Getting Started with the Utilities
The STOICH-utilities R package provides user tools for working with STOICH database files. This package is designed to enable easier and more robust access to the data files from STOICH database releases.
The STOICH API “stoichUtilities” is an R library, as such you will need to have R installed. It is recommended to install RStudio (https://posit.co/) to help manage packages and provide an integrated development environment (IDE).
Installation
There are 3 options for installing the stoichUtilities package. Installing a prebuilt package contains the vignette, while a standard “Install and Restart” won’t contain the vignette. Instructions for building your own package from source to get the vignette are also included.
Option 2: Install a PreBuilt Package
Download the stoichUtilities_x.x.x.xxxx.tar.gz file.
Select the menu option Tools -> Install Packages
A window should pop up. For Install from: choose Package Archive File and navigate to the location of the tar.gz file.
Click “Install” ### Option 3: Install from Source #### Download Source Files There are 2 options to get the source files: * From the GitHub project page click the green Code button and choose “Download Zip” * Use GitHub Desktop or CLI programs to Clone the STOICH-Utilities repository (click the green Code button to copy the URL from the GitHub project page). #### Simple Install: Open and Build the R Library Navigate to the source code location and open stoichUtilities.Rproj in RStudio.
Build the documentation: ctrl + shift + D or with the menu Build -> Document
Install the library and restart the R session: ctrl + shift + B or with the menu Build -> Install and Restart
Build Package Method: Build and Install Package from Source
Navigate to the source code location and open stoichUtilities.Rproj in RStudio.
Select the menu option Build -> Build Source Package
To install the package, follow instructions for Installing a PreBuilt Package.
Unpacking the Data
The Beta STOICH-utilities GitHub code comes with a file: Beta_Release_Data.zip, please unzip this file to a director (later assumed to be in the users home directory with the folder name Data).
Using the R Library
Load the Required Libraries.
Working with STOICH Data
# Load the STOICH data (assumed to be in a directory named "data" inside the home directory)
stoichData <- loadSTOICH(dataPath=file.path(path.expand("~"), "data"))
# Filter the STOICH data
stoichFiltered <- filterSTOICH(dataTables=stoichData,
var="State",
val="North Dakota",
condition="Equal")
# Match organism stoichiometry data with water chemsitry data for samples that weren't taken at the exact same time
stoichPaired <- locateDataPairsSTOICH(stoichData,
timeDiff=2,
timeUnits="weeks",
distance=2,
pairMethod="Min Time",
ignoreExisting=TRUE)
# Join all the tables into one large wide table
stoichTable <- joinSTOICH(stoichPaired)Example of Mapping STOICH Data
Use the tbl_Site table from stoichData for Site information for mapping.
# Count how many sites are in each state.
stateCounts <- stoichData$tbl_Site |>
select(c("State")) |>
drop_na(State) |>
# Change the data to lowercase to allow the table to merge with the Tidyverse state data
mutate(State=str_to_lower(State)) |>
group_by(State) |>
# Summarise to get the number of sites for each state.
summarise(nSites=n(), .groups="keep")
# Use state data from Tidyverse and join the site data to it.
us_states <- map_data("state") |>
left_join(stateCounts, by=c("region"="State"))
# Create a list of states in the Tidyverse dataset so only data from states with boundaries are used.
stateList <- unique(us_states$region)
# Split out the GPS data to map individual sites.
gpsSites <- stoichData$tbl_Site |>
filter(str_to_lower(State) %in% stateList) |>
select(c("Latitude", "Longitude")) |>
drop_na(Latitude, Longitude) |>
# Round the gps coordinates so points that are close will be merged into a single point.
mutate(across(everything(), ~round(.x, 2))) |>
unique()
# Start building the map.
ggplot(data=us_states, mapping=aes(x=long, y=lat, group=group, fill=nSites)) +
geom_polygon(color="black") +
theme(legend.position = "bottom") +
geom_point(data=gpsSites, aes(x=Longitude, y=Latitude, group=NA), size=2, shape=23, fill="orange")
