This homework we will be coding with the strategic coding tools!

Let’s bring in dem libraries first mon

library(pracma)
## Warning: package 'pracma' was built under R version 4.4.3
library(pryr)
## Warning: package 'pryr' was built under R version 4.4.3
## 
## Attaching package: 'pryr'
## The following object is masked from 'package:pracma':
## 
##     bits
library(upscaler)
library(tidyverse)
## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr     1.1.4     ✔ readr     2.1.5
## ✔ forcats   1.0.0     ✔ stringr   1.5.1
## ✔ ggplot2   3.5.1     ✔ tibble    3.2.1
## ✔ lubridate 1.9.3     ✔ tidyr     1.3.1
## ✔ purrr     1.0.2
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ purrr::compose() masks pryr::compose()
## ✖ purrr::cross()   masks pracma::cross()
## ✖ dplyr::filter()  masks stats::filter()
## ✖ dplyr::lag()     masks stats::lag()
## ✖ purrr::partial() masks pryr::partial()
## ✖ dplyr::where()   masks pryr::where()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
library(stringr)
# I put the data into the originalData folder for access but within that we only essentially want one file which is the count data. we will pull out the file names for each

c.file.l <- ""

nam <- list.files("OriginalData/NEON_count-landbird/")

for(i in nam){
  
  fn <- paste("OriginalData/NEON_count-landbird/",i,"/",sep = "")
  
  c.file <- list.files(fn,pattern = "*count",full.names = TRUE)
  
  c.file.l <- c(c.file.l,c.file)
  

  
}

c.file.l <- c.file.l[2:11]

print(c.file.l)
##  [1] "OriginalData/NEON_count-landbird/NEON.D01.BART.DP1.10003.001.2015-06/NEON.D01.BART.DP1.10003.001.brd_countdata.2015-06.expanded.20241118T065914Z.csv"
##  [2] "OriginalData/NEON_count-landbird/NEON.D01.BART.DP1.10003.001.2016-06/NEON.D01.BART.DP1.10003.001.brd_countdata.2016-06.expanded.20241118T142515Z.csv"
##  [3] "OriginalData/NEON_count-landbird/NEON.D01.BART.DP1.10003.001.2017-06/NEON.D01.BART.DP1.10003.001.brd_countdata.2017-06.expanded.20241118T043125Z.csv"
##  [4] "OriginalData/NEON_count-landbird/NEON.D01.BART.DP1.10003.001.2018-06/NEON.D01.BART.DP1.10003.001.brd_countdata.2018-06.expanded.20241118T105926Z.csv"
##  [5] "OriginalData/NEON_count-landbird/NEON.D01.BART.DP1.10003.001.2019-06/NEON.D01.BART.DP1.10003.001.brd_countdata.2019-06.expanded.20241118T064156Z.csv"
##  [6] "OriginalData/NEON_count-landbird/NEON.D01.BART.DP1.10003.001.2020-06/NEON.D01.BART.DP1.10003.001.brd_countdata.2020-06.expanded.20241118T184512Z.csv"
##  [7] "OriginalData/NEON_count-landbird/NEON.D01.BART.DP1.10003.001.2020-07/NEON.D01.BART.DP1.10003.001.brd_countdata.2020-07.expanded.20241118T010504Z.csv"
##  [8] "OriginalData/NEON_count-landbird/NEON.D01.BART.DP1.10003.001.2021-06/NEON.D01.BART.DP1.10003.001.brd_countdata.2021-06.expanded.20241118T105538Z.csv"
##  [9] "OriginalData/NEON_count-landbird/NEON.D01.BART.DP1.10003.001.2022-06/NEON.D01.BART.DP1.10003.001.brd_countdata.2022-06.expanded.20241118T033934Z.csv"
## [10] "OriginalData/NEON_count-landbird/NEON.D01.BART.DP1.10003.001.2023-06/NEON.D01.BART.DP1.10003.001.brd_countdata.2023-06.expanded.20241118T091043Z.csv"
source_batch(folder = "Functions/")
## File "Functions//CalculateAbundance.R" sourced.
## File "Functions//CalculateRichness.R" sourced.
## File "Functions//CleanData.R" sourced.
## File "Functions//ExtractYear.R" sourced.
## File "Functions//GenerateHistograms.R" sourced.
## File "Functions//RunRegression.R" sourced.
clean_data()
## [1] "...checking function: clean_data()"
extract_year()
## [1] "2015" "2016" "2017" "2018" "2019" "2020" "2020" "2021"
calculate_abundance()

# show abundance values

print(read.csv(file = "abundance.csv",header = TRUE,sep = ","))
##   year year_count
## 1 2015        454
## 2 2016        684
## 3 2017        411
## 4 2018        512
## 5 2019        402
## 6 2020        525
## 7 2021        906
## 8 2022        581
## 9 2023        513
calculate_richness()
## `summarise()` has grouped output by 'scientificName'. You can override using
## the `.groups` argument.

#show richness values

print(read.csv(file = "speciesrichness.csv",header = TRUE,sep = ","))
##   year speciescount
## 1 2015           40
## 2 2016           38
## 3 2017           34
## 4 2018           36
## 5 2019           43
## 6 2020           45
## 7 2021           49
## 8 2022           38
## 9 2023           41
run_regression()
## `geom_smooth()` using formula = 'y ~ x'

below here are things that I did in upscaler to get everything setup

# Now that we have the file names we can make some pseudo code that will describe the order of operations for what we are going to do!

# build_function(c("clean_data","extract_year","calculate_abundance","calculate_richness","run_regression","generate_histograms"))
#### these are the things I did to make the

# and using the upscaler package we will create a folder

# add_folder()

```