README.md

In DeclanMolony/teleporrrt: Pulls out information and creates graphics for the Teleport API

teleporrrt

The goal of teleporrrt is to construct detailed visuals and convey information from the Teleport API

Installation

You can install the development version from GitHub with:

# install.packages("devtools")
devtools::install_github("DeclanMolony/teleporrrt")

library(teleporrrt)

Cities in the Teleport API

You can use the function city_lookup() to produce a dataframe of all the cities used in the Teleport API, and their html links.

#city_lookup()

You can use the function country_lookup() to produce a dataframe of all the countries used in the Teleport API, and their html links.

#country_lookup()

Or you can use cities_valid() to check whether a particular city is in the Teleport API

cities_valid(cities = "Constantinople")
#> [1] "FALSE"

Individual City Links

You can use the function city_link() to produce an html link for a particular city’s scores to be used for your own analysis:

city_link("Dublin")
#> [1] "https://api.teleport.org/api/urban_areas/slug:dublin/scores/"

You can use the function area_link() to produce an html link for a particular countries or cities salaries to be used for your own analysis:

area_link("San Diego")
#> [1] "https://api.teleport.org/api/urban_areas/slug:san-diego/salaries/"
area_link("United States", "country")
#> [1] "https://api.teleport.org/api/countries/iso_alpha2:US/salaries/"

Dataframes

You can create a dataframe of a single city’s Teleport metrics:

city_dataframe("New York")
#>   Housing Cost of Living Startups Venture Capital Travel Connectivity Commute
#> 1       1          2.342       10              10               6.675 5.51925
#>   Business Freedom Safety Healthcare Education Environmental Quality Economy
#> 1            8.671  7.022   8.501667    8.0935               5.23375  6.5145
#>   Taxation Internet Access Leisure & Culture Tolerance Outdoors
#> 1   3.9205          7.0985                10    6.7125   5.7475

Or create a dataframe comparing two cities:

city_combine_df("Cairo","Zurich")
#>           Housing Cost of Living Startups Venture Capital Travel Connectivity
#> Max score  10.000             10  10.0000          10.000             10.0000
#> Min score   0.000              0   0.0000           0.000              0.0000
#> Cairo      10.000             10   5.0985           2.958              3.7575
#> Zurich      1.473              1   6.2035           3.872              7.7335
#>            Commute Business Freedom  Safety Healthcare Education
#> Max score 10.00000        10.000000 10.0000  10.000000   10.0000
#> Min score  0.00000         0.000000  0.0000   0.000000    0.0000
#> Cairo      3.99025         5.790667  6.7910   2.897333    0.5000
#> Zurich     5.83950         8.888000  9.0665   9.739333    7.2875
#>           Environmental Quality Economy Taxation Internet Access
#> Max score               10.0000  10.000  10.0000         10.0000
#> Min score                0.0000   0.000   0.0000          0.0000
#> Cairo                    1.1555   2.978   5.1010          2.4050
#> Zurich                   8.6430   6.552   6.4285          7.2675
#>           Leisure & Culture Tolerance Outdoors
#> Max score           10.0000   10.0000   10.000
#> Min score            0.0000    0.0000    0.000
#> Cairo                6.1855    2.6500    0.500
#> Zurich               4.9655    8.7845    5.401

Or create a dataframe with the closest city to a given Longitude/Latitude coordinates

#Coordinates of the White House
nearest_city(lat = 38.8977, lon = -77.0365)
#>      nearest_city       distance_km 
#> [1,] "Washington, D.C." "0.28774607"

Or create a dataframe of the 25th, 50th, and 75th quantiles of salaries for a given city or country.

US_salary <- salaries_qt("United States", "country")

head(US_salary)
#>                  job_names percentile_25 percentile_50 percentile_75
#> 1          Account Manager      48847.73      61157.96      76570.52
#> 2               Accountant      46322.46      55013.10      65334.21
#> 3 Administrative Assistant      28149.28      33900.14      40825.88
#> 4                Architect      49398.82      61169.11      75743.91
#> 5                 Attorney      61244.91      82706.83     111689.62
#> 6         Business Analyst      53502.00      64180.18      76989.56

Spider Chart

You can even create a radar/spider chart comparing two cities’ scores

city_radarchart("Hong Kong","Detroit")

City Ratings

You can input a list of cities you’re interested in comparing based on cost of living, housing, or education, and find out which of the cities has the maximum rating and which has the minimum rating

MaxMinRating(c("Toronto", "Vancouver"), "Cost of Living")
#> [1] "Maximum cost of living rating: 5.271 (Toronto)"  
#> [2] "Minimum cost of living rating: 5.259 (Vancouver)"

DeclanMolony/teleporrrt documentation built on May 12, 2020, 1:02 a.m.