Take-home Exercise 1 - Creating Data Visualisation Beyond Default (Geographical Distribution)

Author

Alicia Loh

Modified

April 24, 2024

Geographical Distribution

Take-home Exercise 1 focuses on statistical graphic methods for visualising frequency distribution and value distribution using ggplot2 and its extensions. The below section is an exploration of the data’s geographical distribution on my own accord. 

Downloading the Dataset

To accomplish the task, transaction data of REALIS will be used.

  1. Access Dataset via SMU e-library

  2. After logging in with SMU credentials, navigate to “Residential” tab

  3. Under Property Types, “Select All”

  4. Under Sale Date, select “2024 Jan” - “2024 Mar”

  5. Click “Search”

  6. Click “Download”

  7. Due to the size of the dataset, it is split into multiple segments. Download all in .csv format

The Designing Tool

The data will be processed using the appropriate tidyverse family of packages and the statistical graphics will be prepared using ggplot2 and its extensions.

Getting Started

Installing and loading the required libraries

Note: Ensure that the pacman package has already been installed.

The code chunk below loads the following packages using uses p_load() of pacman package:

  • tidyverse: (i.e. readr, tidyr, dplyr, ggplot2, lubridate) for performing data science tasks such as importing, tidying, and wrangling data, as well as creating graphics based on The Grammar of Graphics
pacman::p_load(tidyverse)

Importing the Data

  • The data has been split into multiple .csv files

  • list.files() list all CSV files in the specified directory.

  • After looping through each CSV file, read it into a data frame using read_csv(), and store it in a list.

  • bind_rows() combines all data frames in the list into a single big data frame.

csv_directory <- "data/"
csv_files <- list.files(csv_directory, pattern = "\\.csv$", full.names = TRUE)

realis <- list()

for (file in csv_files) {
  realis[[file]] <- read_csv(file)
}
realis_all <- bind_rows(realis)

View Data

  • names() function prints the names of the columns in the tibble data frame.
  • glimpse() function gives a quick overview of the tibble data frame
col_names <- names(realis_all)
col_names
 [1] "Project Name"                "Transacted Price ($)"       
 [3] "Area (SQFT)"                 "Unit Price ($ PSF)"         
 [5] "Sale Date"                   "Address"                    
 [7] "Type of Sale"                "Type of Area"               
 [9] "Area (SQM)"                  "Unit Price ($ PSM)"         
[11] "Nett Price($)"               "Property Type"              
[13] "Number of Units"             "Tenure"                     
[15] "Completion Date"             "Purchaser Address Indicator"
[17] "Postal Code"                 "Postal District"            
[19] "Postal Sector"               "Planning Region"            
[21] "Planning Area"              
glimpse(realis_all)
Rows: 26,806
Columns: 21
$ `Project Name`                <chr> "THE REEF AT KING'S DOCK", "URBAN TREASU…
$ `Transacted Price ($)`        <dbl> 2317000, 1823500, 1421112, 1258112, 1280…
$ `Area (SQFT)`                 <dbl> 882.65, 882.65, 1076.40, 1033.34, 871.88…
$ `Unit Price ($ PSF)`          <dbl> 2625, 2066, 1320, 1218, 1468, 1767, 1095…
$ `Sale Date`                   <chr> "01 Jan 2023", "02 Jan 2023", "02 Jan 20…
$ Address                       <chr> "12 HARBOURFRONT AVENUE #05-32", "205 JA…
$ `Type of Sale`                <chr> "New Sale", "New Sale", "New Sale", "New…
$ `Type of Area`                <chr> "Strata", "Strata", "Strata", "Strata", …
$ `Area (SQM)`                  <dbl> 82.0, 82.0, 100.0, 96.0, 81.0, 308.7, 42…
$ `Unit Price ($ PSM)`          <dbl> 28256, 22238, 14211, 13105, 15802, 19015…
$ `Nett Price($)`               <chr> "-", "-", "-", "-", "-", "-", "-", "-", …
$ `Property Type`               <chr> "Condominium", "Condominium", "Executive…
$ `Number of Units`             <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1…
$ Tenure                        <chr> "99 yrs from 12/01/2021", "Freehold", "9…
$ `Completion Date`             <chr> "Uncompleted", "Uncompleted", "Uncomplet…
$ `Purchaser Address Indicator` <chr> "HDB", "Private", "HDB", "HDB", "HDB", "…
$ `Postal Code`                 <chr> "097996", "419535", "269343", "269294", …
$ `Postal District`             <chr> "04", "14", "27", "27", "28", "19", "10"…
$ `Postal Sector`               <chr> "09", "41", "26", "26", "79", "54", "27"…
$ `Planning Region`             <chr> "Central Region", "East Region", "North …
$ `Planning Area`               <chr> "Bukit Merah", "Bedok", "Yishun", "Yishu…
Note

realis_all contains:

  • Public and Private residential property transaction data from 1st January 2023 to 31st March 2024.

  • There are 26,806 rows and 21 columns.

Data Preparation

Standardise Date Format

The “Sales Date” column is currently a cha type. It needs to be converted into date format.

dmy() is a function from the lubridate package that converts character strings to date format in the day-month-year (DMY) order.

realis_all$`Sale Date` <- dmy(realis_all$`Sale Date`)
head(realis_all$`Sale Date`)
[1] "2023-01-01" "2023-01-02" "2023-01-02" "2023-01-02" "2023-01-03"
[6] "2023-01-03"

Keep Relevant Rows

Duplicate and empty rows are removed.

qa_pte_raw <- realis_all %>%
    distinct() %>%
    drop_na()

View Data

glimpse(qa_pte_raw)
Rows: 26,800
Columns: 21
$ `Project Name`                <chr> "THE REEF AT KING'S DOCK", "URBAN TREASU…
$ `Transacted Price ($)`        <dbl> 2317000, 1823500, 1421112, 1258112, 1280…
$ `Area (SQFT)`                 <dbl> 882.65, 882.65, 1076.40, 1033.34, 871.88…
$ `Unit Price ($ PSF)`          <dbl> 2625, 2066, 1320, 1218, 1468, 1767, 1095…
$ `Sale Date`                   <date> 2023-01-01, 2023-01-02, 2023-01-02, 202…
$ Address                       <chr> "12 HARBOURFRONT AVENUE #05-32", "205 JA…
$ `Type of Sale`                <chr> "New Sale", "New Sale", "New Sale", "New…
$ `Type of Area`                <chr> "Strata", "Strata", "Strata", "Strata", …
$ `Area (SQM)`                  <dbl> 82.0, 82.0, 100.0, 96.0, 81.0, 308.7, 42…
$ `Unit Price ($ PSM)`          <dbl> 28256, 22238, 14211, 13105, 15802, 19015…
$ `Nett Price($)`               <chr> "-", "-", "-", "-", "-", "-", "-", "-", …
$ `Property Type`               <chr> "Condominium", "Condominium", "Executive…
$ `Number of Units`             <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1…
$ Tenure                        <chr> "99 yrs from 12/01/2021", "Freehold", "9…
$ `Completion Date`             <chr> "Uncompleted", "Uncompleted", "Uncomplet…
$ `Purchaser Address Indicator` <chr> "HDB", "Private", "HDB", "HDB", "HDB", "…
$ `Postal Code`                 <chr> "097996", "419535", "269343", "269294", …
$ `Postal District`             <chr> "04", "14", "27", "27", "28", "19", "10"…
$ `Postal Sector`               <chr> "09", "41", "26", "26", "79", "54", "27"…
$ `Planning Region`             <chr> "Central Region", "East Region", "North …
$ `Planning Area`               <chr> "Bukit Merah", "Bedok", "Yishun", "Yishu…
Note

qa_pte_raw contains:

  • Private residential property transaction data from 1st January 2023 to 31st March 2024

  • There are 26,800 rows and 21 columns.

Keep Relevant Columns

Not all 21 columns will be used for analysis e.g. contains overlapping information as another column. Only relevant columns will be kept.

Columns to drop:

  • Area (SQFT): Similar information as Area (SQM)

  • Unit Price ($ PSF): Similar information as Unit Price ($ PSM)

  • Nett Price ($): Similar information as Transacted Price ($)

  • Postal District and Postal Sector: Overlapping information as Postal Code

Columns to be dropped can be specified by prefixing the column names with a minus sign (-) when using the select() function from the dplyr package.

qa_pte <- qa_pte_raw %>%
    select(
        -`Area (SQFT)`,
        -`Unit Price ($ PSF)`,
        -`Postal District`,
        -`Postal Sector`
    )
glimpse(qa_pte)
Rows: 26,800
Columns: 17
$ `Project Name`                <chr> "THE REEF AT KING'S DOCK", "URBAN TREASU…
$ `Transacted Price ($)`        <dbl> 2317000, 1823500, 1421112, 1258112, 1280…
$ `Sale Date`                   <date> 2023-01-01, 2023-01-02, 2023-01-02, 202…
$ Address                       <chr> "12 HARBOURFRONT AVENUE #05-32", "205 JA…
$ `Type of Sale`                <chr> "New Sale", "New Sale", "New Sale", "New…
$ `Type of Area`                <chr> "Strata", "Strata", "Strata", "Strata", …
$ `Area (SQM)`                  <dbl> 82.0, 82.0, 100.0, 96.0, 81.0, 308.7, 42…
$ `Unit Price ($ PSM)`          <dbl> 28256, 22238, 14211, 13105, 15802, 19015…
$ `Nett Price($)`               <chr> "-", "-", "-", "-", "-", "-", "-", "-", …
$ `Property Type`               <chr> "Condominium", "Condominium", "Executive…
$ `Number of Units`             <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1…
$ Tenure                        <chr> "99 yrs from 12/01/2021", "Freehold", "9…
$ `Completion Date`             <chr> "Uncompleted", "Uncompleted", "Uncomplet…
$ `Purchaser Address Indicator` <chr> "HDB", "Private", "HDB", "HDB", "HDB", "…
$ `Postal Code`                 <chr> "097996", "419535", "269343", "269294", …
$ `Planning Region`             <chr> "Central Region", "East Region", "North …
$ `Planning Area`               <chr> "Bukit Merah", "Bedok", "Yishun", "Yishu…
Note

qa_pte contains:

  • Private residential property transaction data from 1st January 2024 to 31st March 2024

  • There are 26,800 rows and 14 columns.

  • Columns:

    • Project Name

    • Transacted Price ($)

    • Sale Date

    • Address

    • Type of Sale

    • Type of Area

    • Area (SQM)

    • Unit Price ($ PSM)

    • Nett Price

    • Property Type

    • Number of Units

    • Tensure

    • Completion Date

    • Purchaser Address Indicator

    • Postal Code

    • Planning Region

    • Planning Area

Separate Data by Quarters

The dataset contains 5 quarters:

  • Quarter 1: 2023 Jan - Mar

  • Quarter 2: 2023 Apr - Jun

  • Quarter 3: 2023 Jul - Sep

  • Quarter 4: 2023 Aug - Dec

  • Quarter 5: 2024 Jan - Mar

To allow for comparison between quarters, qa_pte will be split into the respective quarters by Sale Date.

q1 <- qa_pte %>%
  filter(`Sale Date` <= "2023-03-31")

q2 <- qa_pte %>%
  filter(`Sale Date` > "2023-03-31" & `Sale Date` <= "2023-06-30")

q3 <- qa_pte %>%
  filter(`Sale Date` > "2023-06-30" & `Sale Date` <= "2023-09-30")

q4 <- qa_pte %>%
  filter(`Sale Date` > "2023-09-30" & `Sale Date` <= "2023-12-31")

q5 <- qa_pte %>%
  filter(`Sale Date` > "2023-12-31")

View Data

glimpse(q1)
Rows: 4,722
Columns: 17
$ `Project Name`                <chr> "THE REEF AT KING'S DOCK", "URBAN TREASU…
$ `Transacted Price ($)`        <dbl> 2317000, 1823500, 1421112, 1258112, 1280…
$ `Sale Date`                   <date> 2023-01-01, 2023-01-02, 2023-01-02, 202…
$ Address                       <chr> "12 HARBOURFRONT AVENUE #05-32", "205 JA…
$ `Type of Sale`                <chr> "New Sale", "New Sale", "New Sale", "New…
$ `Type of Area`                <chr> "Strata", "Strata", "Strata", "Strata", …
$ `Area (SQM)`                  <dbl> 82.0, 82.0, 100.0, 96.0, 81.0, 308.7, 42…
$ `Unit Price ($ PSM)`          <dbl> 28256, 22238, 14211, 13105, 15802, 19015…
$ `Nett Price($)`               <chr> "-", "-", "-", "-", "-", "-", "-", "-", …
$ `Property Type`               <chr> "Condominium", "Condominium", "Executive…
$ `Number of Units`             <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1…
$ Tenure                        <chr> "99 yrs from 12/01/2021", "Freehold", "9…
$ `Completion Date`             <chr> "Uncompleted", "Uncompleted", "Uncomplet…
$ `Purchaser Address Indicator` <chr> "HDB", "Private", "HDB", "HDB", "HDB", "…
$ `Postal Code`                 <chr> "097996", "419535", "269343", "269294", …
$ `Planning Region`             <chr> "Central Region", "East Region", "North …
$ `Planning Area`               <chr> "Bukit Merah", "Bedok", "Yishun", "Yishu…
summary(q2)
 Project Name       Transacted Price ($)   Sale Date         
 Length:6125        Min.   :  520000     Min.   :2023-04-01  
 Class :character   1st Qu.: 1268000     1st Qu.:2023-04-24  
 Mode  :character   Median : 1688000     Median :2023-05-13  
                    Mean   : 2116310     Mean   :2023-05-13  
                    3rd Qu.: 2350000     3rd Qu.:2023-05-31  
                    Max.   :66800000     Max.   :2023-06-30  
   Address          Type of Sale       Type of Area         Area (SQM)    
 Length:6125        Length:6125        Length:6125        Min.   :  30.0  
 Class :character   Class :character   Class :character   1st Qu.:  63.0  
 Mode  :character   Mode  :character   Mode  :character   Median :  89.0  
                                                          Mean   : 106.7  
                                                          3rd Qu.: 119.0  
                                                          Max.   :2339.0  
 Unit Price ($ PSM) Nett Price($)      Property Type      Number of Units 
 Min.   : 3364      Length:6125        Length:6125        Min.   : 1.000  
 1st Qu.:14838      Class :character   Class :character   1st Qu.: 1.000  
 Median :19787      Mode  :character   Mode  :character   Median : 1.000  
 Mean   :20665                                            Mean   : 1.002  
 3rd Qu.:26390                                            3rd Qu.: 1.000  
 Max.   :57053                                            Max.   :11.000  
    Tenure          Completion Date    Purchaser Address Indicator
 Length:6125        Length:6125        Length:6125                
 Class :character   Class :character   Class :character           
 Mode  :character   Mode  :character   Mode  :character           
                                                                  
                                                                  
                                                                  
 Postal Code        Planning Region    Planning Area     
 Length:6125        Length:6125        Length:6125       
 Class :character   Class :character   Class :character  
 Mode  :character   Mode  :character   Mode  :character  
                                                         
                                                         
                                                         
summary(q3)
 Project Name       Transacted Price ($)   Sale Date         
 Length:6200        Min.   :  440000     Min.   :2023-07-01  
 Class :character   1st Qu.: 1280000     1st Qu.:2023-07-15  
 Mode  :character   Median : 1642500     Median :2023-08-05  
                    Mean   : 2017893     Mean   :2023-08-09  
                    3rd Qu.: 2255250     3rd Qu.:2023-08-31  
                    Max.   :32000000     Max.   :2023-09-30  
   Address          Type of Sale       Type of Area         Area (SQM)   
 Length:6200        Length:6200        Length:6200        Min.   : 30.0  
 Class :character   Class :character   Class :character   1st Qu.: 67.0  
 Mode  :character   Mode  :character   Mode  :character   Median : 91.0  
                                                          Mean   :105.7  
                                                          3rd Qu.:119.0  
                                                          Max.   :995.0  
 Unit Price ($ PSM) Nett Price($)      Property Type      Number of Units
 Min.   : 2158      Length:6200        Length:6200        Min.   :1      
 1st Qu.:15071      Class :character   Class :character   1st Qu.:1      
 Median :19048      Mode  :character   Mode  :character   Median :1      
 Mean   :19857                                            Mean   :1      
 3rd Qu.:24048                                            3rd Qu.:1      
 Max.   :59949                                            Max.   :2      
    Tenure          Completion Date    Purchaser Address Indicator
 Length:6200        Length:6200        Length:6200                
 Class :character   Class :character   Class :character           
 Mode  :character   Mode  :character   Mode  :character           
                                                                  
                                                                  
                                                                  
 Postal Code        Planning Region    Planning Area     
 Length:6200        Length:6200        Length:6200       
 Class :character   Class :character   Class :character  
 Mode  :character   Mode  :character   Mode  :character  
                                                         
                                                         
                                                         
summary(q4)
 Project Name       Transacted Price ($)   Sale Date         
 Length:4851        Min.   :  570000     Min.   :2023-10-01  
 Class :character   1st Qu.: 1285000     1st Qu.:2023-10-25  
 Mode  :character   Median : 1649340     Median :2023-11-11  
                    Mean   : 2147366     Mean   :2023-11-13  
                    3rd Qu.: 2370000     3rd Qu.:2023-12-04  
                    Max.   :33888000     Max.   :2023-12-31  
   Address          Type of Sale       Type of Area         Area (SQM)    
 Length:4851        Length:4851        Length:4851        Min.   :  30.0  
 Class :character   Class :character   Class :character   1st Qu.:  67.0  
 Mode  :character   Mode  :character   Mode  :character   Median :  96.0  
                                                          Mean   : 110.7  
                                                          3rd Qu.: 122.0  
                                                          Max.   :1423.1  
 Unit Price ($ PSM) Nett Price($)      Property Type      Number of Units
 Min.   : 1484      Length:4851        Length:4851        Min.   :1.000  
 1st Qu.:14900      Class :character   Class :character   1st Qu.:1.000  
 Median :18846      Mode  :character   Mode  :character   Median :1.000  
 Mean   :19682                                            Mean   :1.001  
 3rd Qu.:23492                                            3rd Qu.:1.000  
 Max.   :47026                                            Max.   :7.000  
    Tenure          Completion Date    Purchaser Address Indicator
 Length:4851        Length:4851        Length:4851                
 Class :character   Class :character   Class :character           
 Mode  :character   Mode  :character   Mode  :character           
                                                                  
                                                                  
                                                                  
 Postal Code        Planning Region    Planning Area     
 Length:4851        Length:4851        Length:4851       
 Class :character   Class :character   Class :character  
 Mode  :character   Mode  :character   Mode  :character  
                                                         
                                                         
                                                         
summary(q5)
 Project Name       Transacted Price ($)   Sale Date         
 Length:4902        Min.   :  555000     Min.   :2024-01-01  
 Class :character   1st Qu.: 1330000     1st Qu.:2024-01-26  
 Mode  :character   Median : 1688000     Median :2024-02-17  
                    Mean   : 2100902     Mean   :2024-02-16  
                    3rd Qu.: 2300000     3rd Qu.:2024-03-14  
                    Max.   :39500000     Max.   :2024-03-31  
   Address          Type of Sale       Type of Area         Area (SQM)    
 Length:4902        Length:4902        Length:4902        Min.   :  30.0  
 Class :character   Class :character   Class :character   1st Qu.:  71.0  
 Mode  :character   Mode  :character   Mode  :character   Median :  94.0  
                                                          Mean   : 111.5  
                                                          3rd Qu.: 120.7  
                                                          Max.   :1816.6  
 Unit Price ($ PSM) Nett Price($)      Property Type      Number of Units
 Min.   : 6183      Length:4902        Length:4902        Min.   :1      
 1st Qu.:15237      Class :character   Class :character   1st Qu.:1      
 Median :18688      Mode  :character   Mode  :character   Median :1      
 Mean   :19502                                            Mean   :1      
 3rd Qu.:23548                                            3rd Qu.:1      
 Max.   :58099                                            Max.   :2      
    Tenure          Completion Date    Purchaser Address Indicator
 Length:4902        Length:4902        Length:4902                
 Class :character   Class :character   Class :character           
 Mode  :character   Mode  :character   Mode  :character           
                                                                  
                                                                  
                                                                  
 Postal Code        Planning Region    Planning Area     
 Length:4902        Length:4902        Length:4902       
 Class :character   Class :character   Class :character  
 Mode  :character   Mode  :character   Mode  :character

Visualisation

There are various types of Properties for Private residences.

unique(qa_pte$`Property Type`)
[1] "Condominium"           "Executive Condominium" "Terrace House"        
[4] "Semi-Detached House"   "Apartment"             "Detached House"

The different types in the dataset are:

  • Condominium

  • Terrace House

  • Apartment

  • Executive Condominium

  • Semi-Detached House

  • Detached House

Geographical Distribution - Choropleth Map

A choropleth map is useful for visualizing the variation in average property prices across different areas in Singapore. By shading each region according to its average property price, areas with higher or lower prices can be identified, thus revealing patterns and trends in property values across the city-state. This type of map can reveal hotspots of activity and areas with more affordable or expensive property options.

Two data sets will be used to create the map. They are:

  • MPSZ-2019: This data provides the sub-zone boundary of URA Master Plan 2019. It can be downloaded at data.gov.sg It consists of the geographical boundary of Singapore at the planning subzone level. The data is based on URA Master Plan 2019.

  • Private residential property transaction data from 1st January 2024 to 31st March 2024 in tibble data frame (i.e. q5).

The code chunk below loads the following packages:

  • tmap: for thematic mapping

  • sf: for geospatial data handling

  • httr: Make HTTP requests and handles web APIs

  • future: Allows sequential and parallel processing

  • furrr: combine purrr’s family of mapping functions (within tidyverse) with future’s parallel processing capabilities

pacman::p_load(tmap,sf,httr, future, furrr)

Geospatial Data

Geocoding using Singapore Land Authority (SLA) API

Geocoding - provide geographical coordinates corresponding to a location.

Parallel processing is set up to speed up the process. Results are saved into a .csv file for easy future access.

plan(multisession)

url <- "https://www.onemap.gov.sg/api/common/elastic/search"

postcodes <- unique(q5$`Postal Code`)

# Function to fetch data for a single postal code
fetch_postcode_data <- function(postcode) {
    query <- list('searchVal' = postcode, 'returnGeom' = 'Y', 'getAddrDetails' = 'Y', 'pageNum' = '1')
    res <- GET(url, query = query)
    
    if (http_error(res)) {
        return(NULL) 
    }
    
    content_res <- content(res)
    if (content_res$found != 0) {
        return(data.frame(content_res)[4:13])
    } else {
        return(data.frame(postcode = postcode))
    }
}

results <- future_map(postcodes, fetch_postcode_data)

found <- bind_rows(results, .id = "postcode")

found <- found %>%
    filter(!is.na(postcode))

write.csv(found, file = "data/aspatial/found.csv")
Note

Above code chunk was written with syntax for httr, which has been superseded by httr2. Please make relevant modifications to utilise the newer package.

Import Geospatial Data

The code chunk below uses the st_read() function of sf package to import MPSZ-2019 shapefile into R as a simple feature data frame called mpsz.

mpsz <- st_read(dsn = "data/geospatial",
                layer = "MPSZ-2019") %>%
  st_transform(crs = 3414)
Reading layer `MPSZ-2019' from data source 
  `C:\lnealicia\ISSS608\Take-home_Ex\Take-home_Ex01\data\geospatial' 
  using driver `ESRI Shapefile'
Simple feature collection with 332 features and 6 fields
Geometry type: MULTIPOLYGON
Dimension:     XY
Bounding box:  xmin: 103.6057 ymin: 1.158699 xmax: 104.0885 ymax: 1.470775
Geodetic CRS:  WGS 84

Add coordinates

Link postal code to their relevant coordinates. Empty rows are removed. Not all postal codes may have relevant sales during Q1 of 2024.

q5_coor <- q5 %>%
    left_join(
        found %>% select(results.POSTAL, results.LATITUDE, results.LONGITUDE),
        by = c("Postal Code" = "results.POSTAL")
    ) %>%
    rename(
        Latitude = results.LATITUDE,
        Longitude = results.LONGITUDE
    ) %>%
   filter(!is.na(Longitude) & !is.na(Latitude))

Convert to a sf tibble data frame

q5_sf <- st_as_sf(q5_coor,
                       coords = c("Longitude", "Latitude"),
                       crs =4326) %>%
  st_transform(crs = 3414)

Keep relevant columns

q5_sf_plot <- q5_sf %>%
    select(-`Project Name`,
           -`Sale Date`,
           -`Address`,
           -`Type of Sale`,
           -`Tenure`,
           -`Completion Date`)

Average Transacted Price by Planning Area

avg_txn_px <- q5_sf_plot %>%
    group_by(`Planning Area`) %>%
    summarize(
        Avg_Transacted_Price = mean(`Transacted Price ($)`, na.rm = TRUE)
    )

avg_txn_px <- avg_txn_px %>%
    mutate(`Planning Area` = toupper(`Planning Area`))

avg_txn_px <- st_drop_geometry(avg_txn_px)

Combine avg_txn_px and mpsz

Populates the average transacted price of each planning area into mpsz sf data frame. Not every planning area may have transaction data, so empty rows are dropped.

mpsz_avg_txn_px <- mpsz %>%
    left_join(
        avg_txn_px,
        by = c("PLN_AREA_N" = "Planning Area")
    ) %>%
  drop_na()

Plotting Choropleth Map and Geographical Distribution of Private Properties

The overall plot shows both the average transacted price of the planning area and the distribution of private residences. If viewing the data separately is preferred, click on the relevant tabs.

tmap_mode("view")

tm_shape(mpsz_avg_txn_px) +
    tm_polygons(col = "Avg_Transacted_Price", 
                palette = "YlOrRd", 
                alpha = 0.3,
                style = "quantile",
                n = 7) +
    tmap_options(check.and.fix = TRUE) +
    
    tm_shape(q5_sf_plot) +
    tm_dots(col = "Property Type") +

  tm_view(set.zoom.limits = c(11,14))
+−
Avg_Transacted_Price
1,093,333 to 1,505,422
1,505,422 to 1,632,841
1,632,841 to 1,819,521
1,819,521 to 2,002,916
2,002,916 to 2,204,560
2,204,560 to 2,938,609
2,938,609 to 5,421,500
Property Type
Apartment
Condominium
Detached House
Executive Condominium
Semi-Detached House
Terrace House
Leaflet | Tiles © Esri — Esri, DeLorme, NAVTEQ
tmap_mode("plot")
tmap_mode("view")

tm_shape(mpsz_avg_txn_px) +
    tm_polygons(col = "Avg_Transacted_Price", 
                palette = "YlOrRd", 
                alpha = 0.3,
                style = "quantile",
                n = 7) +
    tmap_options(check.and.fix = TRUE) +
  tm_view(set.zoom.limits = c(11,14))
tmap_mode("plot")
tmap_mode("view")

tm_shape(mpsz_avg_txn_px) +
    tm_polygons() +
    tm_shape(q5_sf_plot) +
    tm_dots(col = "Property Type") +

  tm_view(set.zoom.limits = c(11,14))
tmap_mode("plot")
Note

tmap_mode(“view”):

  • Designed for interactive viewing of spatial data.

  • Enables zooming, panning, and other interactive features, making it easier to explore the data in detail.

tmap_mode(“plot”):

  • Used for static plotting of spatial data that can be saved as static images e.g., PNG

  • Does not support interactions like zooming and panning.

Setting the mode to plot after the map is generated saves on resources and allows the code to run faster, as the plot does not require continuous updating.

Write Up

The plots reveal that of the private properties sold, areas with the highest average transaction price are concentrated around the Central Region, including areas like Bukit Timah and Newton. This is likely due to their proximity to the Central Business District (CBD). Although there are very few transactions in Sentosa and Changi, these areas still have high average transaction prices, mainly consisting of condominiums.

Sales of Executive Condominiums, which were previously noted to have the lowest overall transaction price and low transaction price variability, are predominantly located in the outskirts of Singapore, such as Woodlands and Bukit Batok. This suggests a trend toward more affordable options in these areas.

Apartments and condominiums constitute the majority of private residences sold and are distributed throughout the city-state. This extensive distribution may account for the significant number of outliers observed earlier in transaction prices.