Lære Faceting for Group Comparisons

Faceting is a powerful feature in ggplot2 that allows you to split your data into subsets and display the results in multiple panels within a single plot. This technique is especially useful when you want to compare patterns or trends across different groups, such as regions, product categories, or time periods. By visually separating subgroups, faceting makes it easier to spot similarities, differences, and outliers without the confusion of overlapping data points. You can quickly assess how relationships or distributions vary between groups, which is essential for subgroup analysis in data exploration and reporting.


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library(ggplot2)

# Create a sample data frame
sales_data <- data.frame(
  Month = rep(1:12, times = 3),
  Sales = c(
    round(runif(12, 100, 200)),
    round(runif(12, 120, 250)),
    round(runif(12, 80, 180))
  ),
  Region = rep(c("North", "South", "West"), each = 12)
)

# Scatter plot of Sales by Month, faceted by Region
ggplot(sales_data, aes(x = Month, y = Sales)) +
  geom_point() +
  facet_wrap(~ Region) +
  labs(title = "Monthly Sales Trends by Region",
       x = "Month",
       y = "Sales")

In the code above, the facet_wrap(~ Region) function is used to create a separate scatter plot panel for each region in the sales_data data frame. Faceting functions like facet_wrap and facet_grid enable you to organize your plots based on one or two categorical variables. facet_wrap arranges the panels in a wrapped sequence, making it ideal for one grouping variable such as "Region." If you want to compare more than one grouping variable, facet_grid allows you to lay out panels in a grid format, using both rows and columns. By using these functions, you can easily generate clear, side-by-side comparisons for each subgroup, making patterns and differences more apparent when analyzing regional sales trends or any other grouped data.

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Sektion 3. Kapitel 2

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              1234567891011121314151617181920
            
library(ggplot2)

# Create a sample data frame
sales_data <- data.frame(
  Month = rep(1:12, times = 3),
  Sales = c(
    round(runif(12, 100, 200)),
    round(runif(12, 120, 250)),
    round(runif(12, 80, 180))
  ),
  Region = rep(c("North", "South", "West"), each = 12)
)

# Scatter plot of Sales by Month, faceted by Region
ggplot(sales_data, aes(x = Month, y = Sales)) +
  geom_point() +
  facet_wrap(~ Region) +
  labs(title = "Monthly Sales Trends by Region",
       x = "Month",
       y = "Sales")

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Tak for dine kommentarer!

Sektion 3. Kapitel 2

Faceting for Group Comparisons

1. Which of the following are advantages of using faceting in ggplot2 for subgroup analysis?

2. Which function is used in ggplot2 to create multiple panels based on a grouping variable, as shown in the code sample?

3. When interpreting a faceted plot comparing regional sales, what can you learn from the side-by-side panels?

Faceting for Group Comparisons

1. Which of the following are advantages of using faceting in ggplot2 for subgroup analysis?

2. Which function is used in ggplot2 to create multiple panels based on a grouping variable, as shown in the code sample?

3. When interpreting a faceted plot comparing regional sales, what can you learn from the side-by-side panels?