Introduction

This presentation explains how to create and interpret various types of charts using Plotly in R:

• Histograms and Box Plots for continuous data
• Pie Charts and Bar Charts for categorical data

We’ll use the mtcars dataset as our primary example.

The mtcars Dataset

The mtcars dataset contains fuel consumption and 10 aspects of automobile design and performance for 32 automobiles (1973–74 models).

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

Understanding Histograms

Histograms are used to visualize the distribution of a continuous variable.

Key features:

• Bars represent frequency of data within bins
• X-axis shows the variable’s values
• Y-axis shows the count or frequency
• Shape indicates distribution type (e.g., normal, skewed)

Histogram: Miles Per Gallon (mpg)

plot_ly(mtcars, x = ~mpg, type = "histogram") %>%
  layout(title = "Distribution of Miles Per Gallon",
         xaxis = list(title = "Miles Per Gallon"),
         yaxis = list(title = "Count"))

Interpreting the MPG Histogram

• The distribution appears to be roughly bimodal (two peaks)
• Most cars have MPG between 10 and 25
• There’s a smaller group of more fuel-efficient cars (25-35 MPG)
• Few cars have very high (>30) or very low (<10) MPG

Understanding Box Plots

Box plots show the distribution of a variable through quartiles.

Components:

• Box: Represents the interquartile range (IQR, 25th to 75th percentile)
• Line in box: Median
• Whiskers: Typically extend to 1.5 * IQR
• Points beyond whiskers: Potential outliers

Single-Variable Box Plot: MPG

plot_ly(mtcars, y = ~mpg, type = "box") %>%
  layout(title = "Distribution of Miles Per Gallon",
         yaxis = list(title = "Miles Per Gallon"))

Interpreting the MPG Box Plot

• Median MPG is around 19
• 50% of cars have MPG between about 15 and 22.5
• There are several high-MPG outliers (>30 MPG)
• The distribution is slightly skewed towards higher MPG values

Comparative Box Plots

Box plots are excellent for comparing distributions across groups.

Box Plot: MPG by Number of Cylinders

plot_ly(mtcars, x = ~as.factor(cyl), y = ~mpg, type = "box") %>%
  layout(title = "Miles Per Gallon by Number of Cylinders",
         xaxis = list(title = "Number of Cylinders"),
         yaxis = list(title = "Miles Per Gallon"))

Interpreting MPG by Cylinders Box Plot

• Clear negative relationship between number of cylinders and MPG
• 4-cylinder cars have the highest median MPG and largest spread
• 8-cylinder cars have the lowest median MPG and smallest spread
• There’s an outlier in the 8-cylinder group with unusually high MPG

Understanding Pie Charts

Pie charts are used to show the composition of a categorical variable.

Key features:

• Each slice represents a category
• Size of slice corresponds to the proportion of that category
• Useful for showing parts of a whole
• Best for a small number of categories (typically < 7)

Pie Chart: Distribution of Cylinders

cyl_counts <- mtcars %>% count(cyl)
plot_ly(cyl_counts, labels = ~cyl, values = ~n, type = 'pie') %>%
  layout(title = 'Distribution of Cars by Number of Cylinders')

Interpreting the Cylinders Pie Chart

• The dataset contains cars with 4, 6, and 8 cylinders
• 8-cylinder cars are the most common, making up about half of the dataset
• 4-cylinder and 6-cylinder cars each make up roughly a quarter of the dataset
• This gives us a quick overview of the composition of car types in the dataset

Understanding Bar Charts

Bar charts are used to compare quantities across different categories.

Key features:

• Each bar represents a category
• Height/length of bar corresponds to the quantity for that category
• Can be vertical or horizontal
• Useful for comparing values across categories

Bar Chart: Average MPG by Number of Cylinders

avg_mpg <- mtcars %>% group_by(cyl) %>% summarise(avg_mpg = mean(mpg))
plot_ly(avg_mpg, x = ~factor(cyl), y = ~avg_mpg, type = 'bar') %>%
  layout(title = 'Average MPG by Number of Cylinders',
         xaxis = list(title = 'Number of Cylinders'),
         yaxis = list(title = 'Average MPG'))

Interpreting the MPG by Cylinders Bar Chart

• Clear trend: as the number of cylinders increases, the average MPG decreases
• 4-cylinder cars have the highest average MPG, around 26
• 6-cylinder cars have an average MPG of about 20
• 8-cylinder cars have the lowest average MPG, approximately 15
• This chart makes it easy to compare the fuel efficiency across cylinder types

When to Use Each Chart Type

• Histograms: For continuous data to show distribution and shape
• Box Plots: For continuous data to show distribution, central tendency, and outliers
• Pie Charts: For categorical data to show composition, best for few categories
• Bar Charts: For categorical data to compare quantities across categories

Conclusion

• Each chart type offers unique insights into your data
• Choose the appropriate chart based on your data type and what you want to highlight
• Combine different chart types for a comprehensive understanding of your dataset