Binning Data in R: A Step-by-Step Guide to Computing Average Over 20 Second Intervals and Grouping by Another Column

As a data analyst working with time-series data, you often encounter the need to bin your data into smaller intervals for analysis. In this article, we will explore how to achieve this using the lubridate package for binnning and the dplyr package for grouping and summarization.

Introduction

Time-series data is commonly used in various fields, including finance, economics, and environmental science. One of the challenges when working with time-series data is dealing with large datasets that contain a high volume of observations over time. Binning these observations into smaller intervals can help to reduce the complexity of the dataset while still allowing for meaningful analysis.

In this article, we will focus on two popular R packages: lubridate and dplyr. We will demonstrate how to use these packages to bin data into 20-second intervals and group by another column. The main concepts covered in this article include:

• Using the floor_date function from lubridate to bin data into smaller time intervals
• Grouping data using the group_by function from dplyr
• Summarizing grouped data using the summarise function from dplyr

Prerequisites

Before we begin, make sure you have installed and loaded the required R packages:

# Install necessary libraries
install.packages(c("lubridate", "dplyr"))

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

Binning Data with floor_date from lubridate

To bin data into smaller time intervals, we will use the floor_date function from the lubridate package. This function takes two arguments: a date object and a time interval.

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

# Create a sample dataframe
df <- data.frame(
  datetime = ymd_hms("2018-04-06 14:47:51", "2018-04-06 14:47:52", 
                   "2018-04-06 14:47:53"),
  Depth = c(4.5, 4.5, 4.5),
  MSA = c(0.20154042, 0.11496760, 0.05935992)
)

# Bin the data into 20-second intervals
df$datetime_binned <- floor_date(df$datetime, "20 sec")

Grouping Data with group_by from dplyr

Now that we have binned our data, we need to group it by another column. We will use the group_by function from the dplyr package for this purpose.

# Use group_by() and summarise() functions to calculate the mean of Depth, MSA, rate_s, and HR 
result <- df %>%
  group_by(diveNum, D_phase, datetime = datetime_binned) %>%  
  summarise(across(c(Depth, MSA, rate_s, HR), mean, na.rm = TRUE), .groups = 'drop')

How It Works

Here’s a step-by-step breakdown of how the code works:

1. Bin Data into Smaller Intervals: The floor_date function is used to bin the data into smaller time intervals (20 seconds in this case).
2. Group Data by Another Column: The group_by function groups the binned data by two additional columns: diveNum and D_phase.
3. Summarize Grouped Data: The summarise function calculates the mean of four columns (Depth, MSA, rate_s, and HR) within each group.

Conclusion

In this article, we have demonstrated how to bin data into smaller time intervals using the lubridate package and then group by another column using the dplyr package. By following these steps, you can easily binnify your data and perform meaningful analysis on it.

Note: For a complete example with code, see R Binning Data in 20-second Intervals with Dplyr.

Last modified on 2023-08-09