203_ggplot.Rmd

## ggplot2

**ggplot2** is one of the visualization tools that the R system has. The others are the Base R plotting functions and the **lattice** package. ggplot2 is the most evolved and complete plotting package. The components of a plot, include:
    - the data being plotted, a data frame, or tibble (*tidy* data frame)
    - the geometric objects (circles, lines, etc.) that appear on the plot
    - a set of mappings from variables in the data to the aesthetics (appearance) of the geometric objects: what column x,y is,the color, the size, etc…
    - a statistical transformation used to calculate the data values used in the plot
    - a position adjustment for locating each geometric object on the plot
    - a scale (e.g., range of values) for each aesthetic mapping used: color_manual, x_continuous, 
    - a coordinate system used to organize the geometric objects
    - the facets or groups of data shown in different plots: wrap, grid
    - layers, where each layer has a single geometric object, statistical transformation, and position adjustment. You can think of each plot as a set of layers of images,
    - theme: theme_bw(), theme_light()
    - The typical call to ggplot()
    
    
    ggplot(data=<data>, aes(x=<x>, y=<y>, color=<z>, size=<w>))+
    geom_<geometry>()+
    scale_<scales>()+
    facet_<facets>()+
    <theme>

There are hundreds of geometries and ways to plot the data. 
In summary, to create a plot we need to:
- call `ggplot` function that creates a blank canvas
- specify *aesthetic mappings* between variables and visual aspects
- add new layers of geometric objects such as geom_point, geom_bar, etc. 

Two examples from datasets available in the R system: mtcars and diamonds 

## Diamonds dataset

### Exploratory data analysis
Visualising distributions
```{r}
library(ggplot2)
library(dplyr) # or library(tidyverse)
data("diamonds")
ggplot(data = diamonds) +
  geom_bar(mapping = aes(x = cut))

# the height of the bars is the number of observations
diamonds %>% 
  dplyr::count(cut)

# For continuous variables we use the histogram
ggplot(data = diamonds) +
  geom_histogram(mapping = aes(x = carat), binwidth = 0.5)

diamonds %>% 
  count(cut_width(carat, 0.5))

```

We may subset the data for plotting a smaller part of the data

```{r smaller}
smaller <- diamonds %>% 
  filter(carat < 3)

# set the width of the intervals in a histogram with the binwidth argument 
ggplot(data = smaller, mapping = aes(x = carat)) +
  geom_histogram(binwidth = 0.1)

# multiple histograms, using the variables carat and cut
ggplot(data = smaller, mapping = aes(x = carat, colour = cut)) +
  geom_freqpoly(binwidth = 0.1)
```
Identifying some specific points, outliers, etc. by changing the size of the x or y axis

```{r}
# reducing the width of the binwith
ggplot(data = smaller, mapping = aes(x = carat)) +
  geom_histogram(binwidth = 0.01)

# all values in the x and y axis
ggplot(diamonds) + 
  geom_histogram(mapping = aes(x = y), binwidth = 0.5)

# zoom to small values in the y-axis
ggplot(diamonds) + 
  geom_histogram(mapping = aes(x = y), binwidth = 0.5) +
  coord_cartesian(ylim = c(0, 50))

# we identify those values 
unusual <- diamonds %>% 
  filter(y < 3 | y > 20) %>% 
  select(price, x, y, z) %>%
  arrange(y)
unusual

```

Removing extreme points and plot the new data

```{r}
diamonds2 <- diamonds %>% 
  mutate(y = ifelse(y < 3 | y > 20, NA, y))

ggplot(data = diamonds2, mapping = aes(x = x, y = y)) + 
  geom_point()

```
### Boxplots

```{r}
ggplot(data = diamonds, mapping = aes(x = cut, y = price)) +
  geom_boxplot()
```

### Two categorical variables

Different visualisations

```{r}
ggplot(data = diamonds) +
  geom_count(mapping = aes(x = cut, y = color))

diamonds %>% 
  count(color, cut)

# different geometry
diamonds %>% 
  count(color, cut) %>%  
  ggplot(mapping = aes(x = color, y = cut)) +
    geom_tile(mapping = aes(fill = n))
```


## Plotting relationships diamonds
Simple plot of carats vs price

```{r plotdiamonds}
data("diamonds") # from ggplot2  ?diamonds
p <- ggplot(data = diamonds, aes(x = carat, y = price))
p + geom_point()

# alpha to add transparency
ggplot(data = diamonds) + 
  geom_point(mapping = aes(x = carat, y = price), alpha = 1 / 100)
```

Plot the `smaller` subset with different geometries
```{r}

ggplot(data = smaller) +
  geom_bin2d(mapping = aes(x = carat, y = price))

# install.packages("hexbin")
ggplot(data = smaller) +
  geom_hex(mapping = aes(x = carat, y = price))
```



Cutting data above or equal to 2 carats, adding color depending on the variable cut and adding some transparency to the points (alpha)

```{r}
#carat < 2
data("diamonds")
p <- diamonds %>% filter(carat<2) %>% 
        ggplot(aes(x = carat, y = price, color = cut))
p + geom_point(alpha=0.5)

```
Adding some smooth splines

```{r}
p + geom_point(alpha=0.5) + geom_smooth()
```
Coloring points in a different way to understand the possible relationship
```{r}
p <- diamonds %>% filter(carat<2) %>% 
        ggplot(aes(x = carat, y = price, color = clarity))
p + geom_point(alpha=0.5) + geom_smooth()
```

Since we have several variables that may affect the price we may plot different graphs using `facet_wrap(~cut)`
```{r}
colors <- rainbow(length(unique(diamonds$clarity)))
p <- ggplot(diamonds, aes(x = price, y = carat)) + 
        geom_point(aes(color = clarity), alpha = 0.5, size = 1) + 
        geom_smooth(color = "black") + 
        scale_colour_manual(values = colors, name = "Clarity") +
        facet_wrap(~cut) 
p
```

Or we may change the size of the point

```{r}
p <- ggplot(diamonds, aes(x = price, y = carat, size = cut)) + 
        geom_point(aes(color = clarity), alpha = 0.5) + 
        scale_colour_manual(values = colors, name = "Clarity")
p
```
Sampling the data to unclutter the plot

```{r}
p <- ggplot(diamonds[sample(nrow(diamonds), size=500),], 
            aes(x = carat, y = price, size = cut)) + 
        geom_point(aes(color = clarity), alpha = 0.5) + 
        scale_colour_manual(values = colors, name = "Clarity")
p
```

### Using Themes

theme_grey(), theme_classic(), theme_dark(), theme_minimal()

```{r}
my_theme <- theme_bw()+
            theme(text = element_text(size = 18, family = "Times", face = "bold"),
                  axis.ticks = element_line(size = 1),
                  legend.text = element_text(size = 14, family = "Times"),
                  panel.border = element_rect(size = 2),
                  panel.grid.major = element_blank(), 
                  panel.grid.minor = element_blank()
                  )
p + my_theme
```

## Interactivity with plotly

```{r}
library(plotly)
p <- ggplot(diamonds[sample(nrow(diamonds), size = 100),], 
        aes(x = carat, y = price)) + 
        geom_point(aes(color = clarity), alpha = 0.5, size = 2) + 
        my_theme
ggplotly(p, dynamicTicks = TRUE)
```

Chapter 28 from R for Data Science
```{r plotmtcars}
library(ggplot2)
data("mtcars") # from Base R  ?mtcars

hist(mtcars$mpg)

# create canvas
ggplot(mpg)

# variables of interest mapped
ggplot(mpg, aes(x = displ, y = hwy))

# data plotted
ggplot(mpg, aes(x = displ, y = hwy)) +
  geom_point()
```

### Labels, subtitles, captions

```{r}
ggplot(mpg, aes(displ, hwy)) +
  geom_point(aes(color = class)) +
  geom_smooth(se = FALSE) +
  labs(title = "Fuel efficiency generally decreases with engine size")


ggplot(mpg, aes(displ, hwy)) +
  geom_point(aes(color = class)) +
  geom_smooth(se = FALSE) +
  labs(
    title = "Fuel efficiency generally decreases with engine size",
    subtitle = "Two seaters (sports cars) are an exception because of their light weight",
    caption = "Data from fueleconomy.gov"
  )


ggplot(mpg, aes(displ, hwy)) +
  geom_point(aes(colour = class)) +
  geom_smooth(se = FALSE) +
  labs(
    x = "Engine displacement (L)",
    y = "Highway fuel economy (mpg)",
    colour = "Car type"
  )

df <- tibble(
  x = runif(10),
  y = runif(10)
)
ggplot(df, aes(x, y)) +
  geom_point() +
  labs(
    x = quote(sum(x[i] ^ 2, i == 1, n)),
    y = quote(alpha + beta + frac(delta, theta))
  )


```

### Annotations

We may label individual observations or groups of observations with `geom_text()`, `geom_label` and using some transformation with the package `ggrepel`

```{r}
best_in_class <- mpg %>%
  group_by(class) %>%
  filter(row_number(desc(hwy)) == 1)

ggplot(mpg, aes(displ, hwy)) +
  geom_point(aes(colour = class)) +
  geom_text(aes(label = model), data = best_in_class)


ggplot(mpg, aes(displ, hwy)) +
  geom_point(aes(colour = class)) +
  geom_label(aes(label = model), data = best_in_class, nudge_y = 2, alpha = 0.5)


ggplot(mpg, aes(displ, hwy)) +
  geom_point(aes(colour = class)) +
  geom_point(size = 3, shape = 1, data = best_in_class) +
  ggrepel::geom_label_repel(aes(label = model), data = best_in_class)
```

## References

-   [The book ggplot2](https://ggplot2-book.org/index.html)
-   Video Introduction to ggplot in R [Youtube video](https://www.youtube.com/watch?v=1GmQ5BdAhG4)
-   [Ggplot2 gallery](https://www.r-graph-gallery.com/ggplot2-package.html)
-   [Chapter Data Visualization, R for Data Science](https://r4ds.had.co.nz/data-visualisation.html)
-   [Chapter Graphics for Communication, R for Data Science](https://r4ds.had.co.nz/graphics-for-communication.html)
-   [An introduction to ggplot2](https://uc-r.github.io/ggplot_intro)
- [Top 50 ggplot visualizations](http://r-statistics.co/Top50-Ggplot2-Visualizations-MasterList-R-Code.html)