Ecological Stoichiometry Cooperative

Examples

This page contains several examples of how to use the STOICH-utilites to create plots or find information.

The STOICH-utilities was developed using the Tidyverse. If you need additional resources for using the Tidyverse please check out the cheatsheets.

If you don't have the STOICH-utilities installed please visit Getting Started With STOICH-utilties.

Table of Contents for Examples

Setting Path

Exploring the Data

Creating SampleDate

Plotting

Mapping #1

Mapping #2

Filtering

Summarizing Data

Search

Filter and Search

Filter, Summarize and Search

Setting the path to the data.
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# Three options for creating a variable to store the path to the STOICH data.
# Building the path starting at your Documents directory.
myPath <- file.path(path.expand("~"), "data", "STOICH_Beta_Release_2023-07-21")
# Or with a text string.
myPath <- "C:/Users/chad/Documents/data/STOICH_Beta_Release_2023-07-21"
# Or if you set the working directory to point to the data.
myPath <- getwd()

# Load the STOICH data (using a predefined path variable)
stoichData <- stoichUtilities::loadSTOICH(dataPath=myPath)

Exploring the Data
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Description: Exploring the data with DataExplorer and summarytools.

# Load required packages.
library(tidyverse)
library(summarytools)
library(DataExplorer)

library(stoichUtilities)

myPath <- "C:\..." # Update this with your path to the data

#Load the data and join the tables into one wide table.
stoichData <- stoichUtilities::loadSTOICH(dataPath=myPath) |>
  stoichUtilities::joinSTOICH()

# Create a summary of the STOICH data table using summarytools formated for easy viewing.
view(summarytools::dfSummary(stoichTable))

# Create a report of the STOICH data table using DataExplorer.
DataExplorer::create_report(stoichTable)

Plotting Example
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Description: Plotting latitude/elevation vs CN ratio for specific organisms.

# Load required packages.
library(tidyverse)
library(stoichUtilities)

myPath <- "C:\..." # Update this with your path to the data

#Load the data.
stoichData <- stoichUtilities::loadSTOICH(dataPath=myPath) |>
  # Filter the variables we are interested in to remove any rows that don't have values.
  stoichUtilities::filterSTOICH(var="Elevation", val=NA, condition="not equal") |>
  stoichUtilities::filterSTOICH(var="Latitude", val=NA, condition="not equal") |>
  stoichUtilities::filterSTOICH(var="CarbonToNitrogenRatio", val=NA, condition="not equal") |>
  # Filter to select only rows where the Organism Type is "Seston".
  stoichUtilities::filterSTOICH(var="Type.OrganismStoichiometry", val="Seston", condition="equal") |>
  # Join all the tables into one large wide table.
  stoichUtilities::joinSTOICH() |>
  # Select only the columns we are interested in plotting.
  select(c("Elevation.Site", "Latitude.Site", "CarbonToNitrogenRatio.OrganismStoichiometry")) |>
  # Remove any text with 4 characters or more after a "." at the end ("$") of a string.
  rename_with(~stringr::str_remove(.x, "\\.[a-zA-Z]{4,}$"))

# Plot the elevation versus CN ratio as a line.
ggplot(stoichData, aes(x=Elevation, y=CarbonToNitrogenRatio)) +
  geom_line()

# If you want to look at a plot with average CN for elevation bins, you could use the summarize function after rounding the elevation data.
smoothData <- stoichData |>
  mutate(Elevation_Smooth = 100*round(Elevation/100)) |>
  group_by(Elevation_Smooth) |>
  summarize(mean_CN_ratio = mean(CarbonToNitrogenRatio))

# Plot the averaged data.
ggplot(smoothData, aes(x=Elevation_Smooth, y=mean_CN_ratio)) +
  geom_line()

Mapping Example
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Description: Mapping example with pins for different organism samples color coded.

# Load required packages.
library(tidyverse)
library(stoichUtilities)
library(sf)
library(leaflet)

myPath <- "C:\..." # Update this with your path to the data

#Load the data.
stoichData <- stoichUtilities::loadSTOICH(dataPath=myPath) |>
  # Remove any locations without GPS data
  stoichUtilities::filterSTOICH(var="Longitude", val=NA, condition="not equal") |>
  stoichUtilities::filterSTOICH(var="Latitude", val=NA, condition="not equal") |>
  # Filter to select three orders of Bryophytes.
  stoichUtilities::filterSTOICH(var="TaxonomicOrder", val=c("Hypnales", "Marchantiales", "Polygonales"), condition="equal") |>
  # Join all the tables into one large wide table.
  stoichUtilities::joinSTOICH()

# Pull out just the data we are interested in GPS & taxonomy.
gpsGroups <- stoichData |>
  # Shorten the variable names we are intersted in for convenience.
  rename(Latitude=Latitude.Site, Longitude=Longitude.Site, TaxonomicOrder=TaxonomicOrder.OrganismStoichiometry) |>
  # Select the columns we are interested in.
  select(c("Latitude", "Longitude", "TaxonomicOrder"))

# Initiate the leaflet plot.
myPlot <- leaflet() |>
  # Select the background map style.
  addProviderTiles(providers$OpenTopoMap)

# Create colored markers (one for each order selected).
blueIcon <- awesomeIcons(icon = 'ios-close', iconColor = 'black', library = 'ion', markerColor = "blue")
greenIcon <- awesomeIcons(icon = 'ios-close', iconColor = 'black', library = 'ion', markerColor = "green")
orangeIcon <- awesomeIcons(icon = 'ios-close', iconColor = 'black', library = 'ion', markerColor = "orange")

# Add markers to the plot.
myPlot <- myPlot |>
  # Select (filter) only one order from the data.frame or table for each marker color.
  addAwesomeMarkers(data = data.frame(gpsGroups %>% filter(TaxonomicOrder == "Hypnales")), lng = ~Longitude, lat = ~Latitude, icon=blueIcon) |>
  addAwesomeMarkers(data = data.frame(gpsGroups %>% filter(TaxonomicOrder == "Marchantiales")), lng = ~Longitude, lat = ~Latitude, icon=greenIcon) |>
  addAwesomeMarkers(data = data.frame(gpsGroups %>% filter(TaxonomicOrder == "Polygonales")), lng = ~Longitude, lat = ~Latitude, icon=orangeIcon)

# Display the plot.
myPlot

Mapping Example
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Description: Example of mapping seston samples with points shaded according to C:N ratio and animated to group points by year.

# Load required packages.
library(tidyverse)
library(lubridate)
library(maps)
library(gganimate)
library(stoichUtilities)

myPath <- "C:\..." # Update this with your path to the data

# Load the data
Seston <- stoichUtilities::loadSTOICH(dataPath=myPath) |>
  stoichUtilities::filterSTOICH(var="Type.OrganismStoichiometry", val="Seston", condition="equal") |> 
  stoichUtilities::joinSTOICH()

# Load the state borders
state_info <- map_data("state")

# Map the state borders
base_map <- ggplot(data = state_info, mapping = aes(x=long, y=lat, group=group)) +
  geom_polygon(color = "black", fill = "white") + 
  coord_quickmap() +
  theme_void()

# Add the Seston data to the map of state borders and set the color using the C:N ratio.
map_with_data <- base_map +
  geom_point(data = Seston, aes(x = Longitude.Site, y = Latitude.Site, color=CarbonToNitrogenRatio.OrganismStoichiometry, group=SampleYear.SampleEvent)) + 
  labs(color="C:N")

# Add animation using the year the sample was taken
map_with_animation <- map_with_data +
  transition_time(SampleYear.SampleEvent) +
  ggtitle('Year: {frame_time}',
          subtitle = 'Frame {frame} of {nframes}')

# Calculate the number of years there was sample data to determine the number of frames for the animation.
num_years <- max(Seston$SampleYear.SampleEvent) - min(Seston$SampleYear.SampleEvent) + 1

# Create the animation.
animate(map_with_animation, nframes = num_years)

Filtering Example
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Description: Filter/find all samples from a salt marsh.

# Load required packages.
library(tidyverse)
library(stoichUtilities)

myPath <- "C:\..." # Update this with your path to the data

#Load the data.
stoichData <- stoichUtilities::loadSTOICH(dataPath=myPath) |>
  # Filter to select only rows where the EcosystemType is "Salt Marsh".
  stoichUtilities::filterSTOICH(var="EcosystemType", val="Salt Marsh", condition="equal") |>
  # Join all the tables into one large wide table.
  stoichUtilities::joinSTOICH() |>
  View()

Summarizing Data
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Description: Summarize the number of sites for each source.

# Load required packages.
library(tidyverse)
library(stoichUtilities)

myPath <- "C:\..." # Update this with your path to the data

#Load the data.
stoichData <- stoichUtilities::loadSTOICH(dataPath=myPath) |>
  # Join all the tables into one large wide table.
  stoichUtilities::joinSTOICH() |>
  # Use the dplyr package from the tidyverse to select the columns we are interested in. 
  dplyr::select(c("Title.Source", "FirstAuthor.Source", "YearPublished.Source", "Journal.Source", "LitSurveyId.Source", "Organization.Source", "Id.Site")) |>
  # Shorten column names because I don't want to keep typing ".Source".
  dplyr::rename_all(~stringr::str_remove(.x, "\\.Source")) |>
  # There are probably multiple samples/site so only keep unique entries of sites and sources.
  unique() |>
  # Group the data by Source columns.
  dplyr::group_by(Title, FirstAuthor, YearPublished, Journal, LitSurveyId, Organization) |>
  # Count the number of sites for each group.
  dplyr::summarise(Number_of_Sites = n(), .groups="keep") |>
  View()

Search Example
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Description: Search for the furthest East (minimum longitude) sample site.

Solution 1: pulling a table from the raw data.

# Load required packages.
library(tidyverse)
library(stoichUtilities)

myPath <- "C:\..." # Update this with your path to the data

#Load the data and select just the Site table.
stoichData <- stoichUtilities::loadSTOICH(dataPath=myPath)[["tbl_Site"]]

# Find the row with the minimum Longitude value (drop rows with NA values).
stoichData |> 
  filter(Longitude == min(stoichData[["Longitude"]], na.rm=TRUE))

Solution 2: working with the joined table.

# Load required packages.
library(tidyverse)
library(stoichUtilities)

myPath <- "C:\..." # Update this with your path to the data

#Load the data.
stoichData <- stoichUtilities::loadSTOICH(dataPath=myPath) |>
  # Filter out any rows without Longitude data.
  stoichUtilities::filterSTOICH(var="Longitude", val=NA, condition="not equal") |>
  # Join all the tables into one large wide table.
  stoichUtilities::joinSTOICH() |>
  # Select just the columns from the Site table.
  select(ends_with(".Site"))

# Find the row with the minimum Longitude value
stoichData |> 
  filter(Longitude.Site == min(stoichData[["Longitude.Site"]])) |> 
  # There will probably be duplicates, so keep only unique values.
  unique()

Filter and Summarize Example
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Description: How many samples were taken North of 45 degrees latitude in the month of December and what was the average elevation for those samples.

# Load required packages.
library(tidyverse)
library(stoichUtilities)

myPath <- "C:\..." # Update this with your path to the data

#Load the data and select just the Site table.
stoichData <- stoichUtilities::loadSTOICH(dataPath=myPath) |>
  # Filter just December (month 12) data.
  stoichUtilities::filterSTOICH(var="SampleMonth", val=as.integer(12), condition="equal") |>
  # Filter just samples north of 45 degrees latitude.
  stoichUtilities::filterSTOICH(var="Latitude", val=45, condition="greater than") |>
  # Join all the tables into one large wide table.
  stoichUtilities::joinSTOICH()

# Count the number of rows.
nrow(stoichData)

# Calculate the mean elevation for the samples.
mean(stoichData[["Elevation.Site"]])

Filter and Search Example
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Description: Search for the sample with the lowest Carbon 13 content in Texas.

# Load required packages.
library(tidyverse)
library(stoichUtilities)

myPath <- "C:\..." # Update this with your path to the data

# Load the data and pipe it ("|>" and "%>%" can pass a table into the next function) for further processing
stoichData <- stoichUtilities::loadSTOICH(dataPath=myPath) |>
  # Filter the STOICH data for any samples in Texas with Isotope13C content and remove any samples with unknown species.
  stoichUtilities::filterSTOICH(var="State", val="Texas", condition="Equal") |>
  stoichUtilities::filterSTOICH(var="Isotope13C", val=NA, condition="not equal") |>
  stoichUtilities::filterSTOICH(var="TaxonomicSpecies", val=NA, condition="not equal") |>
  # Join all the tables into one large wide table
  stoichUtilities::joinSTOICH() |>
  # Use the dplyr package from the tidyverse to select the columns we are interested in.
  dplyr::select(c("TaxonomicKingdom.OrganismStoichiometry",
                  "TaxonomicOrder.OrganismStoichiometry",
                  "TaxonomicFamily.OrganismStoichiometry",
                  "TaxonomicGenus.OrganismStoichiometry",
                  "TaxonomicSpecies.OrganismStoichiometry",
                  "Isotope13C.OrganismStoichiometry"))

# Find the row with the minimum Isotope13C value
stoichData |> filter(Isotope13C.OrganismStoichiometry == min(stoichData[["Isotope13C.OrganismStoichiometry"]]))

Filter, Summarize and Search Example
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Description: Search for the organism with the highest average N15 Isotope content in California.

# Load required packages.
library(tidyverse)
library(stoichUtilities)

myPath <- "C:\..." # Update this with your path to the data

# Load the data and pipe it ("|>" and "%>%" can pass a table into the next function) for further processing
stoichData <- stoichUtilities::loadSTOICH(dataPath=myPath) |>
  # Filter the STOICH data for any samples in California with Isotope15N content and remove any samples with unknown species.
  stoichUtilities::filterSTOICH(var="State", val="California", condition="Equal") |>
  stoichUtilities::filterSTOICH(var="Isotope15N", val=NA, condition="not equal") |>
  stoichUtilities::filterSTOICH(var="TaxonomicSpecies", val=NA, condition="not equal") |>
  # Join all the tables into one large wide table
  stoichUtilities::joinSTOICH() |>
  # Use the dplyr package from the tidyverse to select the columns we are interested in.
  dplyr::select(c("TaxonomicKingdom.OrganismStoichiometry",
                  "TaxonomicOrder.OrganismStoichiometry",
                  "TaxonomicFamily.OrganismStoichiometry",
                  "TaxonomicGenus.OrganismStoichiometry",
                  "TaxonomicSpecies.OrganismStoichiometry",
                  "Isotope15N.OrganismStoichiometry")) |>
  # Shorten column names because I don't want to keep typing ".OrganismStoichiometry".
  dplyr::rename_all(~stringr::str_remove(.x, "\\.OrganismStoichiometry")) |>
  # Group the data by Taxonomy.
  dplyr::group_by(TaxonomicKingdom, TaxonomicOrder, TaxonomicFamily, TaxonomicGenus, TaxonomicSpecies) |>
  # Average the N15 Isotope data for each group.
  dplyr::summarise(mean_Isotope15N = mean(Isotope15N), .groups="keep")

# Find the row with the maximum average Isotope15N value
stoichData |> filter(mean_Isotope15N == max(stoichData[["mean_Isotope15N"]]))

Examples

Table of Contents for Examples

Setting the path to the data.back to top

Exploring the Databack to top

Plotting Exampleback to top

Mapping Exampleback to top

Mapping Exampleback to top

Filtering Exampleback to top

Summarizing Databack to top

Search Exampleback to top

Solution 1: pulling a table from the raw data.

Solution 2: working with the joined table.

Filter and Summarize Exampleback to top

Filter and Search Exampleback to top

Filter, Summarize and Search Exampleback to top

Setting the path to the data.
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Exploring the Data
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Plotting Example
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Mapping Example
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Mapping Example
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Filtering Example
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Summarizing Data
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Search Example
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Filter and Summarize Example
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Filter and Search Example
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Filter, Summarize and Search Example
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