In EvaMaeRey/ggextend: What the Package Does (One Line, Title Case)

knitr::opts_chunk$set(echo = TRUE, message = F, warning = F, fig.height = 6, comment = "", cache = T)
options(knitr.duplicate.label = "allow")
library(ggextend)

r flipbookr::chunk_reveal('a_geom_punto_1', title = '### a_geom_punto')

# without the function
library(ggplot2)
ggplot(data = cars) +
 aes(x = speed, y = dist) +
 geom_point()

r flipbookr::chunk_reveal('a_geom_punto_2', title = '### a_geom_punto')

# the function
a_geom_punto
a_geom_punto

r flipbookr::chunk_reveal('a_geom_punto_3', title = '### a_geom_punto')

# using the function
ggplot(data = cars) +
 aes(x = speed, y = dist) +
 a_geom_punto()

r flipbookr::chunk_reveal('b_geom_point_blue_4', title = '### b_geom_point_blue')

# without the function
library(ggplot2)
ggplot(data = cars) +
 aes(x = speed, y = dist) +
 geom_point(color = "blue")

r flipbookr::chunk_reveal('b_geom_point_blue_5', title = '### b_geom_point_blue')

# the function
b_geom_point_blue
b_geom_point_blue

r flipbookr::chunk_reveal('b_geom_point_blue_6', title = '### b_geom_point_blue')

# using the function
ggplot(data = cars) +
 aes(x = speed, y = dist) +
 b_geom_point_blue()

r flipbookr::chunk_reveal('b_geom_ring_7', title = '### b_geom_ring')

# without the function
library(ggplot2)
ggplot(data = cars) +
 aes(x = speed, y = dist) +
 geom_point(shape = 21,
            size = 3)

r flipbookr::chunk_reveal('b_geom_ring_8', title = '### b_geom_ring')

# the function
b_geom_ring
b_geom_ring

r flipbookr::chunk_reveal('b_geom_ring_9', title = '### b_geom_ring')

# using the function
ggplot(data = cars) +
 aes(x = speed, y = dist) +
 b_geom_ring(size = 3) +
 b_geom_ring(aes(fill = speed > 9),
             color = "white",
             size = 8)
# using the function
ggplot(data = cars) +
 aes(x = speed, y = dist) +
 b_geom_ring(fill = "slateblue",
             color = "white",
             size = 8)

r flipbookr::chunk_reveal('c_geom_lm_10', title = '### c_geom_lm')

# without the function
library(ggplot2)
ggplot(data = cars) +
 aes(x = speed, y = dist) +
 geom_point() +
 geom_smooth(method = lm, se = FALSE)

r flipbookr::chunk_reveal('c_geom_lm_11', title = '### c_geom_lm')

# the function
c_geom_lm

r flipbookr::chunk_reveal('c_geom_lm_12', title = '### c_geom_lm')

# using the function
ggplot(data = cars) +
 aes(x = speed, y = dist) +
 geom_point() +
 c_geom_lm()

r flipbookr::chunk_reveal('d_geom_segment_min_max_13', title = '### d_geom_segment_min_max')

# without the function
library(magrittr)
library(dplyr)
cars %>%
 summarize(
   min_speed = min(speed),
   max_speed = max(speed),
   min_dist = min(dist),
   max_dist = max(dist)
 ) ->
 mins_maxs
library(ggplot2)
ggplot(data = cars) +
 aes(x = speed, y = dist) +
 geom_point() +
 geom_segment(data = mins_maxs,
     aes(x = min_speed, xend = max_speed,
         y = min_dist, yend = max_dist))

r flipbookr::chunk_reveal('d_geom_segment_min_max_14', title = '### d_geom_segment_min_max')

# using the function
ggplot(data = cars) +
 aes(x = speed, y = dist) +
 geom_point() +
 d_geom_segment_mins_maxs()

r flipbookr::chunk_reveal('d_geom_segment_min_max_15', title = '### d_geom_segment_min_max')

# the proto
"StatSegmentminmax"
d_geom_segment_mins_maxs

r flipbookr::chunk_reveal('e_geom_lm_intercept_16', title = '### e_geom_lm_intercept')

# without the function
library(magrittr)
library(dplyr)
cars %>%
 lm(dist ~ speed, data = .) ->
 cars_model
 tibble(y_val = cars_model$coefficients[1],
 x_val = 0) ->
 cars_model_intercept
library(ggplot2)
ggplot(data = cars) +
 aes(x = speed, y = dist) +
 geom_point() +
 geom_smooth(method = lm) +
 geom_point(data = cars_model_intercept,
     aes(x = x_val,
         y = y_val),
         color = "red",
         size = 3)

r flipbookr::chunk_reveal('e_geom_lm_intercept_17', title = '### e_geom_lm_intercept')

# using the function
ggplot(data = cars) +
 aes(x = speed, y = dist) +
 geom_point() +
 geom_smooth(method = lm) +
 e_geom_lm_intercept(size = 4) +
 aes(color = speed > 12)

r flipbookr::chunk_reveal('e_geom_lm_intercept_18', title = '### e_geom_lm_intercept')

# the proto and function
"StatOlsintercept"
e_geom_lm_intercept

r flipbookr::chunk_reveal('f_geom_line_endpoint_19', title = '### f_geom_line_endpoint')

# without the function
library(magrittr)
library(dplyr)
cars %>%
  mutate(speed_cat = speed > 8) %>%
  group_by(speed_cat) %>%
  filter(speed == max(speed)) ->
 cars_group_endpoint
library(ggplot2)
ggplot(data = cars) +
 aes(x = speed, y = dist) +
 aes(color = speed > 8) +
 geom_line() +
 geom_point(data = cars_group_endpoint)
layer_data(last_plot(), 2)

r flipbookr::chunk_reveal('f_geom_line_endpoint_20', title = '### f_geom_line_endpoint')

# the function
f_geom_line_endpoint

r flipbookr::chunk_reveal('f_geom_line_endpoint_21', title = '### f_geom_line_endpoint')

# using the function
ggplot(data = cars) +
 aes(x = speed, y = dist) +
 geom_line() +
 f_geom_line_endpoint(size = 4) +
 aes(color = speed > 9)
 layer_data(last_plot(), 2)

r flipbookr::chunk_reveal('f_geom_line_endpoint_22', title = '### f_geom_line_endpoint')

# the proto
"StatEndpoint"
f_geom_line_endpoint

r flipbookr::chunk_reveal('h_geom_prop_in_ts_26', title = '### h_geom_prop_in_ts')

# do it without function
library(tidyverse)
gapminder::gapminder %>%
  mutate(gdp = gdpPercap * pop) ->
my_gapminder
my_gapminder %>%
  group_by(year, continent) %>%
  summarise(gdp = sum(gdp)) %>%
  mutate(prop_gdp = gdp/sum(gdp)) %>%
  ggplot() +
  aes(x = year) +
  aes(y = continent) +
  geom_tile() +
  aes(fill = prop_gdp) +
  scale_fill_viridis_c()

r flipbookr::chunk_reveal('h_geom_prop_in_ts_27', title = '### h_geom_prop_in_ts')

# the function and proto
"StatPropovertime"
"StatPropovertimetext"

r flipbookr::chunk_reveal('h_geom_prop_in_ts_28', title = '### h_geom_prop_in_ts')

# use function
library(ggplot2)
library(magrittr)
my_gapminder %>%
ggplot() +
  aes(x = year) +
  aes(y = continent) +
  geom_tile_prop_over_time() +
  aes(fill = gdp) +
  scale_fill_viridis_c()


library(ggplot2)
library(magrittr)
gapminder::gapminder %>%
dplyr::mutate(gdp = gdpPercap * pop) %>%
ggplot() +
  aes(x = year) +
  aes(y = continent) +
  geom_tile_prop_over_time() +
  aes(fill = gdp) +
  scale_fill_viridis_c() +
  aes(label = gdp) +
  geom_tile_prop_over_time_text()
gapminder::gapminder %>%
dplyr::mutate(gdp = gdpPercap * pop) %>%
ggplot() +
  aes(x = year) +
  aes(y = continent) +
  geom_tile_prop_over_time() +
  aes(fill = gdp) +
  scale_fill_viridis_c() +
  aes(label = gdp) +
  geom_tile_prop_over_time_text() +
  facet_wrap(facets = vars(pop > 1000000))
gapminder::gapminder %>%
ggplot() +
  aes(x = year) +
  aes(y = continent) +
  aes(fill = 1, label = 1) + # use 1 to count times continent is observed
  geom_tile_prop_over_time(color = "oldlace") +
  labs(fill = "proportionnof countriesnin each timenperiod") +
  geom_tile_prop_over_time_text(size = 3) +
  facet_wrap(facets = vars(ifelse(gdpPercap > 10000,
  "gdp per cap > 10000", "gdp per cap < 10000")),
  ncol = 1) +
  scale_fill_viridis_c()

r flipbookr::chunk_reveal('i_geom_prop_label_in_ts_29', title = '### i_geom_prop_label_in_ts')

library(ggplot2)
library(magrittr)
gapminder::gapminder %>%
dplyr::mutate(gdp = gdpPercap * pop) %>%
ggplot() +
  aes(x = year) +
  aes(y = continent) +
  geom_tile_prop_over_time() +
  aes(fill = gdp) +
  scale_fill_viridis_c() +
  aes(label = gdp) +
  geom_tile_prop_over_time_text()
gapminder::gapminder %>%
dplyr::mutate(gdp = gdpPercap * pop) %>%
ggplot() +
  aes(x = year) +
  aes(y = continent) +
  geom_tile_prop_over_time() +
  aes(fill = gdp) +
  scale_fill_viridis_c() +
  aes(label = gdp) +
  geom_tile_prop_over_time_text(size = 2, angle = -90,
                                color = "snow") +
  facet_wrap(facets = vars(pop > 1000000))
gapminder::gapminder %>%
ggplot() +
  aes(x = year) +
  aes(y = continent) +
  aes(fill = 1, label = 1) + # use 1 to count times continent is observed
  geom_tile_prop_over_time(color = "oldlace") +
  labs(fill = "proportionnof countriesnin each timenperiod") +
  geom_tile_prop_over_time_text(size = 3) +
  facet_wrap(facets = vars(ifelse(gdpPercap > 10000,
  "gdp per cap > 10000", "gdp per cap < 10000")),
  ncol = 1) +
  scale_fill_viridis_c()

r flipbookr::chunk_reveal('j_geom_c_hull_30', title = '### j_geom_c_hull')

library(ggplot2)
library(magrittr)
chull(cars$speed, cars$dist) %>% # index of rim points
cars[.,] ->
cars_c_hull_rows
ggplot(data = cars) +
  aes(x = speed) +
  aes(y = dist) +
  geom_point() +
  geom_polygon(data = cars_c_hull_rows,
  alpha = .5
  )
ggplot(data = cars) +
  aes(x = speed) +
  aes(y = dist) +
  geom_point() +
  geom_c_hull(alpha = .5) +
  aes(color = speed >= 10)

r flipbookr::chunk_reveal('k_geom_c_hull_hollow_31', title = '### k_geom_c_hull_hollow')

library(ggplot2)
library(magrittr)
chull(cars$speed, cars$dist) %>% # index of rim points
cars[.,] ->
cars_c_hull_rows
ggplot(data = cars) +
  aes(x = speed) +
  aes(y = dist) +
  geom_point() +
  geom_polygon(data = cars_c_hull_rows,
  alpha = .5
  )
ggplot(data = cars) +
  aes(x = speed) +
  aes(y = dist) +
  geom_point() +
  k_geom_c_hull_hollow(alpha = .5) +
  aes(color = speed >= 10)