In-class Exercise 2

pacman::p_load(tidyverse, sf, tmap)

mpsz <- st_read(dsn = "data/geospatial")

Reading layer `MP14_SUBZONE_WEB_PL' from data source 
  `/home/tropicbliss/GitHub/quarto-project/In-class_Ex/In-class_Ex02/data/geospatial' 
  using driver `ESRI Shapefile'
Simple feature collection with 323 features and 15 fields
Geometry type: MULTIPOLYGON
Dimension:     XY
Bounding box:  xmin: 2667.538 ymin: 15748.72 xmax: 56396.44 ymax: 50256.33
Projected CRS: SVY21

• Simple features are represented via “simple features geometry (SFG)”, and they and geospatial data can be represented with “simple features objects (SFO)” which represent a collection of SFG as a data-frame object. The non-spatial data (metadata) are stored in a separate column separate from the SFG column. The data type of that SFG column is known as “simple features collection (SFC)”.

• SFO is an object, you can get the class by passing in a variable to the class function.

• In the environments tab, “obs” stands for observations which is basically the number of rows. “Variables” represent the number of columns.

• The data types you will be working with for each column would be:

  • int

  • chr

    • Basically a string.

  • Date

  • num

    • A superclass of int and double. R will automatically cast it to its child classes if needed, but this is basically a generic number class.

mpsz2019 <- st_read(dsn = "data/geospatial/MasterPlan2019SubzoneBoundaryNoSeaKML.kml") %>% st_transform(crs = 3414)

Reading layer `URA_MP19_SUBZONE_NO_SEA_PL' from data source 
  `/home/tropicbliss/GitHub/quarto-project/In-class_Ex/In-class_Ex02/data/geospatial/MasterPlan2019SubzoneBoundaryNoSeaKML.kml' 
  using driver `LIBKML'
Simple feature collection with 332 features and 22 fields
Geometry type: MULTIPOLYGON
Dimension:     XYZ
Bounding box:  xmin: 103.6057 ymin: 1.158699 xmax: 104.0885 ymax: 1.470775
z_range:       zmin: 0 zmax: 0
Geodetic CRS:  WGS 84

If you look at the KML version, you can see that it is less tidier than the Shapefile version.

<center><table><tr><th colspan='2' align='center'><em>Attributes</…

You’ll have to manually parse and extract the data. Hence, the Shapefile version will be the one we are going to use.

tm_shape(mpsz)+
  tm_fill("REGION_N", 
          style = "quantile",
          palette = "plasma",
          title = "Subzones") +
  tm_layout(main.title = "Planning subzones of Singapore (2014)",
            main.title.position = "center",
            main.title.size = 1.2,
            legend.height = 0.45, 
            legend.width = 0.35,
            frame = TRUE) +
  tm_borders(alpha = 0.5) +
  tm_compass(type="8star", size = 2) +
  tm_scale_bar() +
  tm_credits("Source: Planning Sub-zone boundary from Urban Redevelopment Authorithy (URA)")

preschool = st_read("data/geospatial/PreSchoolsLocation.kml")

Reading layer `PRESCHOOLS_LOCATION' from data source 
  `/home/tropicbliss/GitHub/quarto-project/In-class_Ex/In-class_Ex02/data/geospatial/PreSchoolsLocation.kml' 
  using driver `LIBKML'
Simple feature collection with 2290 features and 16 fields
Geometry type: POINT
Dimension:     XYZ
Bounding box:  xmin: 103.6878 ymin: 1.247759 xmax: 103.9897 ymax: 1.462134
z_range:       zmin: 0 zmax: 0
Geodetic CRS:  WGS 84

preschool3414 <- st_transform(preschool, crs = 3414)

popdata <- st_read("data/aspatial/respopagesextod2023.csv")

Reading layer `respopagesextod2023' from data source 
  `/home/tropicbliss/GitHub/quarto-project/In-class_Ex/In-class_Ex02/data/aspatial/respopagesextod2023.csv' 
  using driver `CSV'

Warning: no simple feature geometries present: returning a data.frame or tbl_df

popdata2023 <- popdata %>%
  group_by(PA, SZ, AG)