forbesListR - An easy way to access the data contained lists maintained by the fine folks at Forbes in R

Why I Built forbesListR?

Forbes is the preiminant maintainer of covering a wide range of business related topics including sports, entertainment, individual wealth, and locations. The lists are chocked full of phenomonal data that can be analyzed, visualized and merged with other data. Upon discovering that Forbes went to the pains of building an API, even though it is undocumented, I decided to build this package to wrap that API and make it as easy as possible for my fellow #rstats brethren to access this data with a few simple functions.

As of now this package has 3 primary functions that I hope become widely used.

• get_year_forbes_list_data: Gets the data contained in a Forbes list if it exists
• get_years_forbes_list_data: Gets the data contained in multiple years of a specified list
• get_year_list_forbes_bio_data: Gets the page specific for the people contained in a specific list

If and when Forbes adds new lists I will update this package to include them.

Accessible Lists

• Billionaires
• Forbes 400
• Top VCs
• Athletes
• Celebrities
• NBA Valuations
• MLB Valuations
• NFL Valuations
• NHL Valuations
• Soccer Valuations
• NASCAR Valuations
• Powerful Brands
• Growth Companies
• Best Employers
• Powerful People
• Powerful Women
• Top Colleges
• Top Business Schools
• Innovative Companies
• Small Companies
• Best Employers
• Largest Private Companies
• Global 2000
• Richest Families
• Self Made Women
• Most Promising Companies
• Best Countries for Business
• Best Cities for Business
• Best States for Business
• Best Small Cities for Business
• Richest in Tech
• Hong Kong 50
• Australia 50
• China 50
• Taiwan 50
• India 50
• Japan 50
• Africa 50
• Korea 50
• Malaysia 50
• Philippines 50
• Singapore 50
• Indonesia 50
• Thailand 50
• Asia 200
• Asia Fab 50

Installation

devtools::install_github("abresler/forbesListR")

Usage

Get 2016 NBA Team Valuations

library(forbesListR)
nba_2016_values <- 
  get_year_forbes_list_data(list = "NBA Valuations", year = 2016)

top_10_2016 <- 
  nba_2016_values %>% 
  dplyr::select(year, team, valuation.millions) %>% 
  head(10)

year	team	valuation.millions
2016	New York Knicks	3000
2016	Los Angeles Lakers	2700
2016	Chicago Bulls	2300
2016	Boston Celtics	2100
2016	Los Angeles Clippers	2000
2016	Golden State Warriors	1900
2016	Brooklyn Nets	1700
2016	Houston Rockets	1500
2016	Dallas Mavericks	1400
2016	Miami Heat	1300

Get Top Venture Capitalists from 2012 to 2016

vcs_2012_2016 <- 
  get_years_forbes_list_data(years = 2012:2016, list_name = "Top VCs")
vcs_2012_2016 %>% 
  dplyr::glimpse()
# Observations: 475
# Variables: 15
# $ year           (int) 2016, 2016, 2016, 2016, 2016, 2016, 2016, 2016, 2016, 2016, 2016, 2016, 2016, 2016, 2016, 20...
# $ list           (chr) "Top VCs", "Top VCs", "Top VCs", "Top VCs", "Top VCs", "Top VCs", "Top VCs", "Top VCs", "Top...
# $ name           (chr) "Jim Goetz", "Steve Anderson", "Chris Sacca", "Peter Fenton", "Mary Meeker", "Josh Kopelman"...
# $ last_name      (chr) "Goetz", "Anderson", "Sacca", "Fenton", "Meeker", "Kopelman", "Shen", "Gurley", "Leone", "Th...
# $ company        (chr) "Sequoia Capital", "Baseline Ventures", "Lowercase Capital", "Benchmark", "Kleiner Perkins C...
# $ is.government  (lgl) FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, F...
# $ position       (int) NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, ...
# $ rank           (int) 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 2...
# $ age            (int) 50, 47, 40, 43, 56, 44, 46, 49, 58, 48, 54, 59, 50, 51, 44, 52, 54, 48, 48, 51, 45, 64, 42, ...
# $ url.bio.forbes (chr) "http://www.forbes.com/profile/jim-goetz", "http://www.forbes.com/profile/steve-anderson", "...
# $ url.image      (chr) "http://i.forbesimg.com/media/lists/people/jim-goetz_200x200.jpg", "http://i.forbesimg.com/m...
# $ gender         (chr) "M", "M", "M", "M", "F", "M", "M", "M", "M", "M", "M", "M", "M", "M", "M", "M", "M", "M", "M...
# $ country        (chr) "United States", "United States", "United States", "United States", "United States", "United...
# $ description    (chr) "A year-and-a-half after the sale of WhatsApp to Facebook for near $22 billion, Jim Goetz co...
# $ notableDeal    (chr) "WhatsApp", "Instagram", "Twitter", "Twitter", "Facebook", "LinkedIn", "Alibaba", "Uber", "F...

Get Detailed Biography Data for 2015 Top Athletes - Explore Earnings by Maritial Status

library(dplyr)
athletes_2015 <- 
  get_year_list_forbes_bio_data(year = 2015, list_name = 'Athletes')
# Warning: closing unused connection 5 (http://www.forbes.com/profile/russell-westbrook)

athletes_2015 <- 
  athletes_2015 %>% 
  group_by(martial_status) %>% 
  summarise(earnings.millions = sum(salary_earnings.millions, na.rm = T)) %>% 
  arrange(desc(earnings.millions)) %>% 
  ungroup

martial_status	earnings.millions
Married	1257.3
Single	939.1
Divorced	659.0
Engaged	58.5
NA	33.1

Sample Data Visualizations

Waffle Chart of Aggregate Professional Sport Valuations by Year

nba_valuations <-
  2012:2016 %>%
  get_years_forbes_list_data(list_name = "NBA Valuations")

nhl_valuations <-
  2012:2016 %>%
  get_years_forbes_list_data(list_name = "NHL Valuations")

mlb_valuations <-
  2012:2016 %>%
  get_years_forbes_list_data(list_name = "MLB Valuations")

nfl_valuations <-
  2012:2016 %>%
  get_years_forbes_list_data(list_name = "NFL Valuations")

soccer_valuations <-
  2012:2016 %>%
  get_years_forbes_list_data(list_name = "Soccer Valuations")

all_valuation_data <-
  nba_valuations %>%
  bind_rows(nhl_valuations) %>%
  bind_rows(mlb_valuations) %>%
  bind_rows(soccer_valuations) %>%
  bind_rows(nfl_valuations) %>%
  mutate(list = list %>% str_replace_all(' Valuations', '')) %>%
  dplyr::filter(year %in% c(2012:2016))

library(waffle) # devtools::install_github("hrbrmstr/waffle")
# Loading required package: ggplot2
library(ggplot2) # devtools::install_github("hadley/ggplot2")

total_by_year <- 
  all_valuation_data %>% 
  dplyr::filter(year %in% c(2012:2015)) %>% 
  group_by(year) %>% 
  summarise(aggregate_valuation.billions = sum(valuation.millions, na.rm = T) / 1000)

totals <- 
  total_by_year$aggregate_valuation.billions / 10

names(totals) <- 
  total_by_year$year

sports_waffle_valuations <- 
  totals %>% 
  waffle(flip = F, size = 2, rows = 8, xlab = '1 square = ~$10B',  pad = 1,
         colors = c("#FF5A5F","#FFB400", "#007A87", "#FFAA91", "#7B0051")) +
  labs(title = "Aggregate Estimated Professional Sports Valuations",
       subtitle = "MLB, NHL, NBA, NFL, & Soccer -- 2012-2015") +
  theme(plot.subtitle = element_text(
    size = 8,
    hjust = 0
  ),
  plot.title = element_text(size = 10, face = "bold.italic")
  )

sports_waffle_valuations

Streamgraph of Valuations by Sport, 2012 - 2016

  summary_cols <-
    c('valuation.millions', 'revenue.millions', 'operating_income.millions', 'debt.millions')
  
  summary_data <-
    all_valuation_data %>%
    dplyr::rename(sport = list) %>%
    group_by(year, sport) %>%
    summarise_each_(funs(sum(., na.rm = T)), summary_cols) %>%
    mutate(revenue.multiple = valuation.millions / revenue.millions,
           ebitda.multiple = valuation.millions / operating_income.millions) %>%
    ungroup

library(streamgraph) # devtools::install_github('hrbrmstr/streamgraph')

nba_streamgraph <- 
  summary_data %>%
  streamgraph("sport",
              "valuation.millions",
              "year",
              offset = "silhouette",
              interpolate = "step") %>%
  sg_axis_x(1, "year", "%Y") %>%
  sg_fill_brewer(palette = "Spectral") %>%
  sg_legend(show = TRUE, label = "Sport: ")

Choroplething Forbes 2015 Best Cities for Small Business by State

Example from Aaron Miles

library(choroplethr) #install.packages('choroplethr')
library(RColorBrewer)
library(ggplot2)

# Read in Forbes Data
dat <-
  get_year_forbes_list_data(list = "Best Cities for Business", year = 2015)

#Summarize Number of Cities by State
bus <-
  dat %>%
  group_by(state) %>%
  summarize(cities = n_distinct(city)) %>%
  mutate(state = state %>% tolower())


#Read in Choroplethr Data
data("df_state_demographics")

# Merge with cities Data
df_state_demographics <-
  df_state_demographics %>%
  left_join(bus, by = c("region" = "state"))

#Fill NAs as 0 (States with no cities)
df_state_demographics[is.na(df_state_demographics)] <-
  0

#Set value to be city
df_state_demographics <-
  df_state_demographics %>%
  mutate(value = cities)

#Create Plot
choro1 <- StateChoropleth$new(df_state_demographics)
choro1$title = "Forbes' Best Cities for Business by State -- 2015"
choro1$set_num_colors(1)
choro1$ggplot_polygon = geom_polygon(aes(fill = value), color = NA)
choro1$ggplot_scale = scale_fill_gradientn(name = "# Cities",
                                           colours = brewer.pal(8, "Purples"))

choro1$render()