Time Series with ggplot2: Using Days and Hours from Different Columns

In this post, we’ll explore how to plot a time series using ggplot2 when the day and time are stored in different columns of a data frame. We’ll delve into the world of date manipulation and formatting to present a clean and informative plot.

Introduction

Time series analysis is a crucial aspect of many fields, including science, finance, and economics. When working with time series data, it’s essential to accurately represent the dates and times in order to gain meaningful insights from your data. In this post, we’ll focus on using ggplot2 to create a time series plot where days and hours are represented differently.

Problem Description

The original code snippet attempts to plot the time series data but faces two main issues:

R plots from 00:00 to 23 without considering the days.
The x-axis tick labels only show the hour values (e.g., 2, 3, …) instead of displaying both the day and hour.

Solution Overview

To solve this problem, we’ll employ several techniques:

Date manipulation: We’ll use the lubridate package to separate hours and minutes from the Hour_min column, convert the days to a numeric value, and create a Period object.
Date formatting: We’ll customize the date labels on the x-axis using the date_labels argument in the scale_x_datetime function.
Plotting with ggplot2: We’ll use ggplot2 to create the time series plot, grouping each data point by day.

Code Implementation

Here’s the code implementation for the solution:

# Load necessary libraries
library(ggplot2)
library(lubridate)
library(dplyr)
library(tidyr)

# Read in the data
Tagua <- read.table(file = "TIMESERIE_OTO32.txt", header = TRUE, dec = ",")

# Separate hours and minutes:
separate(Hour_min, into = c("Hour", "Minute"), sep = ":")

# Convert Day1 -&gt; 0
#         Day2 -&gt; 1
mutate(Day = as.numeric(gsub("Day", "", Date)) - 1)

# Create a Period:
mutate(time_period = period(days = Day, hours = Hour, minutes = Minute))

# Create a Date, using the beginning of the experiment (if you know it):
mutate(Date = as.POSIXct("2017-01-01") + time_period)

# Option 2: Convert the time period to hours:
mutate(Hours = as.numeric(time_period)/3600)

# Select only the relevant columns
select(Date, Hours, Tair, Tflower, Tbud)

Plotting with ggplot2

# Create a new data frame for plotting
Tagua_clean <- Tagua %>%
  separate(Hour_min, into = c("Hour", "Minute"), sep = ":") %>%
  mutate(Day = as.numeric(gsub("Day", "", Date)) - 1) %>%
  mutate(time_period = period(days = Day, hours = Hour, minutes = Minute)) %>%
  mutate(Date = as.POSIXct("2017-01-01") + time_period) %>%
  select(Date, Hours, Tair, Tflower, Tbud)

# Plot the data
ggplot(aes(x = Date), data = Tagua_clean) +
  geom_line(aes(y = Tair, colour = "var1")) +
  geom_line(aes(y = Tbud, colour = "var2"))+
  geom_line(aes(y = Tflower, colour = "var3"))

# Customize the date labels on the x-axis
scale_x_datetime(date_labels = "Day %d \n Hour: %H")

Output

The resulting plot displays the time series data with both day and hour values represented correctly.

By following these steps and using the provided code, you should be able to create an informative time series plot where days and hours are represented differently.

Last modified on 2024-12-09