Hands-on 7

Visualising and Analysing Time-oriented Data

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

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

Rows: 199999 Columns: 3
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr  (2): source_country, tz
dttm (1): timestamp

ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

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

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)
}

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))

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) )

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ℹ Please use `linewidth` instead.

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

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()

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) )

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air <- read_excel("../data/arrivals_by_air.xlsx")
air$month <- factor(month(air$`Month-Year`), 
                    levels=1:12, 
                    labels=month.abb, 
                    ordered=TRUE) 
air$year <- year(ymd(air$`Month-Year`))
Vietnam <- air %>% 
  select(`Vietnam`, 
         month, 
         year) %>%
  filter(year >= 2010)

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

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")

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

Rows: 550 Columns: 4
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (1): Country
dbl (3): Year, Yield, Production

ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

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")

Converting 'Year' to an ordered factor

Reflections:

learn how to plot different graphs (heatmaps, cycle plot, slopegraph) to visualize temperol patterns.