Author: Andrew J. Felton
Date: 10/29/2024

This R project contains the primary code and data (following pre-processing in python) used for data production, manipulation, visualization, and analysis, and figure production for the study entitled:

"Global estimates of the storage and transit time of water through vegetation"

Please note that 'turnover' and 'transit' are used interchangeably. Also please note that this R project has been updated multiple times as the analysis has updated.

Data information:

The data folder contains key data sets used for analysis. In particular:

"data/turnover_from_python/updated/august_2024_lc/" contains the core datasets used in this study including global arrays summarizing five year (2016-2020) averages of mean (annual) and minimum (monthly) transit time, storage, canopy transpiration, and number of months of data able as both an array (.nc) or data table (.csv). These data were produced in python using the python scripts found in the "supporting_code" folder. The remaining files in the "data" and "data/supporting_data"" folder primarily contain ground-based estimates of storage and transit found in public databases or through a literature search, but have been extensively processed and filtered here. The "supporting_data"" folder also contains annual (2016-2020) MODIS land cover data used in the analysis and contains separate filters containing the original data (.hdf) and then the final process (filtered) data in .nc format. The resulting annual land cover distributions were used in the pre-processing of data in python.

#Code information

Python scripts can be found in the "supporting_code" folder.

Each R script in this project has a role:

"01_start.R": This script sets the working directory, loads in the tidyverse package (the remaining packages in this project are called using the `::` operator), and can run two other scripts: one that loads the customized functions (02_functions.R) and one for importing and processing the key dataset for this analysis (03_import_data.R).

"02_functions.R": This script contains custom functions. Load this using the
`source()` function in the 01_start.R script.

"03_import_data.R": This script imports and processes the .csv transit data. It joins the mean (annual) transit time data with the minimum (monthly) transit data to generate one dataset for analysis: annual_turnover_2. Load this using the
`source()` function in the 01_start.R script.

"04_figures_tables.R": This is the main workhouse for figure/table production and
supporting analyses. This script generates the key figures and summary statistics
used in the study that then get saved in the manuscript_figures folder. Note that all
maps were produced using Python code found in the "supporting_code"" folder.

"supporting_generate_data.R": This script processes supporting data used in the analysis, primarily the varying ground-based datasets of leaf water content.

"supporting_process_land_cover.R": This takes annual MODIS land cover distributions and processes them through a multi-step filtering process so that they can be used in preprocessing of datasets in python.

The following data shows riding information for members vs casual riders at the company Cyclistic(made up name). This is a dataset used as a case study for the google data analytics certificate.

The Changes Done to the Data in Excel: - Removed all duplicated (none were found) - Added a ride_length column by subtracting ended_at by started_at using the following formula "=C2-B2" and then turned that type into a Time, 37:30:55 - Added a day_of_week column using the following formula "=WEEKDAY(B2,1)" to display the day the ride took place on, 1= sunday through 7=saturday. - There was data that can be seen as ########, that data was left the same with no changes done to it, this data simply represents negative data and should just be looked at as 0.

Processing the Data in RStudio: - Installed required packages such as tidyverse for data import and wrangling, lubridate for date functions and ggplot for visualization. - Step 1: I read the csv files into R to collect the data - Step 2: Made sure the data all contained the same column names because I want to merge them into one - Step 3: Renamed all column names to make sure they align, then merged them into one combined data - Step 4: More data cleaning and analyzing - Step 5: Once my data was cleaned and clearly telling a story, I began to visualize it. The visualizations done can be seen below.

Introduction to Primate Data Exploration and Linear Modeling with R was created with the goal of providing training to undergraduate biology students on data management and statistical analysis using authentic data of Cayo Santiago rhesus macaques. Module M.1 introduces basic functions from R, as well as from its package tidyverse, for data exploration and management.

Introduction to Primate Data Exploration and Linear Modeling with R was created with the goal of providing training to undergraduate biology students on data management and statistical analysis using authentic data of Cayo Santiago rhesus macaques. Module M.2 introduces basic functions in R, as well as in its packages tidyverse and rstatix, for estimating descriptive statistics.