what ggplot2 can do?

Example 1

library(tidyverse)

## -- Attaching packages ---------------------------------------------- tidyverse 1.3.0 --

## v ggplot2 3.2.1     v purrr   0.3.3
## v tibble  2.1.3     v dplyr   0.8.3
## v tidyr   1.0.0     v stringr 1.4.0
## v readr   1.3.1     v forcats 0.4.0

## -- Conflicts ------------------------------------------------- tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag()    masks stats::lag()

library(ggplot2)
library(ggflags)
library(countrycode)
library(DT)

dat <- tibble(country = toupper(c("US", "Italy", "Canada", "UK", "Japan", "Germany", "France", "Russia")),
              count = c(3.2, 0.71, 0.5, 0.1, 0, 0.2, 0.1, 0),
              label = c(as.character(c(3.2, 0.71, 0.5, 0.1, 0, 0.2, 0.1)), "No Data"),
              code = c("us", "it", "ca", "gb", "jp", "de", "fr", "ru"))

datatable(dat)

dat %>% mutate(country = reorder(country, -count)) %>%
  ggplot(aes(country, count, label = label)) +
  geom_bar(stat = "identity", fill = "darkred") +
  geom_text(nudge_y = 0.2, color = "darkred", size = 5) +
  geom_flag(y = -.5, aes(country = code), size = 12) +
  scale_y_continuous(breaks = c(0, 1, 2, 3, 4), limits = c(0,4)) +   
  geom_text(aes(6.25, 3.8, label = "Source UNODC Homicide Statistics")) + 
  ggtitle(toupper("Homicide Per 100,000 in G-8 Countries")) + 
  xlab("") + 
  ylab("# of gun-related homicides\nper 100,000 people") +
  ggthemes::theme_economist() +
  theme(axis.text.x = element_text(size = 8, vjust = -16),
        axis.ticks.x = element_blank(),
        axis.line.x = element_blank(),
        plot.margin = unit(c(1,1,1,1), "cm")) 

Example 2

dat <- tibble(country = toupper(c("United States", "Canada", "Portugal", "Ireland", "Italy", "Belgium", "Finland", "France", "Netherlands", "Denmark", "Sweden", "Slovakia", "Austria", "New Zealand", "Australia", "Spain", "Czech Republic", "Hungry", "Germany", "United Kingdom", "Norway", "Japan", "Republic of Korea")),
              count = c(3.61, 0.5, 0.48, 0.35, 0.35, 0.33, 0.26, 0.20, 0.20, 0.20, 0.19, 0.19, 0.18, 0.16,
                        0.16, 0.15, 0.12, 0.10, 0.06, 0.04, 0.04, 0.01, 0.01))
datatable(dat)

dat %>% 
  mutate(country = reorder(country, count)) %>%
  ggplot(aes(country, count, label = count)) +   
  geom_bar(stat = "identity", fill = "darkred", width = 0.5) +
  geom_text(nudge_y = 0.2,  size = 3) +
  xlab("") + ylab("") + 
  ggtitle(toupper("Gun Murders per 100,000 residents")) + 
  theme_minimal() +
  theme(panel.grid.major =element_blank(), panel.grid.minor = element_blank(), 
        axis.text.x = element_blank(),
        axis.ticks.length = unit(-0.4, "cm")) + 
  coord_flip() 

library(dslabs)
data(us_contagious_diseases)
the_disease <- "Measles"
dat <- us_contagious_diseases %>%
  filter(!state%in%c("Hawaii","Alaska") & disease == the_disease) %>%
  mutate(rate = count / population * 10000 * 52 / weeks_reporting) 

jet.colors <- colorRampPalette(c("#F0FFFF", "cyan", "#007FFF", "yellow", "#FFBF00", "orange", "red", "#7F0000"), bias = 2.25)
datatable(dat)

dat %>% mutate(state = reorder(state, desc(state))) %>%
  ggplot(aes(year, state, fill = rate)) +
  geom_tile(color = "white", size = 0.35) +
  scale_x_continuous(expand = c(0,0)) +
  scale_fill_gradientn(colors = jet.colors(16), na.value = 'white') +
  geom_vline(xintercept = 1963, col = "black") +
  theme_minimal() + 
  theme(panel.grid = element_blank()) +
  coord_cartesian(clip = 'off') +
  ggtitle(the_disease) +
  ylab("") +
  xlab("") +  
  theme(legend.position = "bottom", text = element_text(size = 8)) + 
  annotate(geom = "text", x = 1963, y = 50.5, label = "Vaccine introduced", size = 3, hjust = 0)

data("nyc_regents_scores")

datatable(nyc_regents_scores)

nyc_regents_scores$total <- rowSums(nyc_regents_scores[,-1], na.rm=TRUE)
nyc_regents_scores %>% 
  filter(!is.na(score)) %>%
  ggplot(aes(score, total)) + 
  annotate("rect", xmin = 65, xmax = 99, ymin = 0, ymax = 35000, alpha = .5) +
  geom_bar(stat = "identity", color = "black", fill = "#C4843C") + 
  annotate("text", x = 66, y = 28000, label = "MINIMUM\nREGENTS DIPLOMA\nSCORE IS 65", hjust = 0, size = 3) +
  annotate("text", x = 0, y = 12000, label = "2010 Regents scores on\nthe five most common tests", hjust = 0, size = 3) +
  scale_x_continuous(breaks = seq(5, 95, 5), limit = c(0,99)) + 
  scale_y_continuous(position = "right") +
  ggtitle("Scraping By") + 
  xlab("") + ylab("Number of tests") + 
  theme_minimal() + 
  theme(panel.grid.major.x = element_blank(), 
        panel.grid.minor.x = element_blank(),
        axis.ticks.length = unit(-0.2, "cm"),
        plot.title = element_text(face = "bold"))

## Warning: Removed 1 rows containing missing values (position_stack).

## Warning: Removed 2 rows containing missing values (geom_bar).

#### Example 5

my_dgamma <- function(x, mean = 1, sd = 1){
  shape = mean^2/sd^2
  scale = sd^2 / mean
  dgamma(x, shape = shape, scale = scale)
}

my_qgamma <- function(mean = 1, sd = 1){
  shape = mean^2/sd^2
  scale = sd^2 / mean
  qgamma(c(0.1,0.9), shape = shape, scale = scale)
}

tmp <- tibble(candidate = c("Clinton", "Trump", "Johnson"), 
              avg = c(48.5, 44.9, 5.0), 
              avg_txt = c("48.5%", "44.9%", "5.0%"), 
              sd = rep(2, 3), 
              m = my_dgamma(avg, avg, sd)) %>%
  mutate(candidate = reorder(candidate, -avg))

xx <- seq(0, 75, len = 300)

tmp_2 <- map_df(1:3, function(i){
  tibble(candidate = tmp$candidate[i],
         avg = tmp$avg[i],
         sd = tmp$sd[i],
         x = xx,
         y = my_dgamma(xx, tmp$avg[i], tmp$sd[i]))
})

tmp_3 <- map_df(1:3, function(i){
  qq <- my_qgamma(tmp$avg[i], tmp$sd[i])
  xx <- seq(qq[1], qq[2], len = 200)
  tibble(candidate = tmp$candidate[i],
         avg = tmp$avg[i],
         sd = tmp$sd[i],
         x = xx,
         y = my_dgamma(xx, tmp$avg[i], tmp$sd[i]))
})

tmp_2 %>% 
  ggplot(aes(x, ymax = y, ymin = 0)) +
  geom_ribbon(fill = "grey") + 
  facet_grid(candidate~., switch = "y") +
  scale_x_continuous(breaks = seq(0, 75, 25), position = "top",
                     label = paste0(seq(0, 75, 25), "%")) +
  geom_abline(intercept = 0, slope = 0) +
  xlab("") + ylab("") + 
  theme_minimal() + 
  theme(panel.grid.major.y = element_blank(), 
        panel.grid.minor.y = element_blank(),
        axis.title.y = element_blank(),
        axis.text.y = element_blank(),
        axis.ticks.y = element_blank(),
        strip.text.y = element_text(angle = 180, size = 11, vjust = 0.2)) + 
  geom_ribbon(data = tmp_3, mapping = aes(x = x, ymax = y, ymin = 0, fill = candidate), inherit.aes = FALSE, show.legend = FALSE) +
  scale_fill_manual(values = c("#3cace4", "#fc5c34", "#fccc2c")) +
  geom_point(data = tmp, mapping = aes(x = avg, y = m), inherit.aes = FALSE) + 
  geom_text(data = tmp, mapping = aes(x = avg, y = m, label = avg_txt), inherit.aes = FALSE, hjust = 0, nudge_x = 1)