Labels Points Inside Polygons • pointdexter

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:

Chicago Public Schools (CPS) - School Profile Information, School Year (SY) 2018-2019
- as a data frame
City of Chicago boundary
- as a SpatialPolygonsDataFrame; and
- as a simple feature
Chicago’s 77 community areas
- as a SpatialPolygonsDataFrame; and
- as a simple feature
Chicago’s 2010 census tracts
- as a SpatialPolygonsDataFrame; and
- as a simple feature

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