pacman::p_load(sf, tidyverse)Hands-On Exercise 2
Week 2 Hands-on Exercise
mpsz = st_read(dsn = "data/geospatial",
layer = "MP14_SUBZONE_WEB_PL")Reading layer `MP14_SUBZONE_WEB_PL' from data source
`/Users/michelle/Desktop/IS415/shelle-mim/IS415-GAA/Hands-on_Exercise/Wk2/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: SVY21Import polyline feature data as shapefile
cyclingpath = st_read(dsn = "data/geospatial",
layer = "CyclingPathGazette")Reading layer `CyclingPathGazette' from data source
`/Users/michelle/Desktop/IS415/shelle-mim/IS415-GAA/Hands-on_Exercise/Wk2/data/geospatial'
using driver `ESRI Shapefile'
Simple feature collection with 2248 features and 2 fields
Geometry type: MULTILINESTRING
Dimension: XY
Bounding box: xmin: 11854.32 ymin: 28347.98 xmax: 42626.09 ymax: 48948.15
Projected CRS: SVY21preschool = st_read("data/geospatial/preschools-location.kml")Reading layer `PRESCHOOLS_LOCATION' from data source
`/Users/michelle/Desktop/IS415/shelle-mim/IS415-GAA/Hands-on_Exercise/Wk2/data/geospatial/preschools-location.kml'
using driver `KML'
Simple feature collection with 1925 features and 2 fields
Geometry type: POINT
Dimension: XYZ
Bounding box: xmin: 103.6824 ymin: 1.247759 xmax: 103.9897 ymax: 1.462134
z_range: zmin: 0 zmax: 0
Geodetic CRS: WGS 84# Retrive geometry list-col
st_geometry(mpsz)Geometry set for 323 features
Geometry type: MULTIPOLYGON
Dimension: XY
Bounding box: xmin: 2667.538 ymin: 15748.72 xmax: 56396.44 ymax: 50256.33
Projected CRS: SVY21
First 5 geometries:MULTIPOLYGON (((31495.56 30140.01, 31980.96 296...MULTIPOLYGON (((29092.28 30021.89, 29119.64 300...MULTIPOLYGON (((29932.33 29879.12, 29947.32 298...MULTIPOLYGON (((27131.28 30059.73, 27088.33 297...MULTIPOLYGON (((26451.03 30396.46, 26440.47 303...# Display associated attribute info
# Reveals data type of each field
glimpse(mpsz)Rows: 323
Columns: 16
$ OBJECTID <int> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, …
$ SUBZONE_NO <int> 1, 1, 3, 8, 3, 7, 9, 2, 13, 7, 12, 6, 1, 5, 1, 1, 3, 2, 2, …
$ SUBZONE_N <chr> "MARINA SOUTH", "PEARL'S HILL", "BOAT QUAY", "HENDERSON HIL…
$ SUBZONE_C <chr> "MSSZ01", "OTSZ01", "SRSZ03", "BMSZ08", "BMSZ03", "BMSZ07",…
$ CA_IND <chr> "Y", "Y", "Y", "N", "N", "N", "N", "Y", "N", "N", "N", "N",…
$ PLN_AREA_N <chr> "MARINA SOUTH", "OUTRAM", "SINGAPORE RIVER", "BUKIT MERAH",…
$ PLN_AREA_C <chr> "MS", "OT", "SR", "BM", "BM", "BM", "BM", "SR", "QT", "QT",…
$ REGION_N <chr> "CENTRAL REGION", "CENTRAL REGION", "CENTRAL REGION", "CENT…
$ REGION_C <chr> "CR", "CR", "CR", "CR", "CR", "CR", "CR", "CR", "CR", "CR",…
$ INC_CRC <chr> "5ED7EB253F99252E", "8C7149B9EB32EEFC", "C35FEFF02B13E0E5",…
$ FMEL_UPD_D <date> 2014-12-05, 2014-12-05, 2014-12-05, 2014-12-05, 2014-12-05…
$ X_ADDR <dbl> 31595.84, 28679.06, 29654.96, 26782.83, 26201.96, 25358.82,…
$ Y_ADDR <dbl> 29220.19, 29782.05, 29974.66, 29933.77, 30005.70, 29991.38,…
$ SHAPE_Leng <dbl> 5267.381, 3506.107, 1740.926, 3313.625, 2825.594, 4428.913,…
$ SHAPE_Area <dbl> 1630379.27, 559816.25, 160807.50, 595428.89, 387429.44, 103…
$ geometry <MULTIPOLYGON [m]> MULTIPOLYGON (((31495.56 30..., MULTIPOLYGON (…head(mpsz, n=5)Simple feature collection with 5 features and 15 fields
Geometry type: MULTIPOLYGON
Dimension: XY
Bounding box: xmin: 25867.68 ymin: 28369.47 xmax: 32362.39 ymax: 30435.54
Projected CRS: SVY21
OBJECTID SUBZONE_NO SUBZONE_N SUBZONE_C CA_IND PLN_AREA_N
1 1 1 MARINA SOUTH MSSZ01 Y MARINA SOUTH
2 2 1 PEARL'S HILL OTSZ01 Y OUTRAM
3 3 3 BOAT QUAY SRSZ03 Y SINGAPORE RIVER
4 4 8 HENDERSON HILL BMSZ08 N BUKIT MERAH
5 5 3 REDHILL BMSZ03 N BUKIT MERAH
PLN_AREA_C REGION_N REGION_C INC_CRC FMEL_UPD_D X_ADDR
1 MS CENTRAL REGION CR 5ED7EB253F99252E 2014-12-05 31595.84
2 OT CENTRAL REGION CR 8C7149B9EB32EEFC 2014-12-05 28679.06
3 SR CENTRAL REGION CR C35FEFF02B13E0E5 2014-12-05 29654.96
4 BM CENTRAL REGION CR 3775D82C5DDBEFBD 2014-12-05 26782.83
5 BM CENTRAL REGION CR 85D9ABEF0A40678F 2014-12-05 26201.96
Y_ADDR SHAPE_Leng SHAPE_Area geometry
1 29220.19 5267.381 1630379.3 MULTIPOLYGON (((31495.56 30...
2 29782.05 3506.107 559816.2 MULTIPOLYGON (((29092.28 30...
3 29974.66 1740.926 160807.5 MULTIPOLYGON (((29932.33 29...
4 29933.77 3313.625 595428.9 MULTIPOLYGON (((27131.28 30...
5 30005.70 2825.594 387429.4 MULTIPOLYGON (((26451.03 30...Plot, will plot up to a reasonable max by default (in this case 9)
plot(mpsz)Warning: plotting the first 9 out of 15 attributes; use max.plot = 15 to plot
all
Plot out only the geometry
plot(st_geometry(mpsz))
Plot out specific attribute
plot(mpsz["PLN_AREA_N"])
st_crs(mpsz)Coordinate Reference System:
User input: SVY21
wkt:
PROJCRS["SVY21",
BASEGEOGCRS["SVY21[WGS84]",
DATUM["World Geodetic System 1984",
ELLIPSOID["WGS 84",6378137,298.257223563,
LENGTHUNIT["metre",1]],
ID["EPSG",6326]],
PRIMEM["Greenwich",0,
ANGLEUNIT["Degree",0.0174532925199433]]],
CONVERSION["unnamed",
METHOD["Transverse Mercator",
ID["EPSG",9807]],
PARAMETER["Latitude of natural origin",1.36666666666667,
ANGLEUNIT["Degree",0.0174532925199433],
ID["EPSG",8801]],
PARAMETER["Longitude of natural origin",103.833333333333,
ANGLEUNIT["Degree",0.0174532925199433],
ID["EPSG",8802]],
PARAMETER["Scale factor at natural origin",1,
SCALEUNIT["unity",1],
ID["EPSG",8805]],
PARAMETER["False easting",28001.642,
LENGTHUNIT["metre",1],
ID["EPSG",8806]],
PARAMETER["False northing",38744.572,
LENGTHUNIT["metre",1],
ID["EPSG",8807]]],
CS[Cartesian,2],
AXIS["(E)",east,
ORDER[1],
LENGTHUNIT["metre",1,
ID["EPSG",9001]]],
AXIS["(N)",north,
ORDER[2],
LENGTHUNIT["metre",1,
ID["EPSG",9001]]]]# assign correct EPSG code
mpsz3414 <- st_set_crs(mpsz, 3414)Warning: st_crs<- : replacing crs does not reproject data; use st_transform for
thatst_geometry(mpsz3414)Geometry set for 323 features
Geometry type: MULTIPOLYGON
Dimension: XY
Bounding box: xmin: 2667.538 ymin: 15748.72 xmax: 56396.44 ymax: 50256.33
Projected CRS: SVY21 / Singapore TM
First 5 geometries:MULTIPOLYGON (((31495.56 30140.01, 31980.96 296...MULTIPOLYGON (((29092.28 30021.89, 29119.64 300...MULTIPOLYGON (((29932.33 29879.12, 29947.32 298...MULTIPOLYGON (((27131.28 30059.73, 27088.33 297...MULTIPOLYGON (((26451.03 30396.46, 26440.47 303...preschool3414 <- st_transform(preschool,
crs = 3414)
st_crs(preschool3414)Coordinate Reference System:
User input: EPSG:3414
wkt:
PROJCRS["SVY21 / Singapore TM",
BASEGEOGCRS["SVY21",
DATUM["SVY21",
ELLIPSOID["WGS 84",6378137,298.257223563,
LENGTHUNIT["metre",1]]],
PRIMEM["Greenwich",0,
ANGLEUNIT["degree",0.0174532925199433]],
ID["EPSG",4757]],
CONVERSION["Singapore Transverse Mercator",
METHOD["Transverse Mercator",
ID["EPSG",9807]],
PARAMETER["Latitude of natural origin",1.36666666666667,
ANGLEUNIT["degree",0.0174532925199433],
ID["EPSG",8801]],
PARAMETER["Longitude of natural origin",103.833333333333,
ANGLEUNIT["degree",0.0174532925199433],
ID["EPSG",8802]],
PARAMETER["Scale factor at natural origin",1,
SCALEUNIT["unity",1],
ID["EPSG",8805]],
PARAMETER["False easting",28001.642,
LENGTHUNIT["metre",1],
ID["EPSG",8806]],
PARAMETER["False northing",38744.572,
LENGTHUNIT["metre",1],
ID["EPSG",8807]]],
CS[Cartesian,2],
AXIS["northing (N)",north,
ORDER[1],
LENGTHUNIT["metre",1]],
AXIS["easting (E)",east,
ORDER[2],
LENGTHUNIT["metre",1]],
USAGE[
SCOPE["Cadastre, engineering survey, topographic mapping."],
AREA["Singapore - onshore and offshore."],
BBOX[1.13,103.59,1.47,104.07]],
ID["EPSG",3414]]listings <- read_csv("data/aspatial/listings.csv")Rows: 4161 Columns: 75
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (26): listing_url, source, name, description, neighborhood_overview, pi...
dbl (37): id, scrape_id, host_id, host_listings_count, host_total_listings_...
lgl (7): host_is_superhost, host_has_profile_pic, host_identity_verified, ...
date (5): last_scraped, host_since, calendar_last_scraped, first_review, la...
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.list(listings) [[1]]
# A tibble: 4,161 × 75
id listing_url scrap…¹ last_scr…² source name descr…³ neigh…⁴ pictu…⁵
<dbl> <chr> <dbl> <date> <chr> <chr> <chr> <chr> <chr>
1 50646 https://www.a… 2.02e13 2022-09-23 previ… Plea… Fully … The se… https:…
2 71609 https://www.a… 2.02e13 2022-09-22 city … Ensu… For 3 … <NA> https:…
3 71896 https://www.a… 2.02e13 2022-09-22 city … B&B … <b>The… <NA> https:…
4 71903 https://www.a… 2.02e13 2022-09-22 city … Room… Like y… Quiet … https:…
5 275344 https://www.a… 2.02e13 2022-09-22 city … 15 m… Lovely… Bus st… https:…
6 289234 https://www.a… 2.02e13 2022-09-22 city … Book… This w… A quie… https:…
7 294281 https://www.a… 2.02e13 2022-09-22 city … 5 mi… I have… <NA> https:…
8 324945 https://www.a… 2.02e13 2022-09-22 city … Cozy… <b>The… <NA> https:…
9 330089 https://www.a… 2.02e13 2022-09-22 city … Cozy… A unit… <NA> https:…
10 330095 https://www.a… 2.02e13 2022-09-22 city … 10 m… Cosy, … Near I… https:…
# … with 4,151 more rows, 66 more variables: host_id <dbl>, host_url <chr>,
# host_name <chr>, host_since <date>, host_location <chr>, host_about <chr>,
# host_response_time <chr>, host_response_rate <chr>,
# host_acceptance_rate <chr>, host_is_superhost <lgl>,
# host_thumbnail_url <chr>, host_picture_url <chr>, host_neighbourhood <chr>,
# host_listings_count <dbl>, host_total_listings_count <dbl>,
# host_verifications <chr>, host_has_profile_pic <lgl>, …# convert df into simple feature df
# coords arg is c(col name of x coord, col name of y coord)
# crs arg needs coordinates sys in epsg format
# %>% used to nest st_transform() to transform simple feature data frame into svy21 projected coordinates system
listings_sf <- st_as_sf(listings,
coords = c("longitude", "latitude"),
crs=4326) %>%
st_transform(crs = 3414)
glimpse(listings_sf)Rows: 4,161
Columns: 74
$ id <dbl> 50646, 71609, 71896, 7190…
$ listing_url <chr> "https://www.airbnb.com/r…
$ scrape_id <dbl> 2.022092e+13, 2.022092e+1…
$ last_scraped <date> 2022-09-23, 2022-09-22, …
$ source <chr> "previous scrape", "city …
$ name <chr> "Pleasant Room along Buki…
$ description <chr> "Fully furnished bedroom …
$ neighborhood_overview <chr> "The serenity & quiet sur…
$ picture_url <chr> "https://a0.muscache.com/…
$ host_id <dbl> 227796, 367042, 367042, 3…
$ host_url <chr> "https://www.airbnb.com/u…
$ host_name <chr> "Sujatha", "Belinda", "Be…
$ host_since <date> 2010-09-08, 2011-01-29, …
$ host_location <chr> "Singapore", "Singapore",…
$ host_about <chr> "I am a working professio…
$ host_response_time <chr> "a few days or more", "wi…
$ host_response_rate <chr> "0%", "100%", "100%", "10…
$ host_acceptance_rate <chr> "N/A", "100%", "100%", "1…
$ host_is_superhost <lgl> FALSE, FALSE, FALSE, FALS…
$ host_thumbnail_url <chr> "https://a0.muscache.com/…
$ host_picture_url <chr> "https://a0.muscache.com/…
$ host_neighbourhood <chr> "Bukit Timah", "Tampines"…
$ host_listings_count <dbl> 1, 6, 6, 6, 44, 6, 7, 44,…
$ host_total_listings_count <dbl> 4, 15, 15, 15, 57, 15, 8,…
$ host_verifications <chr> "['email', 'phone', 'work…
$ host_has_profile_pic <lgl> TRUE, TRUE, TRUE, TRUE, T…
$ host_identity_verified <lgl> TRUE, TRUE, TRUE, TRUE, T…
$ neighbourhood <chr> "Singapore, Singapore", N…
$ neighbourhood_cleansed <chr> "Bukit Timah", "Tampines"…
$ neighbourhood_group_cleansed <chr> "Central Region", "East R…
$ property_type <chr> "Private room in rental u…
$ room_type <chr> "Private room", "Private …
$ accommodates <dbl> 2, 6, 1, 2, 1, 4, 2, 1, 1…
$ bathrooms <lgl> NA, NA, NA, NA, NA, NA, N…
$ bathrooms_text <chr> "1 bath", "1 private bath…
$ bedrooms <dbl> 1, 2, 1, 1, 1, 3, 1, 1, N…
$ beds <dbl> 1, 3, 1, 2, 1, 5, 1, 1, 1…
$ amenities <chr> "[\"Gym\", \"Washer\", \"…
$ price <chr> "$80.00", "$145.00", "$85…
$ minimum_nights <dbl> 92, 92, 92, 92, 60, 92, 9…
$ maximum_nights <dbl> 730, 1125, 1125, 1125, 99…
$ minimum_minimum_nights <dbl> 92, 92, 92, 92, 60, 92, 9…
$ maximum_minimum_nights <dbl> 92, 92, 92, 92, 60, 92, 9…
$ minimum_maximum_nights <dbl> 730, 1125, 1125, 1125, 99…
$ maximum_maximum_nights <dbl> 730, 1125, 1125, 1125, 99…
$ minimum_nights_avg_ntm <dbl> 92, 92, 92, 92, 60, 92, 9…
$ maximum_nights_avg_ntm <dbl> 730, 1125, 1125, 1125, 99…
$ calendar_updated <lgl> NA, NA, NA, NA, NA, NA, N…
$ has_availability <lgl> TRUE, TRUE, TRUE, TRUE, T…
$ availability_30 <dbl> 30, 5, 0, 30, 0, 0, 30, 5…
$ availability_60 <dbl> 60, 35, 1, 60, 0, 0, 60, …
$ availability_90 <dbl> 90, 65, 1, 90, 21, 10, 90…
$ availability_365 <dbl> 365, 340, 265, 365, 296, …
$ calendar_last_scraped <date> 2022-09-23, 2022-09-22, …
$ number_of_reviews <dbl> 18, 20, 24, 47, 14, 12, 1…
$ number_of_reviews_ltm <dbl> 0, 0, 0, 0, 1, 0, 0, 3, 2…
$ number_of_reviews_l30d <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0…
$ first_review <date> 2014-04-18, 2011-12-19, …
$ last_review <date> 2014-12-26, 2020-01-17, …
$ review_scores_rating <dbl> 4.56, 4.44, 4.16, 4.41, 4…
$ review_scores_accuracy <dbl> 4.72, 4.37, 4.22, 4.39, 4…
$ review_scores_cleanliness <dbl> 4.78, 4.00, 4.09, 4.52, 4…
$ review_scores_checkin <dbl> 4.78, 4.63, 4.43, 4.63, 4…
$ review_scores_communication <dbl> 4.94, 4.78, 4.43, 4.64, 4…
$ review_scores_location <dbl> 4.72, 4.26, 4.17, 4.50, 4…
$ review_scores_value <dbl> 4.50, 4.32, 4.04, 4.36, 4…
$ license <chr> NA, NA, NA, NA, "S0399", …
$ instant_bookable <lgl> FALSE, FALSE, TRUE, FALSE…
$ calculated_host_listings_count <dbl> 1, 6, 6, 6, 44, 6, 7, 44,…
$ calculated_host_listings_count_entire_homes <dbl> 0, 0, 0, 0, 2, 0, 1, 2, 2…
$ calculated_host_listings_count_private_rooms <dbl> 1, 6, 6, 6, 42, 6, 6, 42,…
$ calculated_host_listings_count_shared_rooms <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0…
$ reviews_per_month <dbl> 0.18, 0.15, 0.18, 0.34, 0…
$ geometry <POINT [m]> POINT (22646.02 351…# Calculate area of cycling paths
buffer_cycling <- st_buffer(cyclingpath,
dist=5, nQuadSegs = 30)
glimpse(buffer_cycling)Rows: 2,248
Columns: 3
$ PLANNING_A <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
$ PLANNING_1 <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
$ geometry <POLYGON [m]> POLYGON ((16004.15 36799.78..., POLYGON ((16013.15 …buffer_cycling$AREA <- st_area(buffer_cycling)
glimpse(buffer_cycling$AREA) Units: [m^2] num [1:2248] 186 293 285 145 281 ...sum(buffer_cycling$AREA)1556978 [m^2]# find numbers of pre-schools in each planning subzone
# st_intersects indentified schools located inside planning subzones
# length() calculate num in each zone
mpsz3414$`PreSch Count`<- lengths(st_intersects(mpsz3414, preschool3414))
summary(mpsz3414$`PreSch Count`) Min. 1st Qu. Median Mean 3rd Qu. Max.
0.00 0.00 3.00 5.96 9.00 58.00 # list planning zubzone with most um of pre-school
top_n(mpsz3414, 1, `PreSch Count`)Simple feature collection with 1 feature and 16 fields
Geometry type: MULTIPOLYGON
Dimension: XY
Bounding box: xmin: 39655.33 ymin: 35966 xmax: 42940.57 ymax: 38622.37
Projected CRS: SVY21 / Singapore TM
OBJECTID SUBZONE_NO SUBZONE_N SUBZONE_C CA_IND PLN_AREA_N PLN_AREA_C
1 189 2 TAMPINES EAST TMSZ02 N TAMPINES TM
REGION_N REGION_C INC_CRC FMEL_UPD_D X_ADDR Y_ADDR SHAPE_Leng
1 EAST REGION ER 21658EAAF84F4D8D 2014-12-05 41122.55 37392.39 10180.62
SHAPE_Area geometry PreSch Count
1 4339824 MULTIPOLYGON (((42196.76 38... 58glimpse(mpsz3414)Rows: 323
Columns: 17
$ OBJECTID <int> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, …
$ SUBZONE_NO <int> 1, 1, 3, 8, 3, 7, 9, 2, 13, 7, 12, 6, 1, 5, 1, 1, 3, 2,…
$ SUBZONE_N <chr> "MARINA SOUTH", "PEARL'S HILL", "BOAT QUAY", "HENDERSON…
$ SUBZONE_C <chr> "MSSZ01", "OTSZ01", "SRSZ03", "BMSZ08", "BMSZ03", "BMSZ…
$ CA_IND <chr> "Y", "Y", "Y", "N", "N", "N", "N", "Y", "N", "N", "N", …
$ PLN_AREA_N <chr> "MARINA SOUTH", "OUTRAM", "SINGAPORE RIVER", "BUKIT MER…
$ PLN_AREA_C <chr> "MS", "OT", "SR", "BM", "BM", "BM", "BM", "SR", "QT", "…
$ REGION_N <chr> "CENTRAL REGION", "CENTRAL REGION", "CENTRAL REGION", "…
$ REGION_C <chr> "CR", "CR", "CR", "CR", "CR", "CR", "CR", "CR", "CR", "…
$ INC_CRC <chr> "5ED7EB253F99252E", "8C7149B9EB32EEFC", "C35FEFF02B13E0…
$ FMEL_UPD_D <date> 2014-12-05, 2014-12-05, 2014-12-05, 2014-12-05, 2014-1…
$ X_ADDR <dbl> 31595.84, 28679.06, 29654.96, 26782.83, 26201.96, 25358…
$ Y_ADDR <dbl> 29220.19, 29782.05, 29974.66, 29933.77, 30005.70, 29991…
$ SHAPE_Leng <dbl> 5267.381, 3506.107, 1740.926, 3313.625, 2825.594, 4428.…
$ SHAPE_Area <dbl> 1630379.27, 559816.25, 160807.50, 595428.89, 387429.44,…
$ geometry <MULTIPOLYGON [m]> MULTIPOLYGON (((31495.56 30..., MULTIPOLYG…
$ `PreSch Count` <int> 0, 5, 0, 4, 2, 10, 4, 2, 6, 1, 6, 2, 0, 1, 6, 0, 0, 0, …# Calc density of pre-school by planning subzone
mpsz3414$Area <- mpsz3414 %>%
st_area()
mpsz3414 <- mpsz3414 %>%
mutate(`PreSch Density` = `PreSch Count`/Area * 1000000)
head(mpsz3414)Simple feature collection with 6 features and 18 fields
Geometry type: MULTIPOLYGON
Dimension: XY
Bounding box: xmin: 24468.89 ymin: 28369.47 xmax: 32362.39 ymax: 30542.74
Projected CRS: SVY21 / Singapore TM
OBJECTID SUBZONE_NO SUBZONE_N SUBZONE_C CA_IND PLN_AREA_N
1 1 1 MARINA SOUTH MSSZ01 Y MARINA SOUTH
2 2 1 PEARL'S HILL OTSZ01 Y OUTRAM
3 3 3 BOAT QUAY SRSZ03 Y SINGAPORE RIVER
4 4 8 HENDERSON HILL BMSZ08 N BUKIT MERAH
5 5 3 REDHILL BMSZ03 N BUKIT MERAH
6 6 7 ALEXANDRA HILL BMSZ07 N BUKIT MERAH
PLN_AREA_C REGION_N REGION_C INC_CRC FMEL_UPD_D X_ADDR
1 MS CENTRAL REGION CR 5ED7EB253F99252E 2014-12-05 31595.84
2 OT CENTRAL REGION CR 8C7149B9EB32EEFC 2014-12-05 28679.06
3 SR CENTRAL REGION CR C35FEFF02B13E0E5 2014-12-05 29654.96
4 BM CENTRAL REGION CR 3775D82C5DDBEFBD 2014-12-05 26782.83
5 BM CENTRAL REGION CR 85D9ABEF0A40678F 2014-12-05 26201.96
6 BM CENTRAL REGION CR 9D286521EF5E3B59 2014-12-05 25358.82
Y_ADDR SHAPE_Leng SHAPE_Area geometry PreSch Count
1 29220.19 5267.381 1630379.3 MULTIPOLYGON (((31495.56 30... 0
2 29782.05 3506.107 559816.2 MULTIPOLYGON (((29092.28 30... 5
3 29974.66 1740.926 160807.5 MULTIPOLYGON (((29932.33 29... 0
4 29933.77 3313.625 595428.9 MULTIPOLYGON (((27131.28 30... 4
5 30005.70 2825.594 387429.4 MULTIPOLYGON (((26451.03 30... 2
6 29991.38 4428.913 1030378.8 MULTIPOLYGON (((25899.7 297... 10
Area PreSch Density
1 1630379.3 [m^2] 0.000000 [1/m^2]
2 559816.2 [m^2] 8.931502 [1/m^2]
3 160807.5 [m^2] 0.000000 [1/m^2]
4 595428.9 [m^2] 6.717847 [1/m^2]
5 387429.4 [m^2] 5.162230 [1/m^2]
6 1030378.8 [m^2] 9.705169 [1/m^2]hist(mpsz3414$`PreSch Density`)
ggplot(data=mpsz3414,
aes(x= as.numeric(`PreSch Density`)))+
geom_histogram(bins=20,
color="black",
fill="light blue") +
labs(title = "Are pre-school even distributed in Singapore?",
subtitle= "There are many planning sub-zones with a single pre-school, on the other hand, \nthere are two planning sub-zones with at least 20 pre-schools",
x = "Pre-school density (per km sq)",
y = "Frequency")
ggplot(data=mpsz3414,
aes(y = `PreSch Count`,
x= as.numeric(`PreSch Density`)))+
geom_point(color="black",
fill="light blue") +
xlim(0, 40) +
ylim(0, 40) +
labs(title = "",
x = "Pre-school density (per km sq)",
y = "Pre-school count")Warning: Removed 2 rows containing missing values (`geom_point()`).