CybertownTraining.Rmd## Downloading GitHub repo USEPA/rExpertQuery@develop##
+## sf (1.1-0 -> 1.1-1 ) [CRAN]
+## cpp11 (0.5.4 -> 0.5.5 ) [CRAN]
+## roxygen2 (7.3.3 -> 8.0.0 ) [CRAN]
+## terra (1.9-11 -> 1.9-27) [CRAN]
+## data.table (1.18.2.1 -> 1.18.4) [CRAN]
+## Installing 5 packages: sf, cpp11, roxygen2, terra, data.table
+## Installing packages into 'C:/Users/hmarler/AppData/Local/Temp/RtmpcdnLOt/temp_libpath2fa83ceb49fe'
+## (as 'lib' is unspecified)
+## package 'sf' successfully unpacked and MD5 sums checked
+## package 'cpp11' successfully unpacked and MD5 sums checked
+## package 'roxygen2' successfully unpacked and MD5 sums checked
+## package 'terra' successfully unpacked and MD5 sums checked
+## package 'data.table' successfully unpacked and MD5 sums checked
+##
+## The downloaded binary packages are in
+## C:\Users\hmarler\AppData\Local\Temp\RtmpYnTxPW\downloaded_packages
## ── R CMD build ─────────────────────────────────────────────────────────────────
-## checking for file 'C:\Users\hmarler\AppData\Local\Temp\RtmpywqNsa\remotes2b947e5159e5\USEPA-rExpertQuery-ce1ba15/DESCRIPTION' ... checking for file 'C:\Users\hmarler\AppData\Local\Temp\RtmpywqNsa\remotes2b947e5159e5\USEPA-rExpertQuery-ce1ba15/DESCRIPTION' ... ✔ checking for file 'C:\Users\hmarler\AppData\Local\Temp\RtmpywqNsa\remotes2b947e5159e5\USEPA-rExpertQuery-ce1ba15/DESCRIPTION' (601ms)
-## ─ preparing 'rExpertQuery': (4.7s)
+## checking for file 'C:\Users\hmarler\AppData\Local\Temp\RtmpYnTxPW\remotes59643c4c4d50\USEPA-rExpertQuery-96f363b/DESCRIPTION' ... checking for file 'C:\Users\hmarler\AppData\Local\Temp\RtmpYnTxPW\remotes59643c4c4d50\USEPA-rExpertQuery-96f363b/DESCRIPTION' ... ✔ checking for file 'C:\Users\hmarler\AppData\Local\Temp\RtmpYnTxPW\remotes59643c4c4d50\USEPA-rExpertQuery-96f363b/DESCRIPTION' (753ms)
+## ─ preparing 'rExpertQuery': (33.7s)
## checking DESCRIPTION meta-information ... checking DESCRIPTION meta-information ... ✔ checking DESCRIPTION meta-information
-## ─ checking for LF line-endings in source and make files and shell scripts (430ms)
+## ─ checking for LF line-endings in source and make files and shell scripts (653ms)
## ─ checking for empty or unneeded directories
## Omitted 'LazyData' and 'LazyDataCompression' from DESCRIPTION
-## ─ building 'rExpertQuery_0.1.0.tar.gz'
+## ─ building 'rExpertQuery_0.1.0.tar.gz' (622ms)
##
##
-
+## Installing package into 'C:/Users/hmarler/AppData/Local/Temp/RtmpcdnLOt/temp_libpath2fa83ceb49fe'
+## (as 'lib' is unspecified)
+
This vignette also requires additional packages which can be
installed using the code chunk below:
-
+
# # list of additional required packages
# demo.pkgs <- c("datasets", "data.table", "dplyr", "DT", "ggplot2", "httr", "leaflet", "maps", "plotly", "sf", "usdata", "usmap", "stringi")
#
@@ -156,40 +173,21 @@ Installation# load additional packages for this vignette
library(datasets)
-library(data.table)
-## Warning: package 'data.table' was built under R version 4.4.3
-
-## Warning: package 'dplyr' was built under R version 4.4.3
-
-## Warning: package 'DT' was built under R version 4.4.3
-
-## Warning: package 'httr' was built under R version 4.4.3
-
-## Warning: package 'leaflet' was built under R version 4.4.3
-
-## Warning: package 'maps' was built under R version 4.4.3
-
-## Warning: package 'plotly' was built under R version 4.4.3
-
-## Warning: package 'usdata' was built under R version 4.4.3
-
-## Warning: package 'usmap' was built under R version 4.4.3
-
-## Warning: package 'stringi' was built under R version 4.4.3
+library(data.table)
+library(dplyr)
+library(DT)
+library(ggplot2)
+library(httr)
+library(leaflet)
+library(maps)
+library(plotly)
+library(sf)
+library(usdata)
+library(usmap)
+library(stringi)
As a final setup step, we will turn off scientific notation so that
any catchment id numbers from our queries are displayed correctly.
-
+
# turn off scientific notation (for catchment ids)
options(scipen = 999)
@@ -206,7 +204,7 @@ API KeyTo run this vignette, we can use the following test key. Users should
obtain their own API key if they will be using rExpertQuery to develop
their own workflows.
-
+
# load test api key
testkey <- "53BVce47MQ3KXKibjx35g4ojaDQGh8qWfbdO8cE0"
@@ -311,11 +309,11 @@ EQ_NationalExtractdplyr (data manipulation) and DT (interface to DataTables
library).
-
+
# import natioanl assessments profile
assessments.nat <- rExpertQuery::EQ_NationalExtract("assessments")
-## [1] "EQ_NationalExtract: downloading Assessments Profile (Expert Query National Extract). It was last updated on November 07, 2025 at 08:50 PM EST."
-
+## [1] "EQ_NationalExtract: downloading Assessments Profile (Expert Query National Extract). It was last updated on May 09, 2026 at 12:08 AM EDT."
+
# filter for latest assessments
assessments.nat <- assessments.nat |>
dplyr::group_by(organizationId) |>
@@ -342,7 +340,7 @@ EQ_NationalExtract# create data table of impairment count results
DT::datatable(count.impair, options = list(pageLength = 10, scrollX = TRUE))
-
+
Which waters are impaired for SELENIUM nationally? Which
@@ -350,7 +348,7 @@ EQ_NationalExtractdplyr and DT.
-
+
# filter assessments data set for
param.nat <- assessments.nat |>
# filter for selenium and cause
@@ -360,7 +358,7 @@ EQ_NationalExtract# data table of selenium impairments
DT::datatable(param.nat, options = list(pageLength = 5, scrollX = TRUE))
-Then we can create a list of states that have at least one selenium
+
Then we can create a list of states that have at least one selenium
impairment listed. We can also visualize this information on a map, with
states containing selenium impairments filled in blue. To build this
map, we first create a data frame with all 50 states and a
@@ -371,7 +369,7 @@
EQ_NationalExtractdatasets
(collection of data sets for use in R), usmap (US choropleth plotting) and ggplot2 (creating
graphics).
-
+
# filter to create df of states that have at least one selenium impairment}
state.names.sel <- param.nat |>
# select state column from filtered df
@@ -394,7 +392,7 @@ EQ_NationalExtractdplyr::mutate(SeleniumImpairment = factor(ifelse(state %in% state.names.sel,
"yes", "no"
)))
-
+
# create map, specifying data and values
usmap::plot_usmap(
regions = "state", data = sel.map.data,
@@ -420,7 +418,7 @@ EQ_NationalExtractdplyr and DT, for data manipulation and
creating a data table, respectively.
-
+
# filter national assessments to includ only Fully Supporting w/ TMDL
tmdl.fs <- assessments.nat |>
dplyr::filter(
@@ -446,7 +444,7 @@ EQ_NationalExtract# fully supporting waters with tmdls data table
DT::datatable(tmdl.fs, options = list(pageLength = 5, scrollX = TRUE))
-
+
What are the impaired waters (cat 4 and 5
@@ -454,7 +452,7 @@ EQ_NationalExtractdplyr and DT. This time we are retaining
a few more columns for inclusion in the data table and are filtering by
epaIRCategory for category 4 and 5 waters.
-
+
# select subset of columns to include
imp.waters <- assessments.nat |>
dplyr::select(
@@ -466,11 +464,11 @@ EQ_NationalExtractdplyr::distinct() |>
# filter for category 4 and 5 waters
dplyr::filter(epaIrCategory %in% c("4", "5"))
-The resulting data frame is large, with 128,919 unique impaired
+
The resulting data frame is large, with 137,482 unique impaired
waters nationally. Due to its size, we’ll include a random subset of 250
results in a data table to review during the demo. You can view the full
results by viewing the imp.waters df.
-
+
# data table with random 250 impaired waters
DT::datatable(
imp.waters |>
@@ -478,7 +476,7 @@ EQ_NationalExtract= list(pageLength = 5, scrollX = TRUE)
)
-
+
Which states have included any information about
@@ -491,7 +489,7 @@ EQ_NationalExtractdplyr and DT. We’ll use some additional
options in DT to highlight
useful rows in the final data table.
-
+
# import national extract for assessment units/monitoring locations
nat.ausmls <- rExpertQuery::EQ_NationalExtract("au_mls")
The next step is to filter the data frame and retain only rows that
@@ -507,7 +505,7 @@
EQ_NationalExtract
+
# filter for rows that contain monitoringLocationIds and are from State orgs
filtered.mls <- nat.ausmls |>
dplyr::filter(
@@ -534,13 +532,13 @@ EQ_NationalExtractdplyr::select(organizationId, organizationName, organizationType) |>
# retain distinct rows
dplyr::distinct()
-There are 21 with monitoringLocationId data in ATTAINS. Of these
-states, 3 also contain monitoringLocationDataLinks. We can create a data
+
There are 22 with monitoringLocationId data in ATTAINS. Of these
+states, 4 also contain monitoringLocationDataLinks. We can create a data
table to display this information and apply some conditional formatting
to highlight the states with monitoringLocationIdentifiers in blue and
those with monitoringLocationIdentifiers and monitoringLocationDataLinks
in orange.
-
+
# create data frame summarizing which states have monitoringLocationIds and monitoringLocationDataLinks in ATTAINS
ml.table <- nat.ausmls |>
# create df of all orgs
@@ -573,7 +571,7 @@ EQ_NationalExtract= styleEqual(c("yes"), c("#f9b97d"))
)
-
+
@@ -597,7 +595,7 @@ EQ_Assessments
-
+
# set up query to search assessments for KS, primary contact recreation use, and stream, default behavior is to query latest report cycle
ks.imp.streams <- rExpertQuery::EQ_Assessments(
statecode = "KS",
@@ -628,7 +626,7 @@ EQ_AssessmentsWe start by querying for Montana assessments with zinc as a cause and
can use dplyr and plotly (graphing library) to summarize
the results by useName and create a bar plot of the results.
-
+
# query MT's latest assessments for zinc as a cause
MT.impair <- rExpertQuery::EQ_Assessments(
statecode = "MT",
@@ -640,7 +638,7 @@ EQ_AssessmentsNext we summarize the uses associated with zinc impairments and count
the number of unique assessmentUnitIdentifiers for each use to
facilitate creating a bar plot.
-
+
# summarize by useName
MT.impair.use <- MT.impair |>
dplyr::group_by(useName) |>
@@ -649,7 +647,7 @@ EQ_AssessmentsThere are 3 uses with zinc as a cause in Montana: Agricultural,
Aquatic Life, and Drinking Water . As our last step, we’ll create the
bar plot.
-
+
-
+
Fig 2. Zinc impairments by useName for Montana’s latest assessment
cycle.
@@ -673,7 +671,7 @@ EQ_Assessments
-
+
# get cat 5 waters from 2024 assessment cycle
IL.latest <- rExpertQuery::EQ_Assessments(
statecode = "IL",
@@ -700,7 +698,7 @@ EQ_Assessments
-
+
# filter previous cycle to include only cat 5 waters from 2024
IL.previous <- IL.previous |>
# retain only cat 5 waters from latest cycle
@@ -721,12 +719,12 @@ EQ_AssessmentsFinally, we can summarize by counting the categories the new category
5s were previously in and create a pie chart with a default color scheme
to visualize.
-
+
# summarize what cats the new cat 5s were in previously
IL.change.sum <- IL.changes |>
dplyr::group_by(epaIrCategory.previous) |>
dplyr::summarise(count = dplyr::n_distinct(assessmentUnitId))
-
+
# create plotly plot
plotly::plot_ly() |>
# add pipe based IL.change.sum
@@ -746,7 +744,7 @@ EQ_Assessments ))
-
+
Fig 3. New category 5 waters in Illinois in the 2024 cycle, summarized
by their 2022 categories.
@@ -770,19 +768,19 @@ EQ_AssessmentUnitsDT.
-
+
# search for retired assessment units in Florida
FL.ret.auids <- rExpertQuery::EQ_AssessmentUnits(
statecode = "FL",
au_status = "Retired",
api_key = testkey
)
-## [1] "EQ_AssessmentUnits: The current query will return 35 rows."
-
+## [1] "EQ_AssessmentUnits: The current query will return 0 rows."
+
-
+
@@ -799,7 +797,7 @@ EQ_TMDLsTo answer this question we’ll use dplyr and plotly. The EQ_TMDLs query is simple,
specifying only the state of Hawaii since we want to return all of
Hawaii’s TMDLs.
-
+
# query for Hawaii TMDLs
HI.tmdls <- rExpertQuery::EQ_TMDLs(
statecode = "HI",
@@ -815,7 +813,7 @@ EQ_TMDLs
-
+
# determine the number of tmdls
HI.count <- HI.tmdls |>
# select required columns
@@ -831,7 +829,7 @@ EQ_TMDLs dplyr::summarize(count = dplyr::n_distinct(assessmentUnitId)) |>
# if pollutantGroup is missing, assign to group named "NULL"
dplyr::mutate(pollutantGroup = ifelse(is.na(pollutantGroup), "NULL", pollutantGroup))
-
+
-
+
Fig 4. Number of TMDLs per pollutantGroup in Hawaii.
@@ -854,7 +852,7 @@ EQ_TMDLsdplyr function. The
2025 fiscal year began on October 1, 2024, so that will be set as the
tmdl_start_date parameter along with specifying 10 for the region.
-
+
# r10 query for FY 2025 TMDLs
tmdl.proj <- rExpertQuery::EQ_TMDLs(
region = 10,
@@ -872,7 +870,7 @@ EQ_TMDLsdplyr functions to
manipulate the data frame so that for example, multiple pollutants will
be listed together in the same row.
-
+
tmdl.sum <- tmdl.proj |>
# select columns
dplyr::select(
@@ -901,7 +899,7 @@ EQ_TMDLs options = list(pageLength = 2, scrollX = TRUE)
)
-
+
@@ -918,7 +916,7 @@ EQ_Actions
+
# query Actions from Delaware that are included in Measures
DE.meas <- rExpertQuery::EQ_Actions(
statecode = "DE",
@@ -926,13 +924,13 @@ EQ_Actions= testkey
)
## [1] "EQ_Actions: The current query will return 649 rows."
-
+
# count unique actionIds
DE.count <- dplyr::n_distinct(DE.meas$actionId)
There are 480 actionIds that should be included when calculating
measures for Delaware. In a similar process to the final TMDL example,
we can create a data table with one row per actionId.
-
+
# determine unique actionIds
DE.actids <- DE.meas |>
# select columns
@@ -953,7 +951,7 @@ EQ_Actionsdplyr::select(-parameter, -assessmentUnitId) |>
# retain unique rows
dplyr::distinct()
-
+
DT::datatable(
DE.actids |>
rExpertQuery::EQ_FormatPlanLinks(),
@@ -970,7 +968,7 @@ EQ_Actionsdplyr and DT to summarize and display
results as in the first EQ_Actions example.
-
+
# query for R3 FY2024 actions
act.fy.24 <- rExpertQuery::EQ_Actions(
region = 3,
@@ -981,9 +979,9 @@ EQ_Actions# count unique actionIds
n_act.fy.24 <- dplyr::n_distinct(act.fy.24$actionId)
-There were 7 unique actions by actionId established in R3 in FY 2024.
+
There were 6 unique actions by actionId established in R3 in FY 2024.
We can create data table with counts for each actionType.
-
+
act.fy.24.counts <- act.fy.24 |>
# select columns
dplyr::select(
@@ -999,7 +997,7 @@ EQ_ActionsDT::datatable(act.fy.24.counts)
-
+
@@ -1018,19 +1016,19 @@ EQ_ActionsDocuments
+
# Actions Documents containing "nutrient"
nutrient.doc <- rExpertQuery::EQ_ActionsDocuments(
doc_query = "nutrient",
api_key = testkey
)
-This query yields 17,989 results. As well as providing the actionId
+
This query yields 16,962 results. As well as providing the actionId
and documentName for all results, EQ_Actions() also returns the
column “actionDocumentUrl” containing the URL to link to the document.
Because the query yields so many results, we’ll take a look at the
highest ranking 50 matches in a DT data table to understand the
structure of the results.
-
+
-
+
How many actions documents that contain the keyword
@@ -1049,13 +1047,13 @@ EQ_ActionsDocumentsplotly bar
plot.
-
+
nutrient.doc.regions <- nutrient.doc |>
# group by region
dplyr::group_by(region) |>
# count action ids by region
dplyr::summarize(count = length(unique(actionId)))
-
+
# create bar plot
plotly::plot_ly(
data = nutrient.doc.regions,
@@ -1065,7 +1063,7 @@ EQ_ActionsDocuments)
-
+
Fig 5. Number of documents containing the keyword ‘nutrient’ by EPA
Region.
@@ -1088,7 +1086,7 @@ EQ_AUsMLs
+
# query for assessment units and monitoring locations from Alaska
ak.mls <- rExpertQuery::EQ_AUsMLs(
statecode = "AK",
@@ -1125,7 +1123,7 @@ EQ_AUsMLs
+
# filter Alaska data set for assessmentUnitIds without listed monitoringLocationIds
no.ml.ak <- ak.mls |>
dplyr::filter(!assessmentUnitId %in% filt.ak.mls$assessmentUnitId)
@@ -1147,7 +1145,7 @@ EQ_Sourcesdplyr and usdata to
manipulate the data and create a list of the region 6 states that list
probable sources for any assessments in their latest report cycle.
-
+
# query for probable sources in the latest assessment cycle in R6
r6.sources <- rExpertQuery::EQ_Sources(
region = 6,
@@ -1179,7 +1177,7 @@ EQ_SourcesDT data table of the
results.
-
+
# count the number of times a probable source is listed for each state
r6.sources.summarize <- r6.sources |>
dplyr::select(state, organizationId, assessmentUnitId, sourceName) |>
@@ -1193,7 +1191,7 @@ EQ_Sources# data table for max count
DT::datatable(r6.sources.summarize)
-
+
@@ -1215,7 +1213,7 @@ EQ_CatchCorrdplyr functions to retain the
unique rows make a a DT data
table.
-
+
# query for IL_N-99 catchment data
il.n99.catch <- rExpertQuery::EQ_CatchCorr(
statecode = "IL",
@@ -1250,7 +1248,7 @@ EQ_CatchCorr
+
# set up query params for ATTAINS geospatial web services
query.params <- list(
where = "assessmentunitidentifier IN ('IL_N-99')",
@@ -1290,7 +1288,7 @@ EQ_CatchCorr
+
# create leaflet map
leaflet::leaflet() |>
# add open street map layer
@@ -1332,16 +1330,14 @@ EQ_DomainValuesRunning the EQ_DomainValues function with no value provided for the
“domain” argument will return a list of the parameter names that it can
return domain values for.
-
+
# run EQ_DomainValues with no value provided for "domain" to return list of params
domain.vals <- EQ_DomainValues()
-## [1] "EQ_DomainValues: getting list of available domain names."
+## [1] "EQ_DomainValues: getting list of available domain names. Values in the eq_param column can be used as inputs in EQ_DomainValues."
+## EQ_DomainValues: domain list retrieved from ATTAINS web services.
EQ_DomainValues can provide values for the following query
-parameters: act_agency, act_status, act_type, ad_param, assess_basis,
-assess_methods, assess_types, au_status, cause, delist_reason, doc_type,
-file_type, loc_type, org_id, org_name, param_attain, param_group,
-param_name, param_state_ir_cat, param_status, source_scale, source_type,
-statecode, use_name, use_support and water_type .
+parameters:
+.
How many different values for “water_type” can be used in
@@ -1349,9 +1345,10 @@ EQ_DomainValuesTo find the allowable values for “water_type” in rExpertQuery
function, we can run EQ_DomainValues again, this time specifying
“water_type”.
-
+
water.types <- rExpertQuery::EQ_DomainValues("water_type")
-## [1] "EQ_DomainValues: For water_type the values in the name column of the function output are the allowable values for rExpert Query functions."
+## [1] "EQ_DomainValues: For water_type the values in the 'name' column of the function output are the allowable values for rExpert Query functions."
+## EQ_DomainValues: domain list retrieved from ATTAINS web services.
The printed message informs us that the “name” column contains the
values for use in water_type queries for rExpertQuery functions. For
water_type there are 66 unique values. They are: BAY; BAYOU; BEACH;
diff --git a/docs/articles/CybertownTraining.md b/docs/articles/CybertownTraining.md
index 78c428b..b72bb7c 100644
--- a/docs/articles/CybertownTraining.md
+++ b/docs/articles/CybertownTraining.md
@@ -59,18 +59,39 @@ remotes::install_github("USEPA/rExpertQuery", ref = "develop", dependencies = TR
## Downloading GitHub repo USEPA/rExpertQuery@develop
+ ## sf (1.1-0 -> 1.1-1 ) [CRAN]
+ ## cpp11 (0.5.4 -> 0.5.5 ) [CRAN]
+ ## roxygen2 (7.3.3 -> 8.0.0 ) [CRAN]
+ ## terra (1.9-11 -> 1.9-27) [CRAN]
+ ## data.table (1.18.2.1 -> 1.18.4) [CRAN]
+
+ ## Installing 5 packages: sf, cpp11, roxygen2, terra, data.table
+
+ ## Installing packages into 'C:/Users/hmarler/AppData/Local/Temp/RtmpcdnLOt/temp_libpath2fa83ceb49fe'
+ ## (as 'lib' is unspecified)
+
+ ## package 'sf' successfully unpacked and MD5 sums checked
+ ## package 'cpp11' successfully unpacked and MD5 sums checked
+ ## package 'roxygen2' successfully unpacked and MD5 sums checked
+ ## package 'terra' successfully unpacked and MD5 sums checked
+ ## package 'data.table' successfully unpacked and MD5 sums checked
##
+ ## The downloaded binary packages are in
+ ## C:\Users\hmarler\AppData\Local\Temp\RtmpYnTxPW\downloaded_packages
## ── R CMD build ─────────────────────────────────────────────────────────────────
- ## checking for file 'C:\Users\hmarler\AppData\Local\Temp\RtmpywqNsa\remotes2b947e5159e5\USEPA-rExpertQuery-ce1ba15/DESCRIPTION' ... checking for file 'C:\Users\hmarler\AppData\Local\Temp\RtmpywqNsa\remotes2b947e5159e5\USEPA-rExpertQuery-ce1ba15/DESCRIPTION' ... ✔ checking for file 'C:\Users\hmarler\AppData\Local\Temp\RtmpywqNsa\remotes2b947e5159e5\USEPA-rExpertQuery-ce1ba15/DESCRIPTION' (601ms)
- ## ─ preparing 'rExpertQuery': (4.7s)
+ ## checking for file 'C:\Users\hmarler\AppData\Local\Temp\RtmpYnTxPW\remotes59643c4c4d50\USEPA-rExpertQuery-96f363b/DESCRIPTION' ... checking for file 'C:\Users\hmarler\AppData\Local\Temp\RtmpYnTxPW\remotes59643c4c4d50\USEPA-rExpertQuery-96f363b/DESCRIPTION' ... ✔ checking for file 'C:\Users\hmarler\AppData\Local\Temp\RtmpYnTxPW\remotes59643c4c4d50\USEPA-rExpertQuery-96f363b/DESCRIPTION' (753ms)
+ ## ─ preparing 'rExpertQuery': (33.7s)
## checking DESCRIPTION meta-information ... checking DESCRIPTION meta-information ... ✔ checking DESCRIPTION meta-information
- ## ─ checking for LF line-endings in source and make files and shell scripts (430ms)
+ ## ─ checking for LF line-endings in source and make files and shell scripts (653ms)
## ─ checking for empty or unneeded directories
## Omitted 'LazyData' and 'LazyDataCompression' from DESCRIPTION
- ## ─ building 'rExpertQuery_0.1.0.tar.gz'
+ ## ─ building 'rExpertQuery_0.1.0.tar.gz' (622ms)
##
##
+ ## Installing package into 'C:/Users/hmarler/AppData/Local/Temp/RtmpcdnLOt/temp_libpath2fa83ceb49fe'
+ ## (as 'lib' is unspecified)
+
``` r
library(rExpertQuery)
```
@@ -92,66 +113,19 @@ using the code chunk below:
# load additional packages for this vignette
library(datasets)
library(data.table)
-```
-
- ## Warning: package 'data.table' was built under R version 4.4.3
-
-``` r
library(dplyr)
-```
-
- ## Warning: package 'dplyr' was built under R version 4.4.3
-
-``` r
library(DT)
-```
-
- ## Warning: package 'DT' was built under R version 4.4.3
-
-``` r
library(ggplot2)
library(httr)
-```
-
- ## Warning: package 'httr' was built under R version 4.4.3
-
-``` r
library(leaflet)
-```
-
- ## Warning: package 'leaflet' was built under R version 4.4.3
-
-``` r
library(maps)
-```
-
- ## Warning: package 'maps' was built under R version 4.4.3
-
-``` r
library(plotly)
-```
-
- ## Warning: package 'plotly' was built under R version 4.4.3
-
-``` r
library(sf)
library(usdata)
-```
-
- ## Warning: package 'usdata' was built under R version 4.4.3
-
-``` r
library(usmap)
-```
-
- ## Warning: package 'usmap' was built under R version 4.4.3
-
-``` r
library(stringi)
```
- ## Warning: package 'stringi' was built under R version 4.4.3
-
As a final setup step, we will turn off scientific notation so that any
catchment id numbers from our queries are displayed correctly.
@@ -282,7 +256,7 @@ A screenshot of the Expert Query National Downloads web page
assessments.nat <- rExpertQuery::EQ_NationalExtract("assessments")
```
- ## [1] "EQ_NationalExtract: downloading Assessments Profile (Expert Query National Extract). It was last updated on November 07, 2025 at 08:50 PM EST."
+ ## [1] "EQ_NationalExtract: downloading Assessments Profile (Expert Query National Extract). It was last updated on May 09, 2026 at 12:08 AM EDT."
``` r
# filter for latest assessments
@@ -452,7 +426,7 @@ A screenshot of the Expert Query National Downloads web page
dplyr::filter(epaIrCategory %in% c("4", "5"))
```
- The resulting data frame is large, with 128,919 unique impaired
+ The resulting data frame is large, with 137,482 unique impaired
waters nationally. Due to its size, we’ll include a random subset of
250 results in a data table to review during the demo. You can view
the full results by viewing the imp.waters df.
@@ -531,8 +505,8 @@ A screenshot of the Expert Query National Downloads web page
dplyr::distinct()
```
- There are 21 with monitoringLocationId data in ATTAINS. Of these
- states, 3 also contain monitoringLocationDataLinks. We can create a
+ There are 22 with monitoringLocationId data in ATTAINS. Of these
+ states, 4 also contain monitoringLocationDataLinks. We can create a
data table to display this information and apply some conditional
formatting to highlight the states with
monitoringLocationIdentifiers in blue and those with
@@ -789,7 +763,7 @@ queries ATTAINS Assessment Units data. The default is to only return
)
```
- ## [1] "EQ_AssessmentUnits: The current query will return 35 rows."
+ ## [1] "EQ_AssessmentUnits: The current query will return 0 rows."
``` r
# create data table
@@ -1011,7 +985,7 @@ ATTAINS Actions data by a variety of user supplied parameters.
n_act.fy.24 <- dplyr::n_distinct(act.fy.24$actionId)
```
- There were 7 unique actions by actionId established in R3 in
+ There were 6 unique actions by actionId established in R3 in
FY 2024. We can create data table with counts for each actionType.
``` r
@@ -1053,7 +1027,7 @@ keyword.
)
```
- This query yields 17,989 results. As well as providing the actionId
+ This query yields 16,962 results. As well as providing the actionId
and documentName for all results, *EQ_Actions()* also returns the
column “actionDocumentUrl” containing the URL to link to the
document. Because the query yields so many results, we’ll take a
@@ -1369,15 +1343,14 @@ value that should be used in queries.
domain.vals <- EQ_DomainValues()
```
- ## [1] "EQ_DomainValues: getting list of available domain names."
+ ## [1] "EQ_DomainValues: getting list of available domain names. Values in the eq_param column can be used as inputs in EQ_DomainValues."
+
+ ## EQ_DomainValues: domain list retrieved from ATTAINS web services.
EQ_DomainValues can provide values for the following query
- parameters: act_agency, act_status, act_type, ad_param,
- assess_basis, assess_methods, assess_types, au_status, cause,
- delist_reason, doc_type, file_type, loc_type, org_id, org_name,
- param_attain, param_group, param_name, param_state_ir_cat,
- param_status, source_scale, source_type, statecode, use_name,
- use_support and water_type .
+ parameters:
+
+ .
2. **How many different values for “water_type” can be used in
rExpertQuery functions?**
@@ -1390,7 +1363,9 @@ value that should be used in queries.
water.types <- rExpertQuery::EQ_DomainValues("water_type")
```
- ## [1] "EQ_DomainValues: For water_type the values in the name column of the function output are the allowable values for rExpert Query functions."
+ ## [1] "EQ_DomainValues: For water_type the values in the 'name' column of the function output are the allowable values for rExpert Query functions."
+
+ ## EQ_DomainValues: domain list retrieved from ATTAINS web services.
The printed message informs us that the “name” column contains the
values for use in water_type queries for rExpertQuery functions. For
diff --git a/docs/articles/CybertownTraining_files/htmltools-fill-0.5.8.1/fill.css b/docs/articles/CybertownTraining_files/htmltools-fill-0.5.9/fill.css
similarity index 100%
rename from docs/articles/CybertownTraining_files/htmltools-fill-0.5.8.1/fill.css
rename to docs/articles/CybertownTraining_files/htmltools-fill-0.5.9/fill.css
diff --git a/docs/articles/CybertownTraining_files/plotly-binding-4.11.0/plotly.js b/docs/articles/CybertownTraining_files/plotly-binding-4.12.0/plotly.js
similarity index 100%
rename from docs/articles/CybertownTraining_files/plotly-binding-4.11.0/plotly.js
rename to docs/articles/CybertownTraining_files/plotly-binding-4.12.0/plotly.js
diff --git a/docs/articles/CybertownTraining_files/plotly-htmlwidgets-css-2.11.1/plotly-htmlwidgets.css b/docs/articles/CybertownTraining_files/plotly-htmlwidgets-css-2.25.2/plotly-htmlwidgets.css
similarity index 100%
rename from docs/articles/CybertownTraining_files/plotly-htmlwidgets-css-2.11.1/plotly-htmlwidgets.css
rename to docs/articles/CybertownTraining_files/plotly-htmlwidgets-css-2.25.2/plotly-htmlwidgets.css
diff --git a/docs/articles/CybertownTraining_files/plotly-main-2.11.1/plotly-latest.min.js b/docs/articles/CybertownTraining_files/plotly-main-2.11.1/plotly-latest.min.js
deleted file mode 100644
index 27bfdaa..0000000
--- a/docs/articles/CybertownTraining_files/plotly-main-2.11.1/plotly-latest.min.js
+++ /dev/null
@@ -1,69 +0,0 @@
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