In PatrickEslick/CyAN: Interact with CyAN data

This package is intended to help explore, query, and export data compiled for the CyAN project. The data was downloaded from the Water Quality Portal and loaded into an SQLite database to make querying and processing the data easier. Supplementary information on analytic methods was compiled.

Connecting to a database

To begin working with the data, first connect to a local copy of the database. This vignette will use the small example database that is included with the package.

library(CyAN)
library(dplyr)

path <- system.file("extdata", "example.db", package = "CyAN")
cyan_db <- connect_cyan(path)

Querying

The querying function get_cyan_data() allows filtering by latitude, longitude, year, parameter, tier, and state. Additional filtering can be done using dplyr functions like filter and select. The query isn't actually retrieved from the database unless collect = TRUE is used, or until the collect() function from dplyr is used.

Querying by parameter requires use of the CyAN parameter code built in to the database. To quickly see a list of parameters, you can use generate_parameter_index().

parameter_list <- generate_parameter_index(cyan_db)
head(parameter_list)

#Get all of the Chlorophyll-a data available in Kansas
ks_chla <- get_cyan_data(cyan_db, collect = TRUE,
                         parameters = "P0051",
                         states = "KS")

#Before collecting the data, limit by result values greater than 11.80 ug/L.
ks_chla_high <- get_cyan_data(cyan_db, collect = FALSE,
                              parameters = "P0051",
                              states = "KS") %>%
  filter(RESULT_VALUE > 11.80) %>%
  collect()

Adding columns

Some functions have been included in the package that provide additional calculated or categorical columns such as trophic status (add_trophic_status()), time in GMT (add_GMT_time()), and solar noon (add_solar_noon()). The documentation for each function provides more detail.

ks_chla <- ks_chla %>%
  add_trophic_status() %>%
  add_GMT_time() %>%
  add_solar_noon()

Bivariate data

The package also provides tools to get and plot bivariate data. This is not intended to provide a full bivariate analysis, but to give a cursory preview of the relationships between variables. The basic function used to query for bivariate data is get_bivariate(), and the plotting function is plot_bivariate().

#Look at the relationship between total phosphorus and chlorophyll-a
biv_data <- get_bivariate(cyan_db,
                          parameter_1 = "P0031", #Total phosphorus parameter
                          parameter_2 = "P0051", #Chlorophyll-a parameter,
                          states = "KS")
plot_bivariate(biv_data, log_1 = TRUE, log_2 = TRUE)