Exploratory Analysis II

Data visualization, part 2. Code for Quiz 8.

Introduction

1. Load the R packages we will use.

library(tidyverse)
library(patchwork)

2. Quiz Questions

• Replace all the ???s. These are answers on your moodle quiz.
• Run all the individual code chunks to make sure the answers in this file correspond with your quiz answers.
• After you check all your code chunks run then you can knit it. It won’t knit until the ??? are replaced.
• The quiz assumes you have watched the videos, downloaded (to your examples folder) and worked through the exercises in exercises_slides-50-61.RMD

3. Pick one of your plots to save as your preview plot. Use the ggsave command at the end of teh chunk of the plot that you want to preview.

---

Question: modify slide 51

• Create a plot with the 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 the manufacturer

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

---

Modify facet-ex-2

• Create a plot with the mpg dataset
• Add bars with geom_bar
  • Assign the variable manufacturer to the y-axis
• Add facet_grid to split the data into panels based on the class
  • Let scales vary across columns
  • Let space taken up by panels vary by columns

ggplot(data=mpg)+
  geom_bar(aes(y= manufacturer)) +
  facet_grid(vars(class), space = 'free_y', scale = 'free_y')

---

Patchwork

TO help you complete this question use:

• the patchwork slides and

• the vignette

Download the file spend_time.csv from moodle

• spend_time contains 10 years of data on how many hours Americans spend each day on 5 activities.

• Read it into spend_time

spend_time <- read_csv("spend_time.csv")

---

Start with spend_time

• Extract observations for 2018
• THEN create a 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: 2018
  • set x and y to NULL so they won’t be labeled.
• assign the output to p1
• Display p1

p1 <- spend_time %>% filter(year=="2018") %>%
  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: 2018", x = NULL, y = NULL)

p1

---

Start with spend_time

• THEN create a 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

• See how annotate the composition here
• ADD plot_annotation set
  • 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")

---

Patchwork 2

Use spend_time from last question patchwork slides Start with spend_time

• Extract observations for leisure/sports
• THEN create a plot with that 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 the 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 on for every year on x axis with scale_x_continuous
• ADD coord_cartesian to change range on y axis to 0 to 6
• Add labs to
  • set x and y to NULL so they won’t 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 and p5 on top of p6

(p4 | p5 ) / p6

ggsave(filename = "preview.png",
        path = here::here("_posts", "2022-03-29-exploratory-analysis-ii"))