In class Exercise 6 Time on the Horizon

1. Getting started

Before getting start, make sure that ggHoriPlot has been included in the pacman::p_load(...) statement above.

pacman::p_load(ggHoriPlot, ggthemes, tidyverse)

1.1 Step 1: Data Import

For the purpose of this hands-on exercise, Average Retail Prices Of Selected Consumer Items will be used.

Use the code chunk below to import the AVERP.csv file into R environment.

averp <- read_csv("data/AVERP.csv") %>%   
  mutate(`Date` = dmy(`Date`))

Thing to learn from the code chunk above.

• By default, read_csv will import data in Date field as Character data type. dmy() of lubridate package to palse the Date field into appropriate Date data type in R.

Tip

Thing to learn from the code chunk above.

• By default, read_csv will import data in Date field as Character data type. dmy() of lubridate package to palse the Date field into appropriate Date data type in R.

1.2 Step 2: Plotting the horizon graph

Next, the code chunk below will be used to plot the horizon graph.

averp %>% 
  filter(Date >= "2018-01-01") %>%
  ggplot() +
  geom_horizon(aes(x = Date, y=Values), 
               origin = "midpoint", 
               horizonscale = 6)+
  facet_grid(`Consumer Items`~.) +
    theme_few() +
  scale_fill_hcl(palette = 'RdBu') +   #diverging color red and blue.
  theme(panel.spacing.y=unit(0, "lines"), strip.text.y = element_text(
    size = 5, angle = 0, hjust = 0),
    legend.position = 'none',
    axis.text.y = element_blank(),
    axis.text.x = element_text(size=7),
    axis.title.y = element_blank(),
    axis.title.x = element_blank(),
    axis.ticks.y = element_blank(),
    panel.border = element_blank()
    ) +
    scale_x_date(expand=c(0,0), date_breaks = "3 month", date_labels = "%b%y") +
  ggtitle('Average Retail Prices of Selected Consumer Items (Jan 2018 to Dec 2022)')

attacks <- attacks %>%

mutate(wday = lubridate::wday(timestamp,

label =True

abbr= True,),

hour = lubridate::hour(timestamp))

the code chunk above avoid to change date format.