companies: Startup company information for 1,000 companies

In sailthru/tidyjson: A Grammar for Turning 'JSON' into Tidy Tables

Description

From: http://jsonstudio.com/resources/

Usage

companies

Format

JSON

Examples

library(dplyr)
library(jsonlite)

# Print the first record (do not run automatically)
# companies[[1]] %>% prettify

# Get the key employees data
key_employees <- companies %>%
  spread_values(
    name = jstring("name")
  ) %>%
  mutate(
    company.sort_order = rank(name)
  ) %>%
  enter_object("relationships") %>%
  gather_array("relationship.index") %>%
  spread_values(
    is.past = jlogical("is_past"),
    name = jstring("person", "permalink"),
    title = jstring("title")
  )

# Show the top 10 titles
key_employees %>%
  filter(!is.past) %>%
  group_by(title) %>%
  tally() %>%
  arrange(desc(n)) %>%
  top_n(10)

sailthru/tidyjson documentation built on May 29, 2019, 12:59 p.m.