In rich-iannone/rwr: edr

library(tidyverse)
library(edr)

3.1. Using ggplot to Create Plots

Listing 3.1. Code to make our first ggplot.

ggplot(data = dmd, mapping = aes(x = carats, y = price))

Listing 3.2. Using geom_point() adds a layer of points.

ggplot(data = dmd, mapping = aes(x = carats, y = price)) +
  geom_point()

Listing 3.3. Using a different set of y values.

ggplot(data = dmd, mapping = aes(x = carats, y = depth)) +
  geom_point()

Listing 3.4. Using a different set of y values.

ggplot(dmd, mapping = aes(x = carats, y = price, shape = clarity)) +
  geom_point()

Listing 3.5. Using the alpha argument in geom_point().

ggplot(dmd, mapping = aes(x = carats, y = price, shape = clarity)) +
  geom_point(alpha = 0.25)

Listing 3.6. Setting a fixed color inside of geom_point().

ggplot(dmd, mapping = aes(x = carats, y = price)) +
  geom_point(color = "gray50")

Listing 3.7. Using color and shape aesthetics.

ggplot(dmd, aes(x = carats, y = price)) +
  geom_point(mapping = aes(color = cut, shape = clarity))

Listing 3.8. Using the size aesthetic and a fixed alpha.

ggplot(dmd, aes(x = carats, y = price)) +
  geom_point(mapping = aes(size = depth), alpha = 0.05)

Listing 3.9. Supplying fixed values for color, fill, and shape.

ggplot(dmd, aes(x = carats, y = price)) +
  geom_point(color = "gray50", fill = "#AAAFEF", shape = 23)

Listing 3.10. Applying the color aesthetic to the depth variable.

ggplot(dmd, aes(x = carats, y = price)) +
  geom_point(mapping = aes(color = depth, shape = clarity))

Listing 3.11. Using an additional statement with facet_wrap() gives us a faceted plot.

ggplot(dmd, aes(x = carats, y = price)) +
  geom_point() +
  facet_wrap(facets = vars(clarity))

Listing 3.12. Faceting by two variables: cut and clarity.

ggplot(dmd, aes(x = carats, y = price)) +
  geom_point() +
  facet_wrap(facets = vars(cut, clarity))

Listing 3.13. Using the labeller function label_both to create informative labels for facets.

ggplot(dmd, aes(x = carats, y = price)) +
  geom_point() +
  facet_wrap(facets = vars(cut, clarity), labeller = label_both)

Listing 3.14. We can specify the total number of rows of plot panels with the nrow argument.

ggplot(dmd, aes(x = carats, y = price)) +
  geom_point() +
  facet_wrap(
    facets = vars(cut, clarity),
    nrow = 1, labeller = label_both
  )

Listing 3.15. Using facet_grid() provides a slightly different visualization of the faceted plot panels.

ggplot(dmd, aes(x = carats, y = price)) +
  geom_point(alpha = 0.2) +
  facet_grid(rows = vars(cut), cols = vars(clarity), labeller = label_both)

Listing 3.16. The labs() function gives us the opportunity to provide our own labels for different plot elements.

ggplot(dmd, mapping = aes(x = carats, y = price)) +
  geom_point(mapping = aes(shape = clarity)) +
  labs(
    x = "Weight of the Diamond (carats)",
    y = "Price (USD)",
    shape = "Diamond Clarity"
  )

Listing 3.17. We can specify the plot’s title, subtitle, and caption inside labs() as well.

ggplot(dmd, mapping = aes(x = carats, y = price)) +
  geom_point(mapping = aes(shape = clarity)) +
  labs(
    title = "The Relationship Between Diamond Weight on Price",
    subtitle = "Quality of diamond clarity is indicated by shape",
    caption = "Data taken from the `dmd` dataset",
    x = "Weight of the Diamond (carats)", y = "Price (USD)",
    color = "Diamond Cut", shape = "Diamond Clarity"
  )

Listing 3.18. Using legend.position argument of theme() to put the legend to the right of the plot.

ggplot(dmd, aes(x = carats, y = price)) +
  geom_point(aes(shape = clarity)) +
  labs(shape = "Clarity") +
  theme(legend.position = "right")

Listing 3.19. Using legend.position argument of theme() to put the legend below the plot.

ggplot(dmd, aes(x = carats, y = price)) +
  geom_point(aes(shape = clarity)) +
  labs(shape = "Clarity") +
  theme(legend.position = "bottom")

Listing 3.20. Using legend.justification argument of theme() to justify the legend toward the top of the visualization.

ggplot(dmd, aes(x = carats, y = price)) +
  geom_point(aes(shape = clarity)) +
  labs(shape = "Clarity") +
  theme(legend.justification = "top")

Listing 3.21. Using legend.position argument of theme() to remove the legend entirely.

ggplot(dmd, aes(x = carats, y = price)) +
  geom_point(aes(shape = clarity)) +
  labs(shape = "Clarity") +
  theme(legend.position = "none")

Listing 3.22. Modifying dmd to obtain two new columns: cpc and price_class.

dmd_mod <- 
  dmd %>%
  mutate(cpc = price / carats) %>%
  mutate(price_class = ifelse(cpc >= 3460, "Above Median", "Below Median"))

Listing 3.23. A third and final mutate() statement to add the quality column to our modified dataset (dmd_mod).

dmd_mod <- 
  dmd %>%
  mutate(cpc = price / carats) %>%
  mutate(price_class = ifelse(cpc >= 3460, "Above Median", "Below Median")) %>%
  mutate(quality = ifelse(
    cut == "The Best" & clarity == "The Best",
    "Top Drawer", "The Rest")
  )

Listing 3.24. Modifying dmd to add three new columns, and, plotting dmd_mod with the new variables.

dmd_mod <-
  dmd %>%
  mutate(cpc = price / carats) %>%
  mutate(price_class = ifelse(cpc >= 3460, "Above Median", "Below Median")) %>%
  mutate(quality = ifelse(
    cut == "The Best" & clarity == "The Best", "Top Drawer", "The Rest")
  )

ggplot(dmd_mod, aes(x = carats, y = price)) +
  geom_point() +
  facet_wrap(
    facets = vars(price_class, quality),
    labeller = label_both
  ) +
  labs(x = "Carats", y = "Price")

rich-iannone/rwr documentation built on Jan. 22, 2021, 7:51 p.m.