Introduction

This document presents the solutions for Practicum 3 - Data Visualization.

The visualizations were created using R and the ggplot2 package.

#Exercise 1: Comparison ##Objective

Calculate the average city mileage (cty) for each manufacturer, select the top 10 manufacturers, and visualize the comparison.

data(mpg)

mpg_avg <- mpg %>%
  group_by(manufacturer) %>%
  summarise(mean_cty = mean(cty)) %>%
  arrange(desc(mean_cty))

top10_cty <- mpg_avg %>%
  slice_head(n = 10)

ggplot(top10_cty,
       aes(x = reorder(manufacturer, mean_cty),
           y = mean_cty)) +
  geom_col(fill = "#4DBBD5") +
  coord_flip() +
  labs(
    title = "Top 10 Manufacturers by Average City MPG",
    x = "Manufacturer",
    y = "Average City MPG"
  ) +
  theme_minimal()

###Interpretation

The chart compares the average city mileage among the top 10 manufacturers. Manufacturers with higher average cty values indicate better city fuel efficiency within this group.

#Exercise 2: Distribution

ggplot(diamonds, aes(x = cut, y = price, fill = cut)) +
  geom_boxplot(alpha = 0.7) +
  labs(
    title = "Distribution of Diamond Prices by Cut",
    x = "Cut",
    y = "Price"
  ) +
  theme_minimal() +
  theme(legend.position = "none")

###Interpretation

The boxplot shows how diamond prices are distributed across different cut categories. The distributions show differences in their median prices and variability, with several extreme values appearing in the higher-price range.

#Exercise 3: Relationship

set.seed(123)

diamonds_sample <- diamonds %>%
  slice_sample(n = 1000)
ggplot(diamonds_sample,
       aes(x = carat, y = price, color = cut)) +
  geom_point(alpha = 0.6, size = 2) +
  labs(
    title = "Relationship Between Carat and Price",
    x = "Carat",
    y = "Price",
    color = "Cut"
  ) +
  theme_minimal()

###Interpretation

The scatter plot shows a positive relationship between carat and price. In general, diamonds with larger carat values tend to have higher prices, although there is considerable variation among observations.

#Exercise 4: Time series

economics_change <- economics %>%
  mutate(change = psavert - lag(psavert))

largest_change <- economics_change %>%
  filter(!is.na(change)) %>%
  slice_max(order_by = abs(change), n = 1)

ggplot(economics_change, aes(x = date, y = psavert)) +
  geom_line(
    color = "#3B82F6",
    linewidth = 1
  ) +
  geom_point(
    data = largest_change,
    size = 4,
    color = "#EF476F"
  ) +
  geom_label(
    data = largest_change,
    aes(
      label = paste0(
        "Largest month-to-month change\n",
        format(date, "%b %Y"),
        "\nSaving rate: ", round(psavert, 1), "%"
      )
    ),
    color = "#C2185B",
    fill = "#FFE4EC",
    fontface = "bold",
    size = 3.5,
    hjust = 1,
    vjust = 2
  ) +
  labs(
    title = "Personal Saving Rate Over Time",
    x = "Year",
    y = "Personal Saving Rate (%)"
  ) +
  theme_classic() +
  theme(
    plot.title = element_text(
      face = "bold",
      size = 16
    ),
    axis.title = element_text(
      face = "bold"
    )
  )

###Interpretation

The personal saving rate generally shows a declining pattern over the observed period, although several fluctuations occur. The highlighted point indicates the largest month-to-month change identified in the dataset.

#Exercise 5: Improving a visualization ##Before Improvement

ggplot(mpg,
       aes(x = displ,
           y = hwy)) +
  geom_point() 

##After improvement
ggplot(mpg,
       aes(x = displ,
           y = hwy,
           color = class)) +
  geom_point(
    size = 2.5,
    alpha = 0.8
  ) +
  labs(
    title = "Engine Size and Highway Fuel Efficiency",
    subtitle = "Fuel efficiency varies across car classes",
    x = "Engine Displacement (liters)",
    y = "Highway Fuel Efficiency (mpg)",
    color = "Car Class"
  ) +
  theme_minimal()

###Improvements

The original visualization had several presentation problems, including limited color differentiation, unclear labels, and a basic default appearance.

The redesigned visualization improves the presentation by using color, transparency, clearer labels, and a cleaner theme. These changes make the visualization easier to read and interpret.