ZZ-Templates.Rmd

---
title: "ggplot2 Templates"
author: "Angela Zoss"
date: "9/25/2017"
output: github_document
---

```{r}
knitr::opts_chunk$set(error = TRUE)
```

## Setup your environment

```{r}

# Load required libraries

library(tidyverse)

```

## Load your data

```{r}

# data comes from https://www.kaggle.com/uciml/adult-census-income

# adding a few settings to correct the data types of a couple of the columns

adult <- read_csv("data/adult.csv", na="?")

```

## Try a few charts

### Bar charts

```{r}

# Bar chart, automatically counting number of observations

ggplot(adult) +
  geom_bar(aes(sex))

```

```{r}

# Bar chart, using another column for the length

ggplot(adult) +
  geom_col(aes(x=sex, y=capital.loss))

# or you can use geom_bar and just change the default statistical function ("stat"), 
# which is normally "count"

ggplot(adult) +
  geom_bar(aes(sex, capital.loss), stat="identity")

# What does it do when you have multiple records for each category?

adult %>% group_by(sex) %>% summarise(total = sum(capital.loss))

```


```{r}

# Bar chart, using another column for length and specifying the summary function

ggplot(adult) +
  geom_bar(aes(sex, capital.loss), stat="summary", fun.y="mean")

# In this case, geom_col doesn't work

ggplot(adult) +
  geom_col(aes(sex, capital.loss), stat="summary", fun.y="mean")

```

```{r}

# You can also summarize numerical variables with a bar chart, but consider using histogram or density instead

ggplot(adult) +
  geom_bar(aes(age))

ggplot(adult) +
  geom_histogram(aes(age))

ggplot(adult) +
  geom_histogram(aes(age), binwidth = 1)

ggplot(adult) +
  geom_density(aes(age))

```
```{r}

# Adding another category - do you want to stack or dodge or fill?

# By default, a new category fills the bar (position="stack")
ggplot(adult) +
  geom_bar(aes(sex, fill=race))

# Change position to "dodge" for side-by-side bars
ggplot(adult) +
  geom_bar(aes(sex, fill=race), position="dodge")

# Change position to "fill" for bars scaled up to 100%
ggplot(adult) +
  geom_bar(aes(sex, fill=race), position="fill")

```

```{r}

# Pie charts are...  weird.  In ggplot2, you make a pie chart by taking a single stacked bar and changing the coordinate system to coord_polar.

ggplot(adult) +
  geom_bar(aes(x="", fill=sex))

ggplot(adult) +
  geom_bar(aes(x="", fill=sex)) +
  coord_polar("y")

# Can get rid of hole in the middle by specifying width = 1

ggplot(adult) +
  geom_bar(aes(x="", fill=sex), width=1) +
  coord_polar("y")



```

### Scatter plots

```{r}

# Scatter plots can show relationships between numerical variables, but be careful of overplotting (dots stacked on top of each other)

ggplot(adult) +
  geom_point(aes(age, capital.loss))

ggplot(adult) +
  geom_bin2d(aes(age, capital.loss))

# Note: if your counts are heavily skewed, you can apply a transformation on the color scale

ggplot(adult) +
  geom_bin2d(aes(age, capital.loss)) +
  scale_fill_continuous(trans="log10")

```

### Line charts

```{r}

# Line charts don't include any inherent summary, so individual data points get mapped and connected with a line

ggplot(adult) + 
  geom_line(aes(x=age, y=capital.loss))

ggplot(adult) + 
  geom_line(aes(x=age, y=capital.loss)) +
  geom_point(aes(x=age, y=capital.loss))

# You can add your own function to summarize all of the y values at the same x value

ggplot(adult) + 
  geom_line(aes(x=age, y=capital.loss), stat="summary", fun.y=mean)

# Alternately, you can use geom_smooth to calculate a variety of summary lines

ggplot(adult) + 
  geom_smooth(aes(x=age, y=capital.loss))

ggplot(adult) + 
  geom_smooth(aes(x=age, y=capital.loss), method = "lm")

```

## Design the charts

### Titles

```{r}

# Adding main title and axis labels

ggplot(adult) +
  geom_bar(aes(sex)) +
  labs(title="This sample has about twice as many men as women.",
       x="Sex",
       y="Number of Respondents")

```

```{r}

# Changing the legend title is a little harder; 
# you have to modify the "scale" properties for the non-axis variable

ggplot(adult) +
  geom_bar(aes(sex, fill=race)) +
  labs(title="This sample has about twice as many men as women.",
       x="Sex",
       y="Number of Respondents") +
  scale_fill_discrete(name="Race/Ethnicity")


# The structure is: "scale_" plus whatever aesthetic property you're modifying (e.g., x, fill, size) 
# plus either a.) the kind of variable it is (e.g., continuous, discrete) or 
# b.) a special function that will be applied (e.g., log10, gradient)

# For example, you can't modify the name of the fill aesthetic if you treat it like a continuous variable

ggplot(adult) +
  geom_bar(aes(sex, fill=race)) +
  labs(title="This sample has about twice as many men as women.",
       x="Sex",
       y="Number of Respondents") +
  scale_fill_continuous(name="Race/Ethnicity")


```

### Axes

```{r}

# ggplot2 does a pretty good job guessing what the axes should look like, but you can modify
# individual properties manually

# Changing axis properties requires adding a "scale" layer for one or both axes, 
# just like modifying the legend properties

ggplot(adult) +
  geom_point(aes(age, capital.gain)) +
  scale_x_continuous(breaks=c(20,30,40,50,60,70,80,90))

# Gridlines often show up for both major and minor breaks.  To turn off gridlines for minor
# breaks, an easy way is to set the minor breaks to NULL.

ggplot(adult) +
  geom_point(aes(age, capital.gain)) +
  scale_x_continuous(breaks=c(20,30,40,50,60,70,80,90), minor_breaks = NULL)

# Can also change how the numbers on the axis are spaces out, without doing any mathematical
# transformations to the data

ggplot(adult) +
  geom_point(aes(age, capital.gain)) +
  scale_x_continuous(breaks=c(20,30,40,50,60,70,80,90), minor_breaks = NULL) +
  scale_y_log10()

# Note: log scale spreads out small values, but you can't plot the value "0" on a log scale,
# so we can filter those out first, then do some formatting on the breaks and labels

ggplot(adult %>% filter(capital.gain > 0)) +
  geom_point(aes(age, capital.gain)) +
  scale_x_continuous(breaks=c(20,30,40,50,60,70,80,90), minor_breaks = NULL) +
  scale_y_log10(breaks=c(10,100,1000,10000,100000), 
                labels=function(x){format(x, scientific = FALSE, big.mark=",")})

```

### Coordinate systems

```{r}

# Coordinate layers help control the output of the chart

# coord_fixed helps normalize the units across the two axes; 
# ratio = 1 means that each unit on the x axis is the same length as each unit on the y axis
# the syntax for ratio is y/x

ggplot(adult) +
  geom_point(aes(age, capital.gain)) +
  coord_fixed(ratio = 1)

# a 1:1 ratio for this chart is terrible; the y-axis goes from 0 to 100,000, while the
# x-axis goes from 17 to 90. To have the units look similar, each unit on the x-axis
# should be about 1,000 of the y-axis units.

ggplot(adult) +
  geom_point(aes(age, capital.gain)) +
  coord_fixed(ratio = 1/1000)

# Now the grid on the chart is approximately square

```

```{r}

# Another useful coord layer is coord_flip.  Some charts require certain variables in
# certain slots.  For example, geom_bar requires the categorical variable in the x
# position and the numerical variable in the y position

ggplot(adult) +
  geom_bar(aes(x=sex))

ggplot(adult) +
  geom_bar(aes(y=sex))


# To get the categories on the y-axis, you start with the category on the x-axis and then
# add the coord_flip

ggplot(adult) +
  geom_bar(aes(x=sex)) +
  coord_flip()

```

```{r}

# Scales can change the spacing of numbers on the axis - essentially, changing the grid
# against which the numbers are plotted

ggplot(adult %>% filter(capital.gain > 0)) +
  geom_point(aes(age, capital.gain)) +
  scale_y_log10(breaks=c(10,100,1000,10000,100000), 
                labels=function(x){format(x, scientific = FALSE, big.mark=",")})

# The coord_trans can also change the grid, but it still uses a cartesian approach to the
# major breaks

ggplot(adult %>% filter(capital.gain > 0)) +
  geom_point(aes(age, capital.gain)) +
  coord_trans(y="log10")


```

### Axis/legend labels

```{r}

# As we've already seen, the labels on a numerical axis can be changed by manually setting
# the breaks in the axis and/or formatting the labels

ggplot(adult) +
  geom_point(aes(age, capital.gain))

ggplot(adult) +
  geom_point(aes(age, capital.gain)) +
  scale_x_continuous(breaks=c(20,30,40,50,60,70,80,90))

# regardless of the type of axis, you can set the labels to whatever you want
ggplot(adult) +
  geom_point(aes(age, capital.gain)) +
  scale_x_continuous(breaks=c(20,30,40,50,60,70,80,90),
                     labels=c("A","B","C","D","E","F","G","H"))

# the same works for a categorical variable, either on an axis or in a legend

ggplot(adult) +
  geom_bar(aes(sex, fill=race)) +
  scale_x_discrete(labels=c("A","B")) +
  scale_fill_discrete(labels=c("A","B","C","D","E"))

```

```{r}

# The one thing you can't do with scales is change the order of the categories.  For that,
# you have to turn the variable into a factor and specify the order in the "levels" setting
# of the factor() function

adult$sex <- factor(adult$sex, levels = c("Male","Female"))

adult$race <- factor(adult$race, levels = c("Other","Amer-Indian-Eskimo","Asian-Pac-Islander","Black","White"))

ggplot(adult) +
  geom_bar(aes(sex, fill=race))

```

### Data labels

```{r}

# You can label charts with variables from the dataset or other calculated variables within the chart;
# just like other chart layers, geom_text layers need x and y positions, and they also require "label"

ggplot(adult) +
  geom_bar(aes(sex)) +
  geom_text(aes(sex, label=sex), stat="count")

# Note: geom_bar has stat="count" embedded within it.  If we don't add that to the geom_text
# layer, the text layer will try to process each data point individually and will ask
# for a y value

# Instead of using the exact y position calculated by the stat_count function, you can
# nudge the label up or down.  Remember, this "nudge" value needs to be in the same units
# as the axis.

ggplot(adult) +
  geom_bar(aes(sex)) +
  geom_text(aes(sex, label=..count..), stat="count", nudge_y = 1000)

# geom_label is like geom_text, but it formats the label differently
ggplot(adult) +
  geom_bar(aes(sex)) +
  geom_label(aes(sex, label=..count..), stat="count", nudge_y = 1000)

```

### Themes

```{r}

# Themes control the overall look and feel of the graph.  Several themes are built in to ggplot2.

ggplot(adult) +
  geom_bar(aes(sex)) +
  theme_gray() +
  ggtitle("Gray")

ggplot(adult) +
  geom_bar(aes(sex)) +
  theme_classic() +
  ggtitle("Classic")

ggplot(adult) +
  geom_bar(aes(sex)) +
  theme_bw() +
  ggtitle("Black and white")

ggplot(adult) +
  geom_bar(aes(sex)) +
  theme_dark() +
  ggtitle("Dark")

ggplot(adult) +
  geom_bar(aes(sex)) +
  theme_light() +
  ggtitle("Light")

ggplot(adult) +
  geom_bar(aes(sex)) +
  theme_minimal() +
  ggtitle("Minimal")

ggplot(adult) +
  geom_bar(aes(sex)) +
  theme_void() +
  ggtitle("Void")

```

```{r}

# If the preset themes are insufficient, individual properties can be redefined with theme()

ggplot(adult) +
  geom_bar(aes(sex)) +
  theme_bw() +
  theme(panel.background = element_rect(fill="pink"))

# you can also use themes to remove grid lines without removing tick marks

ggplot(adult) +
  geom_bar(aes(sex)) +
  theme(panel.grid.major.y = element_blank())

```

### Annotation

```{r}

# Earlier, we used geom_text for data labels.  We can also use it for general notes on the graph.

ggplot(adult) +
  geom_point(aes(age, capital.gain)) +
  geom_text(x=25,y=75000,label="A note goes here.")


# This is suboptimal, though, because a text object that doesn't relate to the data will process
# slowly.  In this case, the chart is actually drawing a separate text object for every data point
# in the dataframe.

# Instead, use annotate for unrelated text objects
# Note: you have to specify a "geom" for each annotation - text, rect, segment, pointrange

ggplot(adult) +
  geom_point(aes(age, capital.gain)) +
  annotate("text",x=25,y=75000,label="A note goes here.")

ggplot(adult) +
  geom_point(aes(age, capital.gain)) +
  annotate("rect",xmin=12, xmax=95, ymin=25000, ymax=50000, alpha=.2)

```

### Colors

```{r}

# Most charts use x and y axes to display variables, but you can usually also add another variable to
# the chart by mapping it to color. 

# Note - some geoms use the "color" aesthetic (e.g., geom_point), while others use "fill" (e.g., geom_bar)

ggplot(adult) +
  geom_point(aes(age, capital.gain, fill=race))

ggplot(adult) +
  geom_point(aes(age, capital.gain, color=race))

ggplot(adult) +
  geom_bar(aes(sex, fill=race))

ggplot(adult) +
  geom_bar(aes(sex, color=race))

```


```{r}
# You can change the colors of the chart elements using pre-defined palettes or by manually
# selecting colors

# You can apply pre-defined palettes to a fill or color aesthetic using a scale.
# A useful pre-defined palette is the grey palette, which uses shades of grey to keep a chart
# black and white

ggplot(adult) +
  geom_bar(aes(sex, fill=race)) +
  scale_fill_grey()

# The grey palette picks shades of grey between the start and end value, which by default are 0.2 and 0.8.
# The values can be reversed or made larger or smaller.

ggplot(adult) +
  geom_bar(aes(sex, fill=race)) +
  scale_fill_grey(start=0.8, end=0.2)

# Other default palettes include palettes from colorbrewer2.org

ggplot(adult) +
  geom_bar(aes(sex, fill=race)) +
  scale_fill_brewer(palette="Dark2")

ggplot(adult) +
  geom_bar(aes(sex, fill=race)) +
  scale_fill_brewer(palette="Dark2", direction=-1)

```

```{r}

# Specifying colors manually is as simple as giving a vector of colors to a "manual" scale

ggplot(adult) +
  geom_bar(aes(sex, fill=race)) +
  scale_fill_manual(values=c("cadetblue3","lightpink2","lightskyblue3","lightsalmon2","olivedrab3"))

# R knows names for some colors, but others will need to be specified with codes

ggplot(adult) +
  geom_bar(aes(sex, fill=race)) +
  scale_fill_manual(values=c("#ffffd4","#fed98e","#fe9929","#d95f0e","#993404"))

# For continuous numbers, you can also use scale_color_gradient or scale_fill_gradient

ggplot(adult) +
  geom_point(aes(age, capital.gain, color=age))

ggplot(adult) +
  geom_point(aes(age, capital.gain, color=age)) +
  scale_color_gradient(low="gray80",high="gray20")

# Color Brewer palettes can be used with continuous numbers, but you use "distiller"
# instead of "brewer"

ggplot(adult) +
  geom_point(aes(age, capital.gain, color=age)) +
  scale_color_distiller(palette="Greens")

```

### Facets

```{r}

# Facets are some of the most useful layers when you have a large amount of data. Facets create
# "small multiples", or a series of charts that each has the same specification but that visualizes
# a different subset of the data.  You can think of it as splitting the data into chunks and then
# visualizing each chunk the same way.

# facet_wrap creates a series of charts split by one category and then wraps the charts into
# multiple rows as needed

ggplot(adult) +
  geom_point(aes(age, hours.per.week)) +
  geom_smooth(aes(age, hours.per.week)) +
  facet_wrap(~education)

# facet_grid allows you to split the data by one category in the y direction and another in the x
# syntax: facet_grid(y~x)
# Note: you can use a "." to ignore one direction

ggplot(adult) +
  geom_point(aes(age, hours.per.week)) +
  geom_smooth(aes(age, hours.per.week)) +
  facet_grid(.~race)

ggplot(adult) +
  geom_point(aes(age, hours.per.week)) +
  geom_smooth(aes(age, hours.per.week)) +
  facet_grid(sex~race)

```