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

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

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

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.

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.

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")

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.

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))
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 
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) )
Tip

  • 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.

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

# Step 1: Identify top 4 countries by number of attacks
top4_countries <- attacks %>%
  count(source_country, sort = TRUE) %>%
  slice_max(n, n = 4) %>%
  pull(source_country)

# Step 2: Filter attacks to include only those from top 4 countries
top_attacks <- attacks %>%
  filter(source_country %in% top4_countries)

# Step 3: Group by country, weekday, and hour
grouped_top <- top_attacks %>%
  count(source_country, wkday, hour) %>%
  na.omit()

# Step 4: Plot with ggplot2 and facet_wrap
ggplot(grouped_top, aes(hour, wkday, fill = n)) + 
  geom_tile(color = "white", size = 0.1) + 
  facet_wrap(~source_country, ncol = 2) +
  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(),
        strip.text = element_text(face = "bold", size = 12),
        plot.title = element_text(hjust = 0.5, size = 16),
        legend.title = element_text(size = 8),
        legend.text = element_text(size = 6))

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()

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) )

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.

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")

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`))

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)

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`))

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")

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.

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

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

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")
Tip

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