2025-02-18 Introduction, 1. Data Visualization

[shanmdphd]

https://youtube.com/live/KwOTzMf9G5s

댓글로 Exercise를 풀고 Markdown 형태로

[shanmdphd]

1. How many rows are in penguins?
   How many columns?

344 x 7

2. What does the bill_depth_mm variable in the penguins data frame describe?
   Read the help for ?penguins to find out.

했음.

3. Make a scatterplot of bill_depth_mm vs. bill_length_mm.
   That is, make a scatterplot with bill_depth_mm on the y-axis and bill_length_mm on the x-axis.
   Describe the relationship between these two variables.

ggplot(penguins, aes(x = bill_length_mm, y = bill_depth_mm)) +
  geom_point()

별로 관계 없어보임..

4. What happens if you make a scatterplot of species vs. bill_depth_mm?
   What might be a better choice of geom?

5. Why does the following give an error and how would you fix it?

   #| eval: false
   
   ggplot(data = penguins) + 
     geom_point()
   

6. What does the na.rm argument do in geom_point()?
   What is the default value of the argument?
   Create a scatterplot where you successfully use this argument set to TRUE.

7. Add the following caption to the plot you made in the previous exercise: "Data come from the palmerpenguins package." Hint: Take a look at the documentation for labs().

8. Recreate the following visualization.
   What aesthetic should bill_depth_mm be mapped to?
   And should it be mapped at the global level or at the geom level?

   #| echo: false
   #| warning: false
   #| fig-alt: |
   #|   A scatterplot of body mass vs. flipper length of penguins, colored 
   #|   by bill depth. A smooth curve of the relationship between body mass 
   #|   and flipper length is overlaid. The relationship is positive, 
   #|   fairly linear, and moderately strong.
   
   ggplot(
     data = penguins,
     mapping = aes(x = flipper_length_mm, y = body_mass_g)
   ) +
     geom_point(aes(color = bill_depth_mm)) +
     geom_smooth()
   

9. Run this code in your head and predict what the output will look like.
   Then, run the code in R and check your predictions.

   #| eval: false
   
   ggplot(
     data = penguins,
     mapping = aes(x = flipper_length_mm, y = body_mass_g, color = island)
   ) +
     geom_point() +
     geom_smooth(se = FALSE)
   

10. Will these two graphs look different?
    Why/why not?

    #| eval: false
    
    ggplot(
      data = penguins,
      mapping = aes(x = flipper_length_mm, y = body_mass_g)
    ) +
      geom_point() +
      geom_smooth()
    
    ggplot() +
      geom_point(
        data = penguins,
        mapping = aes(x = flipper_length_mm, y = body_mass_g)
      ) +
      geom_smooth(
        data = penguins,
        mapping = aes(x = flipper_length_mm, y = body_mass_g)
      )
    

[hayoung98]

Exercises

1. How many rows are in penguins?
   How many columns?

row: 344 columns: 8

2. What does the bill_depth_mm variable in the penguins data frame describe?
   Read the help for ?penguins to find out.

bill_depth_mm
a number denoting bill depth (millimeters)

3. Make a scatterplot of bill_depth_mm vs. bill_length_mm.
   That is, make a scatterplot with bill_depth_mm on the y-axis and bill_length_mm on the x-axis.
   Describe the relationship between these two variables.

penguins |> 
  ggplot(aes( x = bill_length_mm, y = bill_depth_mm))+ 
  geom_point()

no relationship

4. What happens if you make a scatterplot of species vs. bill_depth_mm?
   What might be a better choice of geom?
   

penguins |> 
  ggplot(aes( x = species, y = bill_depth_mm)) + 
  geom_boxplot()

5. Why does the following give an error and how would you fix it?

   #| eval: false
   
   ggplot(data = penguins) + 
     geom_point()
   

no axis identified;
ggplot(data = penguins, mapping = aes(x = , y = )) +
geom_point()

6. What does the na.rm argument do in geom_point()?
   What is the default value of the argument?
   Create a scatterplot where you successfully use this argument set to TRUE.

removed 2 missing values from data.

7. Add the following caption to the plot you made in the previous exercise: "Data come from the palmerpenguins package." Hint: Take a look at the documentation for labs().

8. Recreate the following visualization.
   What aesthetic should bill_depth_mm be mapped to?
   And should it be mapped at the global level or at the geom level?

penguins |> 
  ggplot(aes( x = flipper_length_mm, y = body_mass_g))+ 
  geom_point(aes(color = bill_depth_mm)) + 
  geom_smooth()

9. Run this code in your head and predict what the output will look like.
   Then, run the code in R and check your predictions.

10. Will these two graphs look different?
    Why/why not?

No they are the same

[kychoe9547]

Exercises

1. How many rows are in penguins?
   How many columns?

344*8

2. What does the bill_depth_mm variable in the penguins data frame describe?
   Read the help for ?penguins to find out.

a number denoting bill depth (millimeters)

3. Make a scatterplot of bill_depth_mm vs. bill_length_mm.
   That is, make a scatterplot with bill_depth_mm on the y-axis and bill_length_mm on the x-axis.
   Describe the relationship between these two variables.

r ggplot(data = penguins, mapping = aes(x = bill_length_mm, y = bill_depth_mm )) + geom_point()

4. What happens if you make a scatterplot of species vs. bill_depth_mm?
   What might be a better choice of geom?
   

r ggplot(data = penguins, mapping = aes(x = species, y = bill_depth_mm)) + geom_boxplot()

5. Why does the following give an error and how would you fix it?

   #| eval: false
   
   ggplot(data = penguins) + 
     geom_point()
   

ggplot(data = penguins, mapping = aes(x = , y = )) +
geom_point()

6. What does the na.rm argument do in geom_point()?
   What is the default value of the argument?
   Create a scatterplot where you successfully use this argument set to TRUE.

removing missing values from data if they are coded as NA
false
r ggplot(data = penguins, mapping = aes(x = bill_length_mm, y = bill_depth_mm, na.rm = TRUE )) + geom_point()

7. Add the following caption to the plot you made in the previous exercise: "Data come from the palmerpenguins package." Hint: Take a look at the documentation for labs().
   
   r ggplot(data = penguins, mapping = aes(x = bill_length_mm, y = bill_depth_mm, na.rm = TRUE)) + geom_point()+ labs(title = "Data come from the palmerpenguins package")

8. Recreate the following visualization.
   What aesthetic should bill_depth_mm be mapped to?
   And should it be mapped at the global level or at the geom level?

   #| echo: false
   #| warning: false
   #| fig-alt: |
   #|   A scatterplot of body mass vs. flipper length of penguins, colored 
   #|   by bill depth. A smooth curve of the relationship between body mass 
   #|   and flipper length is overlaid. The relationship is positive, 
   #|   fairly linear, and moderately strong.
   
   ggplot(
     data = penguins,
     mapping = aes(x = flipper_length_mm, y = body_mass_g)
   ) +
     geom_point(aes(color = bill_depth_mm)) +
     geom_smooth()
   

  data = penguins,
  mapping = aes(x =  body_mass_g, y =flipper_length_mm)
) +
  geom_point(aes(color = bill_depth_mm)) +
  geom_smooth()

9. Run this code in your head and predict what the output will look like.
   Then, run the code in R and check your predictions.

   #| eval: false
   
   ggplot(
     data = penguins,
     mapping = aes(x = flipper_length_mm, y = body_mass_g, color = island)
   ) +
     geom_point() +
     geom_smooth(se = FALSE)
   

10. Will these two graphs look different?
    Why/why not?

    #| eval: false
    
    ggplot(
      data = penguins,
      mapping = aes(x = flipper_length_mm, y = body_mass_g)
    ) +
      geom_point() +
      geom_smooth()
    
    ggplot() +
      geom_point(
        data = penguins,
        mapping = aes(x = flipper_length_mm, y = body_mass_g)
      ) +
      geom_smooth(
        data = penguins,
        mapping = aes(x = flipper_length_mm, y = body_mass_g)
      )
    

[kychoe9547]

1.Make a bar plot of species of penguins, where you assign species to the y aesthetic. How is this plot different?

ggplot(data = penguins, aes(y=species)) +
geom_bar()
y축에 species가 위치해서 가로로 길게 뻗어나가는 그래프가 됨

2.How are the following two plots different? Which aesthetic, color or fill, is more useful for changing the color of bars?

ggplot(penguins, aes(x = species)) +
geom_bar(color = "red")
테두리만 빨간색
ggplot(penguins, aes(x = species)) +
geom_bar(fill = "red")
bar 전체가 빨간색

3.What does the bins argument in geom_histogram() do?
구간을 몇 개로 나눌 것이냐를 결정함

4.Make a histogram of the carat variable in the diamonds dataset that is available when you load the tidyverse package. Experiment with different binwidths. What binwidth reveals the most interesting patterns?

ggplot(diamonds, aes(x=carat)) +
geom_histogram(binwidth = 0.5)

1.5.5 Exercises

1. The mpg data frame that is bundled with the ggplot2 package contains 234 observations collected by the US Environmental Protection Agency on 38 car models. Which variables in mpg are categorical? Which variables are numerical? (Hint: Type ?mpg to read the documentation for the dataset.) How can you see this information when you run mpg?
   Model, trans, drv, fl, class

2. Make a scatterplot of hwy vs. displ using the mpg data frame. Next, map a third, numerical variable to color, then size, then both color and size, then shape. How do these aesthetics behave differently for categorical vs. numerical variables?
   ggplot(data = mpg, aes(x = displ, y = hwy, color = cyl, size = cyl, shape = cyl)) +
   geom_point()
   size까지는 되는데 shape 에 넣으면 오류 남
   Error in geom_point():
   ! Problem while computing aesthetics.
   ℹ Error occurred in the 1st layer.
   Caused by error in scale_f():
   ! A continuous variable cannot be mapped to the shape aesthetic.
   ℹ Choose a different aesthetic or use scale_shape_binned().
   Run rlang::last_trace() to see where the error occurred.

3. In the scatterplot of hwy vs. displ, what happens if you map a third variable to linewidth?
   아마 선의 굵기 관련이라 아무 것도 일어나지 않은 듯하다

4. What happens if you map the same variable to multiple aesthetics?
   Facet_wrap 이용 cyl 로 나누면 4개의 그리드가 나온다

5. Make a scatterplot of bill_depth_mm vs. bill_length_mm and color the points by species. What does adding coloring by species reveal about the relationship between these two variables? What about faceting by species?
   ggplot(data = penguins, aes(x = bill_length_mm , y = bill_depth_mm , color = species) )+
   geom_point()
   색깔로는 상관관계 파악하기가 어려운듯
   ggplot(data = penguins, aes(x = bill_length_mm , y = bill_depth_mm , color = species) )+
   geom_point()+
   facet_wrap(~ species)
   양의 상관관계가 있는듯, 기울기는 종마다 다를 수도

6. Why does the following yield two separate legends? How would you fix it to combine the two legends?
   ggplot(
   data = penguins,
   mapping = aes(
   x = bill_length_mm, y = bill_depth_mm,
   color = species, shape = species
   )
   ) +
   geom_point() +
   labs(color = "Species")
   ggplot(
   data = penguins,
   mapping = aes(
   x = bill_length_mm, y = bill_depth_mm,
   color = species, shape = species
   )
   ) +
   geom_point()

7. Create the two following stacked bar plots. Which question can you answer with the first one? Which question can you answer with the second one?

ggplot(penguins, aes(x = island, fill = species)) +
geom_bar(position = "fill")
ggplot(penguins, aes(x = species, fill = island)) +
geom_bar(position = "fill")

1. 특정 섬의 펭귄 종 구성이 어떻게 되어 있는지

2. 특정 펭귄이 어느 섬에 얼마나 살고 있는지
   1.6.1 Exercises

3. Run the following lines of code. Which of the two plots is saved as mpg-plot.png? Why?
   ggplot(mpg, aes(x = class)) +
   geom_bar()
   ggplot(mpg, aes(x = cty, y = hwy)) +
   geom_point()
   ggsave("mpg-plot.png")
   마지막 scatterplot, ggsave 코드랑 가까이 있어서?

4. What do you need to change in the code above to save the plot as a PDF instead of a PNG? How could you find out what types of image files would work in ggsave()?
   ggplot(mpg, aes(x = cty, y = hwy)) +
   geom_point()
   ggsave("mpg-plot.pdf")

[kychoe9547]

1. Why does this code not work?

my_variable <- 10
my_varıable
#> Error: object 'my_varıable' not found

Look carefully! (This may seem like an exercise in pointlessness, but training your brain to notice even the tiniest difference will pay off when programming.)

i를 ı로 오타냄

2. Tweak each of the following R commands so that they run correctly:

ggplot(dTA = mpg) + 
  geom_point(maping = aes(x = displ y = hwy)) +
  geom_smooth(method = "lm)

ggplot(data = mpg, aes(x = displ, y = hwy)) + 
  geom_point() +
  geom_smooth(method = "lm")

3. Press Option + Shift + K / Alt + Shift + K. What happens? How can you get to the same place using the menus?

단축키 화면, help tap의 keyboard shortcut

4. Let’s revisit an exercise from the Section 1.6. Run the following lines of code. Which of the two plots is saved as mpg-plot.png? Why?

my_bar_plot <- ggplot(mpg, aes(x = class)) +
geom_bar()
my_scatter_plot <- ggplot(mpg, aes(x = cty, y = hwy)) +
geom_point()
ggsave(filename = "mpg-plot.png", plot = my_bar_plot)

bar_plot 저장함, 뭘 저장할지 지정해주었기 때문에 my _bar_plot 저장

[hayoung98]

1. Make a bar plot of species of penguins, where you assign species to the y aesthetic.
   How is this plot different?
   

2. How are the following two plots different? Which aesthetic, color or fill, is more useful for changing the color of bars?
   bars full of colors vs only color around the boundary. Fill is more useful.

3. What does the bins argument in geom_histogram() do?
   Number of bins. Overridden by binwidth. Defaults to 30.

4. Make a histogram of the carat variable in the diamonds dataset that is available when you load the tidyverse package. Experiment with different binwidths. What binwidth reveals the most interesting patterns?
   

5. The mpg data frame that is bundled with the ggplot2 package contains 234 observations collected by the US Environmental Protection Agency on 38 car models. Which variables in mpg are categorical? Which variables are numerical? (Hint: Type ?mpg to read the documentation for the dataset.) How can you see this information when you run mpg?
   manufacturer, model, trans, drv, fl, class.

---

1. Make a scatterplot of hwy vs. displ using the mpg data frame. Next, map a third, numerical variable to color, then size, then both color and size, then shape. How do these aesthetics behave differently for categorical vs. numerical variables?
   Error in geom_point():
   ! Problem while computing aesthetics.
   ℹ Error occurred in the 1st layer.

2. Make a scatterplot of hwy vs. displ using the mpg data frame. Next, map a third, numerical variable to color, then size, then both color and size, then shape. How do these aesthetics behave differently for categorical vs. numerical variables?

3. In the scatterplot of hwy vs. displ, what happens if you map a third variable to linewidth?
   nothing.

4. What happens if you map the same variable to multiple aesthetics?
???

5. Make a scatterplot of bill_depth_mm vs. bill_length_mm and color the points by species. What does adding coloring by species reveal about the relationship between these two variables? What about faceting by species?
   

longer the bill, deeper the bill.

6. Why does the following yield two separate legends? How would you fix it to combine the two legends?

penguins |> 
  ggplot(aes(x = bill_length_mm , y = bill_depth_mm, color = species, shape = species)) + 
  geom_point() 

7. Create the two following stacked bar plots. Which question can you answer with the first one? Which question can you answer with the second one?

• 각 섬에 펭귄 종이 어떻게 분포가 돼있는지
• 각 펭귄 종이 얼마나 다양한 섬에 살고있는지

ggplot(penguins, aes(x = island, fill = species)) +
geom_bar(position = "fill")
ggplot(penguins, aes(x = species, fill = island)) +
geom_bar(position = "fill")

1. Run the following lines of code. Which of the two plots is saved as mpg-plot.png? Why?
   ggplot(mpg, aes(x = cty, y = hwy)) +
   geom_point()
   ggsave("mpg-plot.png")

2. What do you need to change in the code above to save the plot as a PDF instead of a PNG? How could you find out what types of image files would work in ggsave()?
   ggplot(mpg, aes(x = cty, y = hwy)) +
   geom_point()
   ggsave("mpg-plot.pdf")

[hayoung98]

1. Why does this code not work?
   my_variable <- 10
   my_varıable
   #> Error: object 'my_varıable' not found

Look carefully! (This may seem like an exercise in pointlessness, but training your brain to notice even the tiniest difference will pay off when programming.)

오타

2. Tweak each of the following R commands so that they run correctly:
   ggplot(data = mpg, aes(x = displ, y = hwy)) +
   geom_point() +
   geom_smooth(method = "lm")

3. Press Option + Shift + K / Alt + Shift + K. What happens? How can you get to the same place using the menus?
   keyboard shortcut

4. Let’s revisit an exercise from the Section 1.6. Run the following lines of code. Which of the two plots is saved as mpg-plot.png? Why?
   my_bar_plot <- ggplot(mpg, aes(x = class)) +
   geom_bar()
   my_scatter_plot <- ggplot(mpg, aes(x = cty, y = hwy)) +
   geom_point()
   ggsave(filename = "mpg-plot.png", plot = my_bar_plot)

bar_plot 저장함, 뭘 저장할지 지정해주었기 때문에 my _bar_plot 저장