Take-home Exercise 1 (Part 2): DataVis Makeover

Author

Ho Zi Jun

Published

May 5, 2024

Modified

May 31, 2024

1 Overview

In Take-home Exercise 1 (Part 1), we were tasked to produce two to three data visualisations using ggplot2 and its extensions to reveal the private residential market and sub-markets of Singapore for the 1st quarter of 2024. The data preparation was also processed by using the tidyverse family of packages. The exercise allowed us to explore factors such as Transacted Price ($) and Unit Price ($ PSM) in relation to Property Typeand Planning Region to list a few.

For this Take-home Exercise 1 (Part 2), the objective is to perform a makeover and improve on the original data visualisation from other peers. We will be critiquing one data visualisation in terms of its clarity and aesthetics. A sketch of the alternative design will be done up based on the data visualisation design principles (four quadrants of clarity and aesthetic) and finally a remake of the original design will be implemented.

2 Getting Started

2.1 Installing and loading the required libraries

  • tidyverse: (i.e. readr, tidyr, dplyr, ggplot2) for performing data science tasks such as importing, tidying, and wrangling data, as well as creating graphics based on The Grammar of Graphics,
  • reshape2 for transforming data between wide and long formats
  • ggthemes: provides some extra themes, geoms, and scales for ‘ggplot2’.
  • ggdist: a ggplot2 extension specially designed for visualising distribution and uncertainty
  • patchwork: an R package for preparing composite figure created using ggplot2.
  • ggridges: a ggplot2 extension specially designed for plotting ridgeline plots.
  • ggrepel: an R package which provides geoms for ggplot2 to repel overlapping text labels.
  • knitr: for building static html table to aid us in having a better view of tables
  • lubridate: R package that makes it easier to work with dates and times.
  • patchwork: an R package for preparing composite figure created using ggplot2.

The code chunk below uses p_load() function from pacman package to check if packages listed are already installed in the computer. The packages will be loaded if they are found to be installed. Otherwise, the function will proceed to install and load them into R environment.

pacman::p_load(tidyverse, reshape2, ggthemes,
               ggdist, patchwork, ggridges,
               ggrepel, knitr, lubridate,
               patchwork)

2.2 Data Import and Wrangling

The subsequent code chunks utilises the read_csv function to import the five .csv data files from REALIS into the R environment. The data will also be labelled as such for identification:

  • 2023Q1: ResidentialTransaction20240308160536
  • 2023Q2: ResidentialTransaction20240308160736
  • 2023Q3: ResidentialTransaction20240308161009
  • 2023Q4: ResidentialTransaction20240308161109
  • 2024Q1: ResidentialTransaction20240414220633

The code chunk below utilises the rename_with() function to change the column names accordingly using column_rename as an object.

column_rename <- function(orig_name) {
  # Add underscores to spaces
  gsub(" +", "_",
       # Remove special characters
       gsub("[^A-Z ]", "",
            # Convert to upper case and remove trailing spaces
            toupper(orig_name)) %>% trimws())
}
property_2023q1 <- read_csv('data/ResidentialTransaction20240308160536.csv') %>%
                  rename_with(column_rename)
kable(head(property_2023q1, n=5))
PROJECT_NAME TRANSACTED_PRICE AREA_SQFT UNIT_PRICE_PSF SALE_DATE ADDRESS TYPE_OF_SALE TYPE_OF_AREA AREA_SQM UNIT_PRICE_PSM NETT_PRICE PROPERTY_TYPE NUMBER_OF_UNITS TENURE COMPLETION_DATE PURCHASER_ADDRESS_INDICATOR POSTAL_CODE POSTAL_DISTRICT POSTAL_SECTOR PLANNING_REGION PLANNING_AREA
THE REEF AT KING’S DOCK 2317000 882.65 2625 01 Jan 2023 12 HARBOURFRONT AVENUE #05-32 New Sale Strata 82 28256 - Condominium 1 99 yrs from 12/01/2021 Uncompleted HDB 097996 04 09 Central Region Bukit Merah
URBAN TREASURES 1823500 882.65 2066 02 Jan 2023 205 JALAN EUNOS #08-02 New Sale Strata 82 22238 - Condominium 1 Freehold Uncompleted Private 419535 14 41 East Region Bedok
NORTH GAIA 1421112 1076.40 1320 02 Jan 2023 29 YISHUN CLOSE #08-10 New Sale Strata 100 14211 - Executive Condominium 1 99 yrs from 15/02/2021 Uncompleted HDB 269343 27 26 North Region Yishun
NORTH GAIA 1258112 1033.34 1218 02 Jan 2023 45 YISHUN CLOSE #07-42 New Sale Strata 96 13105 - Executive Condominium 1 99 yrs from 15/02/2021 Uncompleted HDB 269294 27 26 North Region Yishun
PARC BOTANNIA 1280000 871.88 1468 03 Jan 2023 12 FERNVALE STREET #06-16 Resale Strata 81 15802 - Condominium 1 99 yrs from 28/12/2016 2022 HDB 797391 28 79 North East Region Sengkang 
property_2023q2 <- read_csv('data/ResidentialTransaction20240308160736.csv') %>%
                  rename_with(column_rename)
kable(head(property_2023q1, n=5))
PROJECT_NAME TRANSACTED_PRICE AREA_SQFT UNIT_PRICE_PSF SALE_DATE ADDRESS TYPE_OF_SALE TYPE_OF_AREA AREA_SQM UNIT_PRICE_PSM NETT_PRICE PROPERTY_TYPE NUMBER_OF_UNITS TENURE COMPLETION_DATE PURCHASER_ADDRESS_INDICATOR POSTAL_CODE POSTAL_DISTRICT POSTAL_SECTOR PLANNING_REGION PLANNING_AREA
THE REEF AT KING’S DOCK 2317000 882.65 2625 01 Jan 2023 12 HARBOURFRONT AVENUE #05-32 New Sale Strata 82 28256 - Condominium 1 99 yrs from 12/01/2021 Uncompleted HDB 097996 04 09 Central Region Bukit Merah
URBAN TREASURES 1823500 882.65 2066 02 Jan 2023 205 JALAN EUNOS #08-02 New Sale Strata 82 22238 - Condominium 1 Freehold Uncompleted Private 419535 14 41 East Region Bedok
NORTH GAIA 1421112 1076.40 1320 02 Jan 2023 29 YISHUN CLOSE #08-10 New Sale Strata 100 14211 - Executive Condominium 1 99 yrs from 15/02/2021 Uncompleted HDB 269343 27 26 North Region Yishun
NORTH GAIA 1258112 1033.34 1218 02 Jan 2023 45 YISHUN CLOSE #07-42 New Sale Strata 96 13105 - Executive Condominium 1 99 yrs from 15/02/2021 Uncompleted HDB 269294 27 26 North Region Yishun
PARC BOTANNIA 1280000 871.88 1468 03 Jan 2023 12 FERNVALE STREET #06-16 Resale Strata 81 15802 - Condominium 1 99 yrs from 28/12/2016 2022 HDB 797391 28 79 North East Region Sengkang 
property_2023q3 <- read_csv('data/ResidentialTransaction20240308161009.csv') %>%
                  rename_with(column_rename)
kable(head(property_2023q1, n=5))
PROJECT_NAME TRANSACTED_PRICE AREA_SQFT UNIT_PRICE_PSF SALE_DATE ADDRESS TYPE_OF_SALE TYPE_OF_AREA AREA_SQM UNIT_PRICE_PSM NETT_PRICE PROPERTY_TYPE NUMBER_OF_UNITS TENURE COMPLETION_DATE PURCHASER_ADDRESS_INDICATOR POSTAL_CODE POSTAL_DISTRICT POSTAL_SECTOR PLANNING_REGION PLANNING_AREA
THE REEF AT KING’S DOCK 2317000 882.65 2625 01 Jan 2023 12 HARBOURFRONT AVENUE #05-32 New Sale Strata 82 28256 - Condominium 1 99 yrs from 12/01/2021 Uncompleted HDB 097996 04 09 Central Region Bukit Merah
URBAN TREASURES 1823500 882.65 2066 02 Jan 2023 205 JALAN EUNOS #08-02 New Sale Strata 82 22238 - Condominium 1 Freehold Uncompleted Private 419535 14 41 East Region Bedok
NORTH GAIA 1421112 1076.40 1320 02 Jan 2023 29 YISHUN CLOSE #08-10 New Sale Strata 100 14211 - Executive Condominium 1 99 yrs from 15/02/2021 Uncompleted HDB 269343 27 26 North Region Yishun
NORTH GAIA 1258112 1033.34 1218 02 Jan 2023 45 YISHUN CLOSE #07-42 New Sale Strata 96 13105 - Executive Condominium 1 99 yrs from 15/02/2021 Uncompleted HDB 269294 27 26 North Region Yishun
PARC BOTANNIA 1280000 871.88 1468 03 Jan 2023 12 FERNVALE STREET #06-16 Resale Strata 81 15802 - Condominium 1 99 yrs from 28/12/2016 2022 HDB 797391 28 79 North East Region Sengkang 
property_2023q4 <- read_csv('data/ResidentialTransaction20240308161109.csv') %>%
                  rename_with(column_rename)
kable(head(property_2023q1, n=5))
PROJECT_NAME TRANSACTED_PRICE AREA_SQFT UNIT_PRICE_PSF SALE_DATE ADDRESS TYPE_OF_SALE TYPE_OF_AREA AREA_SQM UNIT_PRICE_PSM NETT_PRICE PROPERTY_TYPE NUMBER_OF_UNITS TENURE COMPLETION_DATE PURCHASER_ADDRESS_INDICATOR POSTAL_CODE POSTAL_DISTRICT POSTAL_SECTOR PLANNING_REGION PLANNING_AREA
THE REEF AT KING’S DOCK 2317000 882.65 2625 01 Jan 2023 12 HARBOURFRONT AVENUE #05-32 New Sale Strata 82 28256 - Condominium 1 99 yrs from 12/01/2021 Uncompleted HDB 097996 04 09 Central Region Bukit Merah
URBAN TREASURES 1823500 882.65 2066 02 Jan 2023 205 JALAN EUNOS #08-02 New Sale Strata 82 22238 - Condominium 1 Freehold Uncompleted Private 419535 14 41 East Region Bedok
NORTH GAIA 1421112 1076.40 1320 02 Jan 2023 29 YISHUN CLOSE #08-10 New Sale Strata 100 14211 - Executive Condominium 1 99 yrs from 15/02/2021 Uncompleted HDB 269343 27 26 North Region Yishun
NORTH GAIA 1258112 1033.34 1218 02 Jan 2023 45 YISHUN CLOSE #07-42 New Sale Strata 96 13105 - Executive Condominium 1 99 yrs from 15/02/2021 Uncompleted HDB 269294 27 26 North Region Yishun
PARC BOTANNIA 1280000 871.88 1468 03 Jan 2023 12 FERNVALE STREET #06-16 Resale Strata 81 15802 - Condominium 1 99 yrs from 28/12/2016 2022 HDB 797391 28 79 North East Region Sengkang 
property_2024q1 <- read_csv('data/ResidentialTransaction20240414220633.csv') %>%
                  rename_with(column_rename)
kable(head(property_2023q1, n=5))
PROJECT_NAME TRANSACTED_PRICE AREA_SQFT UNIT_PRICE_PSF SALE_DATE ADDRESS TYPE_OF_SALE TYPE_OF_AREA AREA_SQM UNIT_PRICE_PSM NETT_PRICE PROPERTY_TYPE NUMBER_OF_UNITS TENURE COMPLETION_DATE PURCHASER_ADDRESS_INDICATOR POSTAL_CODE POSTAL_DISTRICT POSTAL_SECTOR PLANNING_REGION PLANNING_AREA
THE REEF AT KING’S DOCK 2317000 882.65 2625 01 Jan 2023 12 HARBOURFRONT AVENUE #05-32 New Sale Strata 82 28256 - Condominium 1 99 yrs from 12/01/2021 Uncompleted HDB 097996 04 09 Central Region Bukit Merah
URBAN TREASURES 1823500 882.65 2066 02 Jan 2023 205 JALAN EUNOS #08-02 New Sale Strata 82 22238 - Condominium 1 Freehold Uncompleted Private 419535 14 41 East Region Bedok
NORTH GAIA 1421112 1076.40 1320 02 Jan 2023 29 YISHUN CLOSE #08-10 New Sale Strata 100 14211 - Executive Condominium 1 99 yrs from 15/02/2021 Uncompleted HDB 269343 27 26 North Region Yishun
NORTH GAIA 1258112 1033.34 1218 02 Jan 2023 45 YISHUN CLOSE #07-42 New Sale Strata 96 13105 - Executive Condominium 1 99 yrs from 15/02/2021 Uncompleted HDB 269294 27 26 North Region Yishun
PARC BOTANNIA 1280000 871.88 1468 03 Jan 2023 12 FERNVALE STREET #06-16 Resale Strata 81 15802 - Condominium 1 99 yrs from 28/12/2016 2022 HDB 797391 28 79 North East Region Sengkang

The code chunk below glimpse() will provide us with an overview of the data.

glimpse(property_2023q1)
Rows: 4,722
Columns: 21
$ PROJECT_NAME                <chr> "THE REEF AT KING'S DOCK", "URBAN TREASURE…
$ TRANSACTED_PRICE            <dbl> 2317000, 1823500, 1421112, 1258112, 128000…
$ 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 2023…
$ ADDRESS                     <chr> "12 HARBOURFRONT AVENUE #05-32", "205 JALA…
$ TYPE_OF_SALE                <chr> "New Sale", "New Sale", "New Sale", "New S…
$ TYPE_OF_AREA                <chr> "Strata", "Strata", "Strata", "Strata", "S…
$ AREA_SQM                    <dbl> 82.0, 82.0, 100.0, 96.0, 81.0, 308.7, 420.…
$ UNIT_PRICE_PSM              <dbl> 28256, 22238, 14211, 13105, 15802, 19015, …
$ NETT_PRICE                  <chr> "-", "-", "-", "-", "-", "-", "-", "-", "-…
$ PROPERTY_TYPE               <chr> "Condominium", "Condominium", "Executive C…
$ 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", "99 …
$ COMPLETION_DATE             <chr> "Uncompleted", "Uncompleted", "Uncompleted…
$ PURCHASER_ADDRESS_INDICATOR <chr> "HDB", "Private", "HDB", "HDB", "HDB", "Pr…
$ POSTAL_CODE                 <chr> "097996", "419535", "269343", "269294", "7…
$ 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 Re…
$ PLANNING_AREA               <chr> "Bukit Merah", "Bedok", "Yishun", "Yishun"…
glimpse(property_2023q2)
Rows: 6,125
Columns: 21
$ PROJECT_NAME                <chr> "THE GAZANIA", "THE GAZANIA", "ONE PEARL B…
$ TRANSACTED_PRICE            <dbl> 1528000, 1938000, 2051000, 1850700, 202150…
$ AREA_SQFT                   <dbl> 678.13, 958.00, 699.66, 882.65, 699.66, 78…
$ UNIT_PRICE_PSF              <dbl> 2253, 2023, 2931, 2097, 2889, 2339, 3560, …
$ SALE_DATE                   <chr> "01 Apr 2023", "01 Apr 2023", "01 Apr 2023…
$ ADDRESS                     <chr> "15 HOW SUN DRIVE #02-31", "7 HOW SUN DRIV…
$ TYPE_OF_SALE                <chr> "New Sale", "New Sale", "New Sale", "New S…
$ TYPE_OF_AREA                <chr> "Strata", "Strata", "Strata", "Strata", "S…
$ AREA_SQM                    <dbl> 63, 89, 65, 82, 65, 73, 191, 46, 62, 93, 8…
$ UNIT_PRICE_PSM              <dbl> 24254, 21775, 31554, 22570, 31100, 25178, …
$ NETT_PRICE                  <chr> "-", "-", "-", "-", "-", "-", "-", "-", "-…
$ PROPERTY_TYPE               <chr> "Condominium", "Condominium", "Apartment",…
$ NUMBER_OF_UNITS             <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, …
$ TENURE                      <chr> "Freehold", "Freehold", "99 yrs from 01/03…
$ COMPLETION_DATE             <chr> "2022", "2022", "Uncompleted", "Uncomplete…
$ PURCHASER_ADDRESS_INDICATOR <chr> "N.A", "Private", "Private", "HDB", "Priva…
$ POSTAL_CODE                 <chr> "538545", "538530", "169016", "419535", "2…
$ POSTAL_DISTRICT             <chr> "19", "19", "03", "14", "10", "10", "09", …
$ POSTAL_SECTOR               <chr> "53", "53", "16", "41", "27", "26", "22", …
$ PLANNING_REGION             <chr> "North East Region", "North East Region", …
$ PLANNING_AREA               <chr> "Serangoon", "Serangoon", "Outram", "Bedok…
glimpse(property_2023q3)
Rows: 6,206
Columns: 21
$ PROJECT_NAME                <chr> "MYRA", "NORTH GAIA", "NORTH GAIA", "NORTH…
$ TRANSACTED_PRICE            <dbl> 1658000, 1449000, 1365000, 1231000, 127200…
$ AREA_SQFT                   <dbl> 667.37, 1076.40, 1076.40, 958.00, 1001.05,…
$ UNIT_PRICE_PSF              <dbl> 2484, 1346, 1268, 1285, 1271, 2062, 1465, …
$ SALE_DATE                   <chr> "01 Jul 2023", "01 Jul 2023", "01 Jul 2023…
$ ADDRESS                     <chr> "9 MEYAPPA CHETTIAR ROAD #02-07", "27 YISH…
$ TYPE_OF_SALE                <chr> "New Sale", "New Sale", "New Sale", "New S…
$ TYPE_OF_AREA                <chr> "Strata", "Strata", "Strata", "Strata", "S…
$ AREA_SQM                    <dbl> 62, 100, 100, 89, 93, 156, 86, 86, 86, 86,…
$ UNIT_PRICE_PSM              <dbl> 26742, 14490, 13650, 13831, 13677, 22192, …
$ NETT_PRICE                  <chr> "-", "-", "-", "-", "-", "-", "-", "-", "-…
$ PROPERTY_TYPE               <chr> "Apartment", "Executive Condominium", "Exe…
$ NUMBER_OF_UNITS             <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, …
$ TENURE                      <chr> "Freehold", "99 yrs from 15/02/2021", "99 …
$ COMPLETION_DATE             <chr> "Uncompleted", "Uncompleted", "Uncompleted…
$ PURCHASER_ADDRESS_INDICATOR <chr> "N.A", "HDB", "HDB", "HDB", "HDB", "Privat…
$ POSTAL_CODE                 <chr> "358456", "769342", "769342", "769299", "7…
$ POSTAL_DISTRICT             <chr> "13", "27", "27", "27", "27", "08", "18", …
$ POSTAL_SECTOR               <chr> "35", "76", "76", "76", "76", "21", "52", …
$ PLANNING_REGION             <chr> "Central Region", "North Region", "North R…
$ PLANNING_AREA               <chr> "Toa Payoh", "Yishun", "Yishun", "Yishun",…
glimpse(property_2023q4)
Rows: 4,851
Columns: 21
$ PROJECT_NAME                <chr> "LEEDON GREEN", "LIV @ MB", "MORI", "THE A…
$ TRANSACTED_PRICE            <dbl> 1749000, 3148740, 2422337, 1330000, 223700…
$ AREA_SQFT                   <dbl> 538.20, 1453.14, 1259.39, 721.19, 1130.22,…
$ UNIT_PRICE_PSF              <dbl> 3250, 2167, 1923, 1844, 1979, 2111, 2131, …
$ SALE_DATE                   <chr> "01 Oct 2023", "01 Oct 2023", "01 Oct 2023…
$ ADDRESS                     <chr> "26 LEEDON HEIGHTS #11-08", "114A ARTHUR R…
$ TYPE_OF_SALE                <chr> "New Sale", "New Sale", "New Sale", "New S…
$ TYPE_OF_AREA                <chr> "Strata", "Strata", "Strata", "Strata", "S…
$ AREA_SQM                    <dbl> 50.0, 135.0, 117.0, 67.0, 105.0, 55.0, 126…
$ UNIT_PRICE_PSM              <dbl> 34980, 23324, 20704, 19851, 21305, 22725, …
$ NETT_PRICE                  <chr> "-", "-", "-", "-", "-", "-", "-", "-", "-…
$ PROPERTY_TYPE               <chr> "Condominium", "Condominium", "Apartment",…
$ NUMBER_OF_UNITS             <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, …
$ TENURE                      <chr> "Freehold", "99 yrs from 23/11/2021", "Fre…
$ COMPLETION_DATE             <chr> "Uncompleted", "Uncompleted", "Uncompleted…
$ PURCHASER_ADDRESS_INDICATOR <chr> "Private", "Private", "Private", "Private"…
$ POSTAL_CODE                 <chr> "266221", "439826", "399738", "668159", "7…
$ POSTAL_DISTRICT             <chr> "10", "15", "14", "23", "26", "22", "26", …
$ POSTAL_SECTOR               <chr> "26", "43", "39", "66", "78", "61", "78", …
$ PLANNING_REGION             <chr> "Central Region", "Central Region", "Centr…
$ PLANNING_AREA               <chr> "Bukit Timah", "Marine Parade", "Geylang",…
glimpse(property_2024q1)
Rows: 4,902
Columns: 21
$ PROJECT_NAME                <chr> "THE LANDMARK", "POLLEN COLLECTION", "SKY …
$ TRANSACTED_PRICE            <dbl> 2726888, 3850000, 2346000, 2190000, 195400…
$ AREA_SQFT                   <dbl> 1076.40, 1808.35, 1087.16, 807.30, 796.54,…
$ UNIT_PRICE_PSF              <dbl> 2533, 2129, 2158, 2713, 2453, 2577, 838, 1…
$ SALE_DATE                   <chr> "01 Jan 2024", "01 Jan 2024", "01 Jan 2024…
$ ADDRESS                     <chr> "173 CHIN SWEE ROAD #22-11", "34 POLLEN PL…
$ TYPE_OF_SALE                <chr> "New Sale", "New Sale", "New Sale", "New S…
$ TYPE_OF_AREA                <chr> "Strata", "Land", "Strata", "Strata", "Str…
$ AREA_SQM                    <dbl> 100.0, 168.0, 101.0, 75.0, 74.0, 123.0, 32…
$ UNIT_PRICE_PSM              <dbl> 27269, 22917, 23228, 29200, 26405, 27741, …
$ NETT_PRICE                  <chr> "-", "-", "-", "-", "-", "-", "-", "-", "-…
$ PROPERTY_TYPE               <chr> "Condominium", "Terrace House", "Apartment…
$ NUMBER_OF_UNITS             <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, …
$ TENURE                      <chr> "99 yrs from 28/08/2020", "99 yrs from 09/…
$ COMPLETION_DATE             <chr> "Uncompleted", "Uncompleted", "Uncompleted…
$ PURCHASER_ADDRESS_INDICATOR <chr> "Private", "N.A", "HDB", "N.A", "Private",…
$ POSTAL_CODE                 <chr> "169878", "807233", "469657", "118992", "5…
$ POSTAL_DISTRICT             <chr> "03", "28", "16", "05", "21", "21", "28", …
$ POSTAL_SECTOR               <chr> "16", "80", "46", "11", "59", "58", "79", …
$ PLANNING_REGION             <chr> "Central Region", "North East Region", "Ea…
$ PLANNING_AREA               <chr> "Outram", "Serangoon", "Bedok", "Queenstow…

Data Wrangling

property_2023q1 <- property_2023q1 %>%
  mutate(
    QUARTER="2023Q1",
    MONTH_YEAR=format(dmy(SALE_DATE), "%b-%y")
  )
property_2023q2 <- property_2023q2 %>%
  mutate(
    QUARTER="2023Q2",
    MONTH_YEAR=format(dmy(SALE_DATE), "%b-%y")
  )
property_2023q3 <- property_2023q3 %>%
  mutate(
    QUARTER="2023Q3",
    MONTH_YEAR=format(dmy(SALE_DATE), "%b-%y")
  )
property_2023q4 <- property_2023q4 %>%
  mutate(
    QUARTER="2023Q4",
    MONTH_YEAR=format(dmy(SALE_DATE), "%b-%y")
  )
property_2024q1 <- property_2024q1 %>%
  mutate(
    QUARTER="2024Q1",
    MONTH_YEAR=format(dmy(SALE_DATE), "%b-%y")
  )
realis <- property_2023q1 %>%
  rbind(property_2023q2) %>%
  rbind(property_2023q3) %>%
  rbind(property_2023q4) %>%
  rbind(property_2024q1)
kable(head(realis, n=10))
PROJECT_NAME TRANSACTED_PRICE AREA_SQFT UNIT_PRICE_PSF SALE_DATE ADDRESS TYPE_OF_SALE TYPE_OF_AREA AREA_SQM UNIT_PRICE_PSM NETT_PRICE PROPERTY_TYPE NUMBER_OF_UNITS TENURE COMPLETION_DATE PURCHASER_ADDRESS_INDICATOR POSTAL_CODE POSTAL_DISTRICT POSTAL_SECTOR PLANNING_REGION PLANNING_AREA QUARTER MONTH_YEAR
THE REEF AT KING’S DOCK 2317000 882.65 2625 01 Jan 2023 12 HARBOURFRONT AVENUE #05-32 New Sale Strata 82.0 28256 - Condominium 1 99 yrs from 12/01/2021 Uncompleted HDB 097996 04 09 Central Region Bukit Merah 2023Q1 Jan-23
URBAN TREASURES 1823500 882.65 2066 02 Jan 2023 205 JALAN EUNOS #08-02 New Sale Strata 82.0 22238 - Condominium 1 Freehold Uncompleted Private 419535 14 41 East Region Bedok 2023Q1 Jan-23
NORTH GAIA 1421112 1076.40 1320 02 Jan 2023 29 YISHUN CLOSE #08-10 New Sale Strata 100.0 14211 - Executive Condominium 1 99 yrs from 15/02/2021 Uncompleted HDB 269343 27 26 North Region Yishun 2023Q1 Jan-23
NORTH GAIA 1258112 1033.34 1218 02 Jan 2023 45 YISHUN CLOSE #07-42 New Sale Strata 96.0 13105 - Executive Condominium 1 99 yrs from 15/02/2021 Uncompleted HDB 269294 27 26 North Region Yishun 2023Q1 Jan-23
PARC BOTANNIA 1280000 871.88 1468 03 Jan 2023 12 FERNVALE STREET #06-16 Resale Strata 81.0 15802 - Condominium 1 99 yrs from 28/12/2016 2022 HDB 797391 28 79 North East Region Sengkang 2023Q1 Jan-23
NANYANG PARK 5870000 3322.85 1767 03 Jan 2023 72 JALAN LIMBOK Resale Land 308.7 19015 - Terrace House 1 999 yrs from 14/02/1881 - Private 548742 19 54 North East Region Hougang 2023Q1 Jan-23
PALMS @ SIXTH AVENUE 4950000 4520.88 1095 03 Jan 2023 231 SIXTH AVENUE Resale Strata 420.0 11786 - Semi-Detached House 1 Freehold 2015 Private 275780 10 27 Central Region Bukit Timah 2023Q1 Jan-23
N.A. 3260000 1555.40 2096 03 Jan 2023 19 TENG TONG ROAD Resale Land 144.5 22561 - Terrace House 1 Freehold 1941 Private 423510 15 42 Central Region Marine Parade 2023Q1 Jan-23
WHISTLER GRAND 850000 441.32 1926 03 Jan 2023 107 WEST COAST VALE #30-04 Sub Sale Strata 41.0 20732 - Apartment 1 99 yrs from 07/05/2018 2022 HDB 126751 05 12 West Region Clementi 2023Q1 Jan-23
NORTHOAKS 1268000 1603.84 791 03 Jan 2023 30 WOODLANDS CRESCENT #01-11 Resale Strata 149.0 8510 - Executive Condominium 1 99 yrs from 16/12/1997 2000 HDB 738086 25 73 North Region Woodlands 2023Q1 Jan-23

After adding the QUARTER columns, there are now 22 variables in the dataframe. However, for this exercise not all of them are necessary to carry out the analysis. We shall filter out the necessary columns and drop the rest for efficiency.

realis <-
  realis %>% select(
    c(
      QUARTER,
      MONTH_YEAR,
      PROPERTY_TYPE,
      PLANNING_REGION,
      PLANNING_AREA,
      TRANSACTED_PRICE,
      AREA_SQFT,
      UNIT_PRICE_PSF,
      SALE_DATE
    )
  )
glimpse(realis) #Overview of transformed data
Rows: 26,806
Columns: 9
$ QUARTER          <chr> "2023Q1", "2023Q1", "2023Q1", "2023Q1", "2023Q1", "20…
$ MONTH_YEAR       <chr> "Jan-23", "Jan-23", "Jan-23", "Jan-23", "Jan-23", "Ja…
$ PROPERTY_TYPE    <chr> "Condominium", "Condominium", "Executive Condominium"…
$ PLANNING_REGION  <chr> "Central Region", "East Region", "North Region", "Nor…
$ PLANNING_AREA    <chr> "Bukit Merah", "Bedok", "Yishun", "Yishun", "Sengkang…
$ TRANSACTED_PRICE <dbl> 2317000, 1823500, 1421112, 1258112, 1280000, 5870000,…
$ AREA_SQFT        <dbl> 882.65, 882.65, 1076.40, 1033.34, 871.88, 3322.85, 45…
$ UNIT_PRICE_PSF   <dbl> 2625, 2066, 1320, 1218, 1468, 1767, 1095, 2096, 1926,…
$ SALE_DATE        <chr> "01 Jan 2023", "02 Jan 2023", "02 Jan 2023", "02 Jan …

Upon using glimpse(), it can be observed that there are 9 variables relevant to our data viz makeover.

3 Data Visualisation Makeover

In this section, we will proceed with a makeover of a peer’s data visualisation and building an improved version. Shown below is the plot of our peer’s plot.

The author stated:

As we mentioned about the individual market is focus on the apartment and condominium above, and we know the distribution of total property, what about the first quarter unit price of these two popular goods?

Upon examination of the violin plots, a clear disparity emerges between the average unit prices of condominiums and apartments, standing at approximately $1,500 and $2,000, respectively, for the period spanning January to March. Noteworthy is the discernible uptick in both unit price and transaction volume from January to March 2024. Despite an overall reduction in total transactions vis-a-vis the preceding year, there is an unmistakable trend towards growth within specific sub-markets, suggesting an increasing inclination towards higher-value properties

filtered_data <- combined_data %>%
  mutate(Sale_Date = dmy(`Sale Date`)) %>%
  filter((year(Sale_Date) == 2023 & 
          month(Sale_Date) %in% 1:12) |
         (year(Sale_Date) == 2024 & 
          month(Sale_Date) %in% 1:3)) %>%
  mutate(Quarter_Sale_Data = case_when(
    between(Sale_Date, as.Date("2023-01-01"), as.Date("2023-03-31")) ~ "Q1_2023",
    between(Sale_Date, as.Date("2023-04-01"), as.Date("2023-06-30")) ~ "Q2_2023",
    between(Sale_Date, as.Date("2023-07-01"), as.Date("2023-09-30")) ~ "Q3_2023",
    between(Sale_Date, as.Date("2023-10-01"), as.Date("2023-12-31")) ~ "Q4_2023",
    between(Sale_Date, as.Date("2024-01-01"), as.Date("2024-03-31")) ~ "Q1_2024",
    TRUE ~ NA_character_
  )) %>%
  filter(!is.na(Quarter_Sale_Data)) %>%
  mutate(Month_Sale_Data = paste0(year(Sale_Date), "-", month(Sale_Date)))

filtered_data <- filtered_data %>%
  filter(`Property Type` %in% c("Apartment", "Condominium"))

ggplot(filtered_data, aes(x = Month_Sale_Data, y = `Unit Price ($ PSF)`, color = `Property Type`)) +
  geom_violin() +
  geom_point(position = "jitter",
             size = 0.1) +
  labs(title = "Unit Price per Square Foot for Apartments and Condominiums",
       x = "Month",
       y = "Unit Price ($ PSF)") +
  theme_light(base_size = 6) +
  xlim(c("2024-1","2024-2","2024-3"))

Peer’s Data Visualisation
ggplot(filtered_data, aes(x = Month_Sale_Data, y = `Unit Price ($ PSF)`, color = `Property Type`)) +
  geom_violin() +
  geom_point(position = "jitter",
             size = 0.1) +
  labs(title = "Unit Price per Square Foot for Apartments and Condominiums",
       x = "Month",
       y = "Unit Price ($ PSF)") +
  theme_light(base_size = 6) +
  xlim(c("2023-1","2023-2","2023-3"))

Peer’s Data Visualisation

3.1 Observations: Clarity and Aesthetics

Clarity

  • The use of a violin plot overlaid with scatter plot points helps illustrate the distribution of prices per square foot for both apartments and condominiums across different months.

  • The red (apartment) and teal (condominium) color distinction or scatterplot is generally clear, but there’s significant overlap in data points, which may confuse the viewer about the exact differences in price distributions between these property types. This might also affect the ease of reading and understanding by audiences from the general public.

Aesthetics

  • The main title while clear could be centralised for easier readability.

  • The plot successfully uses colour to differentiate between the two types of properties. The choice of colors is visually distinct, which is helpful for quick differentiation.

  • However, the presence of outliers, particularly those extreme values shown as vertical lines extending from the main bodies of the violins, can confuse readers from the overall trends from the plot.

3.2 Sketch of alternative design

Sketch ideation Improvements based on the above points mentioned earlier:

  • Main title which was centred to give improved balanced to the plot layout.

  • Combine each different selected property type into each portion of the chart, sharing the same y-axis to reveal the distribution among different property types simultaneously.

  • Added additional pointers and/or labels to highlight summary statistic values such as Mean, Median and IQR.

  • Address the issue of outliers in the plot for this case I have chosen to highlight the outliers to enable readers to be aware and take note of them since they were still actual property transactions.

  • Use widely different colours to differentiate between the variables for better visual distinction.

3.3 Remake of Original Design

1st iteration

Derived from the sketch ideation, this plot shows Unit Price ($ PSF) by Quarter as a start.

ggplot(data= realis,
       aes(x= QUARTER, y= UNIT_PRICE_PSF, color = QUARTER)) +
  geom_violin(aes(fill = QUARTER), size = 0.6, alpha = 0.3, linewidth = 0) +
  geom_boxplot(width= 0.4, outlier.colour = "grey20", outlier.size = 1, 
               outlier.alpha = 0.3) +
  stat_summary(geom = "point",       
               fun.y="mean",         
               colour ="black",        
               size=2) +  
  coord_cartesian(ylim = c(400,6000)) +
  scale_color_manual(values=c("#c73824", "#0477bf", "#9E9E9E", "#0CDBBC", "#0437bf")) +
  theme_economist() +
  labs(title="Unit Price ($PSF) by Quarter") +
  scale_y_continuous(breaks = seq(400, 6000, by = 500)) +
  theme(axis.title.x = element_blank(),
        axis.title.y = element_blank(),
        plot.title=element_text(size= 12, hjust= 0.5),
        axis.text = element_text(size= 10),
        legend.position = "none")

2nd iteration (Filtering of variables)

To accommodate to the peer’s selection of selected property type

filtered_data <- realis %>%
  filter(PROPERTY_TYPE %in% c("Apartment", "Condominium"),
         QUARTER %in% c("2023Q1", "2024Q1"))

ggplot(data= filtered_data,
       aes(x= QUARTER, y= UNIT_PRICE_PSF, color = QUARTER)) +
  geom_violin(aes(fill = QUARTER), size = 0.6, alpha = 0.3, linewidth = 0) +
  geom_boxplot(width= 0.4, outlier.colour = "grey20", outlier.size = 1, 
               outlier.alpha = 0.3) +
  stat_summary(geom = "point",       
               fun.y="mean",         
               colour ="black",        
               size=2) +  
  coord_cartesian(ylim = c(400,6000)) +
  scale_color_manual(values=c("#c73824", "#0477bf", "#9E9E9E", "#0CDBBC", "#0437bf")) +
  theme_economist() +
  labs(title="Unit Price ($PSF) by Quarter") +
  scale_y_continuous(breaks = seq(400, 6000, by = 500)) +
  theme(axis.title.x = element_blank(),
        axis.title.y = element_blank(),
        plot.title=element_text(size= 12, hjust= 0.5),
        axis.text = element_text(size= 10),
        legend.position = "none") +
  facet_wrap(~PROPERTY_TYPE)

3rd iteration (Filtering of variables)

To accommodate to the peer’s selection of time period (Month)

filtered_data <- realis %>%
  filter(PROPERTY_TYPE %in% c("Apartment", "Condominium"),
         MONTH_YEAR %in% c("Jan-23", "Feb-23", "Mar-23")) %>%
  mutate(MONTH_YEAR = factor(MONTH_YEAR, levels = c("Jan-23", "Feb-23", "Mar-23")))

ggplot(data= filtered_data,
       aes(x= MONTH_YEAR, y= UNIT_PRICE_PSF, color = MONTH_YEAR)) +
  geom_violin(aes(fill = MONTH_YEAR), size = 0.6, alpha = 0.3, linewidth = 0) +
  geom_boxplot(width= 0.4, outlier.colour = "grey20", outlier.size = 1, 
               outlier.alpha = 0.3) +
  stat_summary(geom = "point",       
               fun.y="mean",         
               colour ="black",        
               size=2) +  
  coord_cartesian(ylim = c(400,6000)) +
  scale_color_manual(values=c("#c73824", "#0477bf", "#9E9E9E", "#0CDBBC", "#0437bf")) +
  theme_economist() +
  labs(title="Unit Price ($PSF) by Month") +
  scale_y_continuous(breaks = seq(400, 6000, by = 500)) +
  theme(axis.title.x = element_blank(),
        axis.title.y = element_blank(),
        plot.title=element_text(size= 12, hjust= 0.5),
        axis.text = element_text(size= 10),
        legend.position = "none") +
  facet_wrap(~PROPERTY_TYPE)

4th iteration (Addition of Summary Statistics)

For Year 2023

# Filter and order the data as before
filtered_data <- realis %>%
  filter(PROPERTY_TYPE %in% c("Apartment", "Condominium"),
         MONTH_YEAR %in% c("Jan-23", "Feb-23", "Mar-23")) %>%
  mutate(MONTH_YEAR = factor(MONTH_YEAR, levels = c("Jan-23", "Feb-23", "Mar-23")))

# Calculate summary statistics for annotations
stats_data <- filtered_data %>%
  group_by(MONTH_YEAR, PROPERTY_TYPE) %>%
  summarise(
    Mean = mean(UNIT_PRICE_PSF),
    Median = median(UNIT_PRICE_PSF),
    IQR = IQR(UNIT_PRICE_PSF),
    .groups = 'drop'
  )

# Generate the violin plot with statistical annotations
plot1 <- ggplot(data = filtered_data,
       aes(x = MONTH_YEAR, y = UNIT_PRICE_PSF, color = MONTH_YEAR)) +
  geom_violin(aes(fill = MONTH_YEAR), size = 0.6, alpha = 0.3, linewidth = 0) +
  geom_boxplot(width = 0.4, outlier.colour = "grey20", outlier.size = 1, 
               outlier.alpha = 0.3) +
  stat_summary(geom = "point",       
               fun.y="mean",         
               colour ="black",        
               size=2) +
  geom_text(data = stats_data, aes(label = sprintf("Mean: %.2f\nMedian: %.2f\nIQR: %.2f", Mean, Median, IQR), 
                                   y = 5500), size = 3, hjust = 0.5) +
  coord_cartesian(ylim = c(400, 6000)) +
  scale_color_manual(values = c("#c73824", "#0477bf", "#9E9E9E", "#0CDBBC")) +
  theme_economist() +
  labs(title = "Unit Price ($PSF) by Month (Year 2023)") +
  scale_y_continuous(breaks = seq(400, 6000, by = 500)) +
  theme(axis.title.x = element_blank(),
        axis.title.y = element_blank(),
        plot.title = element_text(size = 12, hjust = 0.5),
        axis.text = element_text(size = 10),
        legend.position = "none") +
  facet_wrap(~PROPERTY_TYPE)

# Display the plot
print(plot1)

For Year 2024

# Filter and order the data as before
filtered_data <- realis %>%
  filter(PROPERTY_TYPE %in% c("Apartment", "Condominium"),
         MONTH_YEAR %in% c("Jan-24", "Feb-24", "Mar-24")) %>%
  mutate(MONTH_YEAR = factor(MONTH_YEAR, levels = c("Jan-24", "Feb-24", "Mar-24")))

# Calculate summary statistics for annotations
stats_data <- filtered_data %>%
  group_by(MONTH_YEAR, PROPERTY_TYPE) %>%
  summarise(
    Mean = mean(UNIT_PRICE_PSF),
    Median = median(UNIT_PRICE_PSF),
    IQR = IQR(UNIT_PRICE_PSF),
    .groups = 'drop'
  )

# Generate the violin plot with statistical annotations
plot2 <- ggplot(data = filtered_data,
       aes(x = MONTH_YEAR, y = UNIT_PRICE_PSF, color = MONTH_YEAR)) +
  geom_violin(aes(fill = MONTH_YEAR), size = 0.6, alpha = 0.3, linewidth = 0) +
  geom_boxplot(width = 0.4, outlier.colour = "grey20", outlier.size = 1, 
               outlier.alpha = 0.3) +
  stat_summary(geom = "point",       
               fun.y="mean",         
               colour ="black",        
               size=2) +
  geom_text(data = stats_data, aes(label = sprintf("Mean: %.2f\nMedian: %.2f\nIQR: %.2f", Mean, Median, IQR), 
                                   y = 5500), size = 3, hjust = 0.5) +
  coord_cartesian(ylim = c(400, 6000)) +
  scale_color_manual(values = c("#c73824", "#0477bf", "#9E9E9E", "#0CDBBC")) +
  theme_economist() +
  labs(title = "Unit Price ($PSF) by Month (Year 2024)") +
  scale_y_continuous(breaks = seq(400, 6000, by = 500)) +
  theme(axis.title.x = element_blank(),
        axis.title.y = element_blank(),
        plot.title = element_text(size = 12, hjust = 0.5),
        axis.text = element_text(size = 10),
        legend.position = "none") +
  facet_wrap(~PROPERTY_TYPE)

# Display the plot
print(plot2)

5th iteration (Highlighting Outliers)

# Filter and order the data as before
filtered_data <- realis %>%
  filter(PROPERTY_TYPE %in% c("Apartment", "Condominium"),
         MONTH_YEAR %in% c("Jan-23", "Feb-23", "Mar-23")) %>%
  mutate(MONTH_YEAR = factor(MONTH_YEAR, levels = c("Jan-23", "Feb-23", "Mar-23")))

# Calculate summary statistics for annotations
stats_data <- filtered_data %>%
  group_by(MONTH_YEAR, PROPERTY_TYPE) %>%
  summarise(
    Mean = mean(UNIT_PRICE_PSF),
    Median = median(UNIT_PRICE_PSF),
    IQR = IQR(UNIT_PRICE_PSF),
    .groups = 'drop'
  )

# Generate the violin plot with statistical annotations
plot1 <- ggplot(data = filtered_data,
       aes(x = MONTH_YEAR, y = UNIT_PRICE_PSF, color = MONTH_YEAR)) +
  geom_violin(aes(fill = MONTH_YEAR), size = 0.6, alpha = 0.3, linewidth = 0) +
  geom_boxplot(width = 0.4, outlier.colour = "darkred", outlier.size = 1, outlier.shape = 8, 
               outlier.alpha = 0.8) +
  stat_summary(geom = "point",       
               fun.y="mean",         
               colour ="black",        
               size=2) +
  geom_text(data = stats_data, aes(label = sprintf("Mean: %.2f\nMedian: %.2f\nIQR: %.2f", Mean, Median, IQR), 
                                   y = 5500), size = 3, hjust = 0.5) +
  coord_cartesian(ylim = c(400, 6000)) +
  scale_color_manual(values = c("#c73824", "#0477bf", "#9E9E9E", "#0CDBBC")) +
  theme_economist() +
  labs(title = "Unit Price ($PSF) by Month (Year 2023)") +
  scale_y_continuous(breaks = seq(400, 6000, by = 500)) +
  theme(axis.title.x = element_blank(),
        axis.title.y = element_blank(),
        plot.title = element_text(size = 12, hjust = 0.5),
        axis.text = element_text(size = 10),
        legend.position = "none") +
  facet_wrap(~PROPERTY_TYPE)

# Display the plot
print(plot1)

4 Improved Visualisation

Show the code 
# Filter and order the data as before
filtered_data <- realis %>%
  filter(PROPERTY_TYPE %in% c("Apartment", "Condominium"),
         MONTH_YEAR %in% c("Jan-24", "Feb-24", "Mar-24")) %>%
  mutate(MONTH_YEAR = factor(MONTH_YEAR, levels = c("Jan-24", "Feb-24", "Mar-24")))

# Calculate summary statistics for annotations
stats_data <- filtered_data %>%
  group_by(MONTH_YEAR, PROPERTY_TYPE) %>%
  summarise(
    Mean = mean(UNIT_PRICE_PSF),
    Median = median(UNIT_PRICE_PSF),
    IQR = IQR(UNIT_PRICE_PSF),
    .groups = 'drop'
  )

# Generate the violin plot with statistical annotations
plot1 <- ggplot(data = filtered_data,
       aes(x = MONTH_YEAR, y = UNIT_PRICE_PSF, color = MONTH_YEAR)) +
  geom_violin(aes(fill = MONTH_YEAR), size = 0.6, alpha = 0.3, linewidth = 0) +
  geom_boxplot(width = 0.4, outlier.colour = "darkred", outlier.size = 1, outlier.shape = 8, 
               outlier.alpha = 0.8) +
  stat_summary(geom = "point",       
               fun.y="mean",         
               colour ="black",        
               size=2) +
  geom_text(data = stats_data, aes(label = sprintf("Mean: %.2f\nMedian: %.2f\nIQR: %.2f", Mean, Median, IQR), 
                                   y = 5500), size = 3, hjust = 0.5) +
  coord_cartesian(ylim = c(400, 6000)) +
  scale_color_manual(values = c("#c73824", "#0477bf", "#9E9E9E", "#0CDBBC")) +
  theme_economist() +
  labs(title = "Unit Price ($PSF) by Month (Year 2024)") +
  scale_y_continuous(breaks = seq(400, 6000, by = 500)) +
  theme(axis.title.x = element_blank(),
        axis.title.y = element_blank(),
        plot.title = element_text(size = 12, hjust = 0.5),
        axis.text = element_text(size = 10),
        legend.position = "none") +
  facet_wrap(~PROPERTY_TYPE)

# Display the plot
print(plot1)

Show the code 
# Filter and order the data as before
filtered_data <- realis %>%
  filter(PROPERTY_TYPE %in% c("Apartment", "Condominium"),
         MONTH_YEAR %in% c("Jan-23", "Feb-23", "Mar-23")) %>%
  mutate(MONTH_YEAR = factor(MONTH_YEAR, levels = c("Jan-23", "Feb-23", "Mar-23")))

# Calculate summary statistics for annotations
stats_data <- filtered_data %>%
  group_by(MONTH_YEAR, PROPERTY_TYPE) %>%
  summarise(
    Mean = mean(UNIT_PRICE_PSF),
    Median = median(UNIT_PRICE_PSF),
    IQR = IQR(UNIT_PRICE_PSF),
    .groups = 'drop'
  )

# Generate the violin plot with statistical annotations
plot1 <- ggplot(data = filtered_data,
       aes(x = MONTH_YEAR, y = UNIT_PRICE_PSF, color = MONTH_YEAR)) +
  geom_violin(aes(fill = MONTH_YEAR), size = 0.6, alpha = 0.3, linewidth = 0) +
  geom_boxplot(width = 0.4, outlier.colour = "darkred", outlier.size = 1, outlier.shape = 8, 
               outlier.alpha = 0.8) +
  stat_summary(geom = "point",       
               fun.y="mean",         
               colour ="black",        
               size=2) +
  geom_text(data = stats_data, aes(label = sprintf("Mean: %.2f\nMedian: %.2f\nIQR: %.2f", Mean, Median, IQR), 
                                   y = 5500), size = 3, hjust = 0.5) +
  coord_cartesian(ylim = c(400, 6000)) +
  scale_color_manual(values = c("#c73824", "#0477bf", "#9E9E9E", "#0CDBBC")) +
  theme_economist() +
  labs(title = "Unit Price ($PSF) by Month (Year 2023)") +
  scale_y_continuous(breaks = seq(400, 6000, by = 500)) +
  theme(axis.title.x = element_blank(),
        axis.title.y = element_blank(),
        plot.title = element_text(size = 12, hjust = 0.5),
        axis.text = element_text(size = 10),
        legend.position = "none") +
  facet_wrap(~PROPERTY_TYPE)

# Display the plot
print(plot1)

5 Key Takeaways

Overall, the selected peer’s work was done up relatively well.

The processes for the data visualisation makeover in this take-home exercise illustrates the utmost importance of attention to detail when making a plot. Here are some pointers which I found were useful:

  • To check the data

    • It goes without saying that data is the core element of any chart or graph. If the data is unreliable, the graph will also be unreliable. Therefore, it’s crucial to ensure your data is accurate. Begin by creating straightforward graphs to identify any outliers or unusual spikes. Always double-check anything that looks off. You may find a surprising number of data entry errors in the spreadsheets you receive.

  • Choosing of colours

    • Effective use of color can significantly enhance and clarify a presentation, while poor use of color can lead to confusion and obscurity. Although color adds an aesthetic quality, its primary role in displaying information is functional. The key is to consider what information needs to be conveyed and to determine if and how color can improve the communication of that information.

  • Highlighting what’s important

    • To effectively communicate a message, it’s essential to direct your audience’s attention to the data under analysis. Start with a title that captures the essence of your insight. Then, emphasize your data visually while maintaining other data in a subdued manner in the background, providing context and enabling comparisons.

6 References