Data Visualization

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# Data Visualization
## Making your first plot
### Andrew Irwin, <a href="mailto:a.irwin@dal.ca" class="email">a.irwin@dal.ca</a>
### Math & Stats, Dalhousie University
### 2021-01-13 (updated: 2021-01-01)

---

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# Use the ggplot2 library

Do this once (if you haven't done it already):

```
install.packages("tidyverse")
```

Add this line to every R markdown document:

```r
library(tidyverse)
```

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# Get some data

```r
str(mtcars)
```

```
## 'data.frame':	32 obs. of  11 variables:
##  $ mpg : num  21 21 22.8 21.4 18.7 18.1 14.3 24.4 22.8 19.2 ...
##  $ cyl : num  6 6 4 6 8 6 8 4 4 6 ...
##  $ disp: num  160 160 108 258 360 ...
##  $ hp  : num  110 110 93 110 175 105 245 62 95 123 ...
##  $ drat: num  3.9 3.9 3.85 3.08 3.15 2.76 3.21 3.69 3.92 3.92 ...
##  $ wt  : num  2.62 2.88 2.32 3.21 3.44 ...
##  $ qsec: num  16.5 17 18.6 19.4 17 ...
##  $ vs  : num  0 0 1 1 0 1 0 1 1 1 ...
##  $ am  : num  1 1 1 0 0 0 0 0 0 0 ...
##  $ gear: num  4 4 4 3 3 3 3 4 4 4 ...
##  $ carb: num  4 4 1 1 2 1 4 2 2 4 ...
```

---

# First plot

```r
mtcars %>%
  ggplot(aes(x = wt,
             y = mpg)) +
  geom_point()
```
--
.pull-left[
The "pipe" symbol (`%>%`) is function composition. `f(g(x))` can be written `x %>% g %>% f`.

`aes` is a function to define aesthetic associations between features of your plot and variables in the dataset.

Parts of a ggplot are added togther with `+`

The kind of plot is called its "geometry". A scatterplot is `geom_point`.
]

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<img src="05-making-your-first-plot_files/figure-html/fig0-1.png" width="100%" style="display: block; margin: auto;" />
]

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# What if you forget ... ?

```r
mtcars %>%
  ggplot(aes(x = wt,
             y = mpg))
```

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Try "forgetting" other parts of the code to see what goes wrong.
]
.pull-right[
<img src="05-making-your-first-plot_files/figure-html/fig1-1.png" width="672" height="80%" style="display: block; margin: auto;" />
]

---
# Add some colour

```r
mtcars %>%
  ggplot(aes(x = wt,
             y = mpg,
*            color = factor(cyl))) +
  geom_point()
```

--
.pull-left[
`cyl` is a number, so I must turn it into a categorical variable (factor) to get a discrete colour scale.

Try using `cyl` instead of `factor(cyl)`.
]
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<img src="05-making-your-first-plot_files/figure-html/fig2-1.png" width="768" height="80%" style="display: block; margin: auto;" />
]

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# Make the text larger

```r
mtcars %>%
  ggplot(aes(x = wt,
             y = mpg,
             color = factor(cyl))) + 
  geom_point() +
* theme(text = element_text(size = 28))
```

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# Make the text larger

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# Make the symbols larger

```r
mtcars %>%
  ggplot(aes(x = wt,
             y = mpg,
             color = factor(cyl))) + 
* geom_point(size=3) +
  theme(text = element_text(size = 28)) 
```

I'm setting the size of all the symbols, not connecting a variable to the size aesthetic. So don't use `aes`.

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# Make the symbols larger

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# Customize the labels

```r
mtcars %>%
  ggplot(aes(x = wt,
             y = mpg,
             color = factor(cyl))) + 
  geom_point(size=3) +
  theme(text = element_text(size = 28)) +
* labs(x = "Car mass (x 1000 pounds)",
*      y = "Fuel consumption (mpg)",
*      color = "Number of\nCylinders",
*      title = "Car road test results",
*      caption = "from Motor Trend magazine")
```

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# Customize the labels

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# Summary

* Start with data

* Pipe (`%>%`) into `ggplot`

* Define the aesthetics: `aes(x = ..., y = ..., color = ..., shape = ...)`

* Define the geometry: `geom_point` shown here, but there are lots more

* Customize text

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# Suggested reading

* Course notes: Making your first plot

* Healy. Section 2.6. Make your first figure

* R4DS. Chapter 3: Data visualization

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# Task and Assignment

* Try these plotting commands on your own

* Assigment 1: Your first plotting exercises

* Do Task 4 following Lesson 6 on git and github first

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# Datasets to experiment with

* mtcars, iris and many other well-known data in datasets package
* penguins in palmerpenguins package
* gapminder in gapminder package  (but see website too [Gapminder](https://gapminder.org))
* diamonds in ggplot2 package
* nycflights13 in dbplyr package

Use `str(mtcars)` (or other dataset) and `View(mtcars)` to look at the data.

Use ?mtcars to get documentation for the dataset, or use the search in the "Help" panel.