03-notes.Rmd

---
layout: topic
title: Data visualization with ggplot2 (notes)
author: Data Carpentry contributors
---

```{r, echo=FALSE}
knitr::opts_chunk$set(results='hide', fig.path='img/r-lesson-notes-', results='hide', eval=FALSE)
```

```{r, data cleaning, echo=FALSE}
library(dplyr)
surveys <- read.csv("http://kbroman.org/datacarp/portal_data_joined.csv")
surveys_complete <- surveys %>%
    filter(species_id != "", !is.na(weight)) %>%
    filter(!is.na(hindfoot_length), sex != "")
species_counts <- surveys_complete %>%
    group_by(species_id) %>%
    tally
frequent_species <-  species_counts %>%
    filter(n >= 10) %>%
    select(species_id)
reduced <- surveys_complete %>%
    filter(species_id %in% frequent_species$species_id)
```

Load the cleaned/reduced data:

```{r load_clean_data, eval=FALSE}
reduced <- read.csv("http://kbroman.org/datacarp/portal_data_reduced.csv")
```

Or:

```{r download_then_load, eval=FALSE}
download.file("http://kbroman.org/datacarp/portal_data_reduced.csv",
              "CleanData/portal_data_reduced.csv")
reduced <- read.csv("CleanData/portal_data_reduced.csv")
```


## load libraries

```{r, load_lib}
library(ggplot2)
library(dplyr)
```

**Mention ggplot vs base graphics**

First plot:

```{r first-ggplot}
ggplot(reduced, aes(x = weight, y = hindfoot_length)) + geom_point()
```

Key concepts of "grammar of graphics":
- _aesthetics_ map features of the data to features of the
visualization
(for example, the `weight` variable is mapped to the y-axis coordinate)
- _geoms_ concern what actually gets plotted (here, each row in the data
becomes a point in the plot)

- `ggplot()` creates a graphics objects
- additional controls added with the `+` operator
- actual plot made when object is printed

```{r exlicit_printing, results='hide'}
p1 <- ggplot(reduced, aes(x=weight, y=hindfoot_length))
p2 <- p1 + geom_point()
print(p2)
```

Can be convenient to have saved the results. For example, to make that
last plot have `weight` on the log scale:

```{r weight_on_log_scale}
p2 + scale_x_log10()
```

and on square-root scale:

```{r weight_on_sqrt_scale}
p2 + scale_x_sqrt()
```

### Challenge

Make a scatterplot of `hindfoot_length` vs `weight`, but only for the
`species_id`, `"DM"`.


## Other aesthetics

For scatterplot, `shape`, `size`, `color`, and `alpha`.

```{r create-ggplot-object}
surveys_plot <- ggplot(reduced, aes(x = weight, y = hindfoot_length))

surveys_plot + geom_point(alpha = 0.1)

surveys_plot + geom_point(alpha = 0.1, color = "slateblue")

surveys_plot + geom_point(alpha = 0.1, color = "slateblue", size=0.5)
```

Things get more interesting when we assign these aesthetics to data.


```{r scatter_colored_by_species}
surveys_plot + geom_point(aes(color = species_id))
```


### Challenge

Use dplyr to calculate the mean `weight` and `hindfoot_length` as well
as the sample size for each species.

Make a scatterplot of mean `hindfoot_length` vs `mean_weight`, with
the sizes of the points corresponding to the sample size.

## Layers

You can use `geom_line()` to make a line plot. For example, let's plot
the counts of animals by year.

```{r plot_counts_by_year}
count_by_year <- reduced %>%
    group_by(year) %>%
    tally

p <- ggplot(count_by_year, aes(x=year, y=n))
p + geom_line()
```

You can use _both_ `geom_line` and `geom_point` to make a line plot
with points at the data values.

```{r line_plus_point}
p + geom_line() + geom_point()
```

**layers**: a given plot can have multiple layers of geometric
objects, plotted one on top of another.

If we add colors, we'll see this better.

```{r line_plus_point_wcolors}
p + geom_line(color="lightblue") + geom_point(color="violetred")
```

If we switch the order of `geom_point` and `geom_line`, we'll reverse
the layers.

```{r line_plus_point_wcolors_alt}
p + geom_point(color="violetred") + geom_line(color="lightblue")
```

**global aesthetics**: esthetics included in the call to `ggplot()` (or completely
separately) are made to be the defaults for all layers, but we can
separately control the aesthetics for each layer. For example, we
could color the points by year:

```{r points_colored_by_year}
p + geom_line() + geom_point(aes(color=year))
```

Compare that to the following:

```{r points_colored_by_year_2}
p + geom_line() + geom_point() + aes(color=year)
```



### Challenge

Make a plot of counts of `species_id` `"DM"` and `"DS"` by year.

## Groups

Suppose, in that last challenge, we'd wanted to have black lines but
the points colored by species. We might have done this:

```{r misgrouped}
counts_dm_ds <- reduced %>% filter(species_id %in% c("DM", "DS")) %>%
    group_by(species_id, year) %>% tally
p <- ggplot(counts_dm_ds, aes(x=year, y=n))
p + geom_line() + geom_point(aes(color=species_id))
```

The points get connected left-to-right, which is not what we want.

If we make the `color=species_id` aesthetic _global_, we don't have
this problem.

```{r color_global}
p + geom_line() + geom_point() + aes(color=species_id)
```

Alternatively, we can use the `group` aesthetic, which indicates that
certain data points go together. This way the lines can be a constant
color.

```{r group_aes}
p + geom_line(aes(group=species_id)) + geom_point(aes(color=species_id))
```

We could also make the group aesthetic global

```{r group_global}
p + aes(group=species_id) + geom_line() + geom_point(aes(color=species_id))
```

## Univariate geoms

We've focused so far on scatterplots, but one can also create
one-dimensional summaries, such as histograms or boxplots.


### Challenge

Try using `geom_histogram()` to make a histogram visualization of the
distribution of `weight`.

Hint: You want `weight` as the x-axis aesthetic. Try specifying `bins`
in `geom_histogram()`.

### Boxplot

Visualising the distribution of weight within each species.

```{r boxplot}
ggplot(reduced, aes(x = species_id, y = hindfoot_length)) +
    geom_boxplot()
```

By adding points to boxplot, we can have a better idea of the number of
measurements and of their distribution:

```{r boxplot-with-points}
ggplot(reduced, aes(x = species_id, y = hindfoot_length)) +
    geom_boxplot(alpha = 0) +
    geom_jitter(alpha = 0.3, color = "tomato")
```

Notice how the boxplot layer is behind the jitter layer? What do you need to
change in the code to put the boxplot in front of the points such that it's not
hidden.

### Challenge

A variant on the box plot is the violin plot. Use `geom_violin()` to
make violin plots of `hindfoot_length` by `species_id`.

## Faceting

ggplot has a special technique called *faceting* that allows to split one plot
into multiple plots based on a factor included in the dataset. We will use it to
make one plot for a time series for each species.

```{r first-facet}
yearly_counts <- reduced %>% group_by(year, species_id) %>% tally
ggplot(yearly_counts, aes(x = year, y = n, group = species_id, colour = species_id)) +
    geom_line() +
    facet_wrap(~ species_id)
```

Now we would like to split line in each plot by sex of each individual
measured. To do that we need to make counts in data frame grouped by sex.

### Challenge

- Calculate counts grouped by year, species_id, and sex

- make the faceted plot splitting further by sex (within each panel)

- color by sex rather than species


Suppose I make a similar plot of average weight by species:

```{r average-weight-timeseries}
yearly_weight <- reduced %>%
                 group_by(year, species_id, sex) %>%
                 summarise(avg_weight = mean(weight, na.rm = TRUE))
ggplot(yearly_weight, aes(x=year, y=avg_weight, color = species_id, group = species_id)) +
    geom_line() +
    facet_wrap(~ species_id)
```

Why do we see those steps in the plot?

**Oops** need to group by sex

```{r average-weight-timeseries-fixed}
yearly_weight <- reduced %>%
                 group_by(year, species_id, sex) %>%
                 summarise(avg_weight = mean(weight, na.rm = TRUE))
ggplot(yearly_weight, aes(x=year, y=avg_weight, color = sex, group = sex)) +
    geom_line() +
    facet_wrap(~ species_id)
```

### facet_grid

The `facet_wrap` geometry extracts plots into an arbitrary number of dimensions
to allow them to cleanly fit on one page. On the other hand, the `facet_grid`
geometry allows you to explicitly specify how you want your plots to be
arranged via formula notation (`rows ~ columns`; a `.` can be used as
a placeholder that indicates only one row or column).

```{r average-weight-time-facet_sex_rows}
## One column, facet by rows
yearly_weight %>% filter(species_id %in% c("DM", "DO", "DS")) %>%
    ggplot(aes(x=year, y=avg_weight, color = species_id, group = species_id)) +
    geom_line() +
    facet_grid(sex ~ .)
```

```{r average-weight-time-facet_sex_columns}
# One row, facet by column
yearly_weight %>% filter(species_id %in% c("DM", "DO", "DS")) %>%
    ggplot(aes(x=year, y=avg_weight, color = species_id, group = species_id)) +
    geom_line() +
    facet_grid( ~ sex)
```


```{r average-weight-time-facet_sex_columns_species_rows}
# separate panel for each sex and species
yearly_weight %>% filter(species_id %in% c("DM", "DO", "DS")) %>%
    ggplot(aes(x=year, y=avg_weight, color = species_id, group = species_id)) +
    geom_line() +
    facet_grid(species_id ~ sex)
```

## Saving plots to a file

If you want to save a plot, to share with others, use the `ggsave`
function.

The default is to save the last plot that you created, but I think
it's safer to first save the plot as an object and pass that to
`ggsave`. Also give the height and width in inches.

```{r ggsave_example, eval=FALSE}
p <- ggplot(reduced, aes(x=weight, y=hindfoot_length)) + geom_point()
ggsave("scatter.png", p, height=6, width=8)
```

The image file type is taken from the file name extension.
To make a PDF instead:

```{r ggsave_pdf, eval=FALSE}
ggsave("scatter.pdf", p, height=6, width=8)
```

Use `scale` to adjust the sizes of things, for example for a talk/poster
versus a paper/report. Use `scale < 1` to make the various elements
bigger relative to the plotting area.

```{r ggsave_scale, eval=FALSE}
ggsave("scatter_2.png", p, height=6, width=8, scale=0.8)
```

## Customizing plots

### Axis limits

When faceting, the different panels are given common x- and y-axis
limits. If we were to create separate plots (say one for each
country), we would need to do a bit extra to ensure that common axis
limits are used.

Recall the `scale_x_log10()` function that we had used to create the log
scale for the x axis. This can take an argument `limits` (a
vector of length 2) defining the minimum and maximum values plotted.

There is also a `scale_y_log10()` function, but if you want to change
the y-axis limits without going to a log scale, you would use
`scale_y_continuous()`. (Similarly, there's a `scale_x_continuous`.)

For example, to plot the data for China, using axis limits defined by
the full data, we'd do the following:

```{r limits}
xrange <- range(reduced$weight)
yrange <- range(reduced$hindfoot_length)

p <- reduced %>% filter(species_id=="DM") %>%
    ggplot(aes(x=weight, y=hindfoot_length)) +
    geom_point()
p + scale_x_log10(limits=xrange) +
    scale_y_continuous(limits=yrange)
```

### Color choices

If you don't like the choices for point colors, you can customize
them in a number of ways. First, you can use `scale_color_manual()`
with a vector of your preferred choices. (If it's `fill` rather than
`color` that you want to change, you'll need to use `scale_fill_manual()`.)

```{r custom_colors}
p <- reduced %>% filter(species_id %in% c("DM", "DS", "DO")) %>%
    ggplot(aes(x=weight, y=hindfoot_length)) +
    geom_point(aes(color=species_id))

colors <- c("blue", "green", "orange")
p + scale_color_manual(values=colors)
```

You can also use RGB hex values.

```{r custom_colors_rgb}
hexcolors <- c("#001F3F", "#0074D9", "#01FF70")
p + scale_color_manual(values=hexcolors)
```


## Themes

Not everyone gray background and such in the default ggplot plots.

But you can apply one of a variety of "themes" to control the overall
appearance of plots.

One that a lot of people like is `theme_bw()`. Add it to a plot, and
the overall appearance changes.

```{r theme}
p + theme_bw()
```




<br/> <br/> <br/> <br/> <br/> <br/> <br/> <br/> <br/> <br/>