pacman::p_load(scales, viridis, lubridate, ggthemes, gridExtra, readxl, knitr, data.table, CGPfunctions, ggHoriPlot, tidyverse)Hands-on EX07
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.
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.
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)
}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))Table below shows the tidy tibble table after processing.
kable(head(attacks))| tz | source_country | wkday | hour |
|---|---|---|---|
| Africa/Cairo | BG | NA | 20 |
| Africa/Cairo | TW | NA | 6 |
| Africa/Cairo | TW | NA | 8 |
| Africa/Cairo | CN | NA | 11 |
| Africa/Cairo | US | NA | 15 |
| Africa/Cairo | CA | NA | 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) )
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()andcount()functions.na.omit()is used to exclude missing value.geom_tile()is used to plot tiles (grids) at each x and y position.colorandsizearguments 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.