Exploratory Analysis II

Data visualization, part 2. Code for quiz 8.

1. Load the R package we will use

library(tidyverse)
library(patchwork)

Question: Modify Slide 51

• Create a plot with mpg dataset

• Add points with geom_point

• Assign the variable displ to the x-axis

• Assign the variable hwy to the y-axis

• add facet_wrap to split the data into panels based on manufacturer

ggplot(data = mpg) + 
  geom_point(aes(x = displ, y = hwy)) + 
  facet_wrap(facets = vars(manufacturer))

---

Question: Modify facet-ex-2

• Create a plot with mpg dataset

• Add bars with geom_bar

• Assign the variable manufacturer to the y-axis

• Add facet_grid to split data into panels based on class

• Let scales vary across columns and let space taken up by panels vary by columns

ggplot(mpg) + 
  geom_bar(aes(y = manufacturer)) + 
  facet_grid(vars(class), scales = "free_y", space = "free_y")

---

Question: Spend_time

• Read spend_time into spend_time

spend_time <- read_csv("spend_time.csv")

---

• Start with spend_time

• Extract observations for 2019

• THEN create plot with that data

• Add a barchart with geom_col

  • Assign activity to the x-axis
  • Assign avg_hours to the y-axis
  • Assign activity to fill

• Add scale_y_continuous with breaks every hour from 0 to 6 hours

• Add labs to:

  • set subtitle to Avg hours per day:2019
  • set x and y to NULL so they won’t be labeled

• Assign the output to p1

• Display p1

p1 <- spend_time %>% filter(year == 2019) %>% 
  ggplot()+
  geom_col(aes(x = activity, y = avg_hours, fill = activity)) +
  scale_y_continuous(breaks = seq(0,6, by = 1)) +
  labs(subtitle = "Avg hours per day: 2019", x = NULL, y = NULL)

p1

---

• Start with spend_time

• THEN create plot with it

• Add a barchart with geom_col

  • Assign year to the x-axis
  • Assign avg_hours to the y-axis
  • Assign activity to fill

• Add labs to:

  • Set subtitle to Avg hours per day: 2010-2019
  • Set x and y to NULL so they won’t be labeled

• Assign the output to p2

• Display p2

p2 <- spend_time %>% 
  ggplot() +
  geom_col(aes(x = year, y = avg_hours, fill = activity)) +
  labs(subtitle = "Avg hours per day: 2010-2019", x = NULL, y = NULL)

p2

---

• Use patchwork to display p1 on top of p2

• Assign the output to p_all

• Display p_all

p_all <- (p1/p2)

p_all

---

• Start with p_all

• And set legend.position to “none” to get rid of the legend

• Assign the output to p_all_no_legend

• Display p_all_no_legend

p_all_no_legend <- p_all & theme(legend.position = "none")

p_all_no_legend

---

• Start with p_all_no_legend

• Add plot_annotation set

• Add title to “How much time Americans spent on selected activities”

• Caption to “Source: American Time of Use Survey, https://data.bls.gov/cgi-bin/surveymost?tu

p_all_no_legend +
  plot_annotation(title = "How much time Americans spent on selected activities",
                  caption = "Source: American Time of Use Survey, https://data.bls.gov/cgi-bin/surveymost?tu ")

---

Question: Patchwork 2

• Start with spend_time

• Extract observations for leisure/sports

• THEN create plot with data

• Add points with geom_point

  • Assign year to the x-axis
  • Assign avg_hours to the y-axis

• Add line with geom_smooth

  • Assign year to the x-axis
  • Assign avg_hours to the y-axis

• Add breaks on for every year on x-axis with scale_x_continuous

• Add labs to:

  • Set subtitle to Avg hours per day: leisure/sports
  • Set x and y to NULL so x and y axes won’t be labeled

• Assign the output to p4

• Display p4

p4 <- spend_time %>% 
  filter(activity == "leisure/sports") %>% 
  ggplot() +
  geom_point(aes(x = year, y = avg_hours)) +
  geom_smooth(aes(x = year, y = avg_hours)) +
  scale_x_continuous(breaks = seq(2010, 2019, by = 1)) +
  labs(subtitle = "Avg hours per day: leisure/sports", x = NULL, y = NULL)

p4

---

• Start with p4

• Add coord_cartesian to change range on y axis to 0 to 6

• Assign the output to p5

• Display p5

p5 <- p4 + coord_cartesian(ylim = c(0,6))

p5

---

Start with spend_time

• Create a plot with data

• Add points with geom_point

  • Assign year to the x-axis

  • Assign avg_hours to the y-axis

  • Assign activity to color

  • Assign activity to group

• Add line with geom_smooth

  • Assign year to the x-axis

  • Assign avg_hours to the y-axis

  • Assign activity to color

  • Assign activity to group

• Add breaks for every year on the x-axis with scale_x_continuous

• Add coord_cartesian to change range on y-axis from 0 to 6

• Add labs to:

  • set x and y to NULL so they wont be labeled

• Assign the output to p6

• Display p6

p6 <- spend_time %>% 
  ggplot() +
  geom_point(aes(x = year, y = avg_hours, color = activity, group = activity)) +
  geom_smooth(aes(x = year, y = avg_hours, color = activity, group = activity)) +
  scale_x_continuous(breaks = seq(2010, 2019, by = 1)) +
  coord_cartesian(ylim = c(0,6)) +
  labs(x = NULL, y = NULL)

p6

• Use patchwork to display p4, p5 and p6

(p4/p5) / p6

ggsave(filename = "preview.png", path = here::here("_posts", "2022-03-25-exploratory-analysis-ii"))