14-build-plot.Rmd

# Build a plot layer by layer



```{r, include=FALSE}
library(tidyverse)
```


## Exercises

**1.** The first two arguments to ggplot are `data` and `mapping`. The first
two arguments to all layer functions are `mapping` and `data`. Why does the
order of the arguments differ? (Hint: think about what you set most commonly.)

- Commonly, you first set the data in `ggplot()` and then set aesthetics inside your layer functions, like `geom_point()`, `geom_boxplot()`, or `geom_histogram()`. 

<br>

**2.** 

```{r}
library(dplyr)
class <- mpg %>% 
  group_by(class) %>% 
  summarise(n = n(), hwy = mean(hwy))
```

```{r}
mpg %>% 
  ggplot(aes(class, hwy)) +
  geom_jitter(width = 0.15, height = 0.35) +
  geom_point(data = class, aes(class, hwy), 
             color = "red",
             size = 6) +
  geom_text(data = class, aes(y = 10, x = class, label = paste0("n = ", n)))
```

- I plotted 3 different layers: jittered points, red point for the summary measure, mean, and text for the sample size (n).

<br>

## Exercises

**1.** Simplify the following plot specifications:

```{r}
####################################
####################################
# ggplot(mpg) + 
#   geom_point(aes(mpg$displ, mpg$hwy))

# The above can be simplified:
# ggplot(mpg) +
#   geom_point(aes(displ, hwy))
####################################
####################################


####################################
####################################
# ggplot() + 
#  geom_point(mapping = aes(y = hwy, x = cty),
#             data = mpg) +
#  geom_smooth(data = mpg, 
#              mapping = aes(cty, hwy))

# The above can be simplified:
# ggplot(mpg, aes(cty, hwy)) +
#  geom_point() +
#  geom_smooth()
####################################
####################################


####################################
####################################
# ggplot(diamonds, aes(carat, price)) + 
#   geom_point(aes(log(brainwt), log(bodywt)), 
#              data = msleep)

# The above can be simplified:
# msleep_processed <- msleep %>% 
#   mutate(brainwt_log = log(brainwt),
#          bodywt_log = log(bodywt))

# ggplot(diamonds, aes(carat, price)) +
#   geom_point(aes(brainwt_log, bodywt_log), 
#              data = msleep_processed)
####################################
####################################
```

<br>

**2.** What does the following code do? Does it work? Does it make sense? Why/why not?

```{r}
ggplot(mpg) +
  geom_point(aes(class, cty)) + 
  geom_boxplot(aes(trans, hwy))
```

- It plots points of `class` vs `cty` and then a boxplot of `trans` vs `hwy`. It doesn't make sense to plot layers with different `x` and `y` variables.


<br>

**3.** What happens if you try to use a continuous variable on the x axis in one layer, and a categorical variable in another layer? What happens if you do it in the opposite order?

- Not sure

<br>

## Exercises

1,2,3 omitted.

4. Starting from top left, clockwise direction:

- `geom_violin()`, `geom_point()`, `geom_point()`, `geom_path()`, `geom_area()`, `geom_hex()`.




## Exercises

**1.**
```{r}
mod <- loess(hwy ~ displ, data = mpg)
smoothed <- data.frame(displ = seq(1.6, 7, length = 50))
pred <- predict(mod, newdata = smoothed, se = TRUE) 
smoothed$hwy <- pred$fit
smoothed$hwy_lwr <- pred$fit - 1.96 * pred$se.fit
smoothed$hwy_upr <- pred$fit + 1.96 * pred$se.fit

smoothed %>% 
  ggplot(aes(displ, hwy)) +
  geom_line(color = "dodgerblue1") +
  geom_ribbon(aes(ymin = hwy_lwr,
                  ymax = hwy_upr),
              alpha = 0.4)
```

<br>

**2.** From left to right,

`stat_ecdf()`, `stat_qq()`, `stat_function()`

<br>

**3.**
```{r}
mpg %>% 
  ggplot(aes(drv, trans)) +
  geom_count(aes(size = after_stat(prop), group = 1)) 
```


<br>

## Exercises


**1.** According to the help page, `position_nudge()` is generally useful for adjusting the position of items on discrete scales by a small amount. Nudging is built in to geom_text() because it's so useful for moving labels a small distance from what they're labelling.

<br>


**2.** Not sure

<br>

**3.** `geom_jitter()` adds a small amount of random variation to the location of each point. It is useful for looking at all the overplotted  points. On the other hand, `geom_count()` counts the number of overlapping observations at each location. It is useful for understanding the number of points in a location.

**4.** Stacked area plot seems useful when you want to portray an area whereas a line plot seems useful when you just need a line.