015-wrangling-richness.qmd

---
title: "Data Wrangling & Species Richness"
author: "Jeff Oliver"
date: "`r format(Sys.time(), '%d %B, %Y')`"
---

```{r library-check, echo = FALSE, results = "hide"}
#| output: false

libs <- c("vegan", "tidyverse")
libs_missing <- libs[!(libs %in% installed.packages()[, "Package"])]
if (length(libs_missing) > 0) {
  for (l in libs_missing) {
    install.packages(l)
  }
}
```

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE, fig.height = 3)
```

We collect data from a lot of sources, including field observations. Sometimes,
though, the way will collect and understand data as humans doesn't work well 
with the way computers operate. Here we will work through a real data set to 
use best practices for data organization to analyze species richness and 
diversity.

#### Learning objectives

1. Demonstrate how to organize data in 'tidy' format
2. Develop quality control processes for data
3. Visualize measures of species richness and species diversity

***

## Getting started

> Happy families are all alike; every unhappy family is unhappy in its own way 
- Leo Tolstoy

Since we are working in RStudio, start by making a new project from the File 
menu (File > New Project...). When prompted, select New Directory, then New 
Project. Name the project sweetwater-analysis. When the project opens, create a
folder to hold the data by typing `dir.create(data)` in the Console.

### Tidy data

After Wickham 2014, "tidy" data is defined by:

1. Each variable forms a column.
2. Each observation forms a row.
3. Each type of observational unit forms a table.

The survey data we are working with is available as an Excel file for download 
at [https://tinyurl.com/sweetwater-data](https://tinyurl.com/sweetwater-data). 
(if you want to skip the data wrangling, and work with the "tidy" data, you can
download them [here](https://raw.githubusercontent.com/jcoliver/learn-r/gh-pages/data/sweetwater-data-clean04.csv)). 
Make sure to move the file to the 'data' folder you created.

Open the file in Excel. What can be done to make these data "tidy"?

First, the data are effectively broken in two, with the most recent 
observations separate from observations from preceding days. So in some cases, 
a row has more than one observation. To fix this, we move the cells from the
preceding observations to rows after the most recent observations. 

We also want to:

+ Delete any columns without data
+ Delete any rows without data
+ Add a column title for Date
+ Make sure data for preceding observations lines up with column title 
(currently the Count column has species names and the Species column has count 
data)

Let's save this file as a CSV file so we can read it into R. We don't want to
overwrite the original data file, so we'll call our file 
sweetwater-data-clean.csv.

***

## Data wrangling

Now that the data are in CSV format, we can read it into R and take a look. We 
want to keep a record of all our work, so we are going to type commands into a
script file and save the script. Create a new script via the File menu (File > 
New File > R Script). As we did before, we add a few lines of comments at the 
top of our script with some relevant information.

```{r read-data-01-eval, echo = FALSE}
bird_data <- read.csv(file = "data/sweetwater-data-clean01.csv")
```

```{r read-data-01, eval = FALSE}
# Analyze sweetwater bird species richness
# Jeff Oliver
# jcoliver@email.arizona.edu
# 2019-11-01

bird_data <- read.csv(file = "data/sweetwater-data-clean.csv")
```

We can look at the first few rows with the `head` function:

```{r head-data-01}
head(bird_data)
```

And we see that in rows two through 6 there aren't any values for the Date and 
Site columns. This is a prime example of the difference between how a human
interprets data and how a computer interprets the data. Let's open up our CSV 
file in Excel again. As humans, we can infer that the value for "Date" should 
be October 4th all the way to row 35. But the computer is stupid. The computer 
looks at row 3, sees no date, and infers that the date is _missing_. We need to
explicitly tell the computer what the value for date is. Before we do that, we 
have the opportunity to go ahead and also change the date to the standard ISO 
date format of "YYYY-MM-DD". In this case, October 4th becomes 2019-10-04. 

Go ahead and update all the dates and fill in values appropriately. You should 
also note that for observations prior to October 10th, there is an offset that 
(1) needs to be fixed and (2) prompts deletion of now empty rows.

Also, for the 2019-10-04 observations, do the same thing with data in the Site
column - that is, fill in values where appropriate. Save the file as a CSV (you 
can use the same filename, sweetwater-data-clean.csv) and read it into R.

```{r read-data-02-eval, echo = FALSE}
bird_data <- read.csv(file = "data/sweetwater-data-clean02.csv")
```

```{r read-data-02, eval = FALSE}
# Analyze sweetwater bird species richness
# Jeff Oliver
# jcoliver@email.arizona.edu
# 2019-11-01

bird_data <- read.csv(file = "data/sweetwater-data-clean.csv")
```

Looking at the updated data, we see those first six rows no longer have missing
data.

```{r head-data-02}
head(bird_data)
```

We can also use the `summary` function to get an idea of the data set overall:

```{r summary-data}
summary(bird_data)
```

There are still missing values in the Site column, but that's OK - those are observations from preceding days, which don't have any site data.

Note also though that it is treating "Date" as text data when it really should be treated as, well, a date. So we can change the data in the Date column from categories to dates with the `as.Date` function:

```{r convert-dates}
bird_data$Date <- as.Date(bird_data$Date)
```

***

## Species richness

OK, so now let's actually look at species richness. We are going to look at 
each date separately, and count the total number of species observed. To do 
this, we are going to use an additional package called tidyverse; we will need 
to install the package and load it into memory before we try to use it. First, 
we check to see if the package is installed by looking at the Packages tab in 
the lower-right window. If tidyverse _is_ installed, we don't need to install 
it again. If tidyverse is not listed, you can install it through the R console 
with the command:

```{r install-tidyverse, eval = FALSE}
install.packages("tidyverse")
```

Now that the package is installed, we also have to tell R to load the package 
into memory, so we add a call to `library` to our script. We often add all 
`library` commands to the top of our script.

```{r load-tidyverse, eval = FALSE}
# Analyze sweetwater bird species richness
# Jeff Oliver
# jcoliver@email.arizona.edu
# 2019-11-01

library(tidyverse)
bird_data <- read.csv(file = "data/sweetwater-data-clean.csv")

# Convert the Date column to date data type
bird_data$Date <- as.Date(bird_data$Date)
```

```{r load-tidyverse-eval, echo = FALSE}
library(tidyverse)
```

You might see some read messages when you load tidyverse. As long as you don't 
see the word "Error" everything should be fine.

Now we can use some tidyverse functions to calculate the total number of 
species seen on each day. We use the `group_by` function to first create a 
different pile of data for each day, then count the number of rows using the 
`n` function.

```{r unclean-richness}
# Calculate species richness per day
richness <- bird_data %>%
  group_by(Date) %>%
  summarize(Richness = n())
```

Let's look at the output by typing the name of our variable into the console:

```{r view-unclean-richness}
richness
```

Pretty cool. We can even plot species richness for each day, too. We are going 
to use the ggplot2 package again, which comes with tidyverse.

To plot richness for each day, 

```{r plot-unclean-richness}
# Plot daily species richness
richness_plot <- ggplot(data = richness,
                        mapping = aes(x = Date,
                                      y = Richness)) +
  geom_point()
print(richness_plot)
```

Hey, a plot!

Let's take a moment though to think about the data that went into making these
plots. In fact, let's look at the first six rows again:

```{r show-non-species-01}
head(bird_data)
```

Hmmm...row four has "Finch", which isn't a species. At Sweetwater Wetlands, 
House Finches and Lesser Goldfinches are common, and sometimes other rare 
finches show up. Another example is on row 17. We can take a look at this part 
of the data by specifying which rows to show. Here we ask for R to print out 
rows 15 through 20:

```{r show-non-species-02}
bird_data[15:20, ]
```

In addition to "Finch sp" we all so see two more examples of observations not
identified to species. Because we don't actually know what species these were, 
we will need to exclude them from our analyses.

***

## Computers are stupid

### Or, why quality control matters

We do not actually want to delete these records from our data, but rather we 
need a means of indicating whether or not they are identified to the species 
level. This should probably be done in the sweetwater-data-clean.csv file we've 
been working with. So open that file in Excel and add another column, 
immediately to the right of Species. Call this one "ID_to_species" and fill in
values of 1 for those identified to species (e.g. Gila woodpecker would be 
scored as a 1) and values of 0 for those not identified to species (e.g. Finch 
sp and duck sp. would be scored as a 0). Save the file with the same name as 
before (sweetwater-data-clean.csv).

```{r read-data-03, eval = FALSE}
# Analyze sweetwater bird species richness
# Jeff Oliver
# jcoliver@email.arizona.edu
# 2019-11-01

bird_data <- read.csv(file = "data/sweetwater-data-clean.csv")

# Convert the Date column to date data type
bird_data$Date <- as.Date(bird_data$Date)
```

```{r read-data-03-eval, echo = FALSE}
bird_data <- read.csv(file = "data/sweetwater-data-clean03.csv")
bird_data$Date <- as.Date(bird_data$Date)
```

Now we can update our richness calculation to only include observations that 
were identified to species. We do this by adding a filter to the process. Using 
the code we had before, before calling `group_by`, we use the `filter` function 
to only include those observations that have a 1 in the ID_to_species column.

```{r store-unclean-richness, echo = FALSE}
# For comparing results before and after filter
unclean_richness <- richness
```

```{r richness-clean-01}
# Calculate species richness per day
richness <- bird_data %>%
  filter(ID_to_species == 1) %>%
  group_by(Date) %>%
  summarize(Richness = n())
```

We can compare the old calculations for richness

```{r show-unclean-richness, echo = FALSE}
unclean_richness
```

to the new ones:

```{r view-clean-richness-01}
richness
```

And note the numbers have dropped a little when we exclude those observations. 
Now when we run the code for plotting, we see the results changed, too:

```{r plot-clean-richness-01}
richness_plot <- ggplot(data = richness,
                        mapping = aes(x = Date,
                                      y = Richness)) +
  geom_point()
print(richness_plot)
```

Let's take a quick look at the species that were observed on October 4, just to
double check. We can do that in the console using the pipe (`%>%`) and the 
`filter` command (you'll see more than ten rows):

```{r view-one-day-species-01, eval = FALSE}
bird_data %>% filter(Date == "2019-10-04")
```

```{r view-one-day-species-eval-01, echo = FALSE}
bird_data %>% filter(Date == "2019-10-04") %>% slice(1:10)
```

We should see 34 rows. This lines up with our initial plot, that showed 34 
species. But look a little closer, notably at rows 1 and 9. Both rows are
observations of Mallard Ducks. So this species actually got counted (at least)
twice. Because multiple sites were surveyed, We can't just count the number of 
rows in this data to get the number of species. We should actually do a little 
more data wrangling to get an accurate count of species for the October 4 
sampling. What we are going to do is to create a new data object we'll call
`bird_data_sums` that sums up the values for each species on each day.

```{r sum-sites-01}
# Sum species counts for each day
bird_data_sums <- bird_data %>%
  filter(ID_to_species == 1) %>%
  group_by(Date, Species) %>%
  summarise(Count = sum(Count))
```

Now we can again look at the observations for October 4:

```{r view-one-day-species-02}
bird_data_sums %>% filter(Date == "2019-10-04")
```

Now this doesn't show us all the data, but looking at the end of the output, we 
see that R is showing us 10 rows of data, with 
`r nrow(bird_data_sums %>% filter(Date == "2019-10-04")) - 10` more rows. So we 
have gone from 34 species, down to 
`r nrow(bird_data_sums %>% filter(Date == "2019-10-04"))` species for October 
4.

We can update our code to calculate richness and re-create our plot. Remember 
to replace `bird_data` with `bird_data_sums` in the first line of the code. We 
can also remove the `filter` function, because we already filtered out 
observations that were not identified to species when we created 
`bird_data_sums`.

```{r richness-clean-02}
# Calculate species richness per day
richness <- bird_data_sums %>%
  group_by(Date) %>%
  summarize(Richness = n())

# Plot daily species richness
richness_plot <- ggplot(data = richness,
                        mapping = aes(x = Date,
                                      y = Richness)) +
  geom_point()
print(richness_plot)
```

The only thing to really change in our plot is for October 4. OK, some of you 
might have noticed this already, but let's look again at those October 4 data:

```{r view-one-day-species-03}
bird_data_sums %>% filter(Date == "2019-10-04")
```

So, there's another issue to address. Look at rows 9 and 10. We have a species 
that is represented twice. Why? Here is a great example of computers only being 
able to do what you tell them to. We told R to count the total number of 
individuals for each species. Unfortunately, R sees "mallard" and "Mallard 
Duck" as two different species. 

What do we do?

At this point, the best thing is probably to go into the Excel file and change 
the species names to be consistent. Now so far, we haven't actually _changed_ 
any of the data - we added a column and we filled in values where appropriate, 
but we did not change anything. When changing data, you always want to be 
careful. At this point, we should be keeping track of changes we made to the 
data. We are going to do this in a separate file, with notes about what we did. 
So start by creating a new text file (File > New File > Text File) and adding:

```
README for sweetwater species data
2019-11-01
+ Created CSV from original Excel data file
+ Changed to ISO date format
+ Standardized spelling of species' names
```

And save this file as "README.md".

Now we actually have to do the standardization of the species names, so open up 
the sweetwater-data-clean.csv file in Excel. What's the best way of going about
changing names? We could search for non-standard spellings like "Gambils quail" 
but that assumes we know all the alternative spellings. What might help our 
cause is to sort by the Species column so things like "mallard" and "Mallard 
Duck show up next to one another. **Note**: this sorting is going to rearrange 
the order of the rows, so we should _only_ do this if the order of the rows 
does not matter. Remember the original data? Where the date was only shown in 
the first row? If we had sorted those data it would have been _bad_. Why? 
Because in that case, order _did_ matter. But by using tidy data formats, we 
can re-order rows without losing any information for a single observation.

So go ahead and change names so they are consistent. This includes 

+ consistent spelling (Gambel's vs. Gambils),
+ consistent capitalization (Gila Woodpecker vs. Gila woodpecker), and 
+ constent use of punctuation (Red-winged Blackbird vs. Redwing black bird and
Cooper's Hawk vs. Coopers' Hawk). 

Save the file to sweetwater-data-clean.csv and go back into R.

Re-run the line at the top of our script with the `read.csv` command:

```{r read-data-04, eval = FALSE}
bird_data <- read.csv(file = "data/sweetwater-data-clean.csv")
bird_data$Date <- as.Date(bird_data$Date)
```

```{r read-data-04-eval, echo = FALSE}
bird_data <- read.csv(file = "data/sweetwater-data-clean04.csv")
bird_data$Date <- as.Date(bird_data$Date)
```

Re-run the line counting the number of each species per day:

```{r sum-sites-02}
# Sum species counts for each day
bird_data_sums <- bird_data %>%
  filter(ID_to_species == 1) %>%
  group_by(Date, Species) %>%
  summarise(Count = sum(Count))
```

And calculate richness for each day and draw the plot

```{r richness-clean-03}
# Calculate species richness per day
richness <- bird_data_sums %>%
  group_by(Date) %>%
  summarize(Richness = n())

# Plot daily species richness
richness_plot <- ggplot(data = richness,
                        mapping = aes(x = Date,
                                      y = Richness)) +
  geom_point()
print(richness_plot)
```

Whew! 

So at this point, let's think about what we've done in R. We really haven't had 
to do too much, we wrote code to read in the file, count the total number of 
each species seen each day, calculate species richness, and plot species 
richness. Most of our time was really spent wrangling the data into a format 
that could be used to analyze with a computer. This isn't unexpected. Data 
wrangling can take up as much as 80% of a data scientist's time (See 
[Furche et al. 2016. doi 10.5441/002/edbt.2016.44](http://dx.doi.org/10.5441/002/edbt.2016.44)).

***

## Species diversity

Now we are ready for species diversity! We are going to use Shannon's index 
($H$) of species diversity:

$$
H = -\sum_{i=1}^{s}{p_i \ln p_i}
$$
which is the sum over $s$ species, where $p_i$ is the proportion individuals of 
the $i^th$ species ($n_i$) out of the total number of inviduals ($N$), or:

$$
p_i = \frac{n_i}{N}
$$

To calculate this measure of diversity, we are going to use the vegan package, 
which does _not_ come with tidyverse, so we'll have to install it with:

```{r install-vegan, eval = FALSE}
install.packages("vegan")
```

Add the `library` command to the top of our script:

```{r load-vegan, eval = FALSE}
# Analyze sweetwater bird species richness
# Jeff Oliver
# jcoliver@email.arizona.edu
# 2019-11-01

library(tidyverse)
library(vegan)
bird_data <- read.csv(file = "data/sweetwater-data-clean.csv")

# Convert the Date column to date
bird_data$Date <- as.Date(bird_data$Date)
```

```{r load-vegan-eval, echo = FALSE}
library(vegan)
```

We are going to use functions that come with the vegan package to help with 
these calculations. In order to use those functions, we need to convert the
`bird_data_sums` from the "long" format it is now to "wide" format. That is, we 
need to change from:

| Date       | Species            | Count |
|:-----------|:-------------------|:-----:| 
| 2019-10-04 | Abert's Towhee     | 1     |
| 2019-10-04 | Gila Woodpecker    | 2     |
| 2019-10-03 | Abert's Towhee     | 1     |
| 2019-10-03 | Greater Roadrunner | 1     |

To:

| Date       | Abert's Towhee     | Gila Woodpecker | Greater Roadrunner |
|:-----------|:------------------:|:---------------:|:------------------:|
| 2019-10-04 | 1                  | 2               | 0                  |
| 2019-10-03 | 1                  | 0               | 1                  |

Where

+ each date corresponds to a single row
+ each species corresponds to a single column

We can do this with the `pivot_wider` function that comes with tidyverse. Here 
we tell R to use the values in the Species column as new column names, and the
numbers in the Count column to use as values.

```{r clean-wide}
bird_data_wide <- bird_data_sums %>%
  pivot_wider(names_from = Species, 
              values_from = Count)
```

Let's look at the first five columns of the "wide" formatted data:

```{r view-clean-wide}
bird_data_wide[, 1:5]
```

For the diversity calculations, it only works on count data, and our data 
include one column of dates. We create a new variable, `species_counts` which 
has all the data except the first column (the Date column), then we calculate
species richness and look at the results by running the name of the variable
(`bird_diversity`):

```{r clean-diversity-01}
species_counts <- bird_data_wide[, -1]
bird_diversity <- diversity(x = species_counts)
bird_diversity
```

All the values are `NA` because the input data (`bird_data_wide`) had `NA` 
values for every Date/Species combination with zero observations. In this case, 
we need to replace all those `NA` values with 0.

```{r replace-nas}
bird_data_wide[is.na(bird_data_wide)] <- 0
```

We re-run the diversity calculations after converting those `NA` values to 
zero. We then overwrite `bird_diversity` with a data frame that contains the 
Date and the results of the diversity calculations:

```{r clean-diversity-02}
# Calculate species diversity on count data
species_counts <- bird_data_wide[, -1]
bird_diversity <- diversity(x = species_counts)

# Add date
bird_diversity <- data.frame(Date = bird_data_wide$Date,
                        Diversity = bird_diversity)
```

And plot diversity:

```{r plot-diversity}
# Plot species diversity of each date
diversity_plot <- ggplot(data = bird_diversity, 
                         mapping = aes(x = Date, y = Diversity)) +
  geom_point()
print(diversity_plot)
```

***

## Starting clean

### Setup

If you want to skip all the data wrangling and start with data that are already 
in tidy format, download the data [here](https://raw.githubusercontent.com/jcoliver/learn-r/gh-pages/data/sweetwater-data-clean04.csv). 

Be sure you set up a new project as described in [Getting started](#getting-started), 
above.

Move the file you downloaded to the 'data' folder.

In order to save our work, we will use an R script, which is a text file where 
we place all our R commands. Create a new script through the file menu (File > 
New File > R Script). We start our script with a header of comments:

```{r clean-01}
# Analyze sweetwater bird species richness
# Jeff Oliver
# jcoliver@email.arizona.edu
# 2019-11-01
```

We're going to use some R code from packages that do not come with base R. Run 
these two lines in the console:

```{r clean-02, eval = FALSE}
install.packages("tidyverse")
install.packages("vegan")
```

We also need to tell R in our script that we want to use those packages; we 
load them into memory with the `library` command. Generally, we try to add all 
the calls to `library` to the beginning of our code, so if someone opens the 
script, they can immediately see which additional packages are required.

```{r clean-03}
# Analyze sweetwater bird species richness
# Jeff Oliver
# jcoliver@email.arizona.edu
# 2019-11-01

# Load additional packages
library(tidyverse)
library(vegan)

# Read in data
bird_data <- read.csv(file = "data/sweetwater-data-clean04.csv")

# Convert the Date column to date data type
bird_data$Date <- as.Date(bird_data$Date)
```

The last line reads the data into R. You can look at these data by running
`head(bird_data)` in the Console.

### Wrangle the data

Next we need to sum the counts for each species for each day:

```{R clean-04}
# Sum species counts for each day
bird_data_sums <- bird_data %>%
  filter(ID_to_species == 1) %>%
  group_by(Date, Species) %>%
  summarise(Count = sum(Count))
```

With the daily totals for each species, now we can calculate species richness 
(here we are measuring daily richness as the total number of species each day).

### Richness

```{r clean-05}
# Calculate species richness per day
richness <- bird_data_sums %>%
  group_by(Date) %>%
  summarize(Richness = n())
```

We can investigate the richness data by typing the name of the variable into 
the console:

```{r, eval = FALSE}
> richness
```

```{r, echo = FALSE}
richness
```

Now that we have species richness calculated for each day, we can use the 
ggplot2 package, which comes with tidyverse, to plot the data:

```{r clean-06}
# Plot daily species richness
richness_plot <- ggplot(data = richness,
                        mapping = aes(x = Date,
                                      y = Richness)) +
  geom_point()
print(richness_plot)
```

### Diversity

For species diversity, we are going to use, $H$, Shannon's index of species
diversity:

$$
H = -\sum_{i=1}^{s}{p_i \ln p_i}
$$

which is the sum over $s$ species, where $p_i$ is the proportion individuals of 
the $i^th$ species ($n_i$) out of the total number of inviduals ($N$), or:

$$
p_i = \frac{n_i}{N}
$$

We are going to use functions that come with the vegan package to help with 
these calculations. In order to use those functions, we need to convert the
`bird_data_sums` from the "long" format it is now to "wide" format. That is, we 
need to change from:

| Date       | Species            | Count |
|:-----------|:-------------------|:-----:| 
| 2019-10-04 | Abert's Towhee     | 1     |
| 2019-10-04 | Gila Woodpecker    | 2     |
| 2019-10-03 | Abert's Towhee     | 1     |
| 2019-10-03 | Greater Roadrunner | 1     |

To:

| Date       | Abert's Towhee     | Gila Woodpecker | Greater Roadrunner |
|:-----------|:------------------:|:---------------:|:------------------:|
| 2019-10-04 | 1                  | 2               | 0                  |
| 2019-10-03 | 1                  | 0               | 1                  |

Where

+ each date corresponds to a single row
+ each species corresponds to a single column

We use the `pivot_wider` function of tidyverse to accomplish this. We also need 
to replace NA values produced in this process to zeros:

```{r, clean-07}
# Convert to wide format for species diversity calculation
bird_data_wide <- bird_data_sums %>%
  pivot_wider(names_from = Species,
              values_from = Count)

# Replace NA values with zeros
bird_data_wide[is.na(bird_data_wide)] <- 0
```

The diversity calculation only works on count data, and our data include one 
column of dates. We create a new variable, `species_counts` which has all the 
data except the first column (the Date column), then we calculate species 
richness and look at the results by running the name of the variable
(`bird_diversity`):

```{r clean-08}
# Calculate species diversity on count data
species_counts <- bird_data_wide[, -1]
bird_diversity <- diversity(x = species_counts)

# Add date
bird_diversity <- data.frame(Date = bird_data_wide$Date,
                        Diversity = bird_diversity)
```

Now that we have species diversity, we can plot it using commands similar to 
those we used for species richness:

```{r clean-09}
# Plot species diversity of each date
diversity_plot <- ggplot(data = bird_diversity, 
                         mapping = aes(x = Date, y = Diversity)) +
  geom_point()
print(diversity_plot)
```

Recall the plot for richness:

```{r clean-10, echo = FALSE}
print(richness_plot)
```

**Question**: Why is species richness of October 4 higher than richness of 
October 2, but species diversity is _lower_ on October 4 than on October 2?

***

## Additional resources

+ Wickham, H. 2014. Tidy data. _The Journal of Statistical Software_ **59** [http://www.jstatsoft.org/v59/i10/](http://www.jstatsoft.org/v59/i10/)
+ A nice introduction to [calculating measures of species diversity](https://entnemdept.ifas.ufl.edu/hodges/ProtectUs/lp_webfolder/9_12_grade/Student_Handout_1A.pdf)
+ A [PDF version](https://jcoliver.github.io/learn-r/015-wrangling-richness.pdf) of this lesson