What is the Demographics of the Population in Singapore by Planning Area?
IS428 - Visual Analytics for Business Intelligence – Assignment 4
Author: Peng You Xuan
Date: 21 Mar 2021
1. Overview
This makeover aims to visualize the demographics of the population in Singapore based on a data found on Singstat which provides information on the Planning Area Subzone, Age Group, Sex and Type of Dwelling (https://www.singstat.gov.sg/find-data/search-by-theme/population/geographic-distribution/latest-data) . The scope of this visualization is in 2019 and there are a total of 98,192 rows of data that we can work with.
1.1 Sketch of Proposed Data Visualization
2. Major Data and Design Challenges
The challenge for this visualization is the data preparation of the dataset. This was something new, rather than immediately using the dataset as it is, data have to be looked through and adjusted to ensure that the final visualization is able to provide sufficient insights for readers.
Another challenge for the data visualization is exploring the geofacet library, something which was not covered in class previously. The geofacet library also contains 2 sets of grids which has the grid of Singapore. Hence data has to be seen before we could use it. There were also locations that are named differently from the original dataset, causing it to not work properly initially. This would require changes to be made to the geofacet library dataset, to name the planning area name properly. This issue was prominent especially when exploring with sg_planning_area_grid2. To aid in the usage of the geofacet library, the following website was used as a point of guidance: <https://cran.r-project.org/web/packages/geofacet/vignettes/geofacet.html>.
Lastly, the requirement was to plot a visualization which had to show the demographics based on the planning area. this was difficult as there were many planning areas in Singapore, and it was not easy to make the visualization readable for the readers, as the initial visualization had words that were too small (e.g. name of Planning Area) that were not readable. To overcome this challenge, research was done to find out how to adjust the font sizes of the labels and axes appropriately.
3. Data Visualization
3.1 Installation of packages required
packages = c('tidyverse', "ggplot2", "geofacet")
for(p in packages){
if(!require(p, character.only = T)){
install.packages(p)
}
library(p, character.only = T)
}## Loading required package: tidyverse## -- Attaching packages --------------------------------------- tidyverse 1.3.0 --## v ggplot2 3.3.3 v purrr 0.3.4
## v tibble 3.0.6 v dplyr 1.0.4
## v tidyr 1.1.2 v stringr 1.4.0
## v readr 1.4.0 v forcats 0.5.1## -- Conflicts ------------------------------------------ tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()## Loading required package: geofacet3.2 Data Preparation
- Read the data from the dataset downloaded from Singstat
pop_data <- read_csv("singapore-residents-by-planning-areasubzone-age-group-sex-and-type-of-dwelling-june-20112020/respopagesextod2011to2020.csv")##
## -- Column specification --------------------------------------------------------
## cols(
## PA = col_character(),
## SZ = col_character(),
## AG = col_character(),
## Sex = col_character(),
## TOD = col_character(),
## Pop = col_double(),
## Time = col_double()
## )pop_data## # A tibble: 984,656 x 7
## PA SZ AG Sex TOD Pop Time
## <chr> <chr> <chr> <chr> <chr> <dbl> <dbl>
## 1 Ang Mo K~ Ang Mo Kio Town~ 0_to~ Males HDB 1- and 2-Room Flats 0 2011
## 2 Ang Mo K~ Ang Mo Kio Town~ 0_to~ Males HDB 3-Room Flats 10 2011
## 3 Ang Mo K~ Ang Mo Kio Town~ 0_to~ Males HDB 4-Room Flats 30 2011
## 4 Ang Mo K~ Ang Mo Kio Town~ 0_to~ Males HDB 5-Room and Executive~ 50 2011
## 5 Ang Mo K~ Ang Mo Kio Town~ 0_to~ Males HUDC Flats (excluding th~ 0 2011
## 6 Ang Mo K~ Ang Mo Kio Town~ 0_to~ Males Landed Properties 0 2011
## 7 Ang Mo K~ Ang Mo Kio Town~ 0_to~ Males Condominiums and Other A~ 40 2011
## 8 Ang Mo K~ Ang Mo Kio Town~ 0_to~ Males Others 0 2011
## 9 Ang Mo K~ Ang Mo Kio Town~ 0_to~ Femal~ HDB 1- and 2-Room Flats 0 2011
## 10 Ang Mo K~ Ang Mo Kio Town~ 0_to~ Femal~ HDB 3-Room Flats 10 2011
## # ... with 984,646 more rows- Filter out data that is from 2019
pop_data <- pop_data %>% filter(Time == 2019)
pop_data## # A tibble: 98,192 x 7
## PA SZ AG Sex TOD Pop Time
## <chr> <chr> <chr> <chr> <chr> <dbl> <dbl>
## 1 Ang Mo K~ Ang Mo Kio Town~ 0_to~ Males HDB 1- and 2-Room Flats 0 2019
## 2 Ang Mo K~ Ang Mo Kio Town~ 0_to~ Males HDB 3-Room Flats 10 2019
## 3 Ang Mo K~ Ang Mo Kio Town~ 0_to~ Males HDB 4-Room Flats 10 2019
## 4 Ang Mo K~ Ang Mo Kio Town~ 0_to~ Males HDB 5-Room and Executive~ 20 2019
## 5 Ang Mo K~ Ang Mo Kio Town~ 0_to~ Males HUDC Flats (excluding th~ 0 2019
## 6 Ang Mo K~ Ang Mo Kio Town~ 0_to~ Males Landed Properties 0 2019
## 7 Ang Mo K~ Ang Mo Kio Town~ 0_to~ Males Condominiums and Other A~ 50 2019
## 8 Ang Mo K~ Ang Mo Kio Town~ 0_to~ Males Others 0 2019
## 9 Ang Mo K~ Ang Mo Kio Town~ 0_to~ Femal~ HDB 1- and 2-Room Flats 0 2019
## 10 Ang Mo K~ Ang Mo Kio Town~ 0_to~ Femal~ HDB 3-Room Flats 10 2019
## # ... with 98,182 more rows- Rename the columns for the dataset, to make it clearer for readers to understand the data
names(pop_data)<-c("PlanningArea","Subzone","AgeGroup","Gender","TypeofDwelling","Population","Year")
pop_data## # A tibble: 98,192 x 7
## PlanningArea Subzone AgeGroup Gender TypeofDwelling Population Year
## <chr> <chr> <chr> <chr> <chr> <dbl> <dbl>
## 1 Ang Mo Kio Ang Mo Kio~ 0_to_4 Males HDB 1- and 2-Room ~ 0 2019
## 2 Ang Mo Kio Ang Mo Kio~ 0_to_4 Males HDB 3-Room Flats 10 2019
## 3 Ang Mo Kio Ang Mo Kio~ 0_to_4 Males HDB 4-Room Flats 10 2019
## 4 Ang Mo Kio Ang Mo Kio~ 0_to_4 Males HDB 5-Room and Exe~ 20 2019
## 5 Ang Mo Kio Ang Mo Kio~ 0_to_4 Males HUDC Flats (exclud~ 0 2019
## 6 Ang Mo Kio Ang Mo Kio~ 0_to_4 Males Landed Properties 0 2019
## 7 Ang Mo Kio Ang Mo Kio~ 0_to_4 Males Condominiums and O~ 50 2019
## 8 Ang Mo Kio Ang Mo Kio~ 0_to_4 Males Others 0 2019
## 9 Ang Mo Kio Ang Mo Kio~ 0_to_4 Femal~ HDB 1- and 2-Room ~ 0 2019
## 10 Ang Mo Kio Ang Mo Kio~ 0_to_4 Femal~ HDB 3-Room Flats 10 2019
## # ... with 98,182 more rows- The Age group field would not sort accurately, hence the Age group from 5 to 9 has to be renamed to allot the data to be sorted properly
pop_data <- pop_data %>% mutate(AgeGroup_2 = case_when(AgeGroup == "5_to_9" ~ "05_to_9",
TRUE ~ as.character(AgeGroup)))
pop_data## # A tibble: 98,192 x 8
## PlanningArea Subzone AgeGroup Gender TypeofDwelling Population Year
## <chr> <chr> <chr> <chr> <chr> <dbl> <dbl>
## 1 Ang Mo Kio Ang Mo Kio~ 0_to_4 Males HDB 1- and 2-Room ~ 0 2019
## 2 Ang Mo Kio Ang Mo Kio~ 0_to_4 Males HDB 3-Room Flats 10 2019
## 3 Ang Mo Kio Ang Mo Kio~ 0_to_4 Males HDB 4-Room Flats 10 2019
## 4 Ang Mo Kio Ang Mo Kio~ 0_to_4 Males HDB 5-Room and Exe~ 20 2019
## 5 Ang Mo Kio Ang Mo Kio~ 0_to_4 Males HUDC Flats (exclud~ 0 2019
## 6 Ang Mo Kio Ang Mo Kio~ 0_to_4 Males Landed Properties 0 2019
## 7 Ang Mo Kio Ang Mo Kio~ 0_to_4 Males Condominiums and O~ 50 2019
## 8 Ang Mo Kio Ang Mo Kio~ 0_to_4 Males Others 0 2019
## 9 Ang Mo Kio Ang Mo Kio~ 0_to_4 Femal~ HDB 1- and 2-Room ~ 0 2019
## 10 Ang Mo Kio Ang Mo Kio~ 0_to_4 Femal~ HDB 3-Room Flats 10 2019
## # ... with 98,182 more rows, and 1 more variable: AgeGroup_2 <chr>- View the contents of the geofacet library for the SG planning area grid. To understand what is needed to plot the graphs using geofacet.
sg_planning_area_grid1## row col code name
## 1 1 7 WD Woodlands
## 2 1 9 SE Simpang
## 3 1 8 SB Sembawang
## 4 1 10 SL Seletar
## 5 2 8 YS Yishun
## 6 2 9 SE Sengkang
## 7 2 10 PG Punggol
## 8 2 7 MD Mandai
## 9 3 7 TP Toa Payoh
## 10 3 5 SK Sungei Kadut
## 11 3 9 SG Serangoon
## 12 3 11 PL Paya Lebar
## 13 3 12 PR Pasir Ris
## 14 3 14 NI North Eastern Islands
## 15 3 10 HG Hougang
## 16 3 4 CK Choa Chu Kang
## 17 3 6 BS Bishan
## 18 3 8 AM Ang Mo Kio
## 19 4 4 TH Tengah
## 20 4 6 TL Tanglin
## 21 4 12 TM Tampines
## 22 4 8 RC Rochor
## 23 4 7 NT Newton
## 24 4 3 LK Lim Chu Kang
## 25 4 9 KL Kallang
## 26 4 10 GL Geylang
## 27 4 13 CH Changi
## 28 4 5 CC Central Water Catchment
## 29 4 11 BD Bedok
## 30 5 13 CB Changi Bay
## 31 5 2 WC Western Water Catchment
## 32 5 7 OR Orchard
## 33 5 6 NV Novena
## 34 5 8 MU Museum
## 35 5 10 MP Marine Parade
## 36 5 9 ME Marina East
## 37 5 3 JW Jurong West
## 38 5 5 BP Bukit Panjang
## 39 5 4 BK Bukit Batok
## 40 6 1 TS Tuas
## 41 6 6 RV River Valley
## 42 6 2 PN Pioneer
## 43 6 8 MS Marina South
## 44 6 4 JE Jurong East
## 45 6 7 DT Downtown Core
## 46 6 5 BT Bukit Timah
## 47 6 3 BL Boon Lay
## 48 7 7 SV Straits View
## 49 7 6 SR Singapore River
## 50 7 5 QT Queenstown
## 51 7 4 CL Clementi
## 52 8 7 OT Outram
## 53 8 6 BM Bukit Merah
## 54 9 6 SI Southern Islands
## 55 9 4 WI Western Islandssg_planning_area_grid2## col row code name name_indo
## 1 6 6 ANG MO KIO ANG MO KIO ANG MO KIO
## 2 9 10 BEDOK BEDOK BEDOK
## 3 6 9 BISHAN BISHAN BISHAN
## 4 3 8 BUKIT BATOK BUKIT BATOK BUKIT BATOK
## 5 5 15 BUKIT MERAH BUKIT MERAH BUKIT MERAH
## 6 3 7 BUKIT PANJANG BUKIT PANJANG BUKIT PANJANG
## 7 4 9 BUKIT TIMAH BUKIT TIMAH BUKIT TIMAH
## 8 5 12 CENTRAL CENTRAL CENTRAL
## 9 2 4 CHOA CHU KANG CHOA CHU KANG CHOA CHU KANG
## 10 3 12 CLEMENTI CLEMENTI CLEMENTI
## 11 8 10 GEYLANG GEYLANG GEYLANG
## 12 8 6 HOUGANG HOUGANG HOUGANG
## 13 2 9 JURONG EAST JURONG EAST JURONG EAST
## 14 1 9 JURONG WEST JURONG WEST JURONG WEST
## 15 7 11 KALLANG / WHAMPOA KALLANG / WHAMPOA KALLANG / WHAMPOA
## 16 8 13 MARINE PARADE MARINE PARADE MARINE PARADE
## 17 11 6 PASIR RIS PASIR RIS PASIR RIS
## 18 7 4 PUNGGOL PUNGGOL PUNGGOL
## 19 4 13 QUEENTOWN QUEENTOWN QUEENTOWN
## 20 4 1 SEMBAWANG SEMBAWANG SEMBAWANG
## 21 7 5 SENGKANG SENGKANG SENGKANG
## 22 8 7 SERANGOON SERANGOON SERANGOON
## 23 10 8 TAMPINES TAMPINES TAMPINES
## 24 7 10 TOA PAYOH TOA PAYOH TOA PAYOH
## 25 3 2 WOODLANDS WOODLANDS WOODLANDS
## 26 6 3 YISHUN YISHUN YISHUN- As planning_area_grid1 has more fields that might better represent Singapore and its location, sg_planning_area_grid1 is used for this visualization. To ensure consistency in the dataset, the names were changed to uppercase.
library(geofacet)
sg_planning_area_grid1 <- sg_planning_area_grid1 %>% mutate(name = toupper(name))- Since the to ensure consistency, change the Planning area data to uppercase using toupper()
# convert planning area name to upper case
pop_data <- pop_data %>% mutate(PlanningArea = toupper(PlanningArea))
pop_data## # A tibble: 98,192 x 8
## PlanningArea Subzone AgeGroup Gender TypeofDwelling Population Year
## <chr> <chr> <chr> <chr> <chr> <dbl> <dbl>
## 1 ANG MO KIO Ang Mo Kio~ 0_to_4 Males HDB 1- and 2-Room ~ 0 2019
## 2 ANG MO KIO Ang Mo Kio~ 0_to_4 Males HDB 3-Room Flats 10 2019
## 3 ANG MO KIO Ang Mo Kio~ 0_to_4 Males HDB 4-Room Flats 10 2019
## 4 ANG MO KIO Ang Mo Kio~ 0_to_4 Males HDB 5-Room and Exe~ 20 2019
## 5 ANG MO KIO Ang Mo Kio~ 0_to_4 Males HUDC Flats (exclud~ 0 2019
## 6 ANG MO KIO Ang Mo Kio~ 0_to_4 Males Landed Properties 0 2019
## 7 ANG MO KIO Ang Mo Kio~ 0_to_4 Males Condominiums and O~ 50 2019
## 8 ANG MO KIO Ang Mo Kio~ 0_to_4 Males Others 0 2019
## 9 ANG MO KIO Ang Mo Kio~ 0_to_4 Femal~ HDB 1- and 2-Room ~ 0 2019
## 10 ANG MO KIO Ang Mo Kio~ 0_to_4 Femal~ HDB 3-Room Flats 10 2019
## # ... with 98,182 more rows, and 1 more variable: AgeGroup_2 <chr>2.3 Plotting Visualization
- To be able to test what will be plotted on the grid map, plot a sample graph to view an expanded version of the graph.
ggplot(pop_data, aes(AgeGroup_2, Population, fill = Gender)) +
geom_col(stat="identity", width=1) +
coord_flip()## Warning: Ignoring unknown parameters: stat
4. Final Visualization
Plot all graphs on with the geofacet library. This will show the Planning area location relative to the actual position of the location on the Singapore map.
library(geofacet)
ggplot(pop_data, aes(x=AgeGroup_2, y=Population, fill = Gender)) +
labs(title = "Demographic of Population by Planning Area",
caption = "Source: Singstat", x = "Population", y = "Age Group") +
geom_col(stat="identity") +
theme(axis.title.x = element_text(size=50),
axis.title.y = element_text(size=50),
title = element_text(size=80),
axis.text.x = element_text(size=10),
legend.text = element_text(size = 50),
strip.text.x = element_text(size = 30, face = "bold")) +
coord_flip() +
#facet_wrap(~ Planning Area)
facet_geo(~ PlanningArea, grid = "sg_planning_area_grid1", label = "name")## Warning: Ignoring unknown parameters: stat## Some values in the specified facet_geo column 'PlanningArea' do not
## match the 'name' column of the specified grid and will be removed:
## NORTH-EASTERN ISLANDS
5. Findings from the visualization
- Most of the population of Singapore are found in the upper half of Singapore as seen from the longer bar charts being concentrated in the upper half of the Singapore map layout.
- Senkang and Punggol are planning areas that seems to have a large proportion of their population to be of the older generation as the charts are longer towards the bottom.
- Most of the population is distributed in a normal distribution manner. Suggesting that most of the population are Adults rather than children or Old people.