27_secrets-happy-graphics.Rmd

# Secrets of a happy graphing life {#secrets}

```{r include = FALSE}
source("common.R")
```

<!--Original content: https://stat545.com/block016_secrets-happy-graphing.html-->

##  Load gapminder and the tidyverse

```{r message = FALSE, warning = FALSE}
library(gapminder)
library(tidyverse)
```

##  Hidden data wrangling problems

If you are struggling to make a figure, don't assume it's a problem between you and ggplot2. Stop and ask yourself which of these rules you are breaking:

* Keep stuff in data frames
* Keep your data frames *tidy*; be willing to reshape your data often
* Use factors and be the boss of them
  
In my experience, the vast majority of graphing agony is due to insufficient data wrangling. Tackle your latent data storage and manipulation problems and your graphing problem often melts away.

##  Keep stuff in data frames

I see a fair amount of student code where variables are *copied out* of a data frame, to exist as stand-alone objects in the workspace.

```{r}
life_exp <- gapminder$lifeExp
year <- gapminder$year
```

<!--TODO: This is no longer true, needs to be updated--> 
Problem is, ggplot2 has an incredibly strong preference for variables in data frames; it is virtually a requirement for the main data frame underpinning a plot.

```{r}
ggplot(mapping = aes(x = year, y = life_exp)) + geom_jitter()
```

**Just leave the variables in place and pass the associated data frame!** This advice applies to base and lattice graphics as well. It is not specific to ggplot2.

```{r data-in-situ}
ggplot(data = gapminder, aes(x = year, y = life_exp)) + geom_jitter()
```

What if we wanted to filter the data by country, continent, or year? This is much easier to do safely if all affected variables live together in a data frame, not as individual objects that can get "out of sync."

Don't write-off ggplot2 as a highly opinionated outlier! In fact, keeping data in data frames and computing and visualizing it *in situ* are widely regarded as best practices. The option to pass a data frame via `data =` is a common feature of many high-use R functions, e.g. `lm()`, `aggregate()`, `plot()`, and `t.test()`, so make this your default *modus operandi*.

### Explicit data frame creation via `tibble::tibble()` and `tibble::tribble()`

If your data is already lying around and it's __not__ in a data frame, ask yourself "why not?". Did you create those variables? Maybe you should have created them in a data frame in the first place! The `tibble()` function is an improved version of the built-in `data.frame()`, which makes it possible to define one variable in terms of another and which won't turn character data into factor. If constructing tiny tibbles "by hand", `tribble()` can be an even handier function, in which your code will be laid out like the table you are creating. These functions should remove the most common excuses for data frame procrastination and avoidance.

```{r data_frame-love}
my_dat <-
  tibble(x = 1:5,
         y = x ^ 2,
         text = c("alpha", "beta", "gamma", "delta", "epsilon"))
## if you're truly "hand coding", tribble() is an alternative
my_dat <- tribble(
  ~ x, ~ y,    ~ text,
    1,   1,   "alpha",
    2,   4,    "beta",
    3,   9,   "gamma",
    4,  16,   "delta",
    5,  25, "epsilon"
)
str(my_dat)
ggplot(my_dat, aes(x, y)) + geom_line() + geom_text(aes(label = text))
```

Together with `dplyr::mutate()`, which adds new variables to a data frame, this gives you the tools to work within data frames whenever you're handling related variables of the same length.

### Sidebar: `with()`

Sadly, not all functions offer a `data =` argument. Take `cor()`, for example, which computes correlation. This does __not__ work:

```{r error = TRUE}
cor(year, lifeExp, data = gapminder)
```

Sure, you can always just repeat the data frame name like so:
```{r}
cor(gapminder$year, gapminder$lifeExp)
```

but people hate typing. I suspect subconscious dread of repeatedly typing `gapminder` is what motivates those who copy variables into stand-alone objects in the workspace.

The `with()` function is a better workaround. Provide the data frame as the first argument. The second argument is an expression that will be evaluated in a special environment. It could be a single command or a multi-line snippet of code. What's special is that you can refer to variables in the data frame by name.

```{r}
with(gapminder,
     cor(year, lifeExp))
```

If you use the magrittr package, another option is to use the `%$%` operator to expose the variables inside a data frame for further computation:

```{r message = FALSE, warning = FALSE}
library(magrittr)
gapminder %$%
  cor(year, lifeExp)
```

##  Tidying and reshaping

This is an entire topic covered elsewhere:

Chapter \@ref(tidy-data) - [Tidy data using Lord of the Rings][tidydata-lotr]

##  Factor management
 
This is an entire topic covered elsewhere:

Chapter \@ref(factors-boss) - [Be the boss of your factors](#factors-boss)

##  Worked example

Inspired by this question from a student when we first started using ggplot2: How can I focus in on country, Japan for example, and plot all the quantitative variables against year?

Your first instinct might be to filter the Gapminder data for Japan and then loop over the variables, creating separate plots which need to be glued together. And, indeed, this can be done. But in my opinion, the data reshaping route is more "R native" given our current ecosystem, than the loop way.

### Reshape your data

We filter the Gapminder data and keep only Japan. Then we use `tidyr::gather()` to *gather* up the variables `pop`, `lifeExp`, and `gdpPercap` into a single `value` variable, with a companion variable `key`.

```{r}
japan_dat <- gapminder %>%
  filter(country == "Japan")
japan_tidy <- japan_dat %>%
  gather(key = var, value = value, pop, lifeExp, gdpPercap)
dim(japan_dat)
dim(japan_tidy)
```

The filtered `japan_dat` has `r nrow(japan_dat)` rows. Since we are gathering or stacking three variables in `japan_tidy`, it makes sense to see three times as many rows, namely `r nrow(japan_tidy)` in the reshaped result.

### Iterate over the variables via faceting

Now that we have the data we need in a tidy data frame, with a proper factor representing the variables we want to "iterate" over, we just have to facet.

```{r japan}
p <- ggplot(japan_tidy, aes(x = year, y = value)) +
  facet_wrap(~ var, scales="free_y")
p + geom_point() + geom_line() +
  scale_x_continuous(breaks = seq(1950, 2011, 15))
```

### Recap

Here's the minimal code to produce our Japan example.

```{r eval = FALSE}
japan_tidy <- gapminder %>%
  filter(country == "Japan") %>%
  gather(key = var, value = value, pop, lifeExp, gdpPercap)
ggplot(japan_tidy, aes(x = year, y = value)) +
  facet_wrap(~ var, scales="free_y") +
  geom_point() + geom_line() +
  scale_x_continuous(breaks = seq(1950, 2011, 15))
```

This snippet demonstrates the payoffs from the rules we laid out at the start:

* We isolate the Japan data into its own __data frame__.
* We __reshape__ the data. We gather three columns into one, because we want to depict them via position along the y-axis in the plot.
* We use a __factor__ to distinguish the observations that belong in each mini-plot, which then becomes a simple application of faceting.
* This is an example of expedient data reshaping. I don't actually believe that `gdpPercap`, `lifeExp`, and `pop` naturally belong together in one variable. But gathering them was by far the easiest way to get this plot.


```{r links, child="links.md"}
```