Data collection

by_name data

library(rvest)

by_name_url <- "https://sginvestors.io/sgx/stock-listing/alpha"

by_name_html <- read_html(by_name_url) %>% 
  html_nodes(".stockitem > a") 
  
by_name <- tibble(firm = html_attr(by_name_html, "title"), 
                  link = html_attr(by_name_html, "href")) %>% 
  mutate(firm = str_remove(firm, "\\@.*"), 
         name = str_replace(firm, "\\(SGX.*\\)", ""), 
         code = sub("\\).*", "", sub(".*\\(", "", firm)), 
         code = str_replace_all(code, "\\(|\\)", "")) %>% 
  mutate(code = gsub(" ", "", code))

by_sector data

by_sector_url <- "https://sginvestors.io/sgx/stock-listing/sector"

by_sector_html <- read_html(by_sector_url) %>% 
  html_nodes(".stockitem > a") 

by_sector <- tibble(firm_number = seq(1, 699, 1),
                    firm = html_attr(by_sector_html, "title"), 
                    link = html_attr(by_sector_html, "href")) %>% 
  mutate(sector = case_when(firm_number >=1 & firm_number <= 18 ~ "Communication Services", 
                            firm_number >=19 & firm_number <= 121 ~ "Consumer Discretionary", 
                            firm_number >=122 & firm_number <= 163 ~ "Consumer Staples", 
                            firm_number >=164 & firm_number <= 214 ~ "Energy", 
                            firm_number >=215 & firm_number <= 248 ~ "Financials", 
                            firm_number >=249 & firm_number <= 282 ~ "Health Care", 
                            firm_number >=283 & firm_number <= 506 ~ "Industrials", 
                            firm_number >=507 & firm_number <= 571 ~ "Information Technology", 
                            firm_number >=572 & firm_number <= 616 ~ "Materials", 
                            firm_number >=617 & firm_number <= 689 ~ "Real Estate",
                            firm_number >=690 & firm_number <= 699 ~ "Utilities")) %>% 
  mutate(firm = str_remove(firm, "\\@.*"))

sti data

sti_url <- "https://en.wikipedia.org/wiki/Straits_Times_Index"

sti_html <- read_html(sti_url) %>% 
  html_node("h2~ p+ table td > table") 

sti <- sti_html %>% 
  html_table() %>% 
  tbl_df() %>% 
  rename(code = 1) %>% 
  mutate(code = gsub(" ", "", code), 
         sti = "Yes") %>% 
  select(code, sti)

joint dataset

sg_firm <-  by_name %>% 
  left_join(sti, by = "code") %>% 
  right_join(by_sector, by = c("firm", "link")) %>% 
  mutate(sti = ifelse(is.na(sti), "No", sti)) %>% 
  select(firm_number, firm, name, code, sector, sti, link)


Data table

library(crosstalk)
library(plotly)

tx <- sg_firm %>% highlight_key()

dt <- DT::datatable(tx, rownames = FALSE, filter = "top",
                    caption = 'Table 1: The list of Singapore listed companies as of 8/8/2019', 
                    extensions = 'Buttons', 
                    options = list(dom = 'Bfrtip',
                                   buttons = list('copy', 'print', 
                                                  list(extend = 'collection',
                                                       buttons = c('csv', 'excel', 'pdf'),
                                                       text = 'Download')))) %>% 
  DT::formatStyle(columns = c(1:7), fontSize = "8pt")

widgets <- bscols(
  widths = c(12),
  filter_checkbox("sector", "Sectors", tx, ~sector, inline = FALSE, columns = 1))

crosstalk::bscols(widgets, 
                  dt, 
                  widths = c(2, 10))


Data visualization

library(gridExtra)

mytable <- sg_firm %>% 
  group_by(STI = sti) %>% 
  summarise(Firms = n())

sg_firm %>% 
  group_by(sti, sector) %>% 
  summarise(n = n()) %>% 
  ungroup() %>% 
  mutate(sector = fct_reorder(sector, n, sum)) %>% 
  ggplot(aes(sector, n, fill = sti)) +
  geom_col(key_glyph = draw_key_crossbar) +
  geom_text(aes(label = n), hjust = 1, size = 3) +
  coord_flip() +
  annotation_custom(tableGrob(mytable, 
                              rows = NULL, 
                              theme = ttheme_minimal(base_size = 9)), 
                    xmin = 1.5, xmax = 2, ymin = 180, ymax = 240) +
  theme(legend.title = element_blank(), 
        legend.text = element_blank(),
        legend.background = element_blank(),
        legend.justification=c(1,0), 
        legend.position=c(0.865, 0.025),
        legend.direction='vertical') +
  labs(title = "Singapore listed firms by sectors", 
       subtitle = glue("Total {range(sg_firm$firm_number)[2]} firms as of 8/8/2019"),  
       x = "", 
       y = "", 
       caption = "RAudit Solution LLP | Stewart Li")

sg_firm %>% 
  count(sti, sector) %>% 
  group_by(sti) %>% 
  mutate(pct = n / sum(n)) %>% 
  ungroup() %>% 
  mutate_if(is.character, as.factor) %>% 
  mutate(sector = fct_reorder(sector, n, sum)) %>% 
  ggplot(aes(sector, pct)) +
  geom_col() +
  geom_text(aes(label = n), hjust = 0, size = 3) +
  geom_curve(aes(x = 3, y = .20, xend = 3, yend = .16),
               arrow = arrow(length = unit(0.08, "inch")), size = 0.5,
               color = "gray20", curvature = 0.3) + 
  geom_curve(aes(x = 2, y = .20, xend = 2, yend = .16),
               arrow = arrow(length = unit(0.08, "inch")), size = 0.5,
               color = "gray20", curvature = 0.3) + 
  coord_flip() +
  scale_y_continuous(labels = percent_format()) +
  facet_wrap(~paste0("STI: ", sti), nrow = 1) +
  labs(title = "Is there any concentration risk?", 
       subtitle = "Relatively more firms from financial and communication sectors are included in STI", 
       x = "", 
       y = "", 
       caption = "RAudit Solution LLP | Stewart Li")