https://github.com/brownag/soildbdata
Builds of data sets for use in soilDB unit testing
https://github.com/brownag/soildbdata
Last synced: about 2 months ago
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Builds of data sets for use in soilDB unit testing
- Host: GitHub
- URL: https://github.com/brownag/soildbdata
- Owner: brownag
- Created: 2022-01-11T23:05:40.000Z (over 3 years ago)
- Default Branch: main
- Last Pushed: 2025-02-24T23:15:04.000Z (3 months ago)
- Last Synced: 2025-02-25T00:23:48.397Z (3 months ago)
- Language: R
- Size: 14.3 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.Rmd
- Changelog: NEWS.md
Awesome Lists containing this project
README
---
output: github_document
---
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "man/figures/README-",
out.width = "100%"
)
if (!dir.exists(file.path("inst", "extdata")))
dir.create(file.path("inst", "extdata"), recursive = TRUE)
```
```{r, echo = FALSE}
library(soilDB)
options(soilDB.warn.aliases = FALSE)
```
# {soilDBdata}
The goal of {soilDBdata} is to provide a standard way of building test data sets for the soilDB R package. Most of these test datasets are in the form of SQLite 'snapshots' of NASIS tables.
These data sets are regularly updated to support unit testing of [NASIS functionality in the {soilDB} package](https://github.com/ncss-tech/soilDB/blob/master/tests/testthat/test-soilDBdata.R). A few data sets housed in this repository are also used for training courses provided by the Soil and Plant Science Division such as [Statistics for Soil Survey](https://ncss-tech.github.io/stats_for_soil_survey/book/index.html).
Access to the NASIS transactional database requires eAuth and a NASIS user account. NASIS data are intended for internal use by NRCS staff and authorized cooperators. NASIS-related functions in {soilDB} provide a standard read-only connection to a local NASIS instance for users that have such access, or alternately can be supplied with the optional `dsn` argument. The `dsn` argument can be a _DBIConnection_ to an MSSQL, SQLite or PostgreSQL database with the NASIS schema (for instance), or a path to a SQLite database file.
## Installation
You can install the released version of {soilDBdata} from GitHub with:
``` r
remotes::install_github("brownag/soilDBdata")
```
## Example 1 `soilDB::fetchNASIS('pedons')`
This is an example that shows you how to create a snapshot of the tables in your local database for running `fetchNASIS()` without a local NASIS database.
The sample data for testing the `from="pedons"` functionality are queried into the local database, accepted, and queried into and selected set using SSRO_Northwest query `PedonPC_Plus_DataDump_select` targeting Site and Pedon objects. User Site ID matches `"2015MT663%"`, NASIS site name matches `"%"` and NASIS group name matches `"NW-MIS Point Data"`.
A total of `134` Site and Pedon objects should be found, and `115` of those profiles are retained in `fetchNASIS()` with `rmHzErrors = TRUE` because `19` profiles do not have horizons. These profiles represented with a single `NA` depth horizon when `fill = TRUE`.
Create a "static" snapshot for `soilDB::fetchNASIS('pedons')` from your local NASIS database.
```{r example1}
library(soilDBdata)
con <- soilDB::NASIS()
rebuild <- FALSE
dsn <- "inst/extdata/fetchNASIS_pedons.sqlite"
if (rebuild && inherits(con, 'DBIConnection')) {
res <- create_fetchNASIS_pedons(output_path = dsn)
DBI::dbDisconnect(con)
}
if (file.exists(dsn)) {
f <- try(soilDB::fetchNASIS(
from = "pedons",
dsn = dsn,
SS = TRUE,
rmHzErrors = TRUE
))
f2 <- try(soilDB::fetchNASIS(
from = "pedons",
dsn = dsn,
SS = FALSE,
fill = TRUE
))
}
if (!is.null(f))
show(f)
if (!is.null(f2))
show(f2)
```
## Example 2 `soilDB::fetchNASIS('components')`
Create a "static" snapshot for `soilDB::fetchNASIS('components')` from your local NASIS database.
The sample data for testing the `from="components"` functionality are queried into the local database, accepted, and queried into selected set using SSRO_Southwest query `Whole multiple legends by area sym != addit. and DMU = yes +` targeting Area, Legend, Mapunit and Data Mapunit objects, where Area Symbol matches `"MT663"`.
A total of `1` Legend, `82` Mapunit and `86` Data Mapunit objects should be downloaded into local database, with `63` (non-additional) Mapunit and `63` Data Mapunit objects loaded into selected set. `fetchNASIS(from="components")` will return `191` component profiles with `fill=FALSE` and `546` with `fill=TRUE`.
```{r example2}
library(soilDBdata)
con <- soilDB::NASIS()
rebuild <- FALSE
dsn <- "inst/extdata/fetchNASIS_components.sqlite"
if (rebuild & inherits(con, 'DBIConnection')) {
res <- create_fetchNASIS_components(output_path = dsn)
DBI::dbDisconnect(con)
}
if (file.exists(dsn)) {
f <- try(soilDB::fetchNASIS(
from = "components",
dsn = dsn,
SS = TRUE,
rmHzErrors = TRUE
))
f2 <- try(soilDB::fetchNASIS(
from = "components",
dsn = dsn,
SS = FALSE,
fill = TRUE
))
}
if (!is.null(f))
show(f)
if (!is.null(f2))
show(f2)
```
## Example 3 `soilDB::fetchVegdata()`
Create a "static" snapshot for `soilDB::fetchVegdata()` from your local NASIS database.
The sample data for testing the `fetchVegdata()` functionality are queried using two queries to the national database. First run `POINT - Pedon/Site by User Site ID` with target table "Pedon" to download "Site", "Pedon", and "Transect" objects where User Site ID matches`"2010WY629%"`.. Then run `VEG - Veg Plots by User Site ID and Vegplot ID` with target table "Vegetation Plot" to get "Site" and "Vegetation Plot" objects where User Site ID and Vegetation Plot ID matches `"2010WY629%"`.
The first query will result in `26` Site and `26` Pedon objects. Of the `26` pedons, `11` have vegetation plots linked to the same site observation. The second query will result in `137` Site, and `138` Vegetation Plot objects. One Site Observation has two Vegetation Plot objects linked to it. All `138` Vegetation Plots have `assocuserpedonid` populated, but most of the pedons do not exist in NASIS.
The two queries result in `152` unique sites matching `"2010WY629%"`, of which `127` lack Pedons and `15` lack Vegetation Plots.
```{r example3}
library(soilDBdata)
con <- soilDB::NASIS()
rebuild <- FALSE
dsn <- "inst/extdata/fetchVegdata.sqlite"
if (rebuild && inherits(con, 'DBIConnection')) {
res <- create_fetchVegdata(output_path = dsn)
DBI::dbDisconnect(con)
}
if (file.exists(dsn)) {
f <- try(soilDB::fetchVegdata(
dsn = dsn,
SS = TRUE
))
f2 <- try(soilDB::fetchVegdata(
dsn = dsn,
SS = FALSE,
include_pedon = "assocuserpedonid"
))
f3 <- try(soilDB::fetchVegdata(
dsn = dsn,
SS = FALSE,
include_pedon = FALSE
))
}
if (!is.null(f))
str(f, max.level = 1)
if (!is.null(f2))
str(f2, max.level = 1)
if (!is.null(f3))
str(f3, max.level = 1)
```