Separate Plots for Weekends and Weekdays: A Step-by-Step Guide with ggplot2

Plotting for Weekends and Weekdays Separately from Time-Series Data Set

As a data analyst or scientist working with time-series data, you often encounter datasets that contain information about daily or weekly patterns. One common requirement in such cases is to create separate plots for weekends and weekdays to better understand the differences in behavior between these two periods.

In this article, we will explore how to achieve this using R and the popular ggplot2 library. We’ll start by explaining the basics of time-series data and the concept of days of the week, followed by a step-by-step guide on how to create separate plots for weekends and weekdays.

Time-Series Data and Days of the Week

Time-series data refers to datasets that contain information about events or patterns that occur over time. In this case, our dataset contains temperature readings and load around a substation at regular intervals (e.g., daily).

To analyze these data, it’s essential to understand the concept of days of the week. The wday function in R returns the day of the week as an integer (1-7) or a character string representing the full name of the day.

For example, wday(2018-01-01) would return 2, indicating that January 1st, 2018 was a Monday. Similarly, weekdays(as.Date('2020-06-15')) would return c(“Mon”, “Tue”, “Wed”, “Thu”, “Fri”), where each element represents the day of the week for June 15th, 2020.

Data Preparation

To create separate plots for weekends and weekdays, we need to prepare our data by filtering out non-weekend days. We can achieve this using the dplyr library in R.

First, let’s load the required libraries:

library(dplyr)
library(ggplot2)

Next, we create a sample dataset with temperature readings and load around a substation at regular intervals:

df <- data.frame(
  date = sample(seq(as.Date('2018/01/01'), as.Date('2019/01/01'), by="day"), 100),
  SSLoad = runif(100, 0, 100),
  Temp = runif(100, 70, 100)
)

This dataset contains 100 random temperature readings and load values between January 1st, 2018, and January 1st, 2019.

Filtering Non-Weekend Days

To filter out non-weekend days, we can use the dplyr library’s filter function. Here are two ways to achieve this:

Method 1: Using `dplyr`

df <- df %>%
  filter(weekdays(date) == "Saturday" | weekdays(date) == "Sunday")

In this method, we use the filter function to select only rows where the day of the week is either Saturday or Sunday.

Method 2: Using `mutate` and `subset`

df <- df %>%
  mutate(weekend = (weekdays(date) == "Saturday" | weekdays(date) == "Sunday"))

In this method, we use the mutate function to create a new column called weekend, which contains a logical value indicating whether the day is a weekend. We then select only rows where weekend is true.

Creating Separate Plots

Once we have filtered out non-weekend days, we can create separate plots for weekends and weekdays using the ggplot2 library.

Plotting Weekends Only

ggplot(df[df$weekend == T, ], aes(Temp, SSLoad)) + 
  geom_point()

In this code snippet, we select only rows where weekend is true (i.e., weekends) and create a scatter plot of temperature against load using the geom_point function.

Plotting Weekdays Only

ggplot(df[!df$weekend, ], aes(Temp, SSLoad)) + 
  geom_point()

In this code snippet, we select only rows where weekend is false (i.e., weekdays) and create a scatter plot of temperature against load using the geom_point function.

Additional Tips

To customize your plots further, you can use various options available in the ggplot2 library, such as changing the theme, adding titles, or modifying the color scheme.
When working with time-series data, it’s essential to consider issues like missing values and outliers. You can use techniques like interpolation or robust regression to handle these challenges.
To create more informative plots, you can incorporate additional variables from your dataset, such as weather conditions or geographical information.

By following these steps and examples, you should be able to create separate plots for weekends and weekdays in your time-series data. Remember to explore the capabilities of ggplot2 and dplyr, and don’t hesitate to ask if you have any further questions or need additional guidance.

Last modified on 2023-11-23