Bar, Bended Bar, and Treemap

Gettting sp500_sectora

We will scrap the sp500_sectora from Wikipedia page, with rvest.

library(dplyr)
library(rvest)

url <- "https://en.wikipedia.org/wiki/List_of_S%26P_500_companies"

sp500 <- url %>% 
    read_html() %>% 
    # How do I select css? 
    html_node(css = "table.wikitable") %>% 
    html_table() %>% 
    mutate(sector = factor(`GICS Sector`)) %>% 
    as_tibble()
sp500

## # A tibble: 505 x 10
##    Symbol Security `SEC filings` `GICS Sector` `GICS Sub Indus… `Headquarters L…
##    <chr>  <chr>    <chr>         <chr>         <chr>            <chr>           
##  1 MMM    3M Comp… reports       Industrials   Industrial Cong… St. Paul, Minne…
##  2 ABT    Abbott … reports       Health Care   Health Care Equ… North Chicago, …
##  3 ABBV   AbbVie … reports       Health Care   Pharmaceuticals  North Chicago, …
##  4 ABMD   ABIOMED… reports       Health Care   Health Care Equ… Danvers, Massac…
##  5 ACN    Accentu… reports       Information … IT Consulting &… Dublin, Ireland 
##  6 ATVI   Activis… reports       Communicatio… Interactive Hom… Santa Monica, C…
##  7 ADBE   Adobe S… reports       Information … Application Sof… San Jose, Calif…
##  8 AMD    Advance… reports       Information … Semiconductors   Santa Clara, Ca…
##  9 AAP    Advance… reports       Consumer Dis… Automotive Reta… Raleigh, North …
## 10 AES    AES Corp reports       Utilities     Independent Pow… Arlington, Virg…
## # … with 495 more rows, and 4 more variables: `Date first added` <chr>,
## #   CIK <int>, Founded <chr>, sector <fct>

Note: Write a post about rvest node selector.

Pie Chart

Let’s begin with an awful pie chart.

library(ggplot2)
library(RColorBrewer)

# Set ggplot theme
old <- theme_set(theme_light() + theme(legend.position = "bottom"))

# Set colour palette for repeated use
pal <- scale_fill_brewer(
    palette = "Set3",
    guide = guide_legend(
    title = "Sector",
    title.position = "top",
    label.position = "top",
    keyheight = 0.5,
    ncol = 4
    )
    )

# Pie Chart
sp500 %>% 
    ggplot(aes(factor(1), fill = sector)) + 
    geom_bar(width = 1) + 
    # Pie chart is simply a change in the polar coordinate
    coord_polar(theta = "y") +
    labs(x = "", y = "Count") +
    pal

The disadvantage of using a pie chart is that we cannot clearly visualize the differences, e.g. how much difference is there between the biggest and second biggest industry?

Bar Chart

We should be quite familiar with this.

# Summarize sector by count
sp500_sector <- sp500 %>% group_by(sector) %>% summarise(n = n())

# Normal Bar Chart 
sp500_sector %>% 
    ggplot(aes(reorder(sector, -n), n, fill = sector)) + 
    geom_bar(stat = "identity") +
    # We hide axis x text 
    theme(axis.text.x = element_blank()) +
    # and manually replace with geom_text
    geom_text(aes(y =  n/2, label = sector), 
              show.legend = FALSE, 
              angle = 90, 
              size = 3, 
              nudge_y = +20) +
    labs(x = "", y = "Count") +
    pal

Polar Bar Chart

What if we bend the bars? (Inspired by Apple Watchface)

# The Circular Bar Chart Way
sp500_sector %>% 
    ggplot(aes(reorder(sector, n), n, fill = sector)) + 
    geom_bar(stat = "identity") + 
    # Expand the breaks & limit to look nicer
    scale_y_continuous(breaks = scales::pretty_breaks(10), 
                       expand = c(0, 0.8)) +
    coord_polar(theta = "y", direction = 1) +
    labs(x = "", y = "Count") +
    pal

The main benefit is that we get a more organized display of sorted ranking. Not bad.

Treemap

In my opinion, Treemap is good when you wish to zoom into a particular group of objects. In our case, attention is drawn to the biggest industries. Treemap is also space-efficient. Legend and axis labels are often expendable.

library(treemapify)

sp500_sector %>% 
    mutate(pct = scales::percent(n / sum(n))) %>% 
    ggplot(aes(area = n, fill = sector, label = paste(sector, "\n", pct))) + 
    geom_treemap(show.legend = FALSE) + 
    geom_treemap_text(place = "centre", size = 9) + 
    pal

That’s it. A quick 10. Till next time.