Attached are the scripts I used to execute homework 11. They are in markdown format for easy viewing.
source("/Users/dianeoneal/Desktop/comp_bio_24/StrategicCodingPractices/barracudar/DataTableTemplate.R")
source("/Users/dianeoneal/Desktop/comp_bio_24/StrategicCodingPractices/barracudar/AddFolder.R")
source("/Users/dianeoneal/Desktop/comp_bio_24/StrategicCodingPractices/barracudar/BuildFunction.R")
source("/Users/dianeoneal/Desktop/comp_bio_24/StrategicCodingPractices/barracudar/MetaDataTemplate.R")
source("/Users/dianeoneal/Desktop/comp_bio_24/StrategicCodingPractices/barracudar/CreatePaddedLabel.R")
source("/Users/dianeoneal/Desktop/comp_bio_24/StrategicCodingPractices/barracudar/InitiateSeed.R")
source("/Users/dianeoneal/Desktop/comp_bio_24/StrategicCodingPractices/barracudar/SetUpLog.R")
source("/Users/dianeoneal/Desktop/comp_bio_24/StrategicCodingPractices/barracudar/SourceBatch.R")
source("/Users/dianeoneal/Desktop/comp_bio_24/StrategicCodingPractices/barracudar/QBox.R")
source("/Users/dianeoneal/Desktop/comp_bio_24/StrategicCodingPractices/barracudar/QCon1.R")
source("/Users/dianeoneal/Desktop/comp_bio_24/StrategicCodingPractices/barracudar/QCon2.R")
source("/Users/dianeoneal/Desktop/comp_bio_24/StrategicCodingPractices/barracudar/QHist.R")
source("/Users/dianeoneal/Desktop/comp_bio_24/StrategicCodingPractices/barracudar/QLogis.R")
# source("barracudar/QScat.R") # can't open this
source("/Users/dianeoneal/Desktop/comp_bio_24/StrategicCodingPractices/barracudar/QBub.R")
source("/Users/dianeoneal/Desktop/comp_bio_24/StrategicCodingPractices/barracudar/QContour.R")Set seed
initiate_seed() # preserves random number seed
paths <- list.files(path = "./OriginalData/", pattern = "BART")
filenames <- c()
#use for loop to gather path
for (i in seq_along(paths)) {
# Construct the full path
full_path <- paste0("/Users/dianeoneal/Desktop/comp_bio_24/comp_bio_24/hw11/OriginalData/", paths[i])
# Check if the directory exists
if (dir.exists(full_path)) {
setwd(full_path)
# List files with pattern "countdata"
filenames <- c(filenames, list.files(pattern = "countdata"))
} else {
cat("Directory does not exist:", full_path, "\n")
}
}# pseudocode functions ----
# Step 1 Create Pseudocode: describe project with a list of major steps
# cleaning data
# extract the year from each file name
# calculate abundance
# calculate richness
# Step 2 Each list item becomes a function
# clean_data()
# extract_year()
# calc_abundance()
# calc_richness()
# Step 3: Create function templates as a batch operation
build_function(function_name = c("clean_dat",
"extract_year",
"calc_abundance",
"calc_richness"))
library(tidyverse)
source("Functions/CleanDat.R")
clean_dat(paths)
source("Functions/extract_year")
extract_year(paths)
source("Functions/calc_abundance")
calc_abundance(paths)
source("Functions/calc_richness")
calc_richness(paths)# --------------------------------------
# FUNCTION clean_dat
# required packages: tidyverse
# description: a function that cleans the data for any empty/missing cases
# inputs: paths: file paths that will then be cleaned
# outputs: cleaned data that can then be processed
########################################
clean_dat <- function(path=NULL) {
data <- read.csv(path, header=TRUE, comment.char="#")
cleaned_data <- filter(data, targetTaxaPresent == "Y")
new_name <- paste0("BART_", str_extract(path, pattern="20\\d{2}"), "_countdata_clean.csv")
new_path <- paste0("CleanedData/",new_name)
write.csv(x=cleaned_data, file=new_path)
}
# --------------------------------------
# --------------------------------------
# FUNCTION extract_year
# required packages: none
# description: function required to extract the year
# inputs: file names
# outputs: years
########################################
extract_year <- function(filenames = NULL){
years <- c() # initialize empty vector
years <- str_extract(filenames, pattern="20\\d{2}")
# use regex to identify year
return(years) # return vector
}
# --------------------------------------
# --------------------------------------
# FUNCTION calc_richness
# required packages: none
# description: calculates species richness (unique number of species)
# inputs: cleaned csvs that can be analyzed
# outputs: species richness values
########################################
calc_richness <- function(path=NULL){
df <- read.csv(path, header=TRUE)
return(length(unique(df$scientificName)))
}
# --------------------------------------
# calc_richness()
# --------------------------------------
# FUNCTION calc_abundance
# required packages: none
# description:
# inputs:
# outputs:
########################################
calc_abundance <- function(paths = NULL){
data <- read.csv(path, header = TRUE)
return(length(data[,1]))
}
# --------------------------------------
# calc_abundance()# Initiate and fill data frame ----
df <- data.frame(File = NULL, Year = NULL, Abundance = NULL, Richness = NULL)
for (i in seq_along(file_list)) {
clean.data(path=file_list[i])
}
# outputs list of files that have been cleaned
clean_list <- list.files("CleanedData", full.names=TRUE)
# for loop that iterates along the cleaned data
# makes a data framewith year, abundance, and richness
for (i in seq_along(clean_dat)) {
df <- rbind(df, list(File=clean_dat[i],
Year=extract_year(path=clean_list[i]),
Abundance=calc_abundance(path=clean_list[i]),
Richness=calc_richness(path=clean_list[i])))
}