13-build-plot.Rmd

# (PART\*) The Grammar {-}

# Build a plot layer by layer

**Learning objectives:**

- Understanding ggplot layers
- How to control layers
- Application to real data


## Building a plot

In this chapter we talk about the grammar of graphics plots and their construction layer by layer.

We use data from the {SpatialEpi} package:
```{r 14-load-libs, message=FALSE, warning=FALSE, paged.print=FALSE}
library(tidyverse)
library(SpatialEpi)
library(patchwork)
```

Let's check what data is inside the package, we can use the *NYleukemia* which contains observations about `leukemia` cases in NY, as well as providing other information about `population` and spatials such as latidude and logitude where the cases were located. 

```{r 14-load-data}
data(NYleukemia)
head(NYleukemia$data,3); head(NYleukemia$geo,3)
```


Let's now make a first layer visualization using `ggplot2`
```{r 14-plot1}
data <- NYleukemia$data
geo <- NYleukemia$geo

p <- data %>%
  ggplot(aes(x=population, y=cases))
p
```

The second layer of our plot would take consideration of the `geoms`

```{r 14-geom1}
p + geom_point()
```


In general when we make a ggplot, we build the plot without thinking about the layers, but what is happening inside the hood when we add a layer?

The `layer()` function is called for combining **data**, **stat** and **geom**.

Layers are created using `geom_* or stat_*` calls or directly using the function:

      layer(
        geom = NULL,
        stat = NULL,
        data = NULL,
        mapping = NULL,
        position = NULL,
        params = list(),
        inherit.aes = TRUE,
        check.aes = TRUE,
        check.param = TRUE,
        show.legend = NA,
        key_glyph = NULL,
        layer_class = Layer
      )
      
      
To obtain the same results:
```{r 14-layer-function, include=FALSE}
p + layer(
  mapping = NULL, 
  data = NULL,
  geom = "point", 
  stat = "identity",
  position = "identity"
)
```

`layer()` function components:

- mapping
- data
- geom
- stat
- position
- ...



## Data

The layers of your plot can be populated with different datasets.

```{r 14-df}
df <- data %>%
  inner_join(geo, by = "censustract.FIPS")
```

Here we generate two new datasets from the **df** dataset.

**What `geom_smooth()` does behind the scenes?**

- fit a model, in this case a **loess** model
- generate prediction, about the trend of the data


In this example we create a `grid` of length of 50 to have an average trend to show in a secondary layer of the plot.
```{r 14-loess-model}
mod <- loess(cases ~ population, data = df)
grid <- tibble(population = seq(min(df$population), max(df$population), length = 50))
grid$cases <- predict(mod, newdata = grid)

head(grid,3);dim(grid);dim(df)
```

Next step would be to isolate the **outliers** (observations far away from predicted values), with the help of the `resid()` function to extract model residuals
```{r 14-resid, eval=FALSE, include=T}
?resid()
```

```{r 14-summary}
summary(mod)
```

And build the residuals std error vector:
```{r 14-outliers}
std_resid <- resid(mod) / mod$s
outlier <- filter(df, abs(std_resid) > 2)

head(outlier,3); dim(outlier)
```

Add a new layer with different data: **grid**
```{r 14-plot2}
ggplot(df, aes(population, cases)) + 
  geom_point() + 
  geom_line(data = grid, colour = "blue", size = 1.5)
  
# geom_text(data = outlier, aes(label = ... ))
```

```{r 14-smooth,message=FALSE, warning=FALSE, paged.print=FALSE}
ggplot(df, aes(population, cases)) + 
  geom_point() + 
  geom_smooth(se=F)
```


### Exercises

2.  Recreate the plot in the book
```{r 14-ex1}
class <- mpg %>% 
  group_by(class) %>% 
  summarise(n = n(), hwy = mean(hwy))
```

```{r 14-ex11}
class <- class%>%
  mutate(text_label=paste("n =",n))
```


```{r 14-ex12}
ggplot(data=mpg,aes(x=class,y=hwy,group=class))+
  geom_jitter()+
  geom_point(data=class,aes(x=class,y=hwy,group=n),color="red",size=3)+
  geom_text(data=class,aes(x=class,y=hwy,group=n,label=text_label),
            size=3,
            position = position_stack(vjust = 0))
```


## Aesthetic mappings

The aesthetics: `aes()` allows for some omissions, under certain conditions.
The complete syntax would be:

    ggplot( data = ..., mapping = aes(x = ..., y = ..., ...))

In general `x = ` and `y = ` inside the `aes(x = ..., y = ..., ...)` can be omitted.
Sometimes R asks you about the missing **mapping**, and this is when more than one layer with different datasets is used. To solve the issue would be enough to add all the specifications inside the aesthetics.

One more interesting thing to mention is:

**What manipulation happens when complex tranformations are set inside the aes()?**

As an example , if we apply the **log** transformation:
(the example is from the `diamond` dataset)

    aes(log(carat), log(price))
    
What happens behind the scenes is an explicit call to `dplyr::mutate()`

(The symbol `$` is not allowed inside the `aes()`)


### Specifying the aesthetics in the plot vs. in the layers

All of these alternatives are allowed:

```r
ggplot(mpg, aes(displ, hwy, colour = class)) + 
  geom_point()
  
ggplot(mpg, aes(displ, hwy)) + 
  geom_point(aes(colour = class))
  
ggplot(mpg, aes(displ)) + 
  geom_point(aes(y = hwy, colour = class))
  
ggplot(mpg) + 
  geom_point(aes(displ, hwy, colour = class))
```

But under some conditions, such as the use of a `geom_smooth()`, the position of secondary arguments need to be specified in the layer parameters, as it is important for releasing correct results.

In the first case the smooth line doesn't show up.
```{r 14-plot3}
ggplot(df, aes(population, cases, colour = censustract.FIPS)) + 
  geom_point() + 
  geom_smooth(method = "lm", se = FALSE) +
  theme(legend.position = "none")
#> `geom_smooth()` using formula 'y ~ x'

ggplot(df, aes(population, cases)) + 
  geom_point(aes(colour = censustract.FIPS)) + 
  geom_smooth(method = "lm", se = FALSE) + 
  theme(legend.position = "none")
#> `geom_smooth()` using formula 'y ~ x'
```


### Setting vs. mapping

**What is the difference between *mapping* and *setting* an aesthetic?**

To **map** an aesthetic **to a variable** there are different options, you can put the **color** argument (or other secondary arguments) inside or outside the aesthetic with different results:
      
    geom_...(aes(colour = cut))
    geom_...(colour="red")

Or **set** an aesthetic **to a constant**, a specific color-value, in case of a color argument:

    ...,colour = "red")
    
An alternative would be to use the function:

    scale_colour_identity()

```{r 14-plot4}
ggplot(df, aes(population, cases)) + 
   geom_point(colour = "darkblue") 
   #geom_point(aes(colour = "darkblue")) +
   #scale_colour_identity()
```


In case of more than one `geom_smooth()` being used in the plot, the different colors can be specified with `scale_color_...()` function.
```{r 14-plot5}
ggplot(df, aes(population, cases)) + 
  geom_point(shape=1) +
  geom_smooth(aes(colour = "loess"), method = "loess", se = FALSE) + 
  geom_smooth(aes(colour = "lm"), method = "lm", se = FALSE) +
  scale_color_viridis_d() +
  labs(colour = "Method") +
  theme_bw()
#> `geom_smooth()` using formula 'y ~ x'
#> `geom_smooth()` using formula 'y ~ x'
```


## Geoms

**geoms** stands for **geometric objects** for short. Some geoms requires both `x` and `y` while others not, as well as other require more than simply x and y, such as xmax, ymax etc.

If you do `geom_` and tab all the available geoms appear in a list for you to choose from.


As an example here we use the `geom_quantile()` to represent a smoothed quantile regression and the `geom_rug()` for maginal rugs.
```{r 14-quantile}
ggplot(df, aes(population, cases)) + 
  geom_quantile() +
  geom_point() +
  geom_rug()
```


### Exercises

[Discussion](https://ggplot2-book.org/layers.html)

- The book suggests to download the cheatsheets: [ggplot2 cheatsheet](https://www.rstudio.com/resources/cheatsheets/)

- (Ex.5) Display how a variable has changed over time:
[source](http://www.sthda.com/english/articles/32-r-graphics-essentials/128-plot-time-series-data-using-ggplot/)
```{r 14-economics}
data(economics)
head(economics,3)

ggplot(data = economics, aes(x = date, y = pop))+
  geom_line(color = "#00AFBB", size = 2)
```

- Show the detailed distribution of a single variable
The distribution can be described using a frequency table and histogram.
```{r 14-plot6}
economics%>%
  ggplot(aes(x=uempmed)) +
  geom_histogram(bins = 30,color="white")
```

- Focus attention on the overall trend in a large dataset

[Interesting resource](https://okanbulut.github.io/bigdata/visualizing-big-data.html)

```{r 14-plot7}
economics%>%
  ggplot(aes(x=date,y=unemploy))+
  geom_line()+
  geom_smooth()
```

- Draw a map
```{r 14-plot8}
map <- fortify(scotland$spatial.polygon)

ggplot(data=scotland$data,
                    aes(map_id = county.names)) +
  geom_map(map = map,aes(fill=cases)) +
  expand_limits(x = map$long, y = map$lat) +
  labs(title="Scotland cases") +
  ggthemes::theme_map()+
  theme(legend.position = "bottom")
```

- Label outlying points
```{r 14-plot9}
sc_data <- scotland$data
ggplot(data=sc_data,aes(x=cases,y=expected))+
  geom_point()+
  geom_text(data=sc_data%>%filter(cases>20|expected>50),
            aes(x=cases,y=expected,label=expected),
            vjust="bottom",hjust="left")
```


## Stats


There are several `stat_...()` functions used to transform the data by summarizing information.

For example the `stat_ecdf()` compute the empirical cumulative distribution plot
```{r 14-plot10}
ggplot(data=sc_data,aes(x=cases,y=expected)) +
  #geom_hex() 
  #stat_bin_hex()
  stat_ecdf()
```



Here we use `stat_summary()` function for *categorical data**
```{r 14-plot11}
ggplot(diamonds, aes(cut,carat)) + 
  geom_point(size=1,alpha=0.4) + 
  stat_summary(geom = "point", fun = "median", colour = "red", size = 4) +
  geom_point(stat = "summary", fun = "mean", colour = "blue", size = 2)
```


### Generated variables from the `stat_...()` functions

`stat` takes a data frame as input and returns a data frame as output.

Here we use the `diamonds` dataset, to see hoe the [`after_stat()`](https://ggplot2.tidyverse.org/reference/aes_eval.html) can be applied
```{r 14-plot12}
p1<- ggplot(diamonds, aes(price)) + 
  geom_histogram(binwidth = 500,alpha=0.7,fill="red") 

p2<- ggplot(diamonds, aes(price)) + 
  geom_histogram(aes(y = after_stat(density)), binwidth = 500, 
                 fill="red",alpha=0.7)

# library(patchwork)
p1+ p2 & theme_bw() 
```

### Exercises

- What stats were used to create the Q-Q plot?
```{r 14-plot13}
n<- 10
B<- 100
mns<- apply(matrix(rnorm(n*B),B),1,mean)
mns2<-data.frame(mns)
ggplot(mns2)+
  stat_qq_line(aes(sample=mns),color="red")+
  stat_qq(aes(sample=mns),size=3,alpha=0.5)+
  labs(title="Normal means Normal Q-Q plot",
       x="Theoretical quantiles",
       y="Sample quantiles")+
  theme_bw()
```

- What stats were used to create the Normal density?
```{r 14-plot14}
colors<-c("Simulated distr."="red","Normal distr."="blue")
ggplot(mns2,aes(mns))+
   geom_histogram(aes(y=..density..),color="white",fill="grey55",
                  bins = 30)+
   geom_density(aes(color="Simulated distr."),key_glyph = "path")+
   stat_function(aes(color="Normal distr."),
                 fun = dnorm, key_glyph = "path",
                 args = list(mean = mean(mns), sd = sd(mns)))+
  labs(title="Histogram of the Normal mean distribution",color="")+
  scale_color_manual(values = colors)+
  theme_bw()+
  theme(legend.background = element_blank(),
        legend.box.background = element_blank(),
        legend.position = c(0.8,0.7))
```

## Position adjustments

The position is very important for some `geoms`:

- position_nudge()
- position_jitter()
- position_jitterdodge()

all of them can be used inside the geom:
```{r 14-plot15}
dplot <- ggplot(diamonds, aes(color, fill = cut)) + 
  xlab(NULL) + ylab(NULL) + theme(legend.position = "none")
# position stack is the default for bars, so `geom_bar()` 
# is equivalent to `geom_bar(position = "stack")`.
dplot + geom_bar()
dplot + geom_bar(position = "fill")
dplot + geom_bar(position = "dodge")
ggplot(mpg, aes(displ, hwy)) + 
  geom_point(position = position_jitter(width = 0.05, height = 0.5))
```


- `geom_count()`

```{r 14-plot16}
p3<-ggplot(mpg, aes(cty, hwy)) +
  geom_jitter(alpha=0.4)

p4<-ggplot(mpg, aes(cty, hwy)) +
 geom_count(alpha=0.4) +
 scale_size_area()

p3+p4
```

```{r include=FALSE}
rm(class, map) # Clean up so these objects don't confuse later chapters.
```

## Meeting Videos

### Cohort 1

`r knitr::include_url("https://www.youtube.com/embed/EMozFk_EA88")`

<details>
  <summary> Meeting chat log </summary>
  
```
00:08:06	June Choe:	hey all!
00:08:13	Federica Gazzelloni:	Hi!!
00:08:18	June Choe:	thanks for moving the time to this hour
00:08:37	Federica Gazzelloni:	That’s better for me either
00:08:43	Lydia Gibson:	https://imstat.org/meetings-calendar/ims-international-conference-on-statistics-and-data-science-icsds/
00:08:51	June Choe:	(now i get to call in as I eat lunch at the student common space)
00:57:46	Kent Johnson:	Thank you, this was an interesting chapter!
00:57:52	Michael Haugen:	Thank you!
00:57:57	June Choe:	thanks!
00:58:12	Ryan Metcalf:	Thank you Federica!
00:58:23	Stan Piotrowski:	Thanks for a great presentation!
```
</details>

`r knitr::include_url("https://www.youtube.com/embed/PbrfoBulxfU")`

<details>
  <summary> Meeting chat log </summary>
  
```
00:14:06	June Choe:	gray is default iirc
00:23:25	June Choe:	I wonder if something like this works with datetime values on x

scale_x_date(date_breaks = "2 weeks", offset = 31)
00:23:54	June Choe:	(or offset = -31, maybe)
00:25:30	June Choe:	I see - I'll play around with it more !
00:36:05	Federica Gazzelloni:	rle {base}: Compute the lengths and values of runs of equal values in a vector – or the reverse operation.
00:36:27	Ryan Metcalf:	Sorry team, I have to drop. Great job Kent!
00:48:15	Federica Gazzelloni:	related with cumulative values
00:48:29	Priyanka Gagneja:	Thanks Ryan. See you next time
00:50:34	June Choe:	It's discussed in Advanced R book Ch. 10.2.4! https://adv-r.hadley.nz/function-factories.html?q=stateful#stateful-funs
00:50:43	Federica Gazzelloni:	thanks!
00:52:04	Priyanka Gagneja:	@June this in response the environment() ?
01:05:03	June Choe:	I have the 2nd edition of R Graphics book from 2011 that has a chapter on ggplot2 back then and the code has not changed (i'll see if I can upload a page from that)
01:06:06	June Choe:	they also changed some syntax from tidyr in the new update from like a few days ago
01:06:16	June Choe:	(to make it easier for users especailly with respect to nest!)
01:07:39	June Choe:	thanks!
```
</details>