Strategic Coding Practices

Find main file on desktop and familiarize with contents

folders <- list.files(path = "~/Desktop/NEON_count-landbird", pattern = "-2025", full.names = FALSE)

Prepare to run loop:

Create empty list to hold the files I would like to work with

countdata_list <- list()

Create empty data frame to hold the values generated in loop

stats <- data.frame(
  filename = character(),
  abundance = numeric(),
  richness = numeric(),
  year = numeric(),
  stringsAsFactors = FALSE
)

Create empty data frame for regression model statistics

regression_summary <- data.frame(
  coef_abundance = numeric(),
  intercept = numeric(),
  r_squared = numeric(),
  p_value_abundance = numeric(),
  stringsAsFactors = FALSE
)

Check packages out of the library

library(devtools) library(upscaler)

Process Data

for (i in folders) {
  files <- list.files(
    path = file.path("~/Desktop/NEON_count-landbird", i),
    pattern = "countdata",
    full.names = TRUE
  )
  print(files)

  for (j in files) {
    count_data <- read.csv(j)
    countdata_list[[j]] <- count_data

    # Extract the filename
    file_name <- basename(j)

    # Extract year from filename
    year_match <- regmatches(file_name, regexpr("20[0-9]{2}", file_name))
    year <- as.numeric(year_match)

    # Clean
    na.omit(j)

    # Calculate abundance and richness
    abundance <- sum(count_data$clusterSize, na.rm = TRUE)
    richness <- length(unique(count_data$scientificName))

    # Add row to stats data frame
    stats <- rbind(stats, data.frame(
      filename = file_name,
      abundance = abundance,
      richness = richness,
      year = year,
      stringsAsFactors = FALSE
    ))
  }
}

Run functions for regression and histograms

regression_summary <- regression_model(stats)

create_histograms(stats)

Results

Stats

File Name Abundance Richness Year
NEON.D01.BART.DP1.10003.001.brd_countdata.2015-06.basic.20241118T065914Z.csv 459 40 2015
NEON.D01.BART.DP1.10003.001.brd_countdata.2016-06.basic.20241118T142515Z.csv 696 39 2016
NEON.D01.BART.DP1.10003.001.brd_countdata.2017-06.basic.20241118T043125Z.csv 411 35 2017

NEON.D01.BART.DP1.10003.001.brd_countdata.2018-06.basic.20241118T105926Z.csv		 515 37 2018
NEON.D01.BART.DP1.10003.001.brd_countdata.2019-06.basic.20241118T064156Z.csv 410 44 2019
NEON.D01.BART.DP1.10003.001.brd_countdata.2020-06.basic.20241118T184512Z.csv 489 46 2020
NEON.D01.BART.DP1.10003.001.brd_countdata.2020-07.basic.20241118T010504Z.csv 54 18 2020
NEON.D01.BART.DP1.10003.001.brd_countdata.2021-06.basic.20241118T105538Z.csv 920 50 2021
NEON.D01.BART.DP1.10003.001.brd_countdata.2022-06.basic.20241118T033934Z.csv 592 39 2022
NEON.D01.BART.DP1.10003.001.brd_countdata.2023-06.basic.20241118T091043Z.csv 523 42 2023

Regression Summary

coef_abundance intercept r_squared p_value_abundance n_obs
0.03137201 23.09753 0.6506631 0.004808103 10

Histograms