HW5

Question 1

What is one of the only documented advantages of a pie chart over other types of display? Try to come up with another advantage not noted by the author?*

• One of the only documented advantages is when comparing the sum of adjacent slices of a pie chart than trying to stack and sum the heights of a bar chart.

• An additional advantage might be that pie charts are extremely easy to remember. Because of their simplicity one could use distinct coloring to make a statement to an audience less technical or versed in data visualization.

## Question 2

head(mtcars)

##                    mpg cyl disp  hp drat    wt  qsec vs am gear carb
## Mazda RX4         21.0   6  160 110 3.90 2.620 16.46  0  1    4    4
## Mazda RX4 Wag     21.0   6  160 110 3.90 2.875 17.02  0  1    4    4
## Datsun 710        22.8   4  108  93 3.85 2.320 18.61  1  1    4    1
## Hornet 4 Drive    21.4   6  258 110 3.08 3.215 19.44  1  0    3    1
## Hornet Sportabout 18.7   8  360 175 3.15 3.440 17.02  0  0    3    2
## Valiant           18.1   6  225 105 2.76 3.460 20.22  1  0    3    1

library(plotly)

## Loading required package: ggplot2

## 
## Attaching package: 'plotly'

## The following object is masked from 'package:ggplot2':
## 
##     last_plot

## The following object is masked from 'package:stats':
## 
##     filter

## The following object is masked from 'package:graphics':
## 
##     layout

library(dplyr)

## 
## Attaching package: 'dplyr'

## The following objects are masked from 'package:stats':
## 
##     filter, lag

## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union

mtcars$cyl <- factor(mtcars$cyl)
mtcars$gear <- factor(mtcars$gear)

inner_data <- mtcars %>%
  group_by(cyl) %>%
  summarize(count = n())

outer_data <- mtcars %>%
  group_by(cyl, gear) %>%
  summarize(count = n())

## `summarise()` has grouped output by 'cyl'. You can override using the `.groups`
## argument.

plot_ly() %>%
  add_pie(data = inner_data, labels = ~cyl, values = ~count, 
          hole = 0.4, sort = FALSE,
          textinfo = 'label',
          marker = list(line = list(width = 1)),
          name = "Cylinders",
          domain = list(x = c(0.15, 0.85), y = c(0.15, 0.85))) %>%
    add_pie(data = outer_data, labels = ~gear, values = ~count, 
          hole = 0.7, sort = FALSE,
          textinfo = 'label',
          marker = list(line = list(width = 1)),
          name = "Gears",
          domain = list(x = c(0, 1), y = c(0, 1))) %>%
    layout(title = "Layered Pie Chart of Cylinders and Gears from mtcars)",
         showlegend = TRUE,
         annotations = list(
           list(text = "Cylinders", x = 0.5, y = 0.5, showarrow = FALSE, font = list(size = 12))
         ))

Write a few sentences describing what works and what does not work*

• What works: I think the inner ring cylinders and outer ring are distinct and differntiable, the labeling is clear and there is color differentiation between each category

• What does not work: Some of the colors (4 for both cyl and gear) are the same which may be confusing, there is missing context and also the data does not draw a clear connection between the Cylinders and Gears and the total count, how many is this for each? plotly is interactable which is a plus to see counts, but it takes more effort and has other visualizations that are more optimal.

## Question 3

library(ggplot2)
ggplot(outer_data, aes(x = cyl, y = count, fill = gear)) +
  geom_bar(stat = "identity", position = "stack", color = "white") +
  labs(title = "Stacked Bar Chart of Cylinders and Gears",
       x = "Number of Cylinders",
       y = "Count of Cars",
       fill = "Number of Gears") +
  theme_minimal() 

Address how this display is a more effective visual.*

• The stacked bar chart is a significantly better visualization for comparing Cylinders and Gear types because you can see the count of cars and what is the most common, you can also judge the relative sizes of each segment rather than it being the vague percentages

• The legend and labels make it very easy to interpret. Overall it is better because it simplfiies the comparisons and has a clearer view of proportions.

## Question 4

library(ggalluvial)

mtcars$am <- factor(mtcars$am, labels = c("Automatic", "Manual"))

alluvial_data <- mtcars %>%
  group_by(cyl, gear, am) %>%
  summarize(count = n()) %>%
  ungroup()

## `summarise()` has grouped output by 'cyl', 'gear'. You can override using the
## `.groups` argument.

ggplot(alluvial_data,
       aes(axis1 = cyl, axis2 = gear, axis3 = am, y = count)) +
  geom_alluvium(aes(fill = cyl)) +
  geom_text(stat = "stratum", aes(label = after_stat(stratum))) +
  scale_x_discrete(limits = c("Cylinders", "Gears", "Transmission")) +
  labs(title = "Alluvial Plot of Cylinders, Gears, and Transmission Types (mtcars)",
       x = "", y = "Count of Cars") +
  theme(legend.position = "none")

## Warning in to_lodes_form(data = data, axes = axis_ind, discern =
## params$discern): Some strata appear at multiple axes.
## Warning in to_lodes_form(data = data, axes = axis_ind, discern =
## params$discern): Some strata appear at multiple axes.

• I do not have a ton of experience with Alluvial Plots, but I think that there is a clear distribution and transiton between categories. The width of the blocks helps compare what is popular between different configurations. I was a bit confused by using the same color for the multiple different categories and some of the flows crossing and overlaping make it hard to trace specific paths of types.

## Chat Improved Version

ggplot(alluvial_data,
       aes(axis1 = cyl, axis2 = gear, axis3 = am, y = count)) +
  geom_alluvium(aes(fill = cyl, alpha = count), size = 0.5) +  # Thinner, transparent flows
  geom_stratum(aes(fill = cyl), color = "black") +  # Black borders for clarity
  geom_text(stat = "stratum", aes(label = after_stat(stratum)), color = "black", size = 4) +
  geom_text(aes(label = count), stat = "alluvium", size = 3, color = "black", nudge_x = 0.2) +  # Counts on flows
  scale_x_discrete(limits = c("Cylinders", "Gears", "Transmission")) +
  scale_fill_brewer(palette = "Set2") +  # Distinct colors for clarity
  scale_alpha_continuous(range = c(0.5, 1)) +  # Transparency based on count
  labs(title = "Improved Alluvial Plot of Cylinders, Gears, and Transmission Types (mtcars)",
       x = "", y = "Count of Cars") +
  theme_minimal() +
  theme(legend.position = "none")

## Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
## ℹ Please use `linewidth` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.

## Warning in to_lodes_form(data = data, axes = axis_ind, discern =
## params$discern): Some strata appear at multiple axes.
## Warning in to_lodes_form(data = data, axes = axis_ind, discern =
## params$discern): Some strata appear at multiple axes.
## Warning in to_lodes_form(data = data, axes = axis_ind, discern =
## params$discern): Some strata appear at multiple axes.
## Warning in to_lodes_form(data = data, axes = axis_ind, discern =
## params$discern): Some strata appear at multiple axes.