds, aes(depth)) + geom_histogram(aes(fill = cut), binwidth = 0.1, position = "fill", na.rm = TRUE) + xlim(58, 68) + theme(legend.position = "none")
ggplot(diamonds, aes(depth)) + geom_density(na.rm = TRUE) + xlim(58, 68) + theme(legend.position = "none")
ggplot(diamonds, aes(depth, fill = cut, colour = cut)) + geom_density(alpha = 0.2, na.rm = TRUE) + xlim(58, 68) + theme(legend.position = "none")
ggplot(diamonds, aes(clarity, depth)) + geom_boxplot()
ggplot(diamonds, aes(carat, depth)) + geom_boxplot(aes(group = cut_width(carat, 0.1))) + xlim(NA, 2.05)
ggplot(diamonds, aes(clarity, depth)) + geom_violin() 
ggplot(diamonds, aes(carat, depth)) + geom_violin(aes(group = cut_width(carat, 0.1))) + xlim(NA, 2.05)
```
]
---
### Statistical Summaries
.scroll-output[
```{r warning=T,echo=FALSE}
ggplot(diamonds, aes(color)) + geom_bar()
ggplot(diamonds, aes(color, price)) + geom_bar(stat = "summary_bin", fun.y = mean)
ggplot(diamonds, aes(table, depth)) + geom_bin2d(binwidth = 1, na.rm = TRUE) + xlim(50, 70) + ylim(50, 70)
ggplot(diamonds, aes(table, depth, z = price)) + geom_raster(binwidth = 1, stat = "summary_2d", fun = mean, na.rm = TRUE) + xlim(50, 70) + ylim(50, 70)
```
]
---
### Mastering the Grammar

.scroll-output[
```{r warning=T,echo=FALSE}
ggplot(isr17_head,aes(x=area, y=population,colour=district))+ geom_point()
```
]
-positioned according to the value of two variables
each point also has a size, a color and a shape. These attributes are called aesthetics, and are the properties that can be perceived on the graphic. Each aesthetic can be mapped to a variable, or set to a constant value.
---
.scroll-output-100[
```{r warning=T,echo=FALSE}
ggplot(isr17_head, aes(area, population, colour = district)) + geom_line() + theme(legend.position = "none")
ggplot(isr17_head, aes(area, population, colour = district)) + geom_bar(stat = "identity", position = "identity", fill = NA) + theme(legend.position = "none")
ggplot(isr17_head, aes(area, population, colour = factor(district))) + geom_point() + geom_smooth(method = "lm")
```
]
---
### Scaling
- conversion of data units to graphical units (pixels, colors)
- cords
-render all (data, scales, coordinate system, annotations together 
.scroll-output[
```{r warning=T,echo=FALSE}
ggplot(isr17_head, aes(area, population)) + geom_point() + geom_smooth()+facet_wrap(~status)+scale_x_continuous(trans='log10')+scale_y_continuous(trans='log10')
```
]
---
.scroll-output-100[
```{r warning=FALSE,echo=FALSE}
eduPic <- as.data.frame(read_excel("datasets/eduPic.xlsx",sheet = 1,col_names = TRUE))
e2<-as.data.frame(eduPic)
names(eduPic)
str<-"לא רלוונטי"
str2<-"לא מחושב"
eduPic[eduPic==str|eduPic==str2]<-NA
names(eduPic)[3]<-"Cname"
names(eduPic)[4]<-"eshcol"
names(eduPic)[5]<-"district"
names(eduPic)[9]<-"numSchools"
names(eduPic)[16]<-"a2ndDegreePercent"
names(eduPic)[18]<-"adjustmentsPercent"
names(eduPic)[35]<-"rateBagrut"
names(eduPic)[46]<-"a5pntMathPercent"
names(eduPic)[53]<-"classSizeHigh"

eduPic$a2ndDegreePerecnt<-as.numeric(eduPic$a2ndDegreePercent)
eduPic$a5pntMathPercent<-as.numeric(eduPic$a5pntMathPercent)
eduPic$adjustmentsPercent<-as.numeric(eduPic$adjustmentsPercent)
eduPic$rateBagrut<-as.numeric(eduPic$rateBagrut)
eduPic$classSizeHigh<-as.numeric(eduPic$classSizeHigh)

a<-eduPic %>%
  group_by(eshcol) %>%
  summarise(adjRate=mean(adjustmentsPercent,na.rm=T))
b<-eduPic %>%
  group_by(district) %>%
  summarise(adjRate=mean(adjustmentsPercent,na.rm=T))
```
]
---
### Building plot layer by layer
.scroll-output-80[
```{r warning=T,echo=FALSE}
p<-ggplot(isr17,aes(x=area,y=population))
p
p+geom_point()

p + layer(mapping = NULL,data = NULL,geom = "point",stat = "identity",position = "identity")

eduPic_R<-eduPic[!(is.na(eduPic$a5pntMathPercent)),]

model<-loess(a5pntMathPercent~a2ndDegreePerecnt,data=eduPic_R)
grid<-tibble(a2ndDegreePerecnt=seq(min(eduPic_R$a2ndDegreePercent),max(eduPic_R$a2ndDegreePercent),length=50))
grid$a5pntMathPercent<-predict(model,newdata = grid)
std_resid<-resid(model)/model$s
outliner<-filter(eduPic_R,abs(std_resid)>2)

ggplot(mapping=aes(x=a2ndDegreePerecnt,y=a5pntMathPercent))+geom_point(data=eduPic_R)+geom_line(data=grid)+geom_text(data=outliner, aes(label=Cname,vjust = 1.5))

```
]
- Never refer to a variable with $ (e.g., eduPic$cName) in aes()
---
.scroll-output-100[
```{r warning=T,echo=FALSE}
ggplot(eduPic,aes(x=a2ndDegreePerecnt,y=a5pntMathPercent,color=district))+geom_point()
ggplot(eduPic,aes(x=a2ndDegreePerecnt,y=a5pntMathPercent))+geom_point(aes(color=district))
ggplot(eduPic,aes(x=a2ndDegreePerecnt))+geom_point(aes(y=a5pntMathPercent,color=district))
ggplot(eduPic)+geom_point(aes(x=a2ndDegreePerecnt,y=a5pntMathPercent,color=district))       
```
]
---
.scroll-output-80[
```{r echo=FALSE}
ggplot(eduPic,aes(a2ndDegreePerecnt,a5pntMathPercent))+geom_point(aes(color=district))+geom_smooth(method="lm", se=FALSE)+theme(legend.position="none")
ggplot(eduPic,aes(a2ndDegreePerecnt,a5pntMathPercent,color=district))+geom_point()+geom_smooth(method="lm", se=FALSE)+theme(legend.position="none")
```
]
- If you want appearance to be governed by a variable, put the specification inside aes(); if you want override the default size or colour, put the value outside of aes().
---
.scroll-output-100[
```{r warning=T,echo=FALSE}
ggplot(eduPic,aes(a2ndDegreePerecnt,a5pntMathPercent))+geom_point(colour="darkblue")
ggplot(eduPic,aes(a2ndDegreePerecnt,a5pntMathPercent))+geom_point(aes(colour="darkblue"))
ggplot(eduPic,aes(a2ndDegreePerecnt,a5pntMathPercent))+geom_point(aes(colour="darkblue"))+ scale_color_identity()

#naming color to constant
ggplot(eduPic,aes(a2ndDegreePerecnt,a5pntMathPercent))+geom_point()+
  geom_smooth(aes(colour="loess"),method="loess",se=FALSE)+
  geom_smooth(aes(colour="lm"),method="lm",se=FALSE)+
  labs(colour="Method")

```
]
---
### Geoms

https://raw.githubusercontent.com/rstudio/cheatsheets/main/data-visualization.pdf

- Geoms
- Stats (& stat_spoke, stat_function)
---
.scroll-output-80[
```{r echo=FALSE}
ggplot(isr17,aes(x=age,y=population))+geom_point()+stat_summary(geom="point",fun="mean",color="red",size=4)
ggplot(isr17,aes(x=age,y=population))+geom_point()+geom_point(stat="summary",fun="mean",color="red",size=4)
ggplot(isr17, aes(population))+geom_histogram(binwidth=25)
ggplot(isr17, aes(population))+geom_histogram(aes(y=..density..),binwidth=25)
ggplot(isr17, aes(population))+geom_histogram(aes(y=..ncount..),binwidth=25)
```
]
- position
---
### Scale & Guides
```{r echo=FALSE}
ggplot(isr17,aes(x=age,y=population))+geom_point()+scale_x_continuous("My Label")
```

scale_{aesthetic}_{scale type}
scale_type continuous, discrete, brewer

Axis|Legend|Argument name
----|------|-------------
Label|Title|name
Ticks& Grid Line|Key|breaks
Tick label|Key label|labels
---
.scroll-output-100[
```{r warning=T,echo=FALSE}
df <- data.frame(x = 1:2, y = 1, z = "a")
p <- ggplot(df, aes(x, y)) + geom_point(aes(color=z))
p + scale_x_continuous("X axis")
p + xlab("X axis") + ylab("Y axis")
p + labs(x = "X axis", y = "Y axis", colour = "Colour\nlegend")
p + labs(x = "", y = "")
p + labs(x = NULL, y = NULL)

df <- data.frame(x = c(1, 3, 5) * 1000, y = 1)
axs <- ggplot(df, aes(x, y)) +
geom_point() +
labs(x = NULL, y = NULL)
axs
axs + scale_x_continuous(breaks = c(2000, 4000))
axs + scale_x_continuous(breaks = c(2000, 4000), labels = c("2k", "4k"))

leg <- ggplot(df, aes(y, x, fill = x)) +
geom_tile() +
labs(x = NULL, y = NULL)
leg
leg + scale_fill_continuous(breaks = c(2000, 4000))
leg + scale_fill_continuous(breaks = c(2000, 4000), labels = c("2k", "4k"))

df2 <- data.frame(x = 1:3, y = c("a", "b", "c"))
ggplot(df2, aes(x, y)) +
geom_point()
ggplot(df2, aes(x, y)) +
geom_point() +
scale_y_discrete(labels = c(a = "apple", b = "banana", c = "carrot"))

axs + scale_x_continuous(breaks = NULL)
axs + scale_x_continuous(labels = NULL)

leg + scale_fill_continuous(breaks = NULL)
leg + scale_fill_continuous(labels = NULL)

df <- data.frame(x = c(2, 3, 5, 10, 200, 3000), y = 1)
ggplot(df, aes(x, y)) +
geom_point() +
scale_x_log10()

mb <- as.numeric(1:10 %o% 10^(0:4))
ggplot(df, aes(x, y)) +
geom_point() +
scale_x_log10(minor_breaks = log10(mb))
```
]
---
.scroll-output-100[
```{r warning=T,echo=FALSE}
df<-data.frame(x=c(1,2,3),y=c(1,2,3),z=c("1","2","3"))
ggplot(df, aes(y, y)) + geom_point(size = 4, colour="grey20") + geom_point(aes(colour = z),size=2)
ggplot(df, aes(y, y)) + geom_point(size = 4, colour="grey20", show.legend=TRUE) + geom_point(aes(colour = z),size=2)

norm <- data.frame(x = rnorm(1000), y = rnorm(1000))
norm$z <- cut(norm$x, 3, labels = c("a", "b", "c"))
ggplot(norm, aes(x, y)) +
geom_point(aes(colour = z), alpha = 0.1)
ggplot(norm, aes(x, y)) +
geom_point(aes(colour = z), alpha = 0.1) +
guides(colour = guide_legend(override.aes = list(alpha = 1)))

#df<-data.frame(x=c(1,2,3),y=c(1,2,3),z=c(1,2,3))

ggplot(df, aes(x, y)) + geom_point(aes(colour = z))
ggplot(df, aes(x, y)) + geom_point(aes(shape = z))
ggplot(df, aes(x, y)) + geom_point(aes(shape = z, colour = z))

df <- data.frame(x = 1:3, y = 1:3, z = c("a", "b", "c"))
base <- ggplot(df, aes(x, y)) +
geom_point(aes(colour = z), size = 3) +
xlab(NULL) +
ylab(NULL)
base + theme(legend.position = "right") # the default
base + theme(legend.position = "bottom")
base + theme(legend.position = "none")

base <- ggplot(df, aes(x, y)) +
geom_point(aes(colour = z), size = 3)
base + theme(legend.position = c(0, 1), legend.justification = c(0, 1))
base + theme(legend.position = c(0.5, 0.5), legend.justification = c(0.5, 0.5))
base + theme(legend.position = c(1, 0), legend.justification = c(1, 0))

```
]
---
.scroll-output-100[
```{r warning=T,echo=FALSE}
df <- data.frame(x = 1, y = 1:3, z = 1:3)
base <- ggplot(df, aes(x, y)) + geom_raster(aes(fill = z))
base
base + scale_fill_continuous(guide = guide_legend())
base + guides(fill = guide_legend())

df <- data.frame(x = 1, y = 1:4, z = letters[1:4])
p <- ggplot(df, aes(x, y)) + geom_raster(aes(fill = z))
p
p + guides(fill = guide_legend(ncol = 2))
p + guides(fill = guide_legend(ncol = 2, byrow = TRUE))

p <- ggplot(df, aes(1, y)) + geom_bar(stat = "identity", aes(fill = z))
p
p + guides(fill = guide_legend(reverse = TRUE))
```
]

---
.scroll-output-100[
```{r warning=T,echo=FALSE}
df <- data.frame(x = 1:3, y = 1:3)
base <- ggplot(df, aes(x, y)) + geom_point()
base
base + scale_x_continuous(limits = c(1.5, 2.5))

base + scale_x_continuous(limits = c(0, 4))

base + xlim(0, 4)
base + xlim(4, 0)
base + lims(x = c(0, 4))

```
]