Introducción

library(ggplot2)
library(ragg)
library(palmerpenguins)
devtools::session_info(c("ggplot2", "rag", "palmerpenguins"))
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##  setting  value                       
##  version  R version 3.6.3 (2020-02-29)
##  os       Ubuntu 16.04.6 LTS          
##  system   x86_64, linux-gnu           
##  ui       X11                         
##  language (EN)                        
##  collate  C.UTF-8                     
##  ctype    C.UTF-8                     
##  tz       Etc/UTC                     
##  date     2020-08-30                  
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##    backports        1.1.8    2020-06-17 [1] RSPM (R 3.6.0)
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##    palmerpenguins * 0.1.0    2020-07-23 [1] RSPM (R 3.6.0)
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Descripción de los datos

Medidas del tamaño de los pingüinos adultos en busca de alimento cerca de la estación Palmer, en la Antártida

Incluye medidas para especies de pingüinos, isla en el archipiélago de Palmer, tamaño (longitud de la aleta, masa corporal, dimensiones del pico) y sexo.

Variables

Una archivo de datos con 344 individuos y 8 variables:

Partes del cuerpo de los pingÃœinos

species: Un factor que denota especies de pingüinos (Adelia, Chinstrap y Gentoo)

island: un factor que denota una isla en el archipiélago de Palmer, Antártida (Biscoe, Dream o Torgersen)

bill_length_mm: un número que indica la longitud del pico (en milímetros)

bill_depth_mm: un número que indica la ancho del pico (en milímetros)

flipper_length_mm: un número entero que denota la longitud de la aleta (milímetros)

body_mass_g: un número entero que denota la masa corporal (gramos)

sexo: un factor que denota el sexo del pingüino (hembra, macho)

año: un número entero que denota el año del estudio (2007, 2008 o 2009)

penguins <- palmerpenguins::penguins
penguins <- transform(na.omit(penguins),
  species = factor(species),
  island = factor(island),
  sex = factor(sex),
  year = factor(year)
)

Creación de gráficos

Gráfico de dispersión

ggplot(data = penguins) # data

ggplot(data = penguins) + # data
  aes(x = bill_length_mm, y = bill_depth_mm) # variables

ggplot(data = penguins) + # data
  aes(x = bill_length_mm, y = bill_depth_mm) + # variables
  geom_point() # type of plot

ggplot(data = penguins, mapping = aes(x = bill_length_mm, y = bill_depth_mm)) +
  geom_point()

Gráfico de línea

ggplot(data = penguins) +
  aes(x = bill_length_mm, y = bill_depth_mm, colour = species) +
  geom_line()

Gráfico de líneas y puntos

ggplot(penguins) +
  aes(x = bill_length_mm, y = bill_depth_mm) +
  geom_line(aes(colour = species)) +
  geom_point(aes(shape = sex)) # add line

Histograma

ggplot(penguins) +
  aes(x = flipper_length_mm, colour = species) +
  geom_histogram(bins = 30) + 
  theme(legend.position = "bottom")

ggplot(penguins) +
  aes(x = flipper_length_mm, fill = species) +
  geom_histogram(bins = sqrt(nrow(penguins))) + 
  theme(legend.position = "bottom")

ggplot(penguins, aes(flipper_length_mm)) + geom_histogram(bins = sqrt(nrow(penguins)), aes(colour = species)) + theme(legend.position = "bottom")

ggplot(penguins, aes(flipper_length_mm, colour = species)) + geom_histogram(bins = sqrt(nrow(penguins)))

ggplot(penguins, aes(flipper_length_mm, colour = species)) + geom_freqpoly(bins = sqrt(nrow(penguins)))

ggplot(penguins, aes(bill_depth_mm, colour = species)) + 
  geom_freqpoly(binwidth = 0.5)

ggplot(penguins, aes(bill_depth_mm, colour = species)) + 
  geom_freqpoly(binwidth = 1)

ggplot(penguins, aes(flipper_length_mm, colour = species)) + 
  geom_freqpoly(binwidth = 0.5)

ggplot(penguins, aes(flipper_length_mm, colour = sex)) + 
  geom_histogram(binwidth = 0.5) + 
  facet_wrap(~species, ncol = 3)

ggplot(penguins, aes(flipper_length_mm, colour = species)) + 
  geom_histogram(binwidth = 0.5) + 
  facet_wrap(~sex, ncol = 2)

ggplot(penguins, aes(body_mass_g)) + 
  geom_freqpoly(aes(colour = species), binwidth = 100, na.rm = TRUE) +
  xlim(2500, 6500) + 
  theme(legend.position = "bottom")

ggplot(penguins, aes(body_mass_g)) + 
  geom_histogram(aes(fill = species), binwidth = 100, position = "fill",
    na.rm = TRUE) +
  xlim(2500, 6500) + 
  theme(legend.position = "top")

Gráfico de densidad

ggplot(penguins) +
  aes(x = flipper_length_mm, colour = species) +
  geom_density(aes(fill = species))

ggplot(penguins, aes(body_mass_g, fill = species, colour = species)) + 
  geom_density(na.rm = TRUE) + 
  xlim(2500, 6500) + 
  theme(legend.position = "none")

ggplot(penguins, aes(body_mass_g, fill = species, colour = species)) +
  geom_density(alpha = 0.7, na.rm = TRUE) + 
  xlim(2500, 6500) + 
  theme(legend.position = "left")

Old Faithful

El conjunto de datos comprende longitudes (en minutos) de erupciones del géiser Old Faithful en el Parque Nacional Yellowstone, EE. UU. Los datos están dentro del intervalo [1,67,4,93].

Un marco de datos con 5625 observaciones y 2 variables:

eruptions: Tiempo de erupción en minutos

waiting: Tiempo de espera para la próxima erupción en minutos

Partes del cuerpo de los pingÃœinos

Gráfico de superficie

ggplot(faithfuld, aes(eruptions, waiting)) + 
  geom_contour(aes(z = density, colour = ..level..))

ggplot(faithful, aes(waiting, eruptions)) +
  geom_density_2d()

ggplot(faithfuld, aes(waiting, eruptions, z = density)) + 
  geom_contour_filled()

ggplot(faithfuld, aes(waiting, eruptions, z = density)) + 
  geom_contour(bins = 3)

ggplot(faithfuld, aes(waiting, eruptions, z = density)) + 
  geom_contour(bins = 7)

ggplot(faithfuld, aes(waiting, eruptions, z = density)) + 
  geom_contour(bins = 13)

ggplot(faithfuld, aes(waiting, eruptions, z = density)) + 
  geom_contour(bins = 19)

ggplot(faithfuld, aes(waiting, eruptions, z = density)) + 
  geom_contour(binwidth = 0.003)

ggplot(faithfuld, aes(waiting, eruptions, z = density)) + 
  geom_contour(binwidth = 0.007)

ggplot(faithfuld, aes(waiting, eruptions, z = density)) + 
  geom_contour(binwidth = 0.013)

ggplot(faithfuld, aes(waiting, eruptions, z = density)) + 
  geom_contour(binwidth = 0.019)

ggplot(faithfuld, aes(waiting, eruptions, z = density)) + 
  geom_contour(aes(colour = after_stat(level)))

ggplot(faithfuld, aes(waiting, eruptions, z = density)) + 
  geom_contour(colour = "red")

ggplot(faithfuld, aes(waiting, eruptions, z = density)) + 
  geom_raster(aes(fill = density)) +
  geom_contour(colour = "white")

ggplot(faithfuld, aes(eruptions, waiting)) + 
  geom_raster(aes(fill = density))

small <- faithfuld[seq(1, nrow(faithfuld), by = 10), ]
ggplot(small, aes(eruptions, waiting)) + 
  geom_point(aes(size = density), alpha = 1/3) + 
  scale_size_area()

small <- faithfuld[seq(1, nrow(faithfuld), by = 10), ]
ggplot(small, aes(eruptions, waiting)) + 
  geom_point(aes(size = density), alpha = 1/3) + 
  scale_size_binned()

small <- faithfuld[seq(1, nrow(faithfuld), by = 10), ]
ggplot(small, aes(eruptions, waiting)) + 
  geom_point(aes(size = density), alpha = 1/3) + 
  scale_radius()

Gráfico de dispersión

ggplot(penguins, aes(bill_length_mm, bill_depth_mm)) +
  geom_point(alpha = 1 / 3)

ggplot(penguins, aes(bill_length_mm, bill_depth_mm)) +
  geom_point(alpha = 1 / 3) + 
  xlab("Largo del pico del pingüino (mm)") + 
  ylab("Ancho del pico del pingüino (mm)")

ggplot(penguins, aes(bill_length_mm, bill_depth_mm)) +
  geom_point(alpha = 1 / 3) + 
  xlab(NULL) + 
  ylab(NULL)

ggplot(penguins, aes(x = flipper_length_mm, y = body_mass_g, colour = species)) +
  geom_jitter(width = 0.75, height = 0.75)

ggplot(penguins, aes(x = species, y = flipper_length_mm, colour = species)) +
  geom_jitter(width = 0.50, height = 0.50) + 
  xlim("Adelie", "Chinstrap", "Gentoo") + 
  ylim(160, 240)

ggplot(penguins, aes(x = sex, y = body_mass_g, colour = species)) +
  geom_jitter(width = 0.25, height = 0.50, na.rm = TRUE) + 
  ylim(NA, 6500)

ggplot(penguins, aes(x = flipper_length_mm, y = body_mass_g, colour = species)) +
  geom_point(position = position_jitter(width = 0.75, height = 0.75))

ggplot(penguins, aes(x = species, y = flipper_length_mm, colour = species)) +
  geom_point(position = position_jitter(width = 0.50, height = 0.50)) + 
  xlim("Adelie", "Chinstrap", "Gentoo") + 
  ylim(160, 240)

ggplot(penguins, aes(x = sex, y = body_mass_g, colour = species)) +
    geom_point(position = position_jitter(width = 0.25, height = 0.25)) + 
  ylim(NA, 6500)

Salida del gráfico

p <- ggplot(penguins, aes(flipper_length_mm, body_mass_g, colour = factor(species))) +
  geom_point()
print(p)

# Save png to disk
ggsave("plot.png", p, width = 5, height = 5)
summary(p)
## data: species, island, bill_length_mm, bill_depth_mm,
##   flipper_length_mm, body_mass_g, sex, year [333x8]
## mapping:  x = ~flipper_length_mm, y = ~body_mass_g, colour = ~factor(species)
## faceting: <ggproto object: Class FacetNull, Facet, gg>
##     compute_layout: function
##     draw_back: function
##     draw_front: function
##     draw_labels: function
##     draw_panels: function
##     finish_data: function
##     init_scales: function
##     map_data: function
##     params: list
##     setup_data: function
##     setup_params: function
##     shrink: TRUE
##     train_scales: function
##     vars: function
##     super:  <ggproto object: Class FacetNull, Facet, gg>
## -----------------------------------
## geom_point: na.rm = FALSE
## stat_identity: na.rm = FALSE
## position_identity
saveRDS(p, "plot.rds")
q <- readRDS("plot.rds")

Tipos de geometria individual

df <- data.frame(
  x = runif(n=5, min=1, max=5), 
  y = runif(n=5, min=2, max=6), 
  label = c("a","b","c","d","e")
)
p <- ggplot(df, aes(x, y, label = label)) + 
  labs(x = NULL, y = NULL) + # Hide axis label
  theme(plot.title = element_text(size = 12)) # Shrink plot title
p + geom_point() + ggtitle("point")

p + geom_text() + ggtitle("text")

p + geom_bar(stat = "identity") + ggtitle("bar")

p + geom_tile() + ggtitle("raster")

p + geom_line() + ggtitle("line")

p + geom_area() + ggtitle("area")

p + geom_path() + ggtitle("path")

p + geom_polygon() + ggtitle("polygon")

Tipos de geometría grupos

ggplot(penguins, aes(sex, body_mass_g, group = species)) + 
  geom_point(aes(colour = species)) + 
  geom_line(aes(colour = species))

ggplot(penguins, aes(sex, body_mass_g, group = species)) + 
  geom_jitter(aes(colour = species)) + 
  geom_step(aes(colour = species))

data_summary <- function(data, varname, groupnames){
  require(plyr)
  summary_func <- function(x, col){
    c(mean = mean(x[[col]], na.rm=TRUE),
      sd = sd(x[[col]], na.rm=TRUE))
  }
  data_sum<-ddply(data, groupnames, .fun=summary_func,
                  varname)
  data_sum <- rename(data_sum, c("mean" = varname))
 return(data_sum)
}
PENGUINS <- data_summary(penguins, varname="body_mass_g", 
                    groupnames=c("species", "sex"))
## Loading required package: plyr
head(PENGUINS)
ggplot(PENGUINS, aes(sex, body_mass_g, group = species)) + 
  geom_errorbar(aes(ymin=body_mass_g-sd, ymax=body_mass_g+sd), width=.1, 
    position=position_dodge(0.05)) + 
  geom_path(aes(colour = species))

ggplot(penguins, aes(flipper_length_mm, body_mass_g, group = species)) + 
  geom_point(aes(colour = species)) + 
  geom_smooth(method = "loess", se = TRUE)
## `geom_smooth()` using formula 'y ~ x'

ggplot(penguins, aes(x = flipper_length_mm, y = body_mass_g, colour = species)) + 
  geom_line(aes(group = island)) + 
  geom_smooth(formula = y ~ x, method = "lm", size = 2, se = FALSE)

Gráfico combinado de densidad e histograma

ggplot(penguins, aes(fill = species)) +
  aes(x = flipper_length_mm, y = ..density..) +
  geom_histogram(bins = sqrt(nrow(penguins))) +
  geom_density()

ggplot(penguins) +
  aes(x = flipper_length_mm, color = species, fill = species) +
  geom_density(alpha = 0.25) # add transparency

Gráfico de caja y bigotes

# Boxplot for one variable
ggplot(penguins) +
  aes(x = "Longitud del pico", y = bill_length_mm) +
  geom_boxplot() + 
  stat_summary(fun = mean, geom = "point", shape = rnorm(n = 1), size = runif(n = 1, min = 1, max = 10))

# Boxplot by factor
ggplot(penguins) +
  aes(x = species, y = bill_length_mm) +
  geom_boxplot(outlier.colour = rpois(n = 1, lambda = 100), outlier.shape = rnorm(n = 1), outlier.size = runif(n = 1,min = 1, max = 10)) + 
  stat_summary(fun = mean, geom = "point", shape = runif(1,1,10), size = rnorm(1))

ggplot(penguins) +
  aes(x = species, y = bill_length_mm) +
  geom_boxplot(varwidth = TRUE) 

  geom_jitter(alpha = 0.25, width = 0.2) 
## geom_point: na.rm = FALSE
## stat_identity: na.rm = FALSE
## position_jitter
ggplot(penguins) +
  aes(x = species, y = bill_length_mm) +
  geom_boxplot(varwidth = TRUE) + # vary boxes width according to n obs.
  geom_jitter(alpha = 0.25, width = 0.2) + # adds random noise and limit its width
  facet_wrap(~year) # divide into 2 panels

ggplot(penguins) +
  aes(x = species, y = bill_length_mm, fill = species) + # add color to boxes with fill
  geom_boxplot(varwidth = TRUE) + # vary boxes width according to n obs.
  geom_jitter(alpha = 0.25, width = 0.2) + # adds random noise and limit its width
  facet_wrap(~year) + # divide into 2 panels
  theme(legend.position = "none") # remove legend

ggplot(penguins, aes(species, flipper_length_mm)) + 
  geom_boxplot()

ggplot(penguins, aes(species, flipper_length_mm)) + 
  geom_boxplot() +
  geom_line(colour = "#3366FF", alpha = 0.5)

ggplot(penguins, aes(species, flipper_length_mm)) + 
  geom_boxplot() +
  geom_line(aes(group = sex), colour = "#3366FF", alpha = 0.5)

ggplot(penguins, aes(species, body_mass_g, color = sex)) + geom_jitter()

ggplot(penguins, aes(island, body_mass_g, color = species)) + geom_boxplot()

ggplot(penguins, aes(species, body_mass_g, color = sex)) + geom_violin()

Gráfico de barras

ggplot(penguins) +
  aes(x = species) +
  geom_bar()

ggplot(penguins) +
  aes(x = species, fill = species) + # add colors to bars
  geom_bar() +
  theme(legend.position = "none") # remove legend

ggplot(penguins) +
  aes(x = species, fill = year) + # fill by years
  geom_bar()

ggplot(penguins) +
  geom_bar(aes(x = species, fill = year), position = "fill")

ggplot(penguins) +
  geom_bar(aes(x = species, fill = year), position = "dodge")

Personalización

Etiquetas

p <- ggplot(data = penguins) +
  aes(x = flipper_length_mm, y = body_mass_g, colour = species, shape = sex, alpha = bill_depth_mm) +
  geom_point()

p + labs(
  title = "Body mass for penguins species",
  subtitle = "Period 2007-2009",
  caption = "Data: palmerpenguins::penguins. See more at www.statsandr.com",
  x = "flipper length (milimeters)",
  y = "Body mass (grams)"
)

configuración de los ejes

# Adjust ticks
p + scale_x_continuous(breaks = seq(from = 1, to = 60, by = 5.0)) + # x-axis
  scale_y_continuous(breaks = seq(from = 2500, to = 6500, by = 1000)) # y-axis

Transformación logarítmica

p + scale_x_log10() +
  scale_y_log10()

Límites

p + scale_x_continuous(limits = c(40, 50)) +
  scale_y_continuous(limits = c(3000, 5000))
## Warning: Removed 333 rows containing missing values (geom_point).

Legendas

p + aes(color = species) +
  theme(legend.position = "top")

p + aes(color = species) +
  theme(
    legend.title = element_blank(),
    legend.position = "bottom"
  )

Forma, color, tamaño y transparencia

# Change shape of all points
ggplot(penguins) +
  aes(x = bill_depth_mm, y = body_mass_g) +
  geom_point(shape = 4)

# Change shape of points based on a categorical variable
ggplot(penguins) +
  aes(x = flipper_length_mm, y = body_mass_g, shape = species) +
  geom_point()

p <- ggplot(penguins) +
  aes(x = flipper_length_mm, y = body_mass_g) +
  geom_point()

# Change color for all points
p + geom_point(color = "steelblue")

# Change color based on a qualitative variable
p + aes(color = species)

# Change color based on a quantitative variable
p + aes(color = bill_length_mm)

# Change color based on a criterion (median of cty variable)
p + aes(color = bill_length_mm > median(bill_length_mm))

# Change size of all points
p + geom_point(size = 4)

# Change size of points based on a quantitative variable
p + aes(size = bill_depth_mm)

# Change transparency based on a quantitative variable
p + aes(alpha = bill_length_mm)

p + geom_point(size = 0.5) +
  aes(color = bill_length_mm, shape = year, alpha = bill_depth_mm)

Suavizado y líneas de regresión

p + geom_smooth()
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

p + geom_smooth(method = lm)
## `geom_smooth()` using formula 'y ~ x'

ggplot(penguins, aes(bill_depth_mm, body_mass_g)) + 
  geom_point() + 
  geom_smooth(span = 0.2)
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

ggplot(penguins, aes(bill_length_mm, body_mass_g)) + 
  geom_point() + 
  geom_smooth(span = 1)
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

ggplot(penguins, aes(flipper_length_mm, body_mass_g)) + 
  geom_point() + 
  geom_smooth(span = 1.8)
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

ggplot(penguins, aes(bill_length_mm, body_mass_g)) + 
  geom_point() + 
  geom_smooth(method = "gam", formula = y ~ s(x))

p + aes(color = species, shape = species) +
  geom_smooth(method = lm, se = FALSE)
## `geom_smooth()` using formula 'y ~ x'

# According to one variable
p + facet_grid(. ~ species)

# According to 2 variables
p + facet_grid(species ~ year)

p + facet_grid(. ~ species) +
  geom_smooth(method = lm)
## `geom_smooth()` using formula 'y ~ x'

Temas

# Black and white theme
p + theme_bw()

# Minimal theme
p + theme_minimal()

# Classic theme
p + theme_classic()

theme_set(theme_minimal())

Gráficos interactivos con plotly

library(plotly)
## 
## Attaching package: 'plotly'
## The following objects are masked from 'package:plyr':
## 
##     arrange, mutate, rename, summarise
## The following object is masked from 'package:ggplot2':
## 
##     last_plot
## The following object is masked from 'package:stats':
## 
##     filter
## The following object is masked from 'package:graphics':
## 
##     layout
ggplotly(p + aes(color = year))

Gráficos combinados

p_a <- ggplot(penguins) +
  aes(x = bill_depth_mm, y = body_mass_g) +
  geom_point()

p_b <- ggplot(penguins) +
  aes(x = body_mass_g) +
  geom_histogram()

p_c <- ggplot(penguins) +
  aes(x = sex, y = body_mass_g) +
  geom_boxplot()
library(patchwork)
p_a + p_b + p_c
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

p_a / p_b / p_c
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

p_a + p_b / p_c
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

(p_a + p_b) / p_c
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

Intercambio de coordenadas

# without flipping coordinates
p1 <- ggplot(penguins) +
  aes(x = species, y = flipper_length_mm) +
  geom_boxplot()

# with flipping coordinates
p2 <- ggplot(penguins) +
  aes(x = species, y = flipper_length_mm) +
  geom_boxplot() +
  coord_flip()

library(patchwork)
p1 + p2 # left: without flipping, right: with flipping

ggplot(penguins) +
  aes(x = species) +
  geom_bar() +
  coord_flip()

Guardar el gráfico

ggplot(penguins) +
  aes(x = sex, y = bill_length_mm) +
  geom_point()

ggsave("plot1.pdf")
## Saving 7 x 5 in image

Mapas

library(maps)
## 
## Attaching package: 'maps'
## The following object is masked from 'package:plyr':
## 
##     ozone
mi_counties <- map_data("county", "michigan") %>% 
  select(lon = long, lat, group, id = subregion)
head(mi_counties)
ggplot(mi_counties, aes(lon, lat)) + 
  geom_point(size = .25, show.legend = FALSE) +
  coord_quickmap()

ggplot(mi_counties, aes(lon, lat, group = group)) +
  geom_polygon(fill = "white", colour = "grey50") + 
  coord_quickmap()

library(ozmaps)
library(sf)
## Linking to GEOS 3.5.1, GDAL 2.2.2, PROJ 4.9.2
library(rmapshaper)
## Registered S3 method overwritten by 'geojsonlint':
##   method         from 
##   print.location dplyr
## Warning: v8 Engine is version 3.14.5.9 but version >=6 is required for full functionality. Some rmapshaper functions, notably ms_clip() and ms_erase(), may not work. See https://github.com/jeroen/V8 for help installing a modern version of v8 on your operating system.
oz_states <- ozmaps::ozmap_states
oz_states
plot(quakes[c("long", "lat")], xlim = c(120, 190))
ozmap(add = TRUE)

ggplot(oz_states) + 
  geom_sf() + 
  coord_sf()

sf_oz <- ozmap_data("states")
## plot directly with ggplot2


library(sf)
if (utils::packageVersion("paletteer") < '1.0.0') {
 pal <- paletteer::paletteer_d(package = "ochRe", palette = "namatjira_qual")
} else {
  pal <- paletteer::paletteer_d(palette = "ochRe::namatjira_qual")
} 

opal <- colorRampPalette(pal)
nmjr <- opal(nrow(sf_oz))
plot(st_geometry(sf_oz), col = nmjr)

library(ggplot2)
ggplot(sf_oz, aes(fill = NAME)) + geom_sf() + coord_sf(crs = "+proj=lcc +lon_0=135 +lat_0=-30 +lat_1=-10 +lat_2=-45 +datum=WGS84") + scale_fill_manual(values = nmjr)

ozmap("abs_ced", col = opal(nrow(abs_ced)))

ozmap("abs_ste", col = opal(nrow(abs_ste)))

library(dplyr)
## 
## Attaching package: 'dplyr'
## The following objects are masked from 'package:plyr':
## 
##     arrange, count, desc, failwith, id, mutate, rename, summarise,
##     summarize
## The following objects are masked from 'package:stats':
## 
##     filter, lag
## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union
kbor <- abs_lga %>% dplyr::filter(grepl("Kingborough", NAME))
bb <- st_bbox(kbor)

layout(matrix(c(1, 1, 1, 2, 2, 2, 2, 2, 2), nrow = 3))
plot(kbor, reset = FALSE, main = "Kingborough (TAS)")
rect(bb["xmin"], bb["ymin"], bb["xmax"], bb["ymax"])
library(mapdata)
#> Loading required package: maps
par(mar = rep(0, 4))
plot(c(145, 148.5), c(-43.6, -40.8), type = "n", asp = 1/cos(mean(bb[c(2, 4)]) * pi/180), axes = FALSE, xlab = "", ylab = "")
maps::map(database = "worldHires", regions = "australia", xlim = c(145, 148.5), ylim = c(-43.6, -40.8), add = TRUE)
rect(bb["xmin"], bb["ymin"], bb["xmax"], bb["ymax"])

# filter electorates in the Sydney metropolitan region
sydney_map <- ozmaps::abs_ced %>% filter(NAME %in% c(
  "Sydney", "Wentworth", "Warringah", "Kingsford Smith", "Grayndler", "Lowe", 
  "North Sydney", "Barton", "Bradfield", "Banks", "Blaxland", "Reid", 
  "Watson", "Fowler", "Werriwa", "Prospect", "Parramatta", "Bennelong", 
  "Mackellar", "Greenway", "Mitchell", "Chifley", "McMahon"
))
# draw the electoral map of Sydney
ggplot(sydney_map) + 
  geom_sf(aes(fill = NAME), show.legend = FALSE) + 
  coord_sf(xlim = c(150.97, 151.3), ylim = c(-33.98, -33.79)) + 
  geom_sf_label(aes(label = NAME), label.padding = unit(1, "mm"))
## Warning in st_point_on_surface.sfc(sf::st_zm(x)): st_point_on_surface may not
## give correct results for longitude/latitude data

oz_capitals <- tibble::tribble( 
  ~city,           ~lat,     ~lon,
  "Sydney",    -33.8688, 151.2093,  
  "Melbourne", -37.8136, 144.9631, 
  "Brisbane",  -27.4698, 153.0251, 
  "Adelaide",  -34.9285, 138.6007, 
  "Perth",     -31.9505, 115.8605, 
  "Hobart",    -42.8821, 147.3272, 
  "Canberra",  -35.2809, 149.1300, 
  "Darwin",    -12.4634, 130.8456, 
)

ggplot() + 
  geom_sf(data = oz_states, colour = "black", fill = NA) + 
  geom_point(data = oz_capitals, mapping = aes(x = lon, y = lat), colour = "red") + 
  coord_sf()