This week's data is from the New York Open Data Portal.
As the dataset is >100 MB (GitHub only allows 100 MB), I uploaded a data selection of 300,000 records sampled at random from the original dataset with sample_n(). You could read in the original dataset by using read_csv on the link as seen below.
library(tidyverse)
set.seed(20181209)
# You can use this url to download the data directly into R (will take a few seconds)
df <- read_csv("https://data.cityofnewyork.us/api/views/43nn-pn8j/rows.csv")
# Cleaning names with janitor, sampling 300,000 records, and dropping some variables
sampled_df <- df %>%
janitor::clean_names() %>%
select(-phone, -grade_date, -record_date, -building, -street) %>%
sample_n(size = 300000)
# save the .csv
write_csv(sampled_df, "nyc_restaurants.csv")
The original dataset can be found here.
| Column Name | Description | Type |
|---|---|---|
| camis | This is an unique identifier for the entity (restaurant); 10-digit integer, static per restaurant permit | Plain Text |
| dba | This field represents the name (doing business as) of the entity (restaurant); Public business name, may change at discretion of restaurant owner | Plain Text |
| boro | Borough in which the entity (restaurant) is located.;• 1 = MANHATTAN • 2 = BRONX • 3 = BROOKLYN • 4 = QUEENS • 5 = STATEN ISLAND • Missing; NOTE: There may be discrepancies between zip code and listed boro due to differences in an establishment's mailing address and physical location | Plain Text |
| building | Building number for establishment (restaurant) location | Plain Text |
| street | Street name for establishment (restaurant) location | Plain Text |
| zipcode | Zip code of establishment (restaurant) location | Plain Text |
| phone | Phone Number; Phone number provided by restaurant owner/manager | Plain Text |
| cuisine_description | This field describes the entity (restaurant) cuisine. ; Optional field provided by provided by restaurant owner/manager | Plain Text |
| inspection_type | This field represents the date of inspection; NOTE: Inspection dates of 1/1/1900 mean an establishment has not yet had an inspection | Date & Time |
| action | This field represents the actions that is associated with each restaurant inspection. ; • Violations were cited in the following area(s). • No violations were recorded at the time of this inspection. • Establishment re-opened by DOHMH • Establishment re-closed by DOHMH • Establishment Closed by DOHMH. Violations were cited in the following area(s) and those requiring immediate action were addressed. • "Missing" = not yet inspected; | Plain Text |
| violation_code | Violation code associated with an establishment (restaurant) inspection | Plain Text |
| violation_description | Violation description associated with an establishment (restaurant) inspection | Plain Text |
| critical_flag | Indicator of critical violation; "• Critical • Not Critical • Not Applicable"; Critical violations are those most likely to contribute to food-borne illness | Plain Text |
| score | Total score for a particular inspection; Scores are updated based on adjudication results | Number |
| grade | Grade associated with the inspection; N = Not Yet Graded, A = Grade A, B = Grade B, C = Grade C, Z = Grade Pending, P= Grade Pending issued on re-opening following an initial inspection that resulted in a closure | Plain Text |
| grade_date | The date when the current grade was issued to the entity (restaurant) | Date & Time |
| record_date | The date when the extract was run to produce this data set | Date & Time |
| inspection_type | A combination of the inspection program and the type of inspection performed; See Data Dictionary for full list of expected values | Plain Text |
"How Data Made Me A Believer In New York City’s Restaurant Grades"
