pacman::p_load(treemap, treemapify, tidyverse) Hands-on Exercise 5e: Treemap Visualisation with R
1 Getting Started
In this exercise, we will use treemap and tidyverse in RStudio.
The code chunk below uses p_load() of pacman package to check if these packages are installed in the computer and load them onto your working R environment.
The code chunk below imports the data into R environment by using read_csv() function of readr package.
realis2018 <- read_csv("data/realis2018.csv")We need to manipulate and prepare a data.frame that is appropriate for treemap visualization:
group transaction records by Project Name, Planning Region, Planning Area, Property Type and Type of Sale, and
compute Total Unit Sold, Total Area, Median Unit Price and Median Transacted Price by applying appropriate summary statistics on No. of Units, Area (sqm), Unit Price ($ psm) and Transacted Price ($) respectively.
Two key verbs of dplyr package, namely: group_by() and summarize() will be used to perform these steps.
group_by() breaks down a data.frame into specified groups of rows. When you then apply the verbs above on the resulting object they’ll be automatically applied “by group”.
grouped select() is the same as ungrouped select(), except that grouping variables are always retained.
grouped arrange() is the same as ungrouped; unless you set .by_group = TRUE, in which case it orders first by the grouping variables.
mutate() and filter() are most useful in conjunction with window functions (like rank(), or min(x) == x). They are described in detail in vignette(“window-functions”).
sample_n() and sample_frac() sample the specified number/fraction of rows in each group.
summarise() computes the summary for each group.
Grouped summaries without the Pipe
The code chunk below shows a typical two lines code approach to perform the steps
realis2018_grouped <- group_by(realis2018, `Project Name`,
`Planning Region`, `Planning Area`,
`Property Type`, `Type of Sale`)
realis2018_summarised <- summarise(realis2018_grouped,
`Total Unit Sold` = sum(`No. of Units`, na.rm = TRUE),
`Total Area` = sum(`Area (sqm)`, na.rm = TRUE),
`Median Unit Price ($ psm)` = median(`Unit Price ($ psm)`, na.rm = TRUE),
`Median Transacted Price` = median(`Transacted Price ($)`, na.rm = TRUE))
Note
- Aggregation functions such as sum() and meadian() obey the usual rule of missing values: if there’s any missing value in the input, the output will be a missing value. The argument na.rm = TRUE removes the missing values prior to computation
Grouped summaries with the Pipe
The code chunk below shows a more efficient way to tackle the same processes by using the pipe, %>%:
realis2018_summarised <- realis2018 %>%
group_by(`Project Name`,`Planning Region`,
`Planning Area`, `Property Type`,
`Type of Sale`) %>%
summarise(`Total Unit Sold` = sum(`No. of Units`, na.rm = TRUE),
`Total Area` = sum(`Area (sqm)`, na.rm = TRUE),
`Median Unit Price ($ psm)` = median(`Unit Price ($ psm)`, na.rm = TRUE),
`Median Transacted Price` = median(`Transacted Price ($)`, na.rm = TRUE))2 Designing Treemap with treemap Package
treemap package is a R package specially designed to offer great flexibility in drawing treemaps. The core function, namely: treemap() offers at least 43 arguments.
2.1 Designing a static treemap
In this section, treemap() of Treemap package is used to plot a treemap showing the distribution of median unit prices and total unit sold of resale condominium by geographic hierarchy in 2017.
First, we will select records belongs to resale condominium property type from realis2018_selected data frame.
realis2018_selected <- realis2018_summarised %>%
filter(`Property Type` == "Condominium", `Type of Sale` == "Resale")2.2 Using the basic arguments
The code chunk below designed a treemap by using three core arguments of treemap(), namely: index, vSize and vColor.
treemap(realis2018_selected,
index=c("Planning Region", "Planning Area", "Project Name"),
vSize="Total Unit Sold",
vColor="Median Unit Price ($ psm)",
title="Resale Condominium by Planning Region and Area, 2017",
title.legend = "Median Unit Price (S$ per sq. m)")
Things to learn from the three arguments used
index
The index vector must consist of at least two column names or else no hierarchy treemap will be plotted.
If multiple column names are provided, such as the code chunk above, the first name is the highest aggregation level, the second name the second highest aggregation level, and so on.
vSize
- The column must not contain negative values. This is because it’s vaues will be used to map the sizes of the rectangles of the treemaps.
Warning
The treemap above was wrongly coloured. For a correctly designed treemap, the colours of the rectagles should be in different intensity showing, in our case, median unit prices.
For treemap(), vColor is used in combination with the argument type to determines the colours of the rectangles. Without defining type, like the code chunk above, treemap() assumes type = index, in our case, the hierarchy of planning areas.
2.3 Working with vColor and type arguments
In the code chunk below, type argument is define as value.
treemap(realis2018_selected,
index=c("Planning Region", "Planning Area", "Project Name"),
vSize="Total Unit Sold",
vColor="Median Unit Price ($ psm)",
type = "value",
title="Resale Condominium by Planning Region and Area, 2017",
title.legend = "Median Unit Price (S$ per sq. m)"
)
Thinking to learn from the code chunk above
The rectangles are colored with different intensity of green, reflecting their respective median unit prices.
The legend reveals that the values are binned into ten bins, i.e. 0-5000, 5000-10000, etc. with an equal interval of 5000.
2.4 The “value” type treemap
The code chunk below shows a value type treemap.
treemap(realis2018_selected,
index=c("Planning Region", "Planning Area", "Project Name"),
vSize="Total Unit Sold",
vColor="Median Unit Price ($ psm)",
type="value",
palette="RdYlBu",
title="Resale Condominium by Planning Region and Area, 2017",
title.legend = "Median Unit Price (S$ per sq. m)"
)
Thing to learn from the code chunk above
although the colour palette used is RdYlBu but there are no red rectangles in the treemap above. This is because all the median unit prices are positive.
The reason why we see only 5000 to 45000 in the legend is because the range argument is by default c(min(values, max(values)) with some pretty rounding.
2.5 The “manual” type treemap
The “manual” type does not interpret the values as the “value” type does. Instead, the value range is mapped linearly to the colour palette.
The code chunk below shows a manual type treemap.
treemap(realis2018_selected,
index=c("Planning Region", "Planning Area", "Project Name"),
vSize="Total Unit Sold",
vColor="Median Unit Price ($ psm)",
type="manual",
palette="RdYlBu",
title="Resale Condominium by Planning Region and Area, 2017",
title.legend = "Median Unit Price (S$ per sq. m)"
)
Things to learn from the code chunk above:
- The color scheme used is very confusing. This is because mapping = (min(values), mean(range(values)), max(values)). It is not wise to use diverging color palette such as RdYlBu if the values are all positive or negative
To solve this issue, a single color palette such as Blues should be used.
treemap(realis2018_selected,
index=c("Planning Region", "Planning Area", "Project Name"),
vSize="Total Unit Sold",
vColor="Median Unit Price ($ psm)",
type="manual",
palette="Blues",
title="Resale Condominium by Planning Region and Area, 2017",
title.legend = "Median Unit Price (S$ per sq. m)")
2.6 Working with algorithm argument
The code chunk below plots a squarified treemap by changing the algorithm argument.
treemap(realis2018_selected,
index=c("Planning Region", "Planning Area", "Project Name"),
vSize="Total Unit Sold",
vColor="Median Unit Price ($ psm)",
type="manual",
palette="Blues",
algorithm = "squarified",
title="Resale Condominium by Planning Region and Area, 2017",
title.legend = "Median Unit Price (S$ per sq. m)")
2.7 Using sortID
When “pivotSize” algorithm is used, sortID argument can be used to dertemine the order in which the rectangles are placed from top left to bottom right.
treemap(realis2018_selected,
index=c("Planning Region", "Planning Area", "Project Name"),
vSize="Total Unit Sold",
vColor="Median Unit Price ($ psm)",
type="manual",
palette="Blues",
algorithm = "pivotSize",
sortID = "Median Transacted Price",
title="Resale Condominium by Planning Region and Area, 2017",
title.legend = "Median Unit Price (S$ per sq. m)")
3 Designing Treemap using treemapify Package
treemapify is a R package specially developed to draw treemaps in ggplot2.
3.1 Designing a basic treemap
ggplot(data=realis2018_selected,
aes(area = `Total Unit Sold`,
fill = `Median Unit Price ($ psm)`),
layout = "scol",
start = "bottomleft") +
geom_treemap() +
scale_fill_gradient(low = "light blue", high = "blue")
3.2 Defining hierarchy
Below code chunk is to group by Planning Region
ggplot(data=realis2018_selected,
aes(area = `Total Unit Sold`,
fill = `Median Unit Price ($ psm)`,
subgroup = `Planning Region`),
start = "topleft") +
geom_treemap()
Below code chunk is to group by Planning Area
ggplot(data=realis2018_selected,
aes(area = `Total Unit Sold`,
fill = `Median Unit Price ($ psm)`,
subgroup = `Planning Region`,
subgroup2 = `Planning Area`)) +
geom_treemap()
Adding boundary line
ggplot(data=realis2018_selected,
aes(area = `Total Unit Sold`,
fill = `Median Unit Price ($ psm)`,
subgroup = `Planning Region`,
subgroup2 = `Planning Area`)) +
geom_treemap() +
geom_treemap_subgroup2_border(colour = "gray40",
size = 2) +
geom_treemap_subgroup_border(colour = "gray20")
4 Designing Interactive Treemap using d3treeR
4.1 Installing d3treeR package
install.packages("devtools")library(devtools)
install_github("timelyportfolio/d3treeR",force = TRUE)xfun (0.41 -> 0.42 ) [CRAN]
igraph (2.0.1.1 -> 2.0.2 ) [CRAN]
ggplot2 (3.4.4 -> 3.5.0 ) [CRAN]
data.table (1.15.0 -> 1.15.2) [CRAN]
There is a binary version available but the source version is later:
binary source needs_compilation
data.table 1.15.0 1.15.2 TRUE
package 'xfun' successfully unpacked and MD5 sums checked
package 'igraph' successfully unpacked and MD5 sums checked
The downloaded binary packages are in
C:\Users\kbuat\AppData\Local\Temp\RtmpIJHEIR\downloaded_packages
── R CMD build ─────────────────────────────────────────────────────────────────
* checking for file 'C:\Users\kbuat\AppData\Local\Temp\RtmpIJHEIR\remotes6df03d711346\d3treeR-d3treeR-ebb833d/DESCRIPTION' ... OK
* preparing 'd3treeR':
* checking DESCRIPTION meta-information ... OK
* checking for LF line-endings in source and make files and shell scripts
* checking for empty or unneeded directories
Omitted 'LazyData' from DESCRIPTION
* building 'd3treeR_0.1.tar.gz'
library(d3treeR)4.2 Designing An Interactive Treemap
The codes below perform two processes.
- treemap() is used to build a treemap by using selected variables in condominium data.frame. The treemap created is save as object called tm.
tm <- treemap(realis2018_summarised,
index=c("Planning Region", "Planning Area"),
vSize="Total Unit Sold",
vColor="Median Unit Price ($ psm)",
type="value",
title="Private Residential Property Sold, 2017",
title.legend = "Median Unit Price (S$ per sq. m)")
- Then d3tree() is used to build an interactive treemap.
d3tree(tm,rootname = "Singapore")