/pointdexter

Label longitudinal and latitudinal coordinates located inside a polygon.

Primary LanguageR

pointdexter

CRAN status CRAN checks CRAN Downloads Rdoc Travis build status

The pointdexter package labels longitudinal and latitudinal coordinates located inside a polygon.

Description

For a singular polygon, the label for each coordinate pair is a logical vector of TRUE and FALSE values. For multiple polygons, the label for each coordinate pair is a character vector based on the names of each polygon.

Spatial Packages

The package is designed to work with both sf and SpatialPolygonsDataFrame objects.

By default, pointdexter only installs and loads the sp and splancs packages.

However, pointdexter also works with the sf package. The following is sf installation advice from Matt Herman:

Depending on your operating system and available libraries, sf can be tricky to install the first time. The sf website is a good place to start if you're having trouble. If you're using macOS, this is a good guide to installing the required libraries.

Installation

The latest stable release version can be installed from CRAN:

install.packages("pointdexter")

Development versions can be installed from GitHub:

# note: by default, the development version is the master branch;
#       however, that can be changed by changing the 
#       value in the 'ref' argument
remotes::install_github("cenuno/pointdexter")

Usage

Build-in Data

pointdexter comes with built-in point and polygon data - entirely due to the awesome and accessible Chicago Data Portal - to help you label points in polygons:

Example

Using built-in data, down below is an example of how to use pointdexter to label coordinate pairs with their appropriate polygon:

# load necessary packages ----
library(pointdexter) # label coordinate pairs in polygons
library(sp)          # classes and methods for spatial data
library(knitr)       # general purpose package for dynamic report generation

# load necessary data ----
data("cps_sy1819")
data("community_areas_spdf")

# create list of coordinate pair matrices for each community area ----
community.area.boundaries <-
  GetPolygonBoundaries(my.polygon = community_areas_spdf
                       , labels = community_areas_spdf$community)

# identify the community that each cps school lies in ----
cps_sy1819$community <-
  LabelPointsWithinPolygons(lng = cps_sy1819$school_longitude
                            , lat = cps_sy1819$school_latitude
                            , polygon.boundaries = community.area.boundaries)

# store relevant columns ----
relevant.columns <-
  c("school_id", "short_name"
    , "school_longitude", "school_latitude", "community")

# show first few records ----
kable(head(cps_sy1819[, relevant.columns], n = 10))
school_id short_name school_longitude school_latitude community
609760 CARVER MILITARY HS -87.59062 41.65629 RIVERDALE
609780 MARINE LEADERSHIP AT AMES HS -87.72174 41.91604 LOGAN SQUARE
610304 PHOENIX MILITARY HS -87.68696 41.87912 NEAR WEST SIDE
610513 AIR FORCE HS -87.63276 41.82814 ARMOUR SQUARE
610390 RICKOVER MILITARY HS -87.66579 41.98902 EDGEWATER
609754 CHICAGO MILITARY HS -87.61922 41.83055 DOUGLAS
610132 POE -87.60642 41.70263 PULLMAN
610177 SKINNER -87.65942 41.87910 NEAR WEST SIDE
610534 SKINNER NORTH -87.64484 41.90511 NEAR NORTH SIDE
610066 MCDADE -87.61921 41.73455 CHATHAM 
# plot only CPS high schools in the the Austin, Rogers Park, and West Elsdon community areas ----

# create filter condition ----
filter.condition <- 
  which(cps_sy1819$is_high_school &
          cps_sy1819$community %in% c("AUSTIN"
                                      , "ROGERS PARK"
                                      , "WEST ELSDON"))

# filter cps records to those that matched our condition ---
df <- cps_sy1819[filter.condition, ]

# note: clear plot space
par(mar = c(0, 0, 1, 0))
plot(x = community_areas_spdf
     , main = "CPS High Schools in Austin, Rogers Park, and West Elsdon communities, SY1819"
     , cex.main = 0.75
     , col = "gray85"
     , border = "dodgerblue4")
points(x = df$school_longitude
       , y = df$school_latitude
       , pch = 19
       , col = rgb(red = 212, green = 69, blue = 0
                   , alpha = 90
                   , maxColorValue = 255)
       , cex = 1)

# store relevant columns ----
relevant.columns <-
  c("school_id", "short_name", "is_high_school"
    , "school_longitude", "school_latitude", "community")

# print table of those schools that met the condition ----
kable(df[order(df$community), relevant.columns], row.names = FALSE)
school_id short_name is_high_school school_longitude school_latitude community
400127 YCCS - AUSTIN CAREER TRUE -87.76022 41.89498 AUSTIN
400123 YCCS - SCHOLASTIC ACHIEVEMENT TRUE -87.74254 41.88045 AUSTIN
400144 YCCS - WESTSIDE HOLISTIC TRUE -87.74881 41.90224 AUSTIN
610245 DOUGLASS HS TRUE -87.76767 41.89037 AUSTIN
610244 CLARK HS TRUE -87.75333 41.87288 AUSTIN
610518 AUSTIN CCA HS TRUE -87.76192 41.88599 AUSTIN
400035 CHICAGO MATH & SCIENCE HS TRUE -87.67482 42.01303 ROGERS PARK
609733 SULLIVAN HS TRUE -87.66919 42.00269 ROGERS PARK
609694 HANCOCK HS TRUE -87.72447 41.79146 WEST ELSDON

Resources

After you've installed the package, be sure to view the help files that introduce you to pointdexter's two functions:

  1. ?pointdexter::GetPolygonBoundaries(); and
  2. ?pointdexter::LabelPointsWithinPolygons().

Feedback

Cite

If you use pointdexter for any analysis, I would love to hear about it! You can also cite the package according to citation("pointdexter").

Contribute

Issues and pull requests are welcome anytime!

Code of Conduct

Please note that this project is released with a Contributor Code of Conduct. By participating in this project you agree to abide by its terms.