Creating a Day Trend Scatter Plot by Multiple Variables in R Using Base R and ggplot2

Creating a Day Trend Scatter Plot by Multiple Variables

As data analysts, we often encounter datasets that contain multiple variables of interest. In this article, we will explore how to create a day trend scatter plot using R, specifically focusing on visualizing the daily trends in multiple states.

Introduction

In statistics, a scatter plot is a graphical representation of the relationship between two variables. However, when dealing with multiple variables, creating a meaningful scatter plot can be challenging. In this article, we will discuss how to create a day trend scatter plot by multiple variables using R, highlighting both base R and ggplot2 approaches.

Base R Approach

To create a day trend scatter plot using base R, we first need to understand the importance of data transformation. Since R uses column-first order, we must transpose our data before plotting.

# Load required libraries
library(mapt)

# Define the dataset
df1 <- read.table(text = "
State Day1 Day2 Day3 Day4
CA    1    5     7    9
NY    10   8    20    90 
VT    4   6    9    10 
", header = TRUE)

# Transpose the data (column-first order to row-first order)
df1 <- t(df1[-1])

# Create a scatter plot with lines
matplot(t(df1), type = "l", lty = 1)

# Add legend for state colors
legend("topleft", legend = df1$State, col = 1:3, lty = 1)

As you can see from the code above, we first load the matplot function, which is used to create scatter plots with lines. We then transpose our data using the - operator and apply it to the t() function. The resulting transposed data is then plotted using matplot(). Finally, a legend is added for each state color.

ggplot2 Approach

When working with large datasets or when the data needs to be reshaped, the ggplot2 package provides an efficient solution. To create a day trend scatter plot using ggplot2, we need to reshape our data from wide format to long format.

# Load required libraries
library(ggplot2)
library(tidyr)
library(dplyr)

# Define the dataset
df1 <- read.table(text = "
State Day1 Day2 Day3 Day4
CA    1    5     7    9
NY    10   8    20    90 
VT    4   6    9    10 
", header = TRUE)

# Reshape the data from wide to long format
df1_long <- tidyr::pivot_longer(df1[-1], names_to = "Day") %>%
  dplyr::mutate(Day = as.integer(sub("[^[:digit:]]+", "", Day)))

# Create a scatter plot with lines
ggplot(aes(Day, value, color = State)) +
  geom_line()

In this example, we load the necessary libraries and define our dataset. We then reshape our data using tidyr::pivot_longer() to transform it from wide format to long format. The resulting long-form data is then plotted using ggplot2’s geom_line(). This approach provides a more flexible way of visualizing multiple variables.

Data and Plotting

Before diving into the plotting code, let’s understand how the data is structured.

The dataset consists of four states: CA, NY, VT, with three days each: Day1, Day2, Day3. Each day has two values corresponding to the state’s daily trends in two variables (variable 1 and variable 2). We can see this structure by running head(df1) or exploring the data in a spreadsheet.

Now that we’ve discussed both approaches, let’s combine them into a single code block for demonstration purposes. The combined code below includes both base R and ggplot2 approaches:

# Load required libraries
library(mapt)
library(ggplot2)
library(tidyr)
library(dplyr)

# Define the dataset
df1 <- read.table(text = "
State Day1 Day2 Day3 Day4
CA    1    5     7    9
NY    10   8    20    90 
VT    4   6    9    10 
", header = TRUE)

# Transpose the data (column-first order to row-first order)
df1 <- t(df1[-1])

# Create a scatter plot with lines using base R
matplot(t(df1), type = "l", lty = 1)
legend("topleft", legend = df1$State, col = 1:3, lty = 1)

# Reshape the data from wide to long format for ggplot2 approach
df1_long <- tidyr::pivot_longer(df1[-1], names_to = "Day") %>%
  dplyr::mutate(Day = as.integer(sub("[^[:digit:]]+", "", Day)))

# Create a scatter plot with lines using ggplot2
ggplot(aes(Day, value, color = State)) +
  geom_line()

As shown in the code above, we can visualize our data using both base R and ggplot2. The choice between these two approaches depends on your specific needs and personal preference.

Conclusion

Creating a day trend scatter plot by multiple variables is an essential skill for any data analyst or scientist. In this article, we discussed how to achieve this goal using both base R and ggplot2 packages in R. We covered the importance of data transformation and provided code examples for each approach. Whether you prefer base R or ggplot2, there’s a way to create meaningful scatter plots that showcase your dataset effectively.

Additional Resources

For more information on the matplot function, please visit the MATLAB documentation page: https://www.mathworks.com/help/matlab/ref/matplot.html
For more information on ggplot2 in R, please visit the ggplot2 documentation page: http://ggplot2.tidyverse.org/
To learn more about data transformation and visualization techniques, consider taking an introductory statistics or data analysis course.

Last modified on 2024-10-29