03 - geoms

# 03 - geoms
## Data visualization in R
### <br> dr. mine çetinkaya-rundel <br> duke university & rstudio

---

# 🔗 [bit.ly/dataviz-enar-2022](https://bit.ly/dataviz-enar-2022)

To follow along with the exercises, open and make a permanent copy of the RStudio Cloud project at https://rstudio.cloud/project/3796661.

---

# Setup

---

## Packages

```r
# load packages
library(tidyverse)
library(openintro)
```

---

## ggplot2 theme

```r
# set default theme for ggplot2
ggplot2::theme_set(ggplot2::theme_minimal(base_size = 16))
```

---

## Figure sizing

For more on including figures in R Markdown documents with the right size, resolution, etc. the following resources are great:

- [R for Data Science - Graphics for communication](https://r4ds.had.co.nz/graphics-for-communication.html)
- [Tips and tricks for working with images and figures in R Markdown documents](https://www.zevross.com/blog/2017/06/19/tips-and-tricks-for-working-with-images-and-figures-in-r-markdown-documents/)

```r
# set default figure parameters for knitr
knitr::opts_chunk$set(
  fig.width = 8,     # 8"
  fig.asp = 0.618,   # the golden ratio
  fig.retina = 3,    # dpi multiplier for displaying HTML output on retina
  dpi = 300,         # higher dpi, sharper image
  out.width = "60%"
)
```

---

## Data prep: new variables

```r
duke_forest <- duke_forest %>%
  mutate(
    decade_built = (year_built %/% 10) * 10,
    decade_built_cat = case_when(
      decade_built <= 1940 ~ "1940 or before",
      decade_built >= 1990 ~ "1990 or after",
      TRUE                 ~ as.character(decade_built)
    ),
    decade_built_cat = factor(decade_built_cat, ordered = TRUE)
  )

duke_forest %>%
  select(year_built, decade_built, decade_built_cat)
```

```
## # A tibble: 98 × 3
##    year_built decade_built decade_built_cat
##         <dbl>        <dbl> <ord>           
##  1       1972         1970 1970            
##  2       1969         1960 1960            
##  3       1959         1950 1950            
##  4       1961         1960 1960            
##  5       2020         2020 1990 or after   
##  6       2014         2010 1990 or after   
##  7       1968         1960 1960            
##  8       1973         1970 1970            
##  9       1972         1970 1970            
## 10       1964         1960 1960            
## # … with 88 more rows
```

---

## Data prep: summary table

```r
mean_area_decade <- duke_forest %>%
  group_by(decade_built_cat) %>%
  summarise(mean_area = mean(area))

mean_area_decade
```

```
## # A tibble: 6 × 2
##   decade_built_cat mean_area
##   <ord>                <dbl>
## 1 1940 or before       2072.
## 2 1950                 2545.
## 3 1960                 2873.
## 4 1970                 3413.
## 5 1980                 2889.
## 6 1990 or after        2822.
```

---

# Geoms

---

## Geoms

- Geometric objects, or geoms for short, perform the actual rendering of the layer, controlling the type of plot that you create

- You can think of them as "the geometric shape used to represent the data"

---

## One variable

- Discrete:
  - `geom_bar()`: display distribution of discrete variable.

- Continuous
  - `geom_histogram()`: bin and count continuous variable, display with bars
  - `geom_density()`: smoothed density estimate
  - `geom_dotplot()`: stack individual points into a dot plot
  - `geom_freqpoly()`: bin and count continuous variable, display with lines

---

## .hand[aside...]

Always use "typewriter text" (monospace font) when writing function names, and follow with `()`, e.g.,

- `geom_freqpoly()`
- `mean()`
- `lm()`

---

## `geom_bar()`

```r
ggplot(duke_forest, aes(x = decade_built_cat)) +
  geom_bar()
```

---

## `geom_bar()`

```r
ggplot(duke_forest, aes(y = decade_built_cat)) +
  geom_bar()
```

---

## `geom_histogram()`

```r
ggplot(duke_forest, aes(x = price)) +
  geom_histogram()
```

---

## `geom_histogram()` and `binwidth`

```r
ggplot(duke_forest, aes(x = price)) +
  geom_histogram(binwidth = 20000)
```

<img src="03-geoms_files/figure-html/unnamed-chunk-9-1.png" width="60%" />
]
.panel[.panel-name[50K]

```r
ggplot(duke_forest, aes(x = price)) +
  geom_histogram(binwidth = 50000)
```

<img src="03-geoms_files/figure-html/unnamed-chunk-10-1.png" width="60%" />
]
.panel[.panel-name[100K]

```r
ggplot(duke_forest, aes(x = price)) +
  geom_histogram(binwidth = 100000)
```

<img src="03-geoms_files/figure-html/unnamed-chunk-11-1.png" width="60%" />
]
.panel[.panel-name[200K]

```r
ggplot(duke_forest, aes(x = price)) +
  geom_histogram(binwidth = 200000)
```

<img src="03-geoms_files/figure-html/unnamed-chunk-12-1.png" width="60%" />
]
]

---

## `geom_density()`

```r
ggplot(duke_forest, aes(x = price)) +
  geom_density()
```

---

## `geom_density()` and bandwidth (`bw`)

```r
ggplot(duke_forest, aes(x = price)) +
  geom_density(bw = 1)
```

<img src="03-geoms_files/figure-html/unnamed-chunk-14-1.png" width="60%" />
]
.panel[.panel-name[1000]

```r
ggplot(duke_forest, aes(x = price)) +
  geom_density(bw = 1000)
```

<img src="03-geoms_files/figure-html/unnamed-chunk-15-1.png" width="60%" />
]
.panel[.panel-name[50000]

```r
ggplot(duke_forest, aes(x = price)) +
  geom_density(bw = 50000)
```

<img src="03-geoms_files/figure-html/unnamed-chunk-16-1.png" width="60%" />
]
.panel[.panel-name[500000]

```r
ggplot(duke_forest, aes(x = price)) +
  geom_density(bw = 500000)
```

<img src="03-geoms_files/figure-html/unnamed-chunk-17-1.png" width="60%" />
]
]

---

## `geom_density()` outlines

```r
ggplot(duke_forest, aes(x = price)) +
  geom_density(outline.type = "full")
```

<img src="03-geoms_files/figure-html/unnamed-chunk-18-1.png" width="60%" />
]
.panel[.panel-name[both]

```r
ggplot(duke_forest, aes(x = price)) +
  geom_density(outline.type = "both")
```

<img src="03-geoms_files/figure-html/unnamed-chunk-19-1.png" width="60%" />
]
.panel[.panel-name[upper]

```r
ggplot(duke_forest, aes(x = price)) +
  geom_density(outline.type = "upper")
```

<img src="03-geoms_files/figure-html/unnamed-chunk-20-1.png" width="60%" />
]
.panel[.panel-name[lower]

```r
ggplot(duke_forest, aes(x = price)) +
  geom_density(outline.type = "lower")
```

<img src="03-geoms_files/figure-html/unnamed-chunk-21-1.png" width="60%" />
]
]

---

## `geom_dotplot()`

.task[
What does each point represent? How are their locations determined? What do the x and y axes represent?
]

```r
ggplot(duke_forest, aes(x = price)) +
  geom_dotplot(binwidth = 50000)
```

---

## `geom_freqpoly()`

```r
ggplot(duke_forest, aes(x = price)) +
  geom_freqpoly(binwidth = 50000)
```

---

## `geom_freqpoly()` for comparisons

```r
ggplot(duke_forest, aes(x = price, fill = decade_built_cat)) +
  geom_histogram(binwidth = 100000)
```

<img src="03-geoms_files/figure-html/unnamed-chunk-25-1.png" width="60%" />
]
.panel[.panel-name[Frequency polygon]

```r
ggplot(duke_forest, aes(x = price, color = decade_built_cat)) +
  geom_freqpoly(binwidth = 100000, size = 1)
```

<img src="03-geoms_files/figure-html/unnamed-chunk-26-1.png" width="60%" />
]
]

---

## Two variables - both continuous

- `geom_point()`: scatterplot
- `geom_quantile()`: smoothed quantile regression
- `geom_rug()`: marginal rug plots
- `geom_smooth()`: smoothed line of best fit
- `geom_text()`: text labels

---

## `geom_rug()`

```r
ggplot(duke_forest, aes(x = area, y = price)) +
  geom_point() +
  geom_rug()
```

---

## `geom_rug()` on the outside

```r
ggplot(duke_forest, aes(x = area, y = price)) +
  geom_point() +
  geom_rug(outside = TRUE) +
  coord_cartesian(clip = "off")
```

---

## `geom_rug()` on the outside, but better

```r
ggplot(duke_forest, aes(x = area, y = price)) +
  geom_point() +
  geom_rug(outside = TRUE, sides = "tr") +
  coord_cartesian(clip = "off") +
  theme(plot.margin = margin(1, 1, 1, 1, "cm"))
```

---

## `geom_text()`

```r
ggplot(duke_forest, aes(x = area, y = price)) +
  geom_text(aes(label = bed))
```

---

## `geom_text()` and more

```r
ggplot(duke_forest, aes(x = area, y = price)) +
  geom_text(aes(label = bed, size = bed, color = bed))
```

---

## `geom_text()` and even more

```r
ggplot(duke_forest, aes(x = area, y = price)) +
  geom_text(
    aes(label = bed, size = bed, color = bed),
    show.legend = FALSE
  )
```

---

## Two variables - show distribution

- `geom_bin2d()`: bin into rectangles and count
- `geom_density2d()`: smoothed 2d density estimate
- `geom_hex()`: bin into hexagons and count

---

## `geom_hex()`

```r
ggplot(duke_forest, aes(x = area, y = price)) +
  geom_hex()
```

---

## `geom_hex()` and warnings

- Requires installing the [**hexbin**](https://cran.r-project.org/web/packages/hexbin/index.html) package separately!

```r
install.packages("hexbin")
```

- Otherwise you might see

```
Warning: Computation failed in `stat_binhex()`
```

---

## Two variables

- At least one discrete
  - `geom_count()`: count number of point at distinct locations
  - `geom_jitter()`: randomly jitter overlapping points

- One continuous, one discrete
  - `geom_col()`: a bar chart of pre-computed summaries
  - `geom_boxplot()`: boxplots
  - `geom_violin()`: show density of values in each group

---

## `geom_jitter()`

```r
ggplot(duke_forest, aes(x = bed, y = price)) +
  geom_point()
```

<img src="03-geoms_files/figure-html/unnamed-chunk-35-1.png" width="60%" />
]
.panel[.panel-name[Plot B]

```r
ggplot(duke_forest, aes(x = bed, y = price)) +
  geom_jitter()
```

<img src="03-geoms_files/figure-html/unnamed-chunk-36-1.png" width="60%" />
]
.panel[.panel-name[Plot C]

```r
ggplot(duke_forest, aes(x = bed, y = price)) +
  geom_jitter()
```

<img src="03-geoms_files/figure-html/unnamed-chunk-37-1.png" width="60%" />
]
]

---

## `geom_jitter()` and `set.seed()`

```r
set.seed(1234)
ggplot(duke_forest, aes(x = bed, y = price)) +
  geom_jitter()
```

<img src="03-geoms_files/figure-html/unnamed-chunk-39-1.png" width="60%" />
]
.panel[.panel-name[Plot B]

```r
set.seed(1234)
ggplot(duke_forest, aes(x = bed, y = price)) +
  geom_jitter()
```

<img src="03-geoms_files/figure-html/unnamed-chunk-40-1.png" width="60%" />
]
]

---

## Two variables

- One time, one continuous
  - `geom_area()`: area plot
  - `geom_line()`: line plot
  - `geom_step()`: step plot

- Display uncertainty:
  - `geom_crossbar()`: vertical bar with center
  - `geom_errorbar()`: error bars
  - `geom_linerange()`: vertical line
  - `geom_pointrange()`: vertical line with center

- Spatial
  - `geom_map()`: fast version of `geom_polygon()` for map data (more on this later...)

---

## Average price per year built

```r
mean_price_year <- duke_forest %>%
  group_by(year_built) %>%
  summarise(
    n = n(),
    mean_price = mean(price),
    sd_price = sd(price)
    )

mean_price_year
```

```
## # A tibble: 44 × 4
##    year_built     n mean_price sd_price
##         <dbl> <int>      <dbl>    <dbl>
##  1       1923     1     285000      NA 
##  2       1934     1     600000      NA 
##  3       1938     1     265000      NA 
##  4       1940     1     105000      NA 
##  5       1941     2     432500   28284.
##  6       1945     2     525000  530330.
##  7       1951     2     567500  258094.
##  8       1952     2     531250  469165.
##  9       1953     2     575000   35355.
## 10       1954     4     600000   33912.
## # … with 34 more rows
```

---

## `geom_line()`

```r
ggplot(mean_price_year, aes(x = year_built, y = mean_price)) +
  geom_line()
```

---

## `geom_area()`

```r
ggplot(mean_price_year, aes(x = year_built, y = mean_price)) +
  geom_area()
```

---

## `geom_step()`

```r
ggplot(mean_price_year, aes(x = year_built, y = mean_price)) +
  geom_step()
```

---

## `geom_errorbar()`

.task[
Describe how this plot is constructed and what the points and the lines (error bars) correspond to.
]

```r
ggplot(mean_price_year, aes(x = year_built, y = mean_price)) +
  geom_point() +
  geom_errorbar(aes(ymin = mean_price - sd_price,
                    ymax = mean_price + sd_price))
```
]
.panel[.panel-name[Plot]
<img src="03-geoms_files/figure-html/unnamed-chunk-45-1.png" width="60%" />
]
]

---

## Let's clean things up a bit!

Meet your new best friend, the [**scales**](https://scales.r-lib.org/) package!

```r
library(scales)
```

---

## Let's clean things up a bit!

```r
ggplot(duke_forest, aes(x = area, y = price)) +
  geom_point(alpha = 0.6, size = 2, color = "#012169") +
  scale_x_continuous(labels = label_number(big.mark = ",")) +
  scale_y_continuous(labels = label_dollar(scale = 1/1000, suffix = "K")) +
  labs(
    x = "Area (square feet)",
    y = "Sale price (USD)",
    title = "Sale prices of homes in Duke Forest",
    subtitle = "As of November 2020",
    caption = "Source: Zillow.com"
  )
```

]

]
]

---

.task[
Find a new geom that we haven't introduced so far and use it to visualize the `duke_forest` data.
]