Gather a group of ticker codes, wrangle and visualise weekly, monthly and yearly returns.

ura_wmy_returns.R

# Load libraries ----
library(tidyverse)
library(tidyquant)
library(tidytext)
library(showtext)

showtext_opts(dpi = 300)
showtext_auto(enable = TRUE)

font_add_google("Fira Sans Condensed", "fira sans")
font_add_google("Copse", "copse")
font_add_google("Oswald", "oswald")
font_add_google("Acme", "acme")

theme_set(theme_light(base_size = 12, base_family = "fira sans"))

end <- Sys.Date()

# Setup ticker codes ----
ura_stocks <-  c(
  "DYL.AX", "BMN.AX", "BOE.AX", "LOT.AX", "PEN.AX",
  "PDN.AX", "AGE.AX", "EL8.AX", "92E.AX", "AEE.AX",
  "ACB.AX", "BKY.AX", "TOE.AX", "VAL.AX", "DEV.AX",
  "SLX.AX", "URNM.AX", "ATOM.AX", "THR.AX", "T92.AX",
  "OKR.AX", "EME.AX", "ERA.AX", "TOE.AX", "GTR.AX", 
  "CXU.AX", "GLA.AX", "OKR.AX", "BSN.AX", "1AE.AX",
  "NXG.AX"
)
# Gather stock data ----
stock_prices <- tq_get(ura_stocks,
                       get  = "stock.prices",
                       from = "2022-01-01",
                       to   = end)
# Set correct time zone 
stock_prices$date <- as_date(stock_prices$date, tz="Australia/Brisbane")

# Remove country code
stock_prices$symbol <- gsub("\\..*", "", stock_prices$symbol)


# Data Wrangling ----
stock_returns_weekly <- stock_prices %>%
  group_by(symbol) %>%
  tq_transmute(select     = adjusted, 
               mutate_fun = periodReturn, 
               period     = "weekly", 
               col_rename = "weekly_return") %>% 
  ungroup()

stock_returns_monthly <- stock_prices %>%
  group_by(symbol) %>%
  tq_transmute(select     = adjusted, 
               mutate_fun = periodReturn, 
               period     = "monthly", 
               col_rename = "monthly_return") %>% 
  ungroup()

stock_returns_yearly <- stock_prices %>%
  group_by(symbol) %>%
  tq_transmute(select     = adjusted, 
               mutate_fun = periodReturn, 
               period     = "yearly", 
               col_rename = "yearly_return") %>% 
  ungroup()

returns <- stock_returns_weekly %>% 
  filter(date == max(date)) %>% 
  left_join(stock_returns_monthly, by = c("symbol", "date")) %>% 
  left_join(stock_returns_yearly, by = c("symbol", "date")) %>% 
  pivot_longer(
    cols = ends_with("_return"),
    names_to = "return_period",
    values_to = "returns"
  ) 

# Recode
returns <- returns %>%
  mutate(return_period = recode(return_period,
                                "weekly_return"  = "Weekly",
                                "monthly_return" = "Monthly",
                                "yearly_return"  = "Yearly"))

# Visualisation ----
returns %>%
  mutate(
    return_period = factor(return_period,
                           levels=c("Weekly", "Monthly", "Yearly")),
    symbol = reorder_within(symbol, returns, return_period),
    text_lbl = returns %>% scales::percent(accuracy = 0.1),
    colourtext = case_when(
      returns > 0 ~ "#008B00",
      returns < 0 ~ "#8B3A3A",
      .default    = "grey45"
    )) %>% 
  
  ggplot(aes(x = symbol, y = returns)) + #, fill = return_period
  geom_col(alpha = 0.50, show.legend = FALSE, fill = "grey50") + #, position = "dodge",
  geom_text(aes(symbol, returns, label = text_lbl, colour = colourtext),
            position = position_dodge(0.9),
            size = 2,
            # fontface = "bold",
            hjust="inward",
  ) +
  scale_y_continuous(labels = scales::label_percent(),  expand = c(0,0)) +
  scale_colour_identity() +
 
  facet_wrap(~ return_period, scales = "free") +
  coord_flip() +
  scale_x_reordered() +
  
 
  theme_tq() +
  
  labs(title = "Selected ASX Uranium Stock Returns",
       y = "Return",
       caption = "DataViz: @GrantChalmers | Source: Yahoo Finance") +
  
  theme(legend.position = "none",
        axis.title.y = element_blank(),
        axis.title.x = element_blank(),
        axis.text.x = element_blank(),
        axis.ticks.x = element_blank(), 
        axis.text.y = element_text(size = 7.3, family = "copse"),
        axis.text = element_text(size = 7.5, family = "copse"),
        strip.text = element_text(size = 8.2, family = "acme"), #, colour = "orange"
        plot.title = element_text(size = 10, face = "bold"),
        plot.background = element_rect(fill = 'antiquewhite', colour = 'antiquewhite'),
        panel.background = element_rect(fill = 'snow'),
        panel.grid.major = element_line(linetype = "dashed"),
        panel.grid.major.x = element_blank(),
        panel.spacing = unit(0.75, "lines"),
        plot.caption = element_text(size = 6, colour = "gray50", family = "oswald"))  
  
# Save PNG ----
ggsave("asx_ura_wmy_returns.png", path = "images", width = 5, height = 4)