Refactoring R code for IoT data processing efficiency with dplyr and purrr
I'm trying to implement I'm having trouble with Currently developing an R script to process and analyze data from a fleet of IoT devices. The main challenge lies in refactoring this code for better performance and readability. The original script, written using base R, is becoming increasingly difficult to maintain as the complexity of the data transforms grows. I've begun to explore using the `dplyr` and `purrr` packages for their efficiency and tidyverse compatibility. Initially, I tried converting my data manipulation tasks into `dplyr` functions, but I still have a few bottlenecks. For instance, I have a function that analyzes the temperature data collected every minute. Here’s a simplified version of my existing code: ```r analyze_temperature <- function(data) { result <- data.frame( avg_temp = mean(data$temp), max_temp = max(data$temp), min_temp = min(data$temp) ) return(result) } # Sample data frame sensor_data <- data.frame( timestamp = as.POSIXct('2023-10-01') + seq(0, 59, by = 1), temp = rnorm(60, 25, 5) ) # Applying the function output <- analyze_temperature(sensor_data) ``` While this works, I want to incorporate `dplyr` for a more streamlined approach. I've attempted to rewrite the function as follows: ```r dlibrary(dplyr) analyze_temperature_dplyr <- function(data) { data %>% summarise( avg_temp = mean(temp), max_temp = max(temp), min_temp = min(temp) ) } # Applying the new function output_dplyr <- analyze_temperature_dplyr(sensor_data) ``` This refactor is a step in the right direction, but I’m curious about how to leverage `purrr` for batch processing multiple sensors’ data in a more efficient manner. Given that I receive data in a list format, I've started experimenting with `map`. Here’s what I have: ```r # List of data frames from multiple sensors sensor_data_list <- list( sensor1 = sensor_data, sensor2 = sensor_data + 1, sensor3 = sensor_data - 1 ) analyze_all_sensors <- function(data_list) { map(data_list, analyze_temperature_dplyr) } # Process all sensor data all_output <- analyze_all_sensors(sensor_data_list) ``` This seems promising, but I’m unsure if there’s a more efficient method to aggregate results from different sensors or if I’m missing a best practice in this context. Any insights on optimizing this process further or alternative strategies would be greatly appreciated. Also, are there any pitfalls I should be aware of when processing data in this manner? Looking forward to any advice! I'm developing on Ubuntu 20.04 with R. Any suggestions would be helpful. Could someone point me to the right documentation?