Base Plotting in R: More Tricks

class: center, middle, inverse, title-slide

.title[
# Base Plotting in <strong>R</strong>: More Tricks
]
.subtitle[
## <a href="https://eligurarie.github.io/EFB654/">EFB 654: R and Reproducible Research</a>
]
.author[
### Elie Gurarie
]
.date[
### <strong>February 18, 2026</strong>
]

---

class: inverse

.pull-left-40[
## Goal: plotting many dimensions at once

Many layers of information!

- colors
- sizes
- labels
- legends
- scales

]

.pull-right-60[

<img src="PlottingPartII_files/figure-html/final-plot-1.png" alt="" style="display: block; margin: auto;" />
]

---

## The data
.small[

``` r
wdi <- read.csv("WDI.csv")
str(wdi)
```

```
## 'data.frame':	208 obs. of  16 variables:
##  $ country    : chr  "India" "China" "United States" "Indonesia" ...
##  $ iso2c      : chr  "IN" "CN" "US" "ID" ...
##  $ iso3c      : chr  "IND" "CHN" "USA" "IDN" ...
##  $ year       : int  2022 2022 2022 2022 2022 2022 2022 2022 2022 2022 ...
##  $ status     : logi  NA NA NA NA NA NA ...
##  $ lastupdated: chr  "2026-01-28" "2026-01-28" "2026-01-28" "2026-01-28" ...
##  $ LifeExp    : num  71.7 78.2 77.4 70.9 67.4 ...
##  $ GDP        : num  2347 12971 76657 4731 1538 ...
##  $ Area       : num  2973190 9388210 9147420 1892555 770880 ...
##  $ Population : int  1425423212 1412175000 334017321 278830529 243700667 223150896 210306415 169384897 144236933 128613117 ...
##  $ region     : chr  "South Asia" "East Asia & Pacific" "North America" "East Asia & Pacific" ...
##  $ capital    : chr  "New Delhi" "Beijing" "Washington D.C." "Jakarta" ...
##  $ longitude  : num  77.2 116.3 -77 106.8 72.8 ...
##  $ latitude   : num  28.6 40 38.9 -6.2 30.5 ...
##  $ income     : chr  "Lower middle income" "Upper middle income" "High income" "Upper middle income" ...
##  $ lending    : chr  "IBRD" "IBRD" "Not classified" "IBRD" ...
```
]

---

## Step 1 — a plain scatter plot

.pull-left[

``` r
plot(wdi$GDP/1e3, wdi$LifeExp)
```

Very basic.

- How to deal with clustering?

]

.pull-right[
<img src="PlottingPartII_files/figure-html/plain-1.png" alt="" style="display: block; margin: auto;" />
]

---

## Step 1b — log scale on x

.pull-left[

``` r
par(cex.lab = 1.2, las = 1,
    mgp = c(2, .25, 0), tck = 0.01)

plot(wdi$GDP/1e3, wdi$LifeExp,
     log  = "x",
     xlab = "GDP per capita (1000 USD, log scale)",
     ylab = "Life expectancy (years)")
```

`log = "x"` applies a log axis  
(`log = "y"`, `log = "xy"` also work)

`las = 1` — horizontal tick labels  
`tck = 0.01` — inward ticks  
`mgp` — controls axis label / tick label / tick line positions
]

.pull-right[
<img src="PlottingPartII_files/figure-html/logplot-1.png" alt="" style="display: block; margin: auto;" />
]

---

.pull-left[

## `par()` reference

| parameter | what it does |
|-----------|-------------|
| `cex.lab` | axis label size |
| `cex.axis`| tick label size |
| `mar`     | margins (bottom, left, top, right) |
| `mgp`     | axis title, tick label, tick line positions |
| `las`     | tick label orientation |
| `tck`     | tick length (+inward, −outward) |
| `family`  | font family (`"serif"`, `"sans"`, `"mono"`) |
| `bty` | box type (`"o"`, `"l"`, `"u"`, `"c"`, `"n"`)
]

.pull-right[

``` r
par("mar")   # default margins
```

```
## [1] 5.1 4.1 4.1 2.1
```

``` r
par("mgp")   # default mgp
```

```
## [1] 3 1 0
```

Here are some of my preferences:

``` r
par(mar = c(3,3,2,2), 
    mgp = c(1.5,.25,0), 
    tck = 0.01, 
    bty = "l")
```

]

---

.pull-left[

## Step 2 — size points by population
.small[

``` r
par(cex.lab = 1.2, las = 1,
    mgp = c(2, .25, 0), tck = 0.01)

plot(wdi$GDP/1e3, wdi$LifeExp,
     log  = "x",
     xlab = "GDP per capita ($1000 USD, log scale)",
     ylab = "Life expectancy (years)",
     cex  = sqrt(wdi$Population /
                 max(wdi$Population,
                     na.rm = TRUE)) * 15)
```

]

`cex` controls point size  
`sqrt()` makes **area** proportional to population

]

.pull-right[
<img src="PlottingPartII_files/figure-html/cex-pop-1.png" alt="" style="display: block; margin: auto;" />
]

---

## Why `sqrt()`?

.pull-left[
A circle's **area** = π r²

If we set `cex` proportional to population directly, the *radius* scales linearly — but our eye reads **area**.

To make area proportional to population:

`$$r \propto \sqrt{\text{population}}$$`

So we use:
```r
cex = sqrt(Population / max_pop) * 15
```

The `* 15` just scales up to a visible range.
]

.pull-right[
<img src="PlottingPartII_files/figure-html/sqrt-demo-1.png" alt="" style="display: block; margin: auto;" />
]

---

.pull-left[
## Step 2b — add transparency

.small[

``` r
par(cex.lab = 1.2, las = 1,
    mgp = c(2, .25, 0), tck = 0.01)

plot(wdi$GDP/1e3, wdi$LifeExp,
     log  = "x",
     xlab = "GDP per capita (1000 USD, log scale)",
     ylab = "Life expectancy (years)",
     cex  = sqrt(wdi$Population /
                 max(wdi$Population,
                     na.rm = TRUE)) * 15,
     pch  = 21,
     col  = "grey40",
     bg   = rgb(0, 0, 0, 0.25))
```
]
`pch = 21` — filled circles with separate border/fill  
`rgb(r, g, b, alpha)` — alpha: 0 = transparent, 1 = opaque

Transparency reveals overlapping points.
]

.pull-right[
<img src="PlottingPartII_files/figure-html/transparent-1.png" alt="" style="display: block; margin: auto;" />
]

---

## `rgb()` and color in R

.pull-left[.small[

``` r
# Pure red, 50% transparent
rgb(1, 0, 0, 0.5)

# Any named color with alpha:
adjustcolor("steelblue", alpha.f = 0.4)

# scales package convenience function:
scales::alpha("steelblue", 0.4)
```

``` r
rgb(1, 0, 0, 0.5)
```

```
## [1] "#FF000080"
```

``` r
adjustcolor("steelblue", alpha.f = 0.4)
```

```
## [1] "#4682B466"
```
]]

.pull-right[
All values 0–1 (or use `maxColorValue = 255`)

<img src="PlottingPartII_files/figure-html/unnamed-chunk-6-1.png" alt="" style="display: block; margin: auto;" />
]

---

.pull-left[

## Step 3 — color by region

.small[

``` r
wdi$region <- factor(wdi$region)

cols <- c(
  "East Asia & Pacific"        = "steelblue",
  "Europe & Central Asia"      = "forestgreen",
  "Latin America & Caribbean"  = "goldenrod",
  "Middle East & North Africa" = "darkorange",
  "North America"              = "mediumpurple",
  "South Asia"                 = "firebrick",
  "Sub-Saharan Africa"         = "sienna")

cont_col <- cols[match(wdi$region,
                       levels(wdi$region))]

.pull-right[
<img src="PlottingPartII_files/figure-html/region-col-1.png" alt="" style="display: block; margin: auto;" />
]

---

## How `match()` maps colors to categories
.small[
`levels()` gives factor levels in order

``` r
levels(wdi$region)
```

```
## [1] "East Asia & Pacific"        "Europe & Central Asia"     
## [3] "Latin America & Caribbean"  "Middle East & North Africa"
## [5] "North America"              "South Asia"                
## [7] "Sub-Saharan Africa"
```

`match()` returns each row's position in that ordered list

``` r
wdi$region[1:20]
```

```
##  [1] South Asia                 East Asia & Pacific       
##  [3] North America              East Asia & Pacific       
##  [5] South Asia                 Sub-Saharan Africa        
##  [7] Latin America & Caribbean  South Asia                
##  [9] Europe & Central Asia      Latin America & Caribbean 
## [11] Sub-Saharan Africa         East Asia & Pacific       
## [13] East Asia & Pacific        Middle East & North Africa
## [15] Sub-Saharan Africa         East Asia & Pacific       
## [17] Middle East & North Africa Europe & Central Asia     
## [19] Europe & Central Asia      East Asia & Pacific       
## 7 Levels: East Asia & Pacific ... Sub-Saharan Africa
```

``` r
match(wdi$region, levels(wdi$region))[1:20]
```

```
##  [1] 6 1 5 1 6 7 3 6 2 3 7 1 1 4 7 1 4 2 2 1
```

`cols[match(...)]` picks the right color by index

``` r
cols[match(wdi$region, levels(wdi$region))]
```

```
##                 South Asia        East Asia & Pacific 
##                "firebrick"                "steelblue" 
##              North America        East Asia & Pacific 
##             "mediumpurple"                "steelblue" 
##                 South Asia         Sub-Saharan Africa 
##                "firebrick"                   "sienna" 
##  Latin America & Caribbean                 South Asia 
##                "goldenrod"                "firebrick" 
##      Europe & Central Asia  Latin America & Caribbean 
##              "forestgreen"                "goldenrod" 
##         Sub-Saharan Africa        East Asia & Pacific 
##                   "sienna"                "steelblue" 
##        East Asia & Pacific Middle East & North Africa 
##                "steelblue"               "darkorange" 
##         Sub-Saharan Africa        East Asia & Pacific 
##                   "sienna"                "steelblue" 
## Middle East & North Africa      Europe & Central Asia 
##               "darkorange"              "forestgreen" 
##      Europe & Central Asia        East Asia & Pacific 
##              "forestgreen"                "steelblue" 
##      Europe & Central Asia      Europe & Central Asia 
##              "forestgreen"              "forestgreen" 
##         Sub-Saharan Africa         Sub-Saharan Africa 
##                   "sienna"                   "sienna" 
##      Europe & Central Asia         Sub-Saharan Africa 
##              "forestgreen"                   "sienna" 
##        East Asia & Pacific  Latin America & Caribbean 
##                "steelblue"                "goldenrod" 
##        East Asia & Pacific         Sub-Saharan Africa 
##                "steelblue"                   "sienna" 
##      Europe & Central Asia         Sub-Saharan Africa 
##              "forestgreen"                   "sienna" 
## Middle East & North Africa  Latin America & Caribbean 
##               "darkorange"                "goldenrod" 
## Middle East & North Africa      Europe & Central Asia 
##               "darkorange"              "forestgreen" 
##                 South Asia              North America 
##                "firebrick"             "mediumpurple" 
## Middle East & North Africa      Europe & Central Asia 
##               "darkorange"              "forestgreen" 
##         Sub-Saharan Africa      Europe & Central Asia 
##                   "sienna"              "forestgreen" 
##        East Asia & Pacific  Latin America & Caribbean 
##                "steelblue"                "goldenrod" 
##         Sub-Saharan Africa         Sub-Saharan Africa 
##                   "sienna"                   "sienna" 
## Middle East & North Africa         Sub-Saharan Africa 
##               "darkorange"                   "sienna" 
##         Sub-Saharan Africa                 South Asia 
##                   "sienna"                "firebrick" 
##  Latin America & Caribbean         Sub-Saharan Africa 
##                "goldenrod"                   "sienna" 
##        East Asia & Pacific         Sub-Saharan Africa 
##                "steelblue"                   "sienna" 
##         Sub-Saharan Africa         Sub-Saharan Africa 
##                   "sienna"                   "sienna" 
## Middle East & North Africa                 South Asia 
##               "darkorange"                "firebrick" 
##         Sub-Saharan Africa         Sub-Saharan Africa 
##                   "sienna"                   "sienna" 
##      Europe & Central Asia  Latin America & Caribbean 
##              "forestgreen"                "goldenrod" 
##      Europe & Central Asia         Sub-Saharan Africa 
##              "forestgreen"                   "sienna" 
##  Latin America & Caribbean  Latin America & Caribbean 
##                "goldenrod"                "goldenrod" 
##      Europe & Central Asia         Sub-Saharan Africa 
##              "forestgreen"                   "sienna" 
##        East Asia & Pacific         Sub-Saharan Africa 
##                "steelblue"                   "sienna" 
##         Sub-Saharan Africa         Sub-Saharan Africa 
##                   "sienna"                   "sienna" 
##         Sub-Saharan Africa         Sub-Saharan Africa 
##                   "sienna"                   "sienna" 
## Middle East & North Africa  Latin America & Caribbean 
##               "darkorange"                "goldenrod" 
##      Europe & Central Asia  Latin America & Caribbean 
##              "forestgreen"                "goldenrod" 
## Middle East & North Africa  Latin America & Caribbean 
##               "darkorange"                "goldenrod" 
##  Latin America & Caribbean      Europe & Central Asia 
##                "goldenrod"              "forestgreen" 
##      Europe & Central Asia  Latin America & Caribbean 
##              "forestgreen"                "goldenrod" 
##      Europe & Central Asia      Europe & Central Asia 
##              "forestgreen"              "forestgreen" 
##        East Asia & Pacific      Europe & Central Asia 
##                "steelblue"              "forestgreen" 
##      Europe & Central Asia Middle East & North Africa 
##              "forestgreen"               "darkorange" 
##      Europe & Central Asia Middle East & North Africa 
##              "forestgreen"               "darkorange" 
##      Europe & Central Asia         Sub-Saharan Africa 
##              "forestgreen"                   "sienna" 
##      Europe & Central Asia      Europe & Central Asia 
##              "forestgreen"              "forestgreen" 
##         Sub-Saharan Africa        East Asia & Pacific 
##                   "sienna"                "steelblue" 
##        East Asia & Pacific      Europe & Central Asia 
##                "steelblue"              "forestgreen" 
## Middle East & North Africa      Europe & Central Asia 
##               "darkorange"              "forestgreen" 
##  Latin America & Caribbean  Latin America & Caribbean 
##                "goldenrod"                "goldenrod" 
##      Europe & Central Asia      Europe & Central Asia 
##              "forestgreen"              "forestgreen" 
##  Latin America & Caribbean         Sub-Saharan Africa 
##                "goldenrod"                   "sienna" 
##      Europe & Central Asia Middle East & North Africa 
##              "forestgreen"               "darkorange" 
##        East Asia & Pacific      Europe & Central Asia 
##                "steelblue"              "forestgreen" 
##      Europe & Central Asia      Europe & Central Asia 
##              "forestgreen"              "forestgreen" 
##         Sub-Saharan Africa      Europe & Central Asia 
##                   "sienna"              "forestgreen" 
##  Latin America & Caribbean        East Asia & Pacific 
##                "goldenrod"                "steelblue" 
## Middle East & North Africa         Sub-Saharan Africa 
##               "darkorange"                   "sienna" 
## Middle East & North Africa Middle East & North Africa 
##               "darkorange"               "darkorange" 
##  Latin America & Caribbean      Europe & Central Asia 
##                "goldenrod"              "forestgreen" 
##      Europe & Central Asia        East Asia & Pacific 
##              "forestgreen"                "steelblue" 
##  Latin America & Caribbean      Europe & Central Asia 
##                "goldenrod"              "forestgreen" 
##      Europe & Central Asia         Sub-Saharan Africa 
##              "forestgreen"                   "sienna" 
##  Latin America & Caribbean      Europe & Central Asia 
##                "goldenrod"              "forestgreen" 
## Middle East & North Africa         Sub-Saharan Africa 
##               "darkorange"                   "sienna" 
##      Europe & Central Asia      Europe & Central Asia 
##              "forestgreen"              "forestgreen" 
##         Sub-Saharan Africa         Sub-Saharan Africa 
##                   "sienna"                   "sienna" 
##         Sub-Saharan Africa      Europe & Central Asia 
##                   "sienna"              "forestgreen" 
##         Sub-Saharan Africa      Europe & Central Asia 
##                   "sienna"              "forestgreen" 
##      Europe & Central Asia         Sub-Saharan Africa 
##              "forestgreen"                   "sienna" 
##      Europe & Central Asia Middle East & North Africa 
##              "forestgreen"               "darkorange" 
##        East Asia & Pacific  Latin America & Caribbean 
##                "steelblue"                "goldenrod" 
##      Europe & Central Asia      Europe & Central Asia 
##              "forestgreen"              "forestgreen" 
##         Sub-Saharan Africa         Sub-Saharan Africa 
##                   "sienna"                   "sienna" 
## Middle East & North Africa        East Asia & Pacific 
##               "darkorange"                "steelblue" 
##         Sub-Saharan Africa  Latin America & Caribbean 
##                   "sienna"                "goldenrod" 
##        East Asia & Pacific                 South Asia 
##                "steelblue"                "firebrick" 
##        East Asia & Pacific      Europe & Central Asia 
##                "steelblue"              "forestgreen" 
##      Europe & Central Asia  Latin America & Caribbean 
##              "forestgreen"                "goldenrod" 
## Middle East & North Africa                 South Asia 
##               "darkorange"                "firebrick" 
##         Sub-Saharan Africa        East Asia & Pacific 
##                   "sienna"                "steelblue" 
##  Latin America & Caribbean  Latin America & Caribbean 
##                "goldenrod"                "goldenrod" 
##      Europe & Central Asia        East Asia & Pacific 
##              "forestgreen"                "steelblue" 
##        East Asia & Pacific  Latin America & Caribbean 
##                "steelblue"                "goldenrod" 
##        East Asia & Pacific         Sub-Saharan Africa 
##                "steelblue"                   "sienna" 
##        East Asia & Pacific  Latin America & Caribbean 
##                "steelblue"                "goldenrod" 
##      Europe & Central Asia        East Asia & Pacific 
##              "forestgreen"                "steelblue" 
##  Latin America & Caribbean        East Asia & Pacific 
##                "goldenrod"                "steelblue" 
##         Sub-Saharan Africa  Latin America & Caribbean 
##                   "sienna"                "goldenrod" 
##        East Asia & Pacific  Latin America & Caribbean 
##                "steelblue"                "goldenrod" 
##  Latin America & Caribbean        East Asia & Pacific 
##                "goldenrod"                "steelblue" 
##  Latin America & Caribbean  Latin America & Caribbean 
##                "goldenrod"                "goldenrod" 
##      Europe & Central Asia      Europe & Central Asia 
##              "forestgreen"              "forestgreen" 
##  Latin America & Caribbean  Latin America & Caribbean 
##                "goldenrod"                "goldenrod" 
##              North America      Europe & Central Asia 
##             "mediumpurple"              "forestgreen" 
##      Europe & Central Asia        East Asia & Pacific 
##              "forestgreen"                "steelblue" 
##  Latin America & Caribbean        East Asia & Pacific 
##                "goldenrod"                "steelblue" 
##  Latin America & Caribbean  Latin America & Caribbean 
##                "goldenrod"                "goldenrod" 
##        East Asia & Pacific      Europe & Central Asia 
##                "steelblue"              "forestgreen" 
##      Europe & Central Asia      Europe & Central Asia 
##              "forestgreen"              "forestgreen" 
##  Latin America & Caribbean        East Asia & Pacific 
##                "goldenrod"                "steelblue" 
##        East Asia & Pacific        East Asia & Pacific 
##                "steelblue"                "steelblue"
```
]

---

.pull-left[

## Step 4 — add a legend

``` r
par(cex.lab = 1.2, las = 1,
    mar = c(4, 4, 2, 2),
    mgp = c(2, .25, 0), tck = 0.01)
plot(wdi$GDP/1e3, wdi$LifeExp, log = "x",
     xlab = "GDP per capita (1000 USD, log scale)",
     ylab = "Life expectancy (years)",
     cex = sqrt(wdi$Population /
                max(wdi$Population, na.rm=TRUE))*15,
     pch = 21, col = cont_col,
     bg  = scales::alpha(cont_col, .7))

legend("bottomright", title = "Region",
       legend = levels(wdi$region),
       pt.bg  = cols,
       col    = "grey40",
       pch    = 21,
       pt.cex = 1.8,
       bty    = "n")
```
]

.pull-right[
<img src="PlottingPartII_files/figure-html/legend-plot-1.png" alt="" style="display: block; margin: auto;" />
]

---

## `legend()` key arguments

.pull-left[

``` r
legend(
  x      = "bottomright", # position or coordinates
  legend = ...,           # text labels
  col    = ...,           # point/line colors
  pch    = ...,           # point character
  lty    = ...,           # line types (if lines)
  pt.cex = 2,             # symbol size in legend
  cex    = 0.9,           # text size
  bty    = "n",           # no box around legend
  title  = "Region"       # optional title
)
```

Position keywords:  
`"topleft"`, `"topright"`, `"bottomleft"`, `"bottomright"`, `"top"`, `"right"`, ...

Or give exact coordinates:
```r
legend(x = 0.5, y = 60, ...)
```
]

.pull-right[
For **filled** circles (`pch = 21`), use both `col` (border) and `pt.bg` (fill):

``` r
legend("bottomright",
       legend = levels(wdi$region),
       pt.bg  = cols,      # fill
       col    = "grey40",  # border
       pch    = 21,
       pt.cex = 1.8,
       bty    = "n")
```

`pch 21–25` are **filled** shapes — border color (`col`) and fill (`bg` or `pt.bg`) are independent.
]

---

.pull-left[
## Step 5 — reorder by population
.small[

``` r
# Sort descending so large (populous) countries
# are drawn first — smaller ones on top
wdi <- wdi[order(wdi$Population,
                 decreasing = TRUE), ]

cont_col <- cols[match(wdi$region,
                       levels(wdi$region))]
cex_pop  <- sqrt(wdi$Population /
                 max(wdi$Population,
                     na.rm = TRUE)) * 15

par(cex.lab = 1.2, las = 1,
    mar = c(4, 4, 2, 2),
    mgp = c(2, .25, 0), tck = 0.01)
plot(wdi$GDP/1e3, wdi$LifeExp, log = "x",
     xlab = "GDP per capita (1000 USD, log scale)",
     ylab = "Life expectancy (years)",
     cex = cex_pop, pch = 21,
     col = cont_col,
     bg  = scales::alpha(cont_col, .7))
legend("bottomright", title = "Region",
       legend = levels(wdi$region),
       pt.bg = cols, col = "grey40",
       pch = 21, pt.cex = 1.8, bty = "n")
```
]]

.pull-right[
<img src="PlottingPartII_files/figure-html/reorder-1.png" alt="" style="display: block; margin: auto;" />

Large countries drawn first → small countries on top and visible.
]

---

.pull-left[
## Step 6 — label large countries
.small[

]

``` r
# (continuing from previous slide — plot already drawn)
f()
wdi_big <- subset(wdi, Population > 1e8)

text(labels = wdi_big$country,
     x      = wdi_big$GDP/1e3,
     y      = wdi_big$LifeExp,
     cex    = cex_pop[wdi$Population > 1e8] / 5,
     adj    = 0.5)
```

`adj = 0.5` — centered on the point  
(`adj`: 0=left-justified, 1=right-justified)  
`font = 2` — **bold**  
`font = 3` — *italic*  
`pos = 4` — offset to the right of coordinates
]

.pull-right[
<img src="PlottingPartII_files/figure-html/text-full-1.png" alt="" style="display: block; margin: auto;" />
]

---

.pull-left[

## Step 7 — custom grid lines with `abline()`
.small[

``` r
# type="n" draws axes but NO points —
# then add grid, then points on top

plot(..., type = "n")

abline(h = seq(40, 90, 10),
       col = "grey80", lty = 3)
abline(v = c(.5, 1, 2, 5, 10, 20, 50, 100),
       col = "grey80", lty = 3)

points(...)   # drawn on top of grid
```

`abline(h = ...)` — horizontal lines at given y values  
`abline(v = ...)` — vertical lines at given x values

Preferred over `grid()` here because the x-axis is log-scale — `grid()` spaces lines at tick positions, `abline()` lets you choose exact values.
]]

.pull-right[
<img src="PlottingPartII_files/figure-html/grid-show-1.png" alt="" style="display: block; margin: auto;" />
]

---

.pull-left[

## Step 8 — a second legend for point size

``` r
legend("right",
       title  = "Population (millions)",
       legend = c(10, 50, 100, NA, 500),
       pt.cex = sqrt(
         c(10, 50, 100, NA, 500) * 1e6 /
         max(wdi$Population, na.rm = TRUE)
       ) * 15,
       col   = "darkgrey",
       pch   = 21,
       pt.bg = "grey",
       bty   = "n",
       ncol  = 1)
```

Key trick: `pt.cex` uses the **same formula** as the plot, applied to round reference population values.

`NA` entries create blank rows — useful for spacing.
]

.pull-right[
<img src="PlottingPartII_files/figure-html/size-legend-show-1.png" alt="" style="display: block; margin: auto;" />
]

---

## The full code

``` r
wdi <- read.csv("WDI.csv")
wdi <- wdi[order(wdi$Population, decreasing = TRUE), ]
wdi$region <- factor(wdi$region)

cols <- c("East Asia & Pacific"        = "steelblue",
          "Europe & Central Asia"      = "forestgreen",
          "Latin America & Caribbean"  = "goldenrod",
          "Middle East & North Africa" = "darkorange",
          "North America"              = "mediumpurple",
          "South Asia"                 = "firebrick",
          "Sub-Saharan Africa"         = "sienna")

cont_col <- cols[match(wdi$region, levels(wdi$region))]
cex_pop  <- sqrt(wdi$Population / max(wdi$Population, na.rm = TRUE)) * 15

par(cex.lab = 1.2, mar = c(4, 4, 4, 2),
    mgp = c(2, .25, 0), tck = 0.01,
    family = "serif", las = 1)

plot(wdi$GDP/1e3, wdi$LifeExp, log = "x",
     ylab = "Life expectancy (years)",
     xlab = "GDP per capita (1000 USD, log scale)", type = "n")
title("Health vs. Wealth by Nations (2020)", cex.main = 2, font.main = 1)
title(sub = "World Development Indicators (World Bank) - accessed via the WDI package in R)")
abline(h = seq(40, 90, 5), col = "grey80", lty = 3)
abline(v = c(.5,1,2,5,10,20,50,100), col = "grey80", lty = 3)
points(wdi$GDP/1e3, wdi$LifeExp,
       cex = cex_pop + .5, pch = 21, col = cont_col,
       bg  = scales::alpha(cont_col, .7))
legend("bottomright", title = "Region",
       legend = levels(wdi$region),
       pt.bg = cols, col = "grey40",
       pch = 21, pt.cex = 1.8, bty = "n")
legend("right", title = "Population (millions)",
       legend = c(10, 50, 100, NA, 500),
       pt.cex = sqrt(c(10, 50, 100, NA, 500)*1e6 /
                     max(wdi$Population, na.rm = TRUE))*15,
       col = "darkgrey", pch = 21, pt.bg = "grey", bty = "n")
wdi_big <- subset(wdi, Population > 1e8)
text(labels = wdi_big$country,
     x = wdi_big$GDP/1e3, y = wdi_big$LifeExp,
     cex = cex_pop[wdi$Population > 1e8]/5, adj = 0.5)
```

---

## Summary: dimensions encoded in one plot

| Visual channel | Variable | R tool |
|----------------|----------|--------|
| x position (log) | GDP per capita | `log = "x"` |
| y position | Life expectancy | `plot(x, y)` |
| Point **size** | Population | `cex = sqrt(pop/max_pop) * 15` |
| Point **color** | Region | named `cols` + `match()` |
| Text labels | Country name | `text(labels = ...)` |

**Key functions:**

`par()` · `plot()` · `points()` · `abline()` · `rgb()` · `scales::alpha()` · `match()` · `legend()` · `order()` · `subset()` · `text()` · `title()`

---

## Exercise

Using the `wdi` data make a scatter plot of **latitude** vs. **longitude**

- Size points by **Area**
   - Color the poins by **region**
   - Make the color transparency *proportional to the density: i.e. Population/Area*
   - Make the **shape of the point** (e.g. circle, square, triangle ... options in `?points`) reflect the **income bracket**.  
   - Label the **capitals** of the 20 largest countries
   - Create a legend labeling the **point types / income brackets** and **region colors**
   - Make it PRETTY!