mdsr::CIACountries %>% select(-area, -pop) %>% head 

g <- ggplot(data = CIACountries, aes(y = gdp, x = educ))
g + geom_point(size = 3)

g + geom_point(aes(color = net_users), size = 3)

g + geom_text(aes(label = country, color = net_users), size = 3)

ggplot(data = CIACountries)

g + geom_point(aes(color = net_users, size = roadways)) +
    coord_trans(y = "log10")

g + geom_point(alpha = 0.9, aes(size = roadways)) +
    coord_trans(y = "log10") +
    facet_wrap(~net_users, nrow = 1) +
    theme(legend.position = "top")

    ChargesNJ <- MedicareCharges %>% 
                    filter(stateProvider == "NJ")  # New Jersey only

p <- ggplot(
      data = ChargesNJ,
      aes(x = reorder(drg, mean_charge), y = mean_charge)
     ) +
     geom_col(fill = "gray") +
     ylab("Statewide Average Charges ($)") +
     xlab("Medical Procedure (DRG)") +
     theme(axis.text.x = element_text(angle = 90, hjust = 1, size = rel(0.5)))
p

p + geom_point(data = MedicareCharges, size = 1, alpha = 0.3)

g <- ggplot(data = SAT_2010, aes(x = math))

g + geom_histogram(binwidth = 10) + labs(x = "Average Math SAT score")

g + geom_density(adjust = 0.3)

bc <- ggplot(
  data = head(SAT_2010, 10),  # only the first 10 states (in alphabetical order)
  aes(x = reorder(state, math), y = math)
) +
  geom_col() +  # sort the state names in order of their average math SAT score
  labs(x = "State", y = "Average Math SAT score")

ggplot(data = mosaicData::HELPrct, aes(x = homeless)) + 
  geom_bar(aes(fill = substance), position = "fill") +
  scale_fill_brewer(palette = "Spectral") + 
  coord_flip()

g <- ggplot(
  data = SAT_2010, 
  aes(x = expenditure, y = math)   # expenditure per pupil (1k USD) 
) + 
  geom_point()
g

g <- g + 
  geom_smooth(method = "lm", se = FALSE) + 
  xlab("Average expenditure per student ($1000)") +
  ylab("Average score on math SAT")
g

SAT_2010 <- SAT_2010 %>%
  mutate(
    SAT_rate = cut(
      sat_pct, 
      breaks = c(0, 30, 60, 100), 
      labels = c("low", "medium", "high")
    )
  )
g <- g %+% SAT_2010  # update the data frame that is bound to our plot
g + aes(color = SAT_rate)

g + facet_wrap(~ SAT_rate)

ggplot(
  data = slice_sample(NHANES::NHANES, n = 1000), 
  aes(x = Age, y = Height, color = fct_relevel(Gender, "male")) # reset factor levels
) + 
  geom_point() + 
  geom_smooth() + 
  xlab("Age (years)") + 
  ylab("Height (cm)") +
  labs(color = "Gender")

wp <- ggplot(data = macleish::whately_2015, aes(x = when, y = temperature)) + 
  geom_line(color = "darkgray") + 
  geom_smooth() + 
  xlab(NULL) + 
  ylab("Temperature (degrees Celsius)")

bp <- ggplot(
    data = whately_2015,
    aes(
        x = lubridate::month(when, label = TRUE),
        y = temperature
    )
) +
geom_boxplot() +
xlab("Month") +
ylab("Temperature (degrees Celsius)")

mosaic_to_plot <- NHANES %>%
  filter(Age > 19) %>%
  mutate(AgeDecade = droplevels(AgeDecade)) %>%
  select(AgeDecade, Diabetes, BMI_WHO) %>% 
  na.omit()

mp <- ggplot(mosaic_to_plot) +
  geom_mosaic(
    aes(x = product(BMI_WHO, AgeDecade), fill = Diabetes)
  ) + 
  ylab("BMI") + 
  xlab("Age (by decade)") + 
  coord_flip()

CellEdges <- Cancer
SmallEdges <- head(CellEdges,200)
g <- SmallEdges %>%
  select(cellLine, otherCellLine, correlation) %>%
  as_tbl_graph(directed = FALSE) %>%
  mutate(type = substr(name, 0, 2))
cellnet <- ggraph::ggraph(g, layout = 'kk') +
  geom_edge_arc(aes(width = correlation), color = "lightgray", strength = 0.2) +
  geom_node_point(aes(color = type), size = 10, alpha = 0.6) +
  geom_node_text(aes(label = type)) + 
  scale_edge_width_continuous(range = c(0.1, 1)) +
  guides(color = guide_legend(override.aes = list(size = 6))) + 
  theme_void() + 
  coord_cartesian(clip = "off")

BabynamesDist <- mdsr::make_babynames_dist()
BabynamesDist

joseph <- BabynamesDist %>%
  filter(name == "Joseph" & sex == "M")
name_plot <- ggplot(data = joseph, aes(x = year))

name_plot <- name_plot +
  geom_col(
    aes(y = count_thousands * alive_prob), 
    fill = "#b2d7e9", 
    color = "white",
    size = 0.1
  )

name_plot <- name_plot + 
  geom_line(aes(y = count_thousands), size = 2)
name_plot <- name_plot +
  ylab("Number of People (thousands)") + 
  xlab(NULL)
name_plot

wtd_quantile <- Hmisc::wtd.quantile   # rename
median_yob <- joseph %>%
  summarize(
    year = wtd_quantile(year, est_alive_today, probs = 0.5)
  ) %>% 
  pull(year)
median_yob

name_plot <- name_plot +
  geom_col(
    color = "white", fill = "#008fd5", 
    aes(y = ifelse(year == median_yob, est_alive_today / 1000, 0))
  )

context <- tribble(
  ~year, ~num_people, ~label,
  1935, 40, "Number of Josephs\nborn each year",
  1915, 13, "Number of Josephs\nborn each year
  \nestimated to be alive\non 1/1/2014", 
  2003, 40, "The median\nliving Joseph\nis 37 years old", 
)

joe <- name_plot +
  ggtitle("Age Distribution of American Boys Named Joseph") + 
  geom_text(
    data = context, 
    aes(y = num_people, label = label, color = label)
  ) + 
  geom_curve(
    x = 1990, xend = 1974, y = 40, yend = 24, 
    arrow = arrow(length = unit(0.3, "cm")), curvature = 0.5
  ) + 
  scale_color_manual(
    guide = "none", 
    values = c("black", "#b2d7e9", "darkgray")
  ) + 
  ylim(0, 42)

name_plot %+% filter(
  BabynamesDist, 
  name == "Josephine" & sex == "F"
)

names_plot <- name_plot + 
  facet_wrap(~sex)
names_plot %+% filter(BabynamesDist, name == "Jessie")

many_names_plot <- name_plot + 
  facet_grid(name ~ sex)
mnp <- many_names_plot %+% filter(
  BabynamesDist, 
  name %in% c("Jessie", "Marion", "Jackie")
)
mnp + facet_grid(sex ~ name)

com_fem <- BabynamesDist %>%
  filter(n > 100, sex == "F") %>% 
  group_by(name) %>%
  mutate(wgt = est_alive_today / sum(est_alive_today)) %>%
  summarize(
    N = n(), 
    est_num_alive = sum(est_alive_today),
    quantiles = list(
      wtd_quantile(
        age_today, est_alive_today, probs = 1:3/4, na.rm = TRUE
      )
    )
  ) %>%
  mutate(measures = list(c("q1_age", "median_age", "q3_age"))) %>%
  unnest(cols = c(quantiles, measures)) %>%
  pivot_wider(names_from = measures, values_from = quantiles) %>%
  arrange(desc(est_num_alive)) %>%
  head(25)

w_plot <- ggplot(
  data = com_fem, 
  aes(x = reorder(name, -median_age), y = median_age)
) + 
  xlab(NULL) + 
  ylab("Age (in years)") + 
  ggtitle("Median ages for females with the 25 most common names")

w_plot <- w_plot + 
  geom_linerange(
    aes(ymin = q1_age, ymax = q3_age), 
    color = "#f3d478", 
    size = 4.5, 
    alpha = 0.8
  )
w_plot

w_plot <- w_plot +
  geom_point(
    fill = "#ed3324", 
    color = "white", 
    size = 2, 
    shape = 21
  )
w_plot

context <- tribble(
  ~median_age, ~x, ~label, 
  65, 24, "median",
  29, 16, "25th", 
  48, 16, "75th percentile",
)

age_breaks <- 1:7 * 10 + 5

fp <- w_plot + 
  geom_point(
    aes(y = 60, x = 24), 
    fill = "#ed3324", 
    color = "white", 
    size = 2, 
    shape = 21
  ) + 
  geom_text(data = context, aes(x = x, label = label)) + 
  geom_point(aes(y = 24, x = 16), shape = 17) + 
  geom_point(aes(y = 56, x = 16), shape = 17) +
  geom_hline(
    data = tibble(x = age_breaks), 
    aes(yintercept = x), 
    linetype = 3
  ) +
  scale_y_continuous(breaks = age_breaks) + 
  coord_flip()