Merging and Aggregating Dataframes Based on Time Span

In this article, we’ll explore how to merge two dataframes based on a time span. The goal is to calculate the mean of one column from another dataframe within a specific time window.

Problem Statement

We have two dataframes: test and test2. The test dataframe contains measurements with a 5-minute interval, while test2 contains weather data in 10-minute intervals. We want to merge these two dataframes based on the measurement time from test and calculate the mean of the VPD column from test2 within a 1-hour window.

Data Preparation

Let’s start by preparing our data for merging. The test dataframe contains measurements with the following columns:

• treatment: A factor indicating the treatment type (A or B)
• plot: A factor indicating the plot number
• date: A Date object representing the measurement date
• time: A character string representing the measurement time

The test2 dataframe contains weather data with the following columns:

• datetime: A POSIXct object representing the weather datetime
• VPD: A numeric vector representing the vapor pressure dew point (VPD) value

We need to convert the time column in test to a datetime format that matches the datetime column in test2.

library(dplyr)
library(lubridate)

# Convert time to datetime format
test <- test %>% 
  mutate(date_time = ymd_hms(paste(as.character(date), time)))

# Check the data structure of date_time
str(test$date_time)  # Output: POSIXct[1:50] "2022-01-01 00:00:00" ...

Merging and Aggregation

Now that our data is prepared, let’s merge the two dataframes based on the measurement time from test and calculate the mean of the VPD column from test2 within a 1-hour window.

# Calculate the mean VPD value for each date_time in test
test$mean_VPD <- sapply(test$date_time, function(t){
  # Convert t to minutes
  mins <- gsub(".*:(\\d{2}):00$", "\\1", as.character(t))
  
  if(grepl("5$", mins)){
    t <- t-minutes(5)
  }
  
  # Filter the date_time in test and get the corresponding VPD value from test2
  test2 %>% 
    filter(datetime >= t & datetime <= t + hours(1)) %>% 
    select(VPD) %>% 
    pull() %>% 
    mean()
})

# Print the first few rows of test with calculated mean_VPD
head(test, 10)

In this code:

• We use sapply to apply a function to each date_time in the test dataframe.
• Inside the function, we convert the t variable to minutes using gsub.
• If t is within the 5-minute interval, we subtract 5 minutes from it using minutes(5).
• We then filter the datetime column in test2 based on the updated t value and select only the VPD values.
• We calculate the mean of these VPD values.

Final Output

The final output will be a dataframe with the original columns from test and an additional mean_VPD column containing the calculated mean VPD value for each date_time in test.

library(ggplot2)

# Plot a histogram of mean_VPD
ggplot(test, aes(x = mean_VPD)) + 
  geom_histogram(bins = 20, color = "black") + 
  labs(title = "Histogram of Mean VPD", x = "Mean VPD")

In this article, we demonstrated how to merge two dataframes based on a time span and calculate the mean of one column from another dataframe within that window. The code provided can be used as a starting point for similar tasks in various fields, including climate science, agriculture, and more.

Last modified on 2025-01-24