17 Visualising and Analysing Time-oriented Data

Published

December 4, 2023

Modified

February 11, 2024

17.1 Learning Outcome

By the end of this hands-on exercise you will be able create the followings data visualisation by using R packages:

plotting a calender heatmap by using ggplot2 functions,
plotting a cycle plot by using ggplot2 function,
plotting a slopegraph
plotting a horizon chart

17.2 Getting Started

17.3 Do It Yourself

Write a code chunk to check, install and launch the following R packages: scales, viridis, lubridate, ggthemes, gridExtra, readxl, knitr, data.table and tidyverse.

Show the code

pacman::p_load(scales, viridis, lubridate, ggthemes, gridExtra, readxl, knitr, data.table, CGPfunctions, ggHoriPlot, tidyverse)

package 'rootSolve' successfully unpacked and MD5 sums checked
package 'lmom' successfully unpacked and MD5 sums checked
package 'expm' successfully unpacked and MD5 sums checked
package 'Exact' successfully unpacked and MD5 sums checked
package 'gld' successfully unpacked and MD5 sums checked
package 'productplots' successfully unpacked and MD5 sums checked
package 'libcoin' successfully unpacked and MD5 sums checked
package 'inum' successfully unpacked and MD5 sums checked
package 'DescTools' successfully unpacked and MD5 sums checked
package 'ggmosaic' successfully unpacked and MD5 sums checked
package 'partykit' successfully unpacked and MD5 sums checked
package 'CGPfunctions' successfully unpacked and MD5 sums checked

The downloaded binary packages are in
    C:\Users\tskam\AppData\Local\Temp\Rtmp8Eo0Zn\downloaded_packages
package 'ggHoriPlot' successfully unpacked and MD5 sums checked

The downloaded binary packages are in
    C:\Users\tskam\AppData\Local\Temp\Rtmp8Eo0Zn\downloaded_packages

17.4 Plotting Calendar Heatmap

In this section, you will learn how to plot a calender heatmap programmatically by using ggplot2 package.

By the end of this section, you will be able to:

plot a calender heatmap by using ggplot2 functions and extension,
to write function using R programming,
to derive specific date and time related field by using base R and lubridate packages
to perform data preparation task by using tidyr and dplyr packages.

17.4.1 The Data

For the purpose of this hands-on exercise, eventlog.csv file will be used. This data file consists of 199,999 rows of time-series cyber attack records by country.

17.4.2 Importing the data

First, you will use the code chunk below to import eventlog.csv file into R environment and called the data frame as attacks.

attacks <- read_csv("data/eventlog.csv")

17.4.3 Examining the data structure

It is always a good practice to examine the imported data frame before further analysis is performed.

For example, kable() can be used to review the structure of the imported data frame.

kable(head(attacks))

timestamp	source_country	tz
2015-03-12 15:59:16	CN	Asia/Shanghai
2015-03-12 16:00:48	FR	Europe/Paris
2015-03-12 16:02:26	CN	Asia/Shanghai
2015-03-12 16:02:38	US	America/Chicago
2015-03-12 16:03:22	CN	Asia/Shanghai
2015-03-12 16:03:45	CN	Asia/Shanghai

There are three columns, namely timestamp, source_country and tz.

timestamp field stores date-time values in POSIXct format.
source_country field stores the source of the attack. It is in ISO 3166-1 alpha-2 country code.
tz field stores time zone of the source IP address.

timestamp	source_country	tz
2015-03-12 15:59:16	CN	Asia/Shanghai
2015-03-12 16:00:48	FR	Europe/Paris
2015-03-12 16:02:26	CN	Asia/Shanghai
2015-03-12 16:02:38	US	America/Chicago
2015-03-12 16:03:22	CN	Asia/Shanghai
2015-03-12 16:03:45	CN	Asia/Shanghai

17.4.4 Data Preparation

Step 1: Deriving weekday and hour of day fields

Before we can plot the calender heatmap, two new fields namely wkday and hour need to be derived. In this step, we will write a function to perform the task.

make_hr_wkday <- function(ts, sc, tz) {
  real_times <- ymd_hms(ts, 
                        tz = tz[1], 
                        quiet = TRUE)
  dt <- data.table(source_country = sc,
                   wkday = weekdays(real_times),
                   hour = hour(real_times))
  return(dt)
  }

Note

ymd_hms() and hour() are from lubridate package, and
weekdays() is a base R function.

Step 2: Deriving the attacks tibble data frame

wkday_levels <- c('Saturday', 'Friday', 
                  'Thursday', 'Wednesday', 
                  'Tuesday', 'Monday', 
                  'Sunday')

attacks <- attacks %>%
  group_by(tz) %>%
  do(make_hr_wkday(.$timestamp, 
                   .$source_country, 
                   .$tz)) %>% 
  ungroup() %>% 
  mutate(wkday = factor(
    wkday, levels = wkday_levels),
    hour  = factor(
      hour, levels = 0:23))

Note

Beside extracting the necessary data into attacks data frame, mutate() of dplyr package is used to convert wkday and hour fields into factor so they’ll be ordered when plotting

Table below shows the tidy tibble table after processing.

kable(head(attacks))

tz	source_country	wkday	hour
Africa/Cairo	BG	Saturday	20
Africa/Cairo	TW	Sunday	6
Africa/Cairo	TW	Sunday	8
Africa/Cairo	CN	Sunday	11
Africa/Cairo	US	Sunday	15
Africa/Cairo	CA	Monday	11

17.4.5 Building the Calendar Heatmaps

grouped <- attacks %>% 
  count(wkday, hour) %>% 
  ungroup() %>%
  na.omit()

ggplot(grouped, 
       aes(hour, 
           wkday, 
           fill = n)) + 
geom_tile(color = "white", 
          size = 0.1) + 
theme_tufte(base_family = "Helvetica") + 
coord_equal() +
scale_fill_gradient(name = "# of attacks",
                    low = "sky blue", 
                    high = "dark blue") +
labs(x = NULL, 
     y = NULL, 
     title = "Attacks by weekday and time of day") +
theme(axis.ticks = element_blank(),
      plot.title = element_text(hjust = 0.5),
      legend.title = element_text(size = 8),
      legend.text = element_text(size = 6) )

Things to learn from the code chunk

a tibble data table called grouped is derived by aggregating the attack by wkday and hour fields.
a new field called n is derived by using group_by() and count() functions.
na.omit() is used to exclude missing value.
geom_tile() is used to plot tiles (grids) at each x and y position. color and size arguments are used to specify the border color and line size of the tiles.
theme_tufte() of ggthemes package is used to remove unnecessary chart junk. To learn which visual components of default ggplot2 have been excluded, you are encouraged to comment out this line to examine the default plot.
coord_equal() is used to ensure the plot will have an aspect ratio of 1:1.
scale_fill_gradient() function is used to creates a two colour gradient (low-high).

Then we can simply group the count by hour and wkday and plot it, since we know that we have values for every combination there’s no need to further preprocess the data.

17.4.6 Building Multiple Calendar Heatmaps

Challenge: Building multiple heatmaps for the top four countries with the highest number of attacks.

17.4.7 Plotting Multiple Calendar Heatmaps

Step 1: Deriving attack by country object

In order to identify the top 4 countries with the highest number of attacks, you are required to do the followings:

count the number of attacks by country,
calculate the percent of attackes by country, and
save the results in a tibble data frame.

attacks_by_country <- count(
  attacks, source_country) %>%
  mutate(percent = percent(n/sum(n))) %>%
  arrange(desc(n))

Step 2: Preparing the tidy data frame

In this step, you are required to extract the attack records of the top 4 countries from attacks data frame and save the data in a new tibble data frame (i.e. top4_attacks).

top4 <- attacks_by_country$source_country[1:4]
top4_attacks <- attacks %>%
  filter(source_country %in% top4) %>%
  count(source_country, wkday, hour) %>%
  ungroup() %>%
  mutate(source_country = factor(
    source_country, levels = top4)) %>%
  na.omit()

17.4.8 Plotting Multiple Calendar Heatmaps

Step 3: Plotting the Multiple Calender Heatmap by using ggplot2 package.

ggplot(top4_attacks, 
       aes(hour, 
           wkday, 
           fill = n)) + 
  geom_tile(color = "white", 
          size = 0.1) + 
  theme_tufte(base_family = "Helvetica") + 
  coord_equal() +
  scale_fill_gradient(name = "# of attacks",
                    low = "sky blue", 
                    high = "dark blue") +
  facet_wrap(~source_country, ncol = 2) +
  labs(x = NULL, y = NULL, 
     title = "Attacks on top 4 countries by weekday and time of day") +
  theme(axis.ticks = element_blank(),
        axis.text.x = element_text(size = 7),
        plot.title = element_text(hjust = 0.5),
        legend.title = element_text(size = 8),
        legend.text = element_text(size = 6) )

17.5 Plotting Cycle Plot

In this section, you will learn how to plot a cycle plot showing the time-series patterns and trend of visitor arrivals from Vietnam programmatically by using ggplot2 functions.

17.5.1 Step 1: Data Import

For the purpose of this hands-on exercise, arrivals_by_air.xlsx will be used.

The code chunk below imports arrivals_by_air.xlsx by using read_excel() of readxl package and save it as a tibble data frame called air.

air <- read_excel("data/arrivals_by_air.xlsx")

17.5.2 Step 2: Deriving month and year fields

Next, two new fields called month and year are derived from Month-Year field.

air$month <- factor(month(air$`Month-Year`), 
                    levels=1:12, 
                    labels=month.abb, 
                    ordered=TRUE) 
air$year <- year(ymd(air$`Month-Year`))

17.5.3 Step 4: Extracting the target country

Next, the code chunk below is use to extract data for the target country (i.e. Vietnam)

Vietnam <- air %>% 
  select(`Vietnam`, 
         month, 
         year) %>%
  filter(year >= 2010)

17.5.4 Step 5: Computing year average arrivals by month

The code chunk below uses group_by() and summarise() of dplyr to compute year average arrivals by month.

hline.data <- Vietnam %>% 
  group_by(month) %>%
  summarise(avgvalue = mean(`Vietnam`))

17.5.5 Srep 6: Plotting the cycle plot

The code chunk below is used to plot the cycle plot as shown in Slide 12/23.

ggplot() + 
  geom_line(data=Vietnam,
            aes(x=year, 
                y=`Vietnam`, 
                group=month), 
            colour="black") +
  geom_hline(aes(yintercept=avgvalue), 
             data=hline.data, 
             linetype=6, 
             colour="red", 
             size=0.5) + 
  facet_grid(~month) +
  labs(axis.text.x = element_blank(),
       title = "Visitor arrivals from Vietnam by air, Jan 2010-Dec 2019") +
  xlab("") +
  ylab("No. of Visitors") +
  theme_tufte(base_family = "Helvetica")

17.6 Plotting Slopegraph

In this section you will learn how to plot a slopegraph by using R.

Before getting start, make sure that CGPfunctions has been installed and loaded onto R environment. Then, refer to Using newggslopegraph to learn more about the function. Lastly, read more about newggslopegraph() and its arguments by referring to this link.

17.6.1 Step 1: Data Import

Import the rice data set into R environment by using the code chunk below.

rice <- read_csv("data/rice.csv")

17.6.2 Step 2: Plotting the slopegraph

Next, code chunk below will be used to plot a basic slopegraph as shown below.

rice %>% 
  mutate(Year = factor(Year)) %>%
  filter(Year %in% c(1961, 1980)) %>%
  newggslopegraph(Year, Yield, Country,
                Title = "Rice Yield of Top 11 Asian Counties",
                SubTitle = "1961-1980",
                Caption = "Prepared by: Dr. Kam Tin Seong")

Thing to learn from the code chunk above

For effective data visualisation design, factor() is used convert the value type of Year field from numeric to factor.