07-Data_Visualisation.Rmd

# Data Visualisation

```{r titanic01, include=FALSE}
# Silently load in the data so the rest of the lesson works
titanic_clean <- na.omit(read.csv("https://goo.gl/4Gqsnz", header=TRUE))
```

## Creating publication quality graphics

Once you've completed your data analysis you're going to need to summarise it in some really nice figures for publication and/or presentations. This is where `R` can really shine in comparison to something like Excel.

While you can create plots through various, including base `R`, the most popular method of producing fancy figures is with the `ggplot2` package. First things first, if you haven't done so yet, we need to install the `ggplot2` package:

```{r titanic02, eval=FALSE}
install.packages("ggplot2")
```

And load the package:

```{r titanic03}
library(ggplot2)
```

## Grammar of Graphics

`ggplot2` is built on the grammar of graphics concept: the idea that any plot can be expressed from the same set of components:

+  A **data** set
+  A **coordinate system**
+  A set of **geoms** - the visual representation of data points

The key to understanding `ggplot2` is thinking about a figure in layers. 

To start with we need to create the base layer for our plot. This is done with the `ggplot()` function, and it is here we define are **data** and **coordinate system** components. We set our **data** compnent using the `data` argument, and then use the aesthetic function `aes()` as a second argument to define our **coordinate system**. The `aes()` function can potentially be used multiple times when constructing our figure, so it is important to know that anything defined inside the base `ggplot()` function is a *global options* inherited by all subsequent layers. 

Time for an example:

```{r titanic04}
ggplot(data = titanic_clean,
       aes(x = Age,
           y = Fare))
```

Here we have said that `titanic_clean` is our **data** component, and to use `Age` on the x-axis and `Fare` on the y-axis for our **coordinate system**.

Now lets build onto this base layer by adding **geoms** to represent our data! `ggplot2` employs a really nice syntax where you can add subsequent layers using the `+` operator. This lets `R` know that the plot isn't finished yet and to add the new layer on top. In this case, we want to represent our data as a scatterplot so we use the `geom_point()` function.

```{r titanic05}
ggplot(data = titanic_clean,
       aes(x = Age,
           y = Fare)) +
  geom_point()
```

This plot isn't really telling us much! There are potentially confounding variables in play. What if we want to see the effect of another variable on the `Fare` ~ `Age` relationship? We can tell `R` to colour our points based on a third variable by setting the `col` argument in our global `aes()` call. Lets see what effect passenger class has:

```{r titanic06, warning = FALSE}
ggplot(data = titanic_clean,
       aes(x = Age,
           y = Fare,
           col = as.factor(Pclass))) +
  geom_point()
```

We have used the `as.factor()` function on `Pclass` to ensure it is considered a discrete variable and not continuous for the colour scheme.

Now we can start to tease apart some trends from the data.

## Layers

Instead of a scatterplot We could also make a line plot. This time we will use `geom_line()` instead of `geom_point()`.

```{r titanic07, warning = FALSE}
ggplot(data = titanic_clean,
       aes(x = Age,
           y = Fare,
           col = as.factor(Pclass))) +
  geom_line()
```

But what if we want to visualise both lines and points on the plot? We can simply add another layer to the plot:

```{r titanic08, warning = FALSE}
ggplot(data = titanic_clean,
       aes(x = Age,
           y = Fare,
           col = as.factor(Pclass))) +
  geom_point() +
  geom_line()
```

It's important to note that each layer is drawn on top of the previous layer. In this example, the lines have been drawn *on top of* the points. Here's a demonstration:

```{r titanic09, warning = FALSE}
ggplot(data = titanic_clean,
       aes(x = Age,
           y = Fare,
           col = as.factor(Pclass))) +
  geom_point(col="black") +
  geom_line()
```

```{r Challenge_17}
####################
### Challenge 17 ###
####################

# Switch the order of the point and line layers from the previous example. What happened?
```

## Transformations and statistics

`ggplot2` also makes it easy to overlay statistical models over the data. To demonstrate we'll go back to our first example:

```{r titanic10, warning = FALSE}
ggplot(data = titanic_clean,
       aes(x = Age,
           y = Fare,
           col = as.factor(Pclass))) +
  geom_point()
```

We can fit a simple relationship to the data by adding another layer using `geom_smooth()`. We need to specify the `method` for modelling the relationship so lets use `lm` for a linear model.

```{r titanic11, warning = FALSE}
ggplot(data = titanic_clean,
       aes(x = Age,
           y = Fare,
           col = as.factor(Pclass))) +
  geom_point() +
  geom_smooth(method = "lm")
```

Now we have plotted some linear models and their confidence intervals to show the relationships between `Fare` and `Age` for each `Pclass`, but it is hard to tell second and third class apart. To better view these relationships we can transform the y-axis to make it clearer. `ggplot2` has a variety of scaling functions that can be implemented, but in this case we want to log scale the y axis:

```{r titanic12, warning = FALSE}
ggplot(data = titanic_clean,
       aes(x = Age,
           y = Fare,
           col = as.factor(Pclass))) +
  geom_point() +
  geom_smooth(method = "lm") +
  scale_y_log10()
```

Now we're at the point where we have a layer of the graph whose aesthetic characteristics are partially independent of the other layers: `geom_smooth()`. It has inherited the *global options* set by `aes()` in the `ggplot()` layer, but we can also change *local options* that only apply to the aesthetics of this layer. Depending on the layer in question these *local options* may be controlled with another `aes()` function and/or just with arguments to the function. 

```{r Challenge_18}
####################
### Challenge 18 ###
####################

# Using the following base code:

# ggplot(data = titanic_clean,
#        aes(x = Age,
#            y = Fare,
#            col = as.factor(Pclass))) +
#   geom_point() +
#   geom_smooth(method = "lm") +
#   scale_y_log10()

# Experiment with the additional options you can use to customise the `geom_oint()` and `geom_smooth()` layer. There is no set challenge, just experiment with different options. Consider changing the point/line size, transparency, linetype, etc.
```

## Multi-panel figures

So far we have visualised the relationships between variables in a single plot. Alternatively, we can split out different groups in the data into multiple panels by adding a layer of **facet** panels. The `facet_grid()` and `facet_wrap()` layers do this wit slightly different amounts of control, but for now we can stick with `facet_grid()`. We need to specify which variable we want to split the plots by by supplying it as a formula like `~variable_name`.

This time lets look at the relationship `Sex` and `Survived` as a bar plot using `geom_bar()` (the `position = dodge` argument means have them side by side not stacked). First as a single plot:

```{r titanic13, warning = FALSE}
ggplot(data = titanic_clean,
       aes(x = Sex,
           fill = as.factor(Survived))) +
  geom_bar(position = "dodge")
```

And now facetted by passenger class:

```{r titanic14, warning = FALSE}
ggplot(data = titanic_clean,
       aes(x = Sex,
           fill = as.factor(Survived))) +
  geom_bar(position = "dodge") +
  facet_grid(~ Pclass)
```

## Further Customisation

Once the broad layout of your figure is settled on you can start to customise the more minor details of the figure to get it ready for publication. This includes things like changing labels, adding titles, fixing up the legend, adding themes and so on.

First, lets take a look at themes. Themes are a quick and easy way to make some big stylistic changes to a figure, and they just require adding on of `ggplot2`'s `theme_*()` layers.

```{r titanic15, warning = FALSE}
ggplot(data = titanic_clean,
       aes(x = Sex,
           fill = as.factor(Survived))) +
  geom_bar(position = "dodge") +
  facet_grid(~ Pclass) +
  theme_bw()
```

Now lets add some more information (like units!) to our axis labels as well as a title. This involves a mix of the `xlab()`, `ylab()`, and `ggtitle()` functions.

```{r titanic16, warning = FALSE}
ggplot(data = titanic_clean,
       aes(x = Sex,
           fill = as.factor(Survived))) +
  geom_bar(position = "dodge") +
  facet_grid(~ Pclass) +
  theme_bw() +
  xlab("Passenger age (in years)") +
  ylab("Number of passengers survived") +
  ggtitle("Titanic passenger survival rates")
```

Now lets clean up our facets so that they read "First class" etc instead of just the numeric data they are stored as. This requires the `labeller` argument and `labeller()` function inside our `facet_grid()` call:

To clean this figure up for a publication we need to change some of the text
elements. 

We can do this by adding a couple of different layers. The **theme** layer
controls the axis text, and overall text size, and there are special layers
for changing the axis labels. To change the legend title, we need to use the
**scales** layer.

```{r titanic17, warning = FALSE}
ggplot(data = titanic_clean,
       aes(x = Sex,
           fill = as.factor(Survived))) +
  geom_bar(position = "dodge") +
  facet_grid(~ Pclass,
             labeller = labeller(Pclass = c(`1` = "First Class", 
                                                      `2` = "Second Class", 
                                                      `3` = "Third Class"))) +
  theme_bw() +
  xlab("Passenger age (in years)") +
  ylab("Number of passengers survived") +
  ggtitle("Titanic passenger survival rates")
```

And now lets tidy up the legend. How this is done will depend on the type of data being plotted, but it will be one of the various `scale_*()` functions. In our case we use `scale_fill_discrete()` since we have discrete data where the colour is filled by this third variable. 

```{r titanic18, warning = FALSE}
ggplot(data = titanic_clean,
       aes(x = Sex,
           fill = as.factor(Survived))) +
  geom_bar(position = "dodge") +
  facet_grid(~ Pclass,
             labeller = labeller(Pclass = c(`1` = "First Class", 
                                                      `2` = "Second Class", 
                                                      `3` = "Third Class"))) +
  theme_bw() +
  xlab("Passenger age (in years)") +
  ylab("Number of passengers survived") +
  ggtitle("Titanic passenger survival rates") +
  scale_fill_discrete(name = "Survival",
                      label = c("Died", "Survived"))
```

## Saving Plots

Once you have a finished plot you will want to save it to file from `R` so that you can embed it in your document of choice. The easiest option is to use the `ggsave()` function that by default saves the last plot from your `Plot` tab to file using the current size of the graphics device, but you can set your own `height`, `width`, and `units` arguments.

```{r  titanic19, eval=FALSE}
ggsave("images/My_Plot.jpg")

ggsave("images/My_Plot.jpg",
       height = 10,
       width = 15,
       units = "cm")
```

An alternative is to use one of the variety of image file creation functions available in `R` like `png()`, `jpeg()`, or `pdf()`. These functions will open a new file of that specific type, add to it anything that you plot, and then needs to be specifically told to close and save the file using the `dev.off()` function. This is the ideal method of doing so since it gives you greater control over the file generation process, including the ability to save multiple plots to a single file (depending on file type chosen). Unless you have a **very** good reason why not to, always use the `pdf()` function here for plots since it saves as a vector graphic so it can be rescaled in the future without pixellation problems.

```{r titanic20, eval=FALSE}
pdf("images/My_Plot.pdf",
    width = 10,
    height = 6)

ggplot(data = titanic_clean,
       aes(x = Sex,
           fill = as.factor(Survived))) +
  geom_bar(position = "dodge") +
  facet_grid(~ Pclass,
             labeller = labeller(Pclass = c(`1` = "First Class", 
                                                      `2` = "Second Class", 
                                                      `3` = "Third Class"))) +
  theme_bw() +
  xlab("Passenger age (in years)") +
  ylab("Number of passengers survived") +
  ggtitle("Titanic passenger survival rates") +
  scale_fill_discrete(name = "Survival",
                      label = c("Died", "Survived"))

dev.off()
```

Alternatively if you have saved your plot in your environment as the variable `my_plot`:

```{r titanic21, eval=FALSE}
pdf("images/My_Plot.pdf",
    width = 10,
    height = 6)

my_plot

dev.off()
```

```{r Challenge_19}
####################
### Challenge 19 ###
####################

# This is the final challenge of the course and the ONLY assessed component.
#
# Plot the titanic_clean dataset in any way you wish. Customise the plot to your heart's content! If you're lacking inspiration try some of these:
#
# - Change the variables on the axis
# - Change line/point widths/clours/transparency
# - Change the title and axis labels
# - Use a theme!
# - Facet the data
# - Change the colours!
#
# If you have ideas and don't know how to do it ask a helper!
#
# Save the plot to a pdf file in your images/ directory
#
# Show the plot in the file to a helper to get a tick next to your name on the assessment list. We don't care what it looks like as long as the pdf has a plot in it! Make it as pretty or ugly as you wish!
```