Real World Example
Los Angeles has a lot of people, and just as many businesses, and the
list of active businesses in LA is contained in the dataset, found
here. In R, the laOpenData package can be used to pull
this data directly.
By using the la_pull_dataset() function, we can gather
information on these businesses, and filter based upon any of the
columns inside the dataset.
Let’s take an example of 3 requests that occur in the actual city of
Los Angeles. The la_pull_dataset() function can filter
based off any of the columns in the dataset. To filter, we add
filters = list() and put whatever filters we would like
inside. From our colnames call before, we know that there
is a column called “city” which we can use to accomplish this.
la_businesses <- la_pull_dataset(dataset = "6rrh-rzua",limit = 3, timeout_sec = 90, filters = list(city = "LOS ANGELES"))
la_businesses
#> # A tibble: 3 × 16
#> location_account business_name street_address city zip_code
#> <chr> <chr> <chr> <chr> <chr>
#> 1 0003301266-0001-1 WEST OF HOLLYWOOD, INC. 2608 AIKEN AVENUE LOS … 90064-3…
#> 2 0002772430-0001-9 GISELLE LUZA STUDIO, LLC 5500 HOLLYWOOD BL… LOS … 90028-6…
#> 3 0002173704-0001-5 ATLANTIC RECOVERY SERVICES 5306 N FIGUEROA S… LOS … 90042-4…
#> # ℹ 11 more variables: location_description <chr>, mailing_address <chr>,
#> # mailing_city <chr>, mailing_zip_code <chr>, naics <dbl>,
#> # primary_naics_description <chr>, council_district <dbl>,
#> # location_start_date <dttm>, location_1_latitude <dbl>,
#> # location_1_longitude <dbl>, location_1_human_address <chr>
# Checking to see the filtering worked
la_businesses |>
distinct(city)
#> # A tibble: 1 × 1
#> city
#> <chr>
#> 1 LOS ANGELESSuccess! From calling the la_businesses dataset we see
there are only 3 rows of data, and from the distinct() call
we see the only location featured in our dataset is LOS ANGELES.
One of the strongest qualities this function has is its ability to filter based off of multiple columns. Let’s put everything together and get a dataset of 50 businesses that occur in LOS ANGELES in council district 8.
# Creating the dataset
la_businesses_8 <- la_pull_dataset(dataset = "6rrh-rzua", limit = 50, timeout_sec = 90, filters = list(city = "LOS ANGELES", council_district = 8))
# Calling head of our new dataset
la_businesses_8 |>
slice_head(n = 6)
#> # A tibble: 6 × 17
#> location_account business_name dba_name street_address city zip_code
#> <chr> <chr> <chr> <chr> <chr> <chr>
#> 1 0000688586-0001-8 G & P RECYCLING INC G & P R… 1329 W JEFFER… LOS … 90007-3…
#> 2 0000609648-0002-5 LARRY & DARNELLA SCA… L AND D… 6715 2ND AVEN… LOS … 90043-4…
#> 3 0002865522-0001-3 DOLORES G AMAYA <NA> 1601 W 46TH S… LOS … 90062-1…
#> 4 0002983638-0001-6 ANTHONY CANTERO THE MEN… 4427 S NORMAN… LOS … 90037-2…
#> 5 0002109702-0001-5 FRANCISCO DIAZ <NA> 1228 W 25TH S… LOS … 90007-1…
#> 6 0002277536-0001-3 SAUNDRA BISHOP TRUST <NA> 627 W IMPERIA… LOS … 90044-4…
#> # ℹ 11 more variables: location_description <chr>, mailing_address <chr>,
#> # mailing_city <chr>, mailing_zip_code <chr>, naics <dbl>,
#> # primary_naics_description <chr>, council_district <dbl>,
#> # location_start_date <dttm>, location_1_latitude <dbl>,
#> # location_1_longitude <dbl>, location_1_human_address <chr>
# Quick check to make sure our filtering worked
la_businesses_8 |>
summarize(rows = n())
#> # A tibble: 1 × 1
#> rows
#> <int>
#> 1 50
la_businesses_8 |>
distinct(city)
#> # A tibble: 1 × 1
#> city
#> <chr>
#> 1 LOS ANGELES
la_businesses_8 |>
distinct(council_district)
#> # A tibble: 1 × 1
#> council_district
#> <dbl>
#> 1 8We successfully created our dataset that contains 50 requests regarding the businesses in district 8 of LA.
Mini analysis
Now that we have successfully pulled the data and have it in R, let’s
do a mini analysis on using the primary_naics_description
column, to figure out what are the main types of businesses
To do this, we will create a bar graph of the business types.
# Visualizing the distribution, ordered by frequency
la_businesses_8 |>
count(primary_naics_description) |>
ggplot(aes(
x = n,
y = reorder(primary_naics_description, n)
)) +
geom_col(fill = "steelblue") +
theme_minimal() +
labs(
title = "Top 50 Business Types in District 8 of LA",
x = "Number of Businesses",
y = "Business Type"
)
Bar chart showing the frequency of business types in LA in district 8.
This graph shows us not only which businesses are in the area, but how many of each there are.