05-visualisation-ggplot2.Rmd

---
layout: topic
title: Data visualisation with ggplot2
subtitle: Visualising data in R with ggplot2 package
minutes: 60
---

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```{r}
knitr::opts_chunk$set(fig.keep='last')
```

```{r setup, echo=FALSE, purl=FALSE}
source("setup.R")
```

Authors: **Mateusz Kuzak**, **Diana Marek**, **Hedi Peterson**



#### Disclaimer

We will here using functions of ggplot2 package. There are basic ploting
capabilities in basic R, but ggplot2 adds more powerful plotting capabilities.

> ### Learning Objectives
>
> -	Visualise some of the
>[mammals data](http://figshare.com/articles/Portal_Project_Teaching_Database/1314459)
>from Figshare [surveys.csv](http://files.figshare.com/1919744/surveys.csv)
> -	Understand how to plot these data using R ggplot2 package. For more details
>on using ggplot2 see
>[official documentation](http://docs.ggplot2.org/current/).
> -	Building step by step complex plots with ggplot2 package

Load required packages

```{r}
# plotting package
library(ggplot2)
# piping / chaining
library(magrittr)
# modern dataframe manipulations
library(dplyr)
```

Load data directly from figshare.

```{r}
surveys_raw <- read.csv("http://files.figshare.com/1919744/surveys.csv")
```

`surveys.csv` data contains some measurements of the animals caught in plots.

## Data cleaning and preparing for plotting

Let's look at the summary

```{r}
summary(surveys_raw)
```

There are few things we need to clean in the dataset.

There is missing species_id in some records. Let's remove those.

```{r}
surveys <- surveys_raw %>%
           filter(species_id != "")
```

There are a lot of species with low counts, let's remove the ones below 10 counts

```{r}
# count records per species
species_counts <- surveys %>%
                  group_by(species_id) %>%
                  summarise(n=n())

# get names of those frequent species
frequent_species <- species_counts %>%
                    filter(n >= 10) %>%
                    select(species_id)

surveys <- surveys %>%
           filter(species_id %in% frequent_species$species_id)
```

We saw in summary, there were NA's in weight and hindfoot_length. Let's remove
rows with missing weights.

```{r}
surveys_weight_present <- surveys %>%
                      filter(!is.na(weight))
```

> ### Challenge
>
> - Do the same to remove rows without `hindfoot_length`. Save results in the new dataframe.


```{r}
surveys_length_present <- surveys %>%
                      filter(!is.na(hindfoot_length))
```

- How would you get the dataframe without missing values?

```{r}
surveys_complete <- surveys_weight_present %>%
                    filter(!is.na(hindfoot_length))
```

> We can chain filtering together using pipe operator (`%>%`) introduced earlier.

```{r}
surveys_complete <- surveys %>%
                    filter(!is.na(weight)) %>%
                    filter(!is.na(hindfoot_length))
```

> Make simple scatter plot of `hindfoot_length` (in millimeters) as a function of
> `weight` (in grams), using basic R plotting capabilities.

```{r}
plot(x=surveys_complete$weight, y=surveys_complete$hindfoot_length)
```

## Plotting with ggplot2

We will make the same plot using `ggplot2` package.

`ggplot2` is a plotting package that makes it sipmple to create complex plots
from data in a dataframe. It uses default settings, which help creating
publication quality plotts with minimal amount of settings and tweaking.

With ggplot graphics are build step by step by adding new elements.

To build a ggplot we need to:

- bind plot to a specific data frame

```{r, eval=FALSE}
ggplot(surveys_complete)
```

- define aestetics (`aes`), that maps variables in the data to axes on the plot
     or to plotting size, shape color, etc.,

```{r}
ggplot(surveys_complete, aes(x = weight, y = hindfoot_length))
```

- add `geoms` -- graphical representation of the data in the plot (points,
     lines, bars). To add a geom to the plot use `+` operator:

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

## Modifying plots

- adding transparency (alpha)

```{r}
ggplot(surveys_complete, aes(x = weight, y = hindfoot_length)) +
  geom_point(alpha=0.1)
```

- adding colors

```{r}
ggplot(surveys_complete, aes(x = weight, y = hindfoot_length)) +
  geom_point(alpha=0.1, color="blue")
```

Example of complex visualisation in which plot area is divided into hexagonal
sections and points are counted wihin hexagons. The number of points per hexagon
is encoded by color.

```{r}
ggplot(surveys_complete, aes(x = weight, y = hindfoot_length)) + stat_binhex(bins=50) +
  scale_fill_gradientn(trans="log10", colours = heat.colors(10, alpha=0.5))
```

## Boxplot

Visualising the distribution of weight within each species.

```{r}
ggplot(surveys_weight_present, aes(factor(species_id), weight)) +
                   geom_boxplot()
```

By adding points to boxplot, we can see particular measurements and the
abundance of measurements.

```{r}
ggplot(surveys_weight_present, aes(factor(species_id), weight)) +
                   geom_jitter(alpha=0.3, color="tomato") +
                   geom_boxplot(alpha=0)
```

> ### Challenge
>
> Create boxplot for `hindfoot_length`.

## Plotting time series data

Let's calculate number of counts per year for each species. To do that we need
to group data first and count records within each group.

```{r}
yearly_counts <- surveys %>%
                 group_by(year, species_id) %>%
                 summarise(count=n())
```

Timelapse data can be visualised as a line plot with years on x axis and counts
on y axis.

```{r}
ggplot(yearly_counts, aes(x=year, y=count)) +
                  geom_line()
```

Unfortunately this does not work, because we plot data for all the species
together. We need to tell ggplot to split graphed data by `species_id`

```{r}
ggplot(yearly_counts, aes(x=year, y=count, group=species_id)) +
  geom_line()
```

We will be able to distiguish species in the plot if we add colors.

```{r}
ggplot(yearly_counts, aes(x=year, y=count, group=species_id, color=species_id)) +
  geom_line()
```

## Faceting

ggplot has a special technique called *faceting* that allows to split one plot
into mutliple plots based on some factor. We will use it to plot one time series
for each species separately.

```{r}
ggplot(yearly_counts, aes(x=year, y=count, color=species_id)) +
  geom_line() + facet_wrap(~species_id)
```

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

> ### Challenges:
>
> - filter the dataframe so that we only keep records with sex "F" or "M"s
>

```{r}
sex_values = c("F", "M")
surveys <- surveys %>%
           filter(sex %in% sex_values)
```

> - group by year, species_id, sex

```{r}
yearly_sex_counts <- surveys %>%
                     group_by(year, species_id, sex) %>%
                     summarise(count=n())
```

> - make the faceted plot spliting further by sex (within single plot)

```{r}
ggplot(yearly_sex_counts, aes(x=year, y=count, color=species_id, group=sex)) +
  geom_line() + facet_wrap(~ species_id)
```

> We can improve the plot by coloring by sex instead of species (species are
> already in separate plots, so we don't need to distinguish them better)

```{r}
ggplot(yearly_sex_counts, aes(x=year, y=count, color=sex, group=sex)) +
  geom_line() + facet_wrap(~ species_id)
```