6  Spatial Data Visualisation

6.1 R packages for spatial data analysis

  • rgdal

  • sp

  • rgeos

  • raster

  • sf

  • tmap

  • leaflet

  • ggmap

  • maptools

  • gstat

  • spatstat

  • stars

  • geosphere

  • RgoogleMaps

  • rasterVis

6.2 Geospatial vector data structures

  • Point: Individual longitude and latitude of locations

    Eg: Locations, Buildings

  • Lines: Two or more vertices or points that are connected

    Eg: Roads, rivers

  • Polygons: Three or more vertices and closed

    Eg: Area of a country, state, district

6.3 Challenge in Spatial Data Viualisation

Move from

to

How to transform this three dimensional angular system to a two dimensional cartesian system?

Solution: Spatial Projections

6.4 Map Projection

  • A projection is about the geometric transformation from 3D to 2D.

  • A map projection is a method or mathematical formula to represent the curved surface of the Earth on a flat map.

  • It focuses on how the Earth’s surface is “flattened”, which always introduces some distortion in area, shape, distance, or direction.

Examples:

Mercator (preserves direction)

Albers Equal-Area Conic (preserves area)

Goode Homolosine (minimizes distortion globally)

6.5 Types of Map Projections

Cylindrical Projections

Imagine wrapping a cylinder around the Earth and projecting the surface onto it.

Characteristics:

Meridians and parallels are straight and perpendicular.

Distortion increases away from the equator.

Example:

Mercator Projection – preserves direction, but greatly distorts area near the poles.

Conical Projections

Imagine wrapping a cone over the Earth, usually touching at one or two standard parallels.

Characteristics:

Parallels appear as arcs; meridians converge toward a point.

Best for mid-latitude regions with an east–west extent.

Example:

Albers Equal-Area Conic – preserves area, commonly used for countries or regions.

Planar (Azimuthal) Projections

The Earth’s surface is projected onto a flat plane, usually touching at a single point.

Characteristics:

Distortion increases outward from the point of tangency.

Can preserve distance, area, or shape depending on the type.

Example:

Goode Homolosine – minimizes global distortion by combining multiple projection methods.

Visit here to see the visual illustration.

6.6 Example maps

p1

p2

p3

p4

6.7 Why Multiple Map Projection Types Exist

The Earth is round, but maps are flat. Because no flat map can perfectly preserve all properties of the globe, every projection introduces some distortion. Different projections are therefore designed to preserve certain properties depending on the map’s purpose.

Key Reasons for Multiple Projection Types

  1. Different Types of Distortion

Shape (conformal) – keeps angles and shapes correct (e.g., Mercator).

Area (equal-area / equivalent) – keeps relative sizes correct (e.g., Albers Equal-Area Conic).

Distance (equidistant) – preserves distances from a point or along certain lines (e.g., Azimuthal Equidistant).

Direction (azimuthal / navigational) – preserves angles and compass bearings (e.g., Mercator for navigation).

  1. Geographical Focus

Some projections are better for global maps, others for regional or local maps.

Example: Conical projections work well for mid-latitude countries, while planar projections work for polar regions.

  1. Practical Applications

Navigation – need accurate direction → Mercator.

Thematic / statistical maps – need accurate area → Albers Equal-Area.

Minimizing overall distortion – Goode Homolosine for global thematic maps.

6.8 Quick Comparison Table

In-class

6.9 Coordinate Reference System (CRS)

A CRS is a complete spatial reference framework that defines:

  • How coordinates relate to the Earth (datum)

    A datum defines the mathematical Earth model and reference point for coordinates, ensuring that latitude, longitude, and height correspond to real positions on the Earth.)

  • The map projection used (if any)

6.10 What is a Datum?

A datum is a mathematical model of the Earth that defines:

The size and shape of the Earth (ellipsoid or spheroid)

The origin and orientation of the coordinate system (where latitude 0°, longitude 0° are defined)

In short, a datum tells us where “zero” is and how the coordinates are measured on the Earth’s surface.

6.11 Why is a Datum Important?

Different datums model the Earth slightly differently, so the same latitude/longitude may correspond to different locations depending on the datum.

Example:

WGS84 → used by GPS

NAD83 → used in North America

A coordinate of 40°N, 75°W will be slightly different on WGS84 vs NAD83 because the reference ellipsoid is slightly shifted.

6.12 Projection vs CRS

  • Projection = method to flatten the Earth

  • CRS = full system that defines the Earth model + projection + coordinate rules

6.13 Shapefile

A shapefile is a popular geospatial vector data format used to store geographic features like points, lines, and polygons, along with their attributes. It’s commonly used in GIS (Geographic Information Systems) and mapping software, including R (sf or rgdal packages).

A shapefile actually consists of several files that work together. The main ones are:

File Extension Purpose
.shp Geometry of features (points, lines, polygons)
.shx Shape index (helps software find features quickly)
.dbf Attribute data (like a spreadsheet)
.prj Projection information (coordinate reference system)
.cpg Encoding of text attributes (optional)

R Example 1

# Read shapefile
world <- ne_countries(scale = "medium", returnclass = "sf")
head(world)
Simple feature collection with 6 features and 168 fields
Geometry type: MULTIPOLYGON
Dimension:     XY
Bounding box:  xmin: -73.36621 ymin: -22.40205 xmax: 109.4449 ymax: 41.9062
Geodetic CRS:  WGS 84
       featurecla scalerank labelrank sovereignt sov_a3 adm0_dif level
1 Admin-0 country         1         3   Zimbabwe    ZWE        0     2
2 Admin-0 country         1         3     Zambia    ZMB        0     2
3 Admin-0 country         1         3      Yemen    YEM        0     2
4 Admin-0 country         3         2    Vietnam    VNM        0     2
5 Admin-0 country         5         3  Venezuela    VEN        0     2
6 Admin-0 country         6         6    Vatican    VAT        0     2
               type tlc     admin adm0_a3 geou_dif   geounit gu_a3 su_dif
1 Sovereign country   1  Zimbabwe     ZWE        0  Zimbabwe   ZWE      0
2 Sovereign country   1    Zambia     ZMB        0    Zambia   ZMB      0
3 Sovereign country   1     Yemen     YEM        0     Yemen   YEM      0
4 Sovereign country   1   Vietnam     VNM        0   Vietnam   VNM      0
5 Sovereign country   1 Venezuela     VEN        0 Venezuela   VEN      0
6 Sovereign country   1   Vatican     VAT        0   Vatican   VAT      0
    subunit su_a3 brk_diff      name name_long brk_a3  brk_name brk_group
1  Zimbabwe   ZWE        0  Zimbabwe  Zimbabwe    ZWE  Zimbabwe      <NA>
2    Zambia   ZMB        0    Zambia    Zambia    ZMB    Zambia      <NA>
3     Yemen   YEM        0     Yemen     Yemen    YEM     Yemen      <NA>
4   Vietnam   VNM        0   Vietnam   Vietnam    VNM   Vietnam      <NA>
5 Venezuela   VEN        0 Venezuela Venezuela    VEN Venezuela      <NA>
6   Vatican   VAT        0   Vatican   Vatican    VAT   Vatican      <NA>
  abbrev postal                        formal_en
1  Zimb.     ZW             Republic of Zimbabwe
2 Zambia     ZM               Republic of Zambia
3   Yem.     YE                Republic of Yemen
4  Viet.     VN    Socialist Republic of Vietnam
5   Ven.     VE Bolivarian Republic of Venezuela
6   Vat.      V        State of the Vatican City
                           formal_fr              name_ciawf note_adm0 note_brk
1                               <NA>                Zimbabwe      <NA>     <NA>
2                               <NA>                  Zambia      <NA>     <NA>
3                               <NA>                   Yemen      <NA>     <NA>
4                               <NA>                 Vietnam      <NA>     <NA>
5 República Bolivariana de Venezuela               Venezuela      <NA>     <NA>
6                               <NA> Holy See (Vatican City)      <NA>     <NA>
           name_sort name_alt mapcolor7 mapcolor8 mapcolor9 mapcolor13  pop_est
1           Zimbabwe     <NA>         1         5         3          9 14645468
2             Zambia     <NA>         5         8         5         13 17861030
3        Yemen, Rep.     <NA>         5         3         3         11 29161922
4            Vietnam     <NA>         5         6         5          4 96462106
5      Venezuela, RB     <NA>         1         3         1          4 28515829
6 Vatican (Holy See) Holy See         1         3         4          2      825
  pop_rank pop_year gdp_md gdp_year                    economy
1       14     2019  21440     2019    5. Emerging region: G20
2       14     2019  23309     2019  7. Least developed region
3       15     2019  22581     2019  7. Least developed region
4       16     2019 261921     2019    5. Emerging region: G20
5       15     2019 482359     2014    5. Emerging region: G20
6        2     2019    -99     2019 2. Developed region: nonG7
               income_grp fips_10 iso_a2 iso_a2_eh iso_a3 iso_a3_eh iso_n3
1           5. Low income      ZI     ZW        ZW    ZWE       ZWE    716
2  4. Lower middle income      ZA     ZM        ZM    ZMB       ZMB    894
3  4. Lower middle income      YM     YE        YE    YEM       YEM    887
4  4. Lower middle income      VM     VN        VN    VNM       VNM    704
5  3. Upper middle income      VE     VE        VE    VEN       VEN    862
6 2. High income: nonOECD      VT     VA        VA    VAT       VAT    336
  iso_n3_eh un_a3 wb_a2 wb_a3   woe_id woe_id_eh                   woe_note
1       716   716    ZW   ZWE 23425004  23425004 Exact WOE match as country
2       894   894    ZM   ZMB 23425003  23425003 Exact WOE match as country
3       887   887    RY   YEM 23425002  23425002 Exact WOE match as country
4       704   704    VN   VNM 23424984  23424984 Exact WOE match as country
5       862   862    VE   VEN 23424982  23424982 Exact WOE match as country
6       336   336   -99   -99 23424986  23424986 Exact WOE match as country
  adm0_iso adm0_diff adm0_tlc adm0_a3_us adm0_a3_fr adm0_a3_ru adm0_a3_es
1      ZWE      <NA>      ZWE        ZWE        ZWE        ZWE        ZWE
2      ZMB      <NA>      ZMB        ZMB        ZMB        ZMB        ZMB
3      YEM      <NA>      YEM        YEM        YEM        YEM        YEM
4      VNM      <NA>      VNM        VNM        VNM        VNM        VNM
5      VEN      <NA>      VEN        VEN        VEN        VEN        VEN
6      VAT      <NA>      VAT        VAT        VAT        VAT        VAT
  adm0_a3_cn adm0_a3_tw adm0_a3_in adm0_a3_np adm0_a3_pk adm0_a3_de adm0_a3_gb
1        ZWE        ZWE        ZWE        ZWE        ZWE        ZWE        ZWE
2        ZMB        ZMB        ZMB        ZMB        ZMB        ZMB        ZMB
3        YEM        YEM        YEM        YEM        YEM        YEM        YEM
4        VNM        VNM        VNM        VNM        VNM        VNM        VNM
5        VEN        VEN        VEN        VEN        VEN        VEN        VEN
6        VAT        VAT        VAT        VAT        VAT        VAT        VAT
  adm0_a3_br adm0_a3_il adm0_a3_ps adm0_a3_sa adm0_a3_eg adm0_a3_ma adm0_a3_pt
1        ZWE        ZWE        ZWE        ZWE        ZWE        ZWE        ZWE
2        ZMB        ZMB        ZMB        ZMB        ZMB        ZMB        ZMB
3        YEM        YEM        YEM        YEM        YEM        YEM        YEM
4        VNM        VNM        VNM        VNM        VNM        VNM        VNM
5        VEN        VEN        VEN        VEN        VEN        VEN        VEN
6        VAT        VAT        VAT        VAT        VAT        VAT        VAT
  adm0_a3_ar adm0_a3_jp adm0_a3_ko adm0_a3_vn adm0_a3_tr adm0_a3_id adm0_a3_pl
1        ZWE        ZWE        ZWE        ZWE        ZWE        ZWE        ZWE
2        ZMB        ZMB        ZMB        ZMB        ZMB        ZMB        ZMB
3        YEM        YEM        YEM        YEM        YEM        YEM        YEM
4        VNM        VNM        VNM        VNM        VNM        VNM        VNM
5        VEN        VEN        VEN        VEN        VEN        VEN        VEN
6        VAT        VAT        VAT        VAT        VAT        VAT        VAT
  adm0_a3_gr adm0_a3_it adm0_a3_nl adm0_a3_se adm0_a3_bd adm0_a3_ua adm0_a3_un
1        ZWE        ZWE        ZWE        ZWE        ZWE        ZWE        -99
2        ZMB        ZMB        ZMB        ZMB        ZMB        ZMB        -99
3        YEM        YEM        YEM        YEM        YEM        YEM        -99
4        VNM        VNM        VNM        VNM        VNM        VNM        -99
5        VEN        VEN        VEN        VEN        VEN        VEN        -99
6        VAT        VAT        VAT        VAT        VAT        VAT        -99
  adm0_a3_wb     continent region_un          subregion
1        -99        Africa    Africa     Eastern Africa
2        -99        Africa    Africa     Eastern Africa
3        -99          Asia      Asia       Western Asia
4        -99          Asia      Asia South-Eastern Asia
5        -99 South America  Americas      South America
6        -99        Europe    Europe    Southern Europe
                   region_wb name_len long_len abbrev_len tiny homepart
1         Sub-Saharan Africa        8        8          5  -99        1
2         Sub-Saharan Africa        6        6          6  -99        1
3 Middle East & North Africa        5        5          4  -99        1
4        East Asia & Pacific        7        7          5    2        1
5  Latin America & Caribbean        9        9          4  -99        1
6      Europe & Central Asia        7        7          4    4        1
  min_zoom min_label max_label   label_x    label_y      ne_id wikidataid
1        0       2.5       8.0  29.92544 -18.911640 1159321441       Q954
2        0       3.0       8.0  26.39530 -14.660804 1159321439       Q953
3        0       3.0       8.0  45.87438  15.328226 1159321425       Q805
4        0       2.0       7.0 105.38729  21.715416 1159321417       Q881
5        0       2.5       7.5 -64.59938   7.182476 1159321411       Q717
6        0       5.0      10.0  12.45342  41.903323 1159321407       Q237
    name_ar       name_bn      name_de      name_en             name_es
1  زيمبابوي      জিম্বাবুয়ে     Simbabwe     Zimbabwe            Zimbabue
2    زامبيا       জাম্বিয়া       Sambia       Zambia              Zambia
3     اليمن        ইয়েমেন        Jemen        Yemen               Yemen
4    فيتنام      ভিয়েতনাম      Vietnam      Vietnam             Vietnam
5   فنزويلا     ভেনেজুয়েলা    Venezuela    Venezuela           Venezuela
6 الفاتيكان ভ্যাটিকান সিটি Vatikanstadt Vatican City Ciudad del Vaticano
   name_fa         name_fr    name_el       name_he   name_hi   name_hu
1 زیمبابوه        Zimbabwe Ζιμπάμπουε      זימבבואה   ज़िम्बाब्वे  Zimbabwe
2   زامبیا          Zambie     Ζάμπια         זמביה   ज़ाम्बिया    Zambia
3      یمن           Yémen     Υεμένη          תימן       यमन     Jemen
4   ویتنام        Viêt Nam    Βιετνάμ       וייטנאם   वियतनाम   Vietnám
5  ونزوئلا       Venezuela Βενεζουέλα       ונצואלה    वेनेज़ुएला Venezuela
6  واتیکان Cité du Vatican   Βατικανό קריית הוותיקן वैटिकन नगर   Vatikán
    name_id            name_it    name_ja     name_ko      name_nl   name_pl
1  Zimbabwe           Zimbabwe ジンバブエ    짐바브웨     Zimbabwe  Zimbabwe
2    Zambia             Zambia   ザンビア      잠비아       Zambia    Zambia
3     Yaman              Yemen   イエメン        예멘        Jemen     Jemen
4   Vietnam            Vietnam   ベトナム      베트남      Vietnam   Wietnam
5 Venezuela          Venezuela ベネズエラ  베네수엘라    Venezuela Wenezuela
6   Vatikan Città del Vaticano   バチカン 바티칸 시국 Vaticaanstad   Watykan
    name_pt   name_ru       name_sv   name_tr   name_uk    name_ur
1  Zimbábue  Зимбабве      Zimbabwe  Zimbabve  Зімбабве    زمبابوے
2    Zâmbia    Замбия        Zambia   Zambiya    Замбія     زیمبیا
3     Iémen     Йемен         Jemen     Yemen      Ємен        یمن
4  Vietname   Вьетнам       Vietnam   Vietnam   В'єтнам     ویتنام
5 Venezuela Венесуэла     Venezuela Venezuela Венесуела  وینیزویلا
6  Vaticano   Ватикан Vatikanstaten   Vatikan   Ватикан ویٹیکن سٹی
        name_vi  name_zh name_zht      fclass_iso tlc_diff      fclass_tlc
1      Zimbabwe 津巴布韦   辛巴威 Admin-0 country     <NA> Admin-0 country
2        Zambia   赞比亚   尚比亞 Admin-0 country     <NA> Admin-0 country
3         Yemen     也门     葉門 Admin-0 country     <NA> Admin-0 country
4      Việt Nam     越南     越南 Admin-0 country     <NA> Admin-0 country
5     Venezuela 委内瑞拉 委內瑞拉 Admin-0 country     <NA> Admin-0 country
6 Thành Vatican   梵蒂冈   梵蒂岡 Admin-0 country     <NA> Admin-0 country
  fclass_us fclass_fr fclass_ru fclass_es fclass_cn fclass_tw fclass_in
1      <NA>      <NA>      <NA>      <NA>      <NA>      <NA>      <NA>
2      <NA>      <NA>      <NA>      <NA>      <NA>      <NA>      <NA>
3      <NA>      <NA>      <NA>      <NA>      <NA>      <NA>      <NA>
4      <NA>      <NA>      <NA>      <NA>      <NA>      <NA>      <NA>
5      <NA>      <NA>      <NA>      <NA>      <NA>      <NA>      <NA>
6      <NA>      <NA>      <NA>      <NA>      <NA>      <NA>      <NA>
  fclass_np fclass_pk fclass_de fclass_gb fclass_br fclass_il fclass_ps
1      <NA>      <NA>      <NA>      <NA>      <NA>      <NA>      <NA>
2      <NA>      <NA>      <NA>      <NA>      <NA>      <NA>      <NA>
3      <NA>      <NA>      <NA>      <NA>      <NA>      <NA>      <NA>
4      <NA>      <NA>      <NA>      <NA>      <NA>      <NA>      <NA>
5      <NA>      <NA>      <NA>      <NA>      <NA>      <NA>      <NA>
6      <NA>      <NA>      <NA>      <NA>      <NA>      <NA>      <NA>
  fclass_sa fclass_eg fclass_ma fclass_pt fclass_ar fclass_jp fclass_ko
1      <NA>      <NA>      <NA>      <NA>      <NA>      <NA>      <NA>
2      <NA>      <NA>      <NA>      <NA>      <NA>      <NA>      <NA>
3      <NA>      <NA>      <NA>      <NA>      <NA>      <NA>      <NA>
4      <NA>      <NA>      <NA>      <NA>      <NA>      <NA>      <NA>
5      <NA>      <NA>      <NA>      <NA>      <NA>      <NA>      <NA>
6      <NA>      <NA>      <NA>      <NA>      <NA>      <NA>      <NA>
  fclass_vn fclass_tr fclass_id fclass_pl fclass_gr fclass_it fclass_nl
1      <NA>      <NA>      <NA>      <NA>      <NA>      <NA>      <NA>
2      <NA>      <NA>      <NA>      <NA>      <NA>      <NA>      <NA>
3      <NA>      <NA>      <NA>      <NA>      <NA>      <NA>      <NA>
4      <NA>      <NA>      <NA>      <NA>      <NA>      <NA>      <NA>
5      <NA>      <NA>      <NA>      <NA>      <NA>      <NA>      <NA>
6      <NA>      <NA>      <NA>      <NA>      <NA>      <NA>      <NA>
  fclass_se fclass_bd fclass_ua                       geometry
1      <NA>      <NA>      <NA> MULTIPOLYGON (((31.28789 -2...
2      <NA>      <NA>      <NA> MULTIPOLYGON (((30.39609 -1...
3      <NA>      <NA>      <NA> MULTIPOLYGON (((53.08564 16...
4      <NA>      <NA>      <NA> MULTIPOLYGON (((104.064 10....
5      <NA>      <NA>      <NA> MULTIPOLYGON (((-60.82119 9...
6      <NA>      <NA>      <NA> MULTIPOLYGON (((12.43916 41...
# Check CRS
st_crs(world)
Coordinate Reference System:
  User input: WGS 84 
  wkt:
GEOGCRS["WGS 84",
    DATUM["World Geodetic System 1984",
        ELLIPSOID["WGS 84",6378137,298.257223563,
            LENGTHUNIT["metre",1]]],
    PRIMEM["Greenwich",0,
        ANGLEUNIT["degree",0.0174532925199433]],
    CS[ellipsoidal,2],
        AXIS["latitude",north,
            ORDER[1],
            ANGLEUNIT["degree",0.0174532925199433]],
        AXIS["longitude",east,
            ORDER[2],
            ANGLEUNIT["degree",0.0174532925199433]],
    ID["EPSG",4326]]
#> CRS: EPSG:4326 (WGS84) → unprojected lat/lon in degrees

# Project to UTM
world_utm <- st_transform(world, crs = 32633)
#> CRS: EPSG:32633 → projected in meters

R Example 2

#devtools::install_github("thiyangt/ceylon")
library(ceylon)
data(sf_sl_0)
class(sf_sl_0)
[1] "sf"         "tbl_df"     "tbl"        "data.frame"
sf_sl_0
Simple feature collection with 1 feature and 1 field
Geometry type: MULTIPOLYGON
Dimension:     XY
Bounding box:  xmin: 362203.3 ymin: 380301.9 xmax: 621918.1 ymax: 813560.9
Projected CRS: SLD99 / Sri Lanka Grid 1999
# A tibble: 1 × 2
                                                                geometry COUNTRY
                                                      <MULTIPOLYGON [m]> <chr>  
1 (((481925.5 381353.7, 481922.9 381350.3, 481919 381348.2, 481914.6 38… SRI LA…

or

st_crs(sf_sl_0)
Coordinate Reference System:
  User input: EPSG:5235 
  wkt:
PROJCRS["SLD99 / Sri Lanka Grid 1999",
    BASEGEOGCRS["SLD99",
        DATUM["Sri Lanka Datum 1999",
            ELLIPSOID["Everest 1830 (1937 Adjustment)",6377276.345,300.8017,
                LENGTHUNIT["metre",1]]],
        PRIMEM["Greenwich",0,
            ANGLEUNIT["degree",0.0174532925199433]],
        ID["EPSG",5233]],
    CONVERSION["Sri Lanka Grid 1999",
        METHOD["Transverse Mercator",
            ID["EPSG",9807]],
        PARAMETER["Latitude of natural origin",7.00047152777778,
            ANGLEUNIT["degree",0.0174532925199433],
            ID["EPSG",8801]],
        PARAMETER["Longitude of natural origin",80.7717130833333,
            ANGLEUNIT["degree",0.0174532925199433],
            ID["EPSG",8802]],
        PARAMETER["Scale factor at natural origin",0.9999238418,
            SCALEUNIT["unity",1],
            ID["EPSG",8805]],
        PARAMETER["False easting",500000,
            LENGTHUNIT["metre",1],
            ID["EPSG",8806]],
        PARAMETER["False northing",500000,
            LENGTHUNIT["metre",1],
            ID["EPSG",8807]]],
    CS[Cartesian,2],
        AXIS["(E)",east,
            ORDER[1],
            LENGTHUNIT["metre",1]],
        AXIS["(N)",north,
            ORDER[2],
            LENGTHUNIT["metre",1]],
    USAGE[
        SCOPE["unknown"],
        AREA["Sri Lanka - onshore"],
        BBOX[5.86,79.64,9.88,81.95]],
    ID["EPSG",5235]]

Visualizing using shapefiles: map of Sri Lanka

ggplot(sf_sl_0) +
  geom_sf()

ggplot(sf_sl_0) +
  geom_sf(fill='beige') +
theme_minimal()

library(knitr)
sf_sl_0 %>% kable()
geometry COUNTRY
MULTIPOLYGON (((481925.5 38… SRI LANKA

6.14 Exercise

  1. Draw district and province maps

data:

province
district
  1. Guess the output.
ggplot() +
  geom_sf(data=province, lwd=2, col="black") + 
  geom_sf(data=district, linetype=21, col="red")
  1. Colour provinces according to the population.

6.15 Annotations

ggplot(sf_sl_0) +
  geom_sf(fill='beige') +
ggspatial::annotation_north_arrow(location = "br")+
  ggspatial::annotation_scale(location = "bl")

ggplot(sf_sl_0) +
  geom_sf(fill='beige') +
ggspatial::annotation_north_arrow(location = "br")+
  ggspatial::annotation_scale(location = "bl")

6.16 Spatial Data Wrangling

  1. Convert regular data frame to sf
# Example data frame: cities with lat/lon
cities <- data.frame(
  name = c("New York", "London", "Tokyo"),
  lon = c(-74.0060, -0.1276, 139.6917),
  lat = c(40.7128, 51.5074, 35.6895)
)

# Convert to sf object
cities_sf <- st_as_sf(cities, coords = c("lon", "lat"), crs = 4326)
st_crs(cities_sf)
Coordinate Reference System:
  User input: EPSG:4326 
  wkt:
GEOGCRS["WGS 84",
    ENSEMBLE["World Geodetic System 1984 ensemble",
        MEMBER["World Geodetic System 1984 (Transit)"],
        MEMBER["World Geodetic System 1984 (G730)"],
        MEMBER["World Geodetic System 1984 (G873)"],
        MEMBER["World Geodetic System 1984 (G1150)"],
        MEMBER["World Geodetic System 1984 (G1674)"],
        MEMBER["World Geodetic System 1984 (G1762)"],
        MEMBER["World Geodetic System 1984 (G2139)"],
        MEMBER["World Geodetic System 1984 (G2296)"],
        ELLIPSOID["WGS 84",6378137,298.257223563,
            LENGTHUNIT["metre",1]],
        ENSEMBLEACCURACY[2.0]],
    PRIMEM["Greenwich",0,
        ANGLEUNIT["degree",0.0174532925199433]],
    CS[ellipsoidal,2],
        AXIS["geodetic latitude (Lat)",north,
            ORDER[1],
            ANGLEUNIT["degree",0.0174532925199433]],
        AXIS["geodetic longitude (Lon)",east,
            ORDER[2],
            ANGLEUNIT["degree",0.0174532925199433]],
    USAGE[
        SCOPE["Horizontal component of 3D system."],
        AREA["World."],
        BBOX[-90,-180,90,180]],
    ID["EPSG",4326]]
#> EPSG:4326 (WGS84)
  1. Transforming coordinates
# Transform to UTM (meters)
cities_utm <- st_transform(cities_sf, crs = 32618)  # UTM zone 18N

st_crs(cities_utm)
Coordinate Reference System:
  User input: EPSG:32618 
  wkt:
PROJCRS["WGS 84 / UTM zone 18N",
    BASEGEOGCRS["WGS 84",
        ENSEMBLE["World Geodetic System 1984 ensemble",
            MEMBER["World Geodetic System 1984 (Transit)"],
            MEMBER["World Geodetic System 1984 (G730)"],
            MEMBER["World Geodetic System 1984 (G873)"],
            MEMBER["World Geodetic System 1984 (G1150)"],
            MEMBER["World Geodetic System 1984 (G1674)"],
            MEMBER["World Geodetic System 1984 (G1762)"],
            MEMBER["World Geodetic System 1984 (G2139)"],
            MEMBER["World Geodetic System 1984 (G2296)"],
            ELLIPSOID["WGS 84",6378137,298.257223563,
                LENGTHUNIT["metre",1]],
            ENSEMBLEACCURACY[2.0]],
        PRIMEM["Greenwich",0,
            ANGLEUNIT["degree",0.0174532925199433]],
        ID["EPSG",4326]],
    CONVERSION["UTM zone 18N",
        METHOD["Transverse Mercator",
            ID["EPSG",9807]],
        PARAMETER["Latitude of natural origin",0,
            ANGLEUNIT["degree",0.0174532925199433],
            ID["EPSG",8801]],
        PARAMETER["Longitude of natural origin",-75,
            ANGLEUNIT["degree",0.0174532925199433],
            ID["EPSG",8802]],
        PARAMETER["Scale factor at natural origin",0.9996,
            SCALEUNIT["unity",1],
            ID["EPSG",8805]],
        PARAMETER["False easting",500000,
            LENGTHUNIT["metre",1],
            ID["EPSG",8806]],
        PARAMETER["False northing",0,
            LENGTHUNIT["metre",1],
            ID["EPSG",8807]]],
    CS[Cartesian,2],
        AXIS["(E)",east,
            ORDER[1],
            LENGTHUNIT["metre",1]],
        AXIS["(N)",north,
            ORDER[2],
            LENGTHUNIT["metre",1]],
    USAGE[
        SCOPE["Navigation and medium accuracy spatial referencing."],
        AREA["Between 78°W and 72°W, northern hemisphere between equator and 84°N, onshore and offshore. Bahamas. Canada - Nunavut; Ontario; Quebec. Colombia. Cuba. Ecuador. Greenland. Haiti. Jamaica. Panama. Turks and Caicos Islands. United States (USA). Venezuela."],
        BBOX[0,-78,84,-72]],
    ID["EPSG",32618]]
#> EPSG:32618 → coordinates in meters
# Original coordinates (degrees)
cities_sf
Simple feature collection with 3 features and 1 field
Geometry type: POINT
Dimension:     XY
Bounding box:  xmin: -74.006 ymin: 35.6895 xmax: 139.6917 ymax: 51.5074
Geodetic CRS:  WGS 84
      name                 geometry
1 New York  POINT (-74.006 40.7128)
2   London  POINT (-0.1276 51.5074)
3    Tokyo POINT (139.6917 35.6895)
# Transformed coordinates (meters)
cities_utm
Simple feature collection with 3 features and 1 field
Geometry type: POINT
Dimension:     XY
Bounding box:  xmin: -2693059 ymin: 4507351 xmax: 4935636 ymax: 15438700
Projected CRS: WGS 84 / UTM zone 18N
      name                  geometry
1 New York  POINT (583959.4 4507351)
2   London   POINT (4935636 8691571)
3    Tokyo POINT (-2693059 15438701)

6.17 Two types of maps

  1. Simple feature map

  2. Polygon maps

6.18 Simple feature map

ggplot(sf_sl_0) +
  geom_sf() +
  coord_sf()

ggplot(sf_sl_0) +
  geom_sf() +
  coord_sf(crs = st_crs(3577))

6.19 Polygon map

Example 1

library(ggplot2)
library(sf)

# Create a simple polygon (square) as sf object
coords <- matrix(c(
  0, 0,
  0, 1,
  1, 1,
  1, 0,
  0, 0  # close the polygon
), ncol = 2, byrow = TRUE)

polygon_sf <- st_sf(
  geometry = st_sfc(st_polygon(list(coords)))
)
polygon_sf
Simple feature collection with 1 feature and 0 fields
Geometry type: POLYGON
Dimension:     XY
Bounding box:  xmin: 0 ymin: 0 xmax: 1 ymax: 1
CRS:           NA
                        geometry
1 POLYGON ((0 0, 0 1, 1 1, 1 ...
# Extract coordinates for geom_point
polygon_points <- st_coordinates(polygon_sf)
polygon_points
     X Y L1 L2
[1,] 0 0  1  1
[2,] 0 1  1  1
[3,] 1 1  1  1
[4,] 1 0  1  1
[5,] 0 0  1  1
# Convert to data frame
polygon_df <- as.data.frame(polygon_points)
head(polygon_df)
  X Y L1 L2
1 0 0  1  1
2 0 1  1  1
3 1 1  1  1
4 1 0  1  1
5 0 0  1  1
# Plot using geom_point
ggplot(polygon_df, aes(x = X, y = Y)) +
  geom_point(color = "blue", size = 3) +
  coord_fixed() +
  labs(title = "Polygon represented by points")

Example 2

devtools::install_github("UrbanInstitute/urbnmapr")
library(urbnmapr)
statepop1 <- statepop %>% rename(state_name = full)
head(statepop1)
# A tibble: 6 × 4
  fips  abbr  state_name pop_2022
  <chr> <chr> <chr>         <dbl>
1 01    AL    Alabama     5074296
2 02    AK    Alaska       733583
3 04    AZ    Arizona     7359197
4 05    AR    Arkansas    3045637
5 06    CA    California 39029342
6 08    CO    Colorado    5839926
statepop2 <- full_join(statepop1, states, by = "state_name")
head(statepop2)
# A tibble: 6 × 12
  fips  abbr  state_name pop_2022  long   lat order hole  piece group state_fips
  <chr> <chr> <chr>         <dbl> <dbl> <dbl> <int> <lgl> <fct> <fct> <chr>     
1 01    AL    Alabama     5074296 -88.5  31.9     1 FALSE 1     01.1  01        
2 01    AL    Alabama     5074296 -88.5  31.9     2 FALSE 1     01.1  01        
3 01    AL    Alabama     5074296 -88.5  31.9     3 FALSE 1     01.1  01        
4 01    AL    Alabama     5074296 -88.5  32.0     4 FALSE 1     01.1  01        
5 01    AL    Alabama     5074296 -88.5  32.0     5 FALSE 1     01.1  01        
6 01    AL    Alabama     5074296 -88.5  32.1     6 FALSE 1     01.1  01        
# ℹ 1 more variable: state_abbv <chr>
ggplot() + 
  geom_point(data = statepop2, mapping = aes(x = long, y = lat, group = group), fill = "grey", color = "black") 

ggplot() + 
  geom_polygon(data = statepop2, mapping = aes(x = long, y = lat, group = group),fill = "grey", color = "black")

Example 3

mi_counties <- map_data("county", "michigan") %>% 
  select(lon = long, lat, group, id = subregion)
head(mi_counties)
        lon      lat group     id
1 -83.88675 44.85686     1 alcona
2 -83.36536 44.86832     1 alcona
3 -83.36536 44.86832     1 alcona
4 -83.33098 44.83968     1 alcona
5 -83.30806 44.80530     1 alcona
6 -83.30233 44.77665     1 alcona

Plot: in class

Example 4: Does as.data.frame(sf_object) work?

library(ceylon)
data(sf_sl_0)
sf_sl_0
Simple feature collection with 1 feature and 1 field
Geometry type: MULTIPOLYGON
Dimension:     XY
Bounding box:  xmin: 362203.3 ymin: 380301.9 xmax: 621918.1 ymax: 813560.9
Projected CRS: SLD99 / Sri Lanka Grid 1999
# A tibble: 1 × 2
                                                                geometry COUNTRY
                                                      <MULTIPOLYGON [m]> <chr>  
1 (((481925.5 381353.7, 481922.9 381350.3, 481919 381348.2, 481914.6 38… SRI LA…
# Convert to data frame
polygon_df_sl <- as.data.frame(sf_sl_0)
polygon_df_sl
                        geometry   COUNTRY
1 MULTIPOLYGON (((481925.5 38... SRI LANKA

6.20 rnaturalearth

rnaturalearth is an R package that provides ready-to-use maps of the world, countries, and regions as sf (spatial) or data frames. It’s a super convenient way to get geographic data without downloading shapefiles manually.

Example 1

library(ggplot2)
library(dplyr)
library(rnaturalearth)
library(rnaturalearthdata)
# Get Sri Lanka admin-1 regions (provinces)
sri_lanka <- ne_states(country = "Sri Lanka", returnclass = "sf")

# Check the first few rows
head(sri_lanka)
Simple feature collection with 6 features and 121 fields
Geometry type: MULTIPOLYGON
Dimension:     XY
Bounding box:  xmin: 79.65577 ymin: 7.272089 xmax: 81.41668 ymax: 9.829576
Geodetic CRS:  WGS 84
                         featurecla scalerank adm1_code diss_me iso_3166_2
1860 Admin-1 states provinces lakes         9  LKA-2454    2454      LK-53
1861 Admin-1 states provinces lakes         9  LKA-2458    2458      LK-45
1862 Admin-1 states provinces lakes         9  LKA-2459    2459      LK-41
1863 Admin-1 states provinces lakes         9  LKA-2460    2460      LK-42
1864 Admin-1 states provinces lakes         9  LKA-2457    2457      LK-43
1865 Admin-1 states provinces lakes         9  LKA-2462    2462      LK-62
     wikipedia iso_a2 adm0_sr          name            name_alt name_local
1860      <NA>     LK       1 Trikuṇāmalaya         Trincomalee       <NA>
1861      <NA>     LK       1       Mulativ Mulativu|Mullaitivu       <NA>
1862      <NA>     LK       3      Yāpanaya              Jaffna       <NA>
1863      <NA>     LK       1   Kilinŏchchi         Kilinochchi       <NA>
1864      <NA>     LK       3     Mannārama              Mannar       <NA>
1865      <NA>     LK       1     Puttalama            Puttalam       <NA>
            type  type_en code_local code_hasc
1860 Distrikkaya District       <NA>     LK.TC
1861 Distrikkaya District       <NA>     LK.MP
1862 Distrikkaya District       <NA>     LK.JA
1863 Distrikkaya District       <NA>     LK.KL
1864 Distrikkaya District       <NA>     LK.MB
1865 Distrikkaya District       <NA>     LK.PX
                                  note hasc_maybe            region region_cod
1860                              <NA>       <NA> Næ̆gĕnahira paḷāta       LK-5
1861                              <NA>       <NA>      Uturu paḷāta       LK-4
1862 Split into Jaffna and Kilinochchi       <NA>      Uturu paḷāta       LK-4
1863                              <NA>       <NA>      Uturu paḷāta       LK-4
1864                              <NA>       <NA>      Uturu paḷāta       LK-4
1865                              <NA>       <NA>    Vayamba paḷāta       LK-6
     provnum_ne gadm_level check_me datarank abbrev postal area_sqkm sameascity
1860      20013          1       10        6   <NA>     TC         0          7
1861      20005          1       10        6   <NA>     MP         0        -99
1862      20002          1       10        6   <NA>     JA         0        -99
1863      20001          1        0        6   <NA>     KL         0        -99
1864      20004          1       10        6   <NA>     MB         0        -99
1865      20006          1       10        6   <NA>     PX         0        -99
     labelrank name_len mapcolor9 mapcolor13 fips fips_alt   woe_id woe_label
1860         7       13         4          9 CE21     <NA> 23706507      <NA>
1861         9        7         4          9 CE27     <NA> 23706505      <NA>
1862         9        8         4          9 CE25     <NA> 23706489      <NA>
1863         9       11         4          9 <NA>     <NA> 23706490      <NA>
1864         9        9         4          9 CE26     <NA> 23706506      <NA>
1865         9        9         4          9 CE19     <NA> 23706502      <NA>
        woe_name latitude longitude sov_a3 adm0_a3 adm0_label     admin
1860 Trincomalee  8.56202   81.0848    LKA     LKA          2 Sri Lanka
1861  Mullaitivu  9.24043   80.5667    LKA     LKA          2 Sri Lanka
1862      Jaffna  9.50669   79.6933    LKA     LKA          2 Sri Lanka
1863 Kilinochchi  9.38429   80.3341    LKA     LKA          2 Sri Lanka
1864      Mannar  8.84371   80.0854    LKA     LKA          2 Sri Lanka
1865    Puttalam  7.94944   79.9111    LKA     LKA          2 Sri Lanka
      geonunit gu_a3   gn_id              gn_name   gns_id             gns_name
1860 Sri Lanka   LKA 1226258 Trincomalee District -2237626 Trincomalee District
1861 Sri Lanka   LKA 1234392 Mullaittivu District -2229490 Mullaittivu District
1862 Sri Lanka   LKA 1242831      Jaffna District -2221042      Jaffna District
1863 Sri Lanka   LKA 1240371 Kilinochchi District -2223504 Kilinochchi District
1864 Sri Lanka   LKA 1236148      Mannar District -2227730      Mannar District
1865 Sri Lanka   LKA 1229292    Puttalam District -2234591             Puttalam
     gn_level gn_region gn_a1_code region_sub sub_code gns_level gns_lang
1860        2      <NA> LK.1226258       <NA>     <NA>         2     <NA>
1861        2      <NA> LK.1234392       <NA>     <NA>         2     <NA>
1862        2      <NA> LK.1242831       <NA>     <NA>         2     <NA>
1863        2      <NA> LK.1240371       <NA>     <NA>         2     <NA>
1864        2      <NA> LK.1236148       <NA>     <NA>         2     <NA>
1865        2      <NA> LK.1229292       <NA>     <NA>         2     <NA>
     gns_adm1 gns_region min_label max_label min_zoom wikidataid
1860     <NA>       CE37       8.7        11      8.7   Q1493318
1861     <NA>       CE38       8.7        11      8.7   Q1587508
1862     <NA>       CE38       8.7        11      8.7   Q1520182
1863     <NA>       CE38       8.7        11      8.7   Q1584007
1864     <NA>       CE38       8.7        11      8.7    Q178003
1865     <NA>       CE32       8.7        11      8.7   Q1665318
               name_ar        name_bn     name_de     name_en     name_es
1860 مديرية ترينكومالي       ত্রিকুমালয় Trincomalee Trincomalee Trincomalee
1861  مديرية مولايتيفو মোলাইতিবো জেলা  Mullaitivu  Mullaitivu  Mullaitivu
1862      مديرية جافنا       ইয়াপানায়      Jaffna      Jaffna      Jaffna
1863  مديرية كيلينوتشي ক্লিনোকাচি জেলা Kilinochchi Kilinochchi Kilinochchi
1864       مديرية منار     মানার জেলা      Mannar      Mannar      Mannar
1865    مديرية بوتالام   পোতালাম জেলা    Puttalam    Puttalam    Puttalam
          name_fr        name_el         name_hi       name_hu       name_id
1860 Trinquemalay Τρικουμαμαλάια त्रिंकोन्माली जिला Trikuṇāmalaya Trikuṇamalaya
1861   Mullaitivu   Μουλαϊτιβούν      मुलैतिवु जिला    Mullaitivu    Mullaitivu
1862       Jaffna      Γιαπανάια       जफ़ना जिला      Yāpanaya      Yapanaya
1863  Kilinochchi      Κιλινότσι  किलिनोच्चि जिला   Kilinŏchchi   Kilinochchi
1864       Mannar          Μανάρ      मन्नार जिला     Mannārama        Mannar
1865     Puttalam      Πουτταλάμ      पुत्तलम जिला     Puttalama      Puttalam
         name_it                name_ja       name_ko     name_nl       name_pl
1860 Trincomalee       トリンコマリー県 트링코말리 구 Trincomalee Trikunamalaja
1861  Mullaitivu ムッライッティーヴー県 물라이티부 구  Mullaitivu   Mullajttiwu
1862      Jaffna             ジャフナ県     자프나 구      Jaffna        Dżafna
1863 Kilinochchi           キリノッチ県 킬리노치치 구 Kilinochchi     Kilinoćći
1864      Mannar           マンナール県     만나르 구      Mannar        Mannar
1865    Puttalam           プッタラム県     푸탈람 구    Puttalam      Puttalam
         name_pt          name_ru     name_sv       name_tr          name_vi
1860 Trincomalee Тринкомали округ Trincomalee Trikuṇāmalaya      Trincomalee
1861 Mullaittivu       Муллайтиву  Mullaitivu    Mullaitivu  Quận Mullaitivu
1862      Jaffna     округ Джафна      Jaffna      Yāpanaya         Yāpanaya
1863 Kilinochchi        Килиноччи Kilinochchi   Kilinochchi Quận Kilinochchi
1864      Mannar           Маннар      Mannar        Mannar      Quận Mannar
1865    Puttalam         Путталам    Puttalam      Puttalam    Quận Puttalam
        name_zh      ne_id      name_he    name_uk        name_ur
1860 亭可馬里區 1159311407 טריקונהמלאיה Трінкомалі ترینکومالی ضلع
1861 穆莱蒂武区 1159311415        מולטה Муллайтіву  مولایتیوو ضلع
1862   贾夫纳区 1159311395     יאפאנאיה     Джафна       جففناضلع
1863 基利诺奇区 1159311417     קילינצ'י  Кіліноччі  کیلینوچچی ضلع
1864   马纳尔区 1159311387     האי מנסל     Маннар      مانار ضلع
1865 普塔勒姆区 1159311421     פוטאלאמה   Путталам    پوٹالام ضلع
              name_fa   name_zht FCLASS_ISO FCLASS_US FCLASS_FR FCLASS_RU
1860 ناحیه ترینکومالی 亭可馬里區       <NA>      <NA>      <NA>      <NA>
1861  ناحیه مولایتیوو 穆萊蒂武區       <NA>      <NA>      <NA>      <NA>
1862       ناحیه جفنا   賈夫納區       <NA>      <NA>      <NA>      <NA>
1863   ناحیه کیلینوچی 基利諾奇區       <NA>      <NA>      <NA>      <NA>
1864       ناحیه منار   馬納爾區       <NA>      <NA>      <NA>      <NA>
1865    ناحیه پوتالام 普塔勒姆區       <NA>      <NA>      <NA>      <NA>
     FCLASS_ES FCLASS_CN FCLASS_TW FCLASS_IN FCLASS_NP FCLASS_PK FCLASS_DE
1860      <NA>      <NA>      <NA>      <NA>      <NA>      <NA>      <NA>
1861      <NA>      <NA>      <NA>      <NA>      <NA>      <NA>      <NA>
1862      <NA>      <NA>      <NA>      <NA>      <NA>      <NA>      <NA>
1863      <NA>      <NA>      <NA>      <NA>      <NA>      <NA>      <NA>
1864      <NA>      <NA>      <NA>      <NA>      <NA>      <NA>      <NA>
1865      <NA>      <NA>      <NA>      <NA>      <NA>      <NA>      <NA>
     FCLASS_GB FCLASS_BR FCLASS_IL FCLASS_PS FCLASS_SA FCLASS_EG FCLASS_MA
1860      <NA>      <NA>      <NA>      <NA>      <NA>      <NA>      <NA>
1861      <NA>      <NA>      <NA>      <NA>      <NA>      <NA>      <NA>
1862      <NA>      <NA>      <NA>      <NA>      <NA>      <NA>      <NA>
1863      <NA>      <NA>      <NA>      <NA>      <NA>      <NA>      <NA>
1864      <NA>      <NA>      <NA>      <NA>      <NA>      <NA>      <NA>
1865      <NA>      <NA>      <NA>      <NA>      <NA>      <NA>      <NA>
     FCLASS_PT FCLASS_AR FCLASS_JP FCLASS_KO FCLASS_VN FCLASS_TR FCLASS_ID
1860      <NA>      <NA>      <NA>      <NA>      <NA>      <NA>      <NA>
1861      <NA>      <NA>      <NA>      <NA>      <NA>      <NA>      <NA>
1862      <NA>      <NA>      <NA>      <NA>      <NA>      <NA>      <NA>
1863      <NA>      <NA>      <NA>      <NA>      <NA>      <NA>      <NA>
1864      <NA>      <NA>      <NA>      <NA>      <NA>      <NA>      <NA>
1865      <NA>      <NA>      <NA>      <NA>      <NA>      <NA>      <NA>
     FCLASS_PL FCLASS_GR FCLASS_IT FCLASS_NL FCLASS_SE FCLASS_BD FCLASS_UA
1860      <NA>      <NA>      <NA>      <NA>      <NA>      <NA>      <NA>
1861      <NA>      <NA>      <NA>      <NA>      <NA>      <NA>      <NA>
1862      <NA>      <NA>      <NA>      <NA>      <NA>      <NA>      <NA>
1863      <NA>      <NA>      <NA>      <NA>      <NA>      <NA>      <NA>
1864      <NA>      <NA>      <NA>      <NA>      <NA>      <NA>      <NA>
1865      <NA>      <NA>      <NA>      <NA>      <NA>      <NA>      <NA>
     FCLASS_TLC                       geometry
1860       <NA> MULTIPOLYGON (((80.92292 8....
1861       <NA> MULTIPOLYGON (((80.62598 9....
1862       <NA> MULTIPOLYGON (((80.44174 9....
1863       <NA> MULTIPOLYGON (((80.29067 9....
1864       <NA> MULTIPOLYGON (((79.91278 8....
1865       <NA> MULTIPOLYGON (((79.82789 7....

Example 2

library(rnaturalearth)
library(sf)
library(ggplot2)

# Get world countries as sf object
world <- ne_countries(scale = "medium", returnclass = "sf")

# Plot the map
ggplot(world) +
  geom_sf(fill = "lightblue", color = "black") +
  labs(title = "World Map")

6.21 Practical

  1. Mark following locations on the map
oz_capitals <- tibble::tribble( 
  ~city,           ~lat,     ~lon,
  "Sydney",    -33.8688, 151.2093,  
  "Melbourne", -37.8136, 144.9631, 
  "Brisbane",  -27.4698, 153.0251, 
  "Adelaide",  -34.9285, 138.6007, 
  "Perth",     -31.9505, 115.8605, 
  "Hobart",    -42.8821, 147.3272, 
  "Canberra",  -35.2809, 149.1300, 
  "Darwin",    -12.4634, 130.8456, 
)

Help:

library(ozmaps)
library(sf)
oz_states <- ozmaps::ozmap_states
oz_states
Simple feature collection with 9 features and 1 field
Geometry type: MULTIPOLYGON
Dimension:     XY
Bounding box:  xmin: 105.5507 ymin: -43.63203 xmax: 167.9969 ymax: -9.229287
Geodetic CRS:  GDA94
# A tibble: 9 × 2
  NAME                                                                  geometry
* <chr>                                                       <MULTIPOLYGON [°]>
1 New South Wales              (((150.7016 -35.12286, 150.6611 -35.11782, 150.6…
2 Victoria                     (((146.6196 -38.70196, 146.6721 -38.70259, 146.6…
3 Queensland                   (((148.8473 -20.3457, 148.8722 -20.37575, 148.85…
4 South Australia              (((137.3481 -34.48242, 137.3749 -34.46885, 137.3…
5 Western Australia            (((126.3868 -14.01168, 126.3625 -13.98264, 126.3…
6 Tasmania                     (((147.8397 -40.29844, 147.8902 -40.30258, 147.8…
7 Northern Territory           (((136.3669 -13.84237, 136.3339 -13.83922, 136.3…
8 Australian Capital Territory (((149.2317 -35.222, 149.2346 -35.24047, 149.271…
9 Other Territories            (((167.9333 -29.05421, 167.9188 -29.0344, 167.93…
  1. Projecting your data
# devtools::install_github("hrbrmstr/albersusa")
library(albersusa)
crs_use = "+proj=laea +lat_0=30 +lon_0=-95"

d_points = data.frame(long = c(-110, -103, -84), 
                      lat  = c(45, 40, 41))