# Start Here

#### Michael Dumelle, Tom Kincaid, Anthony R. Olsen, and Marc Weber

# 1 Installing and loading `spsurvey`

If this is your first time using the `spsurvey` package, run

    install.packages("spsurvey")

to install the package. You only need to run this code once per version of R.

After the `spsurvey` package is installed, load it into R each new R session by running

    library(spsurvey)

# 2 Citation information

If you used `spsurvey` in your work, please cite it. You can view the most recent citation by running

    citation("spsurvey")
    #> To cite spsurvey in publications use:
    #>
    #>   Michael Dumelle, Tom Kincaid, Anthony R. Olsen, Marc Weber (2023).
    #>   spsurvey: Spatial Sampling Design and Analysis in R. Journal of
    #>   Statistical Software, 105(3), 1-29. doi:10.18637/jss.v105.i03
    #>
    #> A BibTeX entry for LaTeX users is
    #>
    #>   @Article{,
    #>     title = {{spsurvey}: Spatial Sampling Design and Analysis in {R}},
    #>     author = {Michael Dumelle and Tom Kincaid and Anthony R. Olsen and Marc Weber},
    #>     journal = {Journal of Statistical Software},
    #>     year = {2023},
    #>     volume = {105},
    #>     number = {3},
    #>     pages = {1--29},
    #>     doi = {10.18637/jss.v105.i03},
    #>   }

# 3 `spsurvey` terminology

`spsurvey` implements a design-based approach to statistical inference, with a focus on spatial data. There are a few terms helpful to define before we move forward with `spsurvey`, as these terms will be used throughout the vignettes and documentation:

- Survey design: All aspects of a survey from establishment of a need for data to the production of final results.
- Sampling frame: The set of all sites (i.e. units) from which a sample is selected.
- Sampling design: The process by which sites are selected from the sampling frame.
- Design sites: The set of sites selected from the sampling frame according to the sampling design. Sometimes the term “sample” is used to refer to a collection of design sites (e.g. the term “GRTS sample” more formally means “the collection of design sites selected using the GRTS algorithm according to the sampling design”).
- Analysis data: Data collected at the design sites. This includes information from the survey design such as design weights, stratification variables, subpopulation variables, etc.
- Analysis results: The results from an appropriate statistical analysis of the analysis data.

# 4 Vignettes in `spsurvey`

There are five additional vignettes in `spsurvey` that are available on our website here under the “Articles” tab:

1. An Overview of Statistical Surveys for Ecological Applications
2. Exploratory Data Analysis: Summarizing and Visualizing Sampling Frames, Design Sites, and Analysis Data
   - Use the `summary()` and `plot()` functions to summarize and visualize sampling frames, design sites, and analysis data.
3. Spatially Balanced Sampling
   - Use the `grts()` function to implement the Generalized Random Tessellation Stratified (GRTS) algorithm (Stevens and Olsen, 2004) to select spatially balanced samples.
4. Weight Adjustments in Practice
   - Use the `adjwgt()` and `adjwgtNR()` functions to adjust weights for changing sample sizes and nonresponse, respectively.
5. Analyzing Data
   - Use several analysis functions to analyze data.
6. Technical Details
   - See the technical details underlying various functions in `spsurvey`.

These vignettes cover some of the core functions (and arguments within those functions) in `spsurvey`. To learn more about features of `spsurvey` that are not covered in these vignettes, we encourage you to read `spsurvey`’s documentation available for download here or viewable interactively on our website here. Help files for a particular function are viewable by running `?function_name` after loading `spsurvey`. For example, to learn more about the `grts()` function, run `?grts`.

# 5 Installing a previous version of `spsurvey`

The version 5.0.0 update to `spsurvey` implemented many significant changes to existing functions. As a result, some of your old code may not run properly while using version 5.0.0. Though we recommend adapting your code to work with the version 5.0.0 update (and later), you may also install a previous version of `spsurvey`. For information regarding the installation of previous version of R packages, please see the RStudio support page here. Additionally, old versions of `spsurvey` are also available for download in the release tags section of our GitHub repository here.

# 6 `sf` objects

The sampling functions in `spsurvey` (`grts()` and `irs()`) require that your sampling frame is an `sf` object. An `sf` object (shorthand for a “simple features” object) is an R object with a unique structure used to conveniently store spatial data. `sf` objects are constructed using the sf package (Pebesma, 2018). The sf package is loaded and installed alongside the `spsurvey` package, so you do not need to run `install.packages("sf")` or `library(sf)` to access the sf package if `spsurvey` is already installed and loaded. For more on the sf package, see here.

Next we discuss a few ways to construct `sf` objects in R. The first is to read a shapefile directly into R using `sf::read_sf()`. The second is to use the `sf::st_sf()` function or the `sf::st_as_sf()` function to combine an appropriate R object (most commonly a data frame) and an appropriate geometry object into an `sf` object. To illustrate one approach for turning a data frame into an `sf` object, we start with `NE_Lakes_df`, a data frame in `spsurvey` that contains variables and geographic coordinates (latitude and longitude coordinates) for lakes in the Northeastern United States. To turn `NE_Lakes_df` into `NE_Lakes_geo`, an `sf` object with geographic coordinates, run

    NE_Lakes_geo <- st_as_sf(NE_Lakes_df, coords = c("XCOORD", "YCOORD"), crs = 4326)
    NE_Lakes_geo
    #> Simple feature collection with 195 features and 4 fields
    #> Geometry type: POINT
    #> Dimension:     XY
    #> Bounding box:  xmin: -73.64778 ymin: 41.07065 xmax: -69.96715 ymax: 42.73616
    #> Geodetic CRS:  WGS 84
    #> First 10 features:
    #>         AREA AREA_CAT   ELEV ELEV_CAT                   geometry
    #> 1  10.648825    large 264.69     high POINT (-72.08896 42.55508)
    #> 2   2.504606    small 557.63     high POINT (-73.18199 42.36727)
    #> 3   3.979199    small  28.79      low POINT (-71.14074 42.15596)
    #> 4   1.645657    small 212.60     high   POINT (-73.06726 41.783)
    #> 5   7.489052    small 239.67     high  POINT (-72.2602 42.36255)
    #> 6  86.533725    large 195.37     high POINT (-71.74634 41.87624)
    #> 7   1.926996    small 158.96     high POINT (-73.48408 41.34238)
    #> 8   6.514217    small  29.26      low POINT (-73.25487 41.20551)
    #> 9   3.100221    small 204.62     high POINT (-72.20897 42.12512)
    #> 10  1.868094    small  78.77      low POINT (-72.70233 42.18012)

The `coords` argument to `sf::st_as_sf` specifies the columns in `NE_Lakes_df` that are the x-coordinates and y-coordinates. The `crs` argument specifies the coordinate reference system, which we discuss in more detail next.

# 7 Coordinate reference systems

Spatial data and `sf` objects rely on coordinate reference systems. A coordinate reference system (CRS) provides a structure by which to identify unique locations on the Earth’s surface. CRSs are either geographic or projected. A geographic CRS uses longitude (east-west direction) and latitude (north-south direction) coordinates to represent location with respect to a specific ellipsoid or spheroid surface. Geographic CRSs are measured in degrees, not units like meters or feet – this has important consequences. For example, a one degree difference in latitude is different at different longitudes. Projected CRSs are measured in standard Cartesian coordinates with respect to a flat surface. They have x and y locations, an origin, and a unit of measurement (like meters or feet).

You can move between coordinate systems using `sf::st_transform()`. For example, we can transform `NE_Lakes_geo` (which uses a geographic CRS) to `NE_Lakes` (which uses a projected CRS) by running

    NE_Lakes <- st_transform(NE_Lakes_geo, crs = 5070)
    NE_Lakes
    #> Simple feature collection with 195 features and 4 fields
    #> Geometry type: POINT
    #> Dimension:     XY
    #> Bounding box:  xmin: 1834001 ymin: 2225021 xmax: 2127632 ymax: 2449985
    #> Projected CRS: NAD83 / Conus Albers
    #> First 10 features:
    #>         AREA AREA_CAT   ELEV ELEV_CAT                geometry
    #> 1  10.648825    large 264.69     high POINT (1930929 2417191)
    #> 2   2.504606    small 557.63     high POINT (1849399 2375085)
    #> 3   3.979199    small  28.79      low POINT (2017323 2393723)
    #> 4   1.645657    small 212.60     high POINT (1874135 2313865)
    #> 5   7.489052    small 239.67     high POINT (1922712 2392868)
    #> 6  86.533725    large 195.37     high POINT (1977163 2350744)
    #> 7   1.926996    small 158.96     high POINT (1852292 2257784)
    #> 8   6.514217    small  29.26      low POINT (1874421 2247388)
    #> 9   3.100221    small 204.62     high POINT (1933352 2368181)
    #> 10  1.868094    small  78.77      low POINT (1892582 2364213)

CRSs in R have traditionally been stored using EPSG codes or `proj4string` values. This meant that in order to transform your coordinates from one CRS to another, you needed two EPSG codes or `proj4string` values, one for each CRS. Recent updates to R’s handling of spatial data follow GDAL and PROJ (more information available here), and CRSs in `sf` objects are stored in R as lists with two components: `input`, which contains information regarding the EPSG code and `proj4string`; and `wkt`, an open geospatial standard format. For more information on CRSs and EPSG codes, see Pebesma (2018) and Lovelace et al. (2019). To search for various CRSs and EPSG codes, see here and here.

`spsurvey` will use the CRS from your `sf` object, so it is your responsibility to make sure the `sf` object has an appropriate CRS. If the CRS is not specified correctly, you may get misleading results.

# 8 References

Lovelace, R., Nowosad, J., & Muenchow, J. (2019). Geocomputation with R. CRC Press.

Pebesma, E., (2018). Simple Features for R: Standardized Support for Spatial Vector Data. The R Journal, 10 (1):439-446. https://doi.org/10.32614/RJ-2018-009

Stevens Jr, D. L. and Olsen, A. R. (2004). Spatially balanced sampling of natural resources. Journal of the American Statistical Association, 99(465):262-278.