Your First Five Minutes with tampaBayOpenData

This tutorial searches for a public parks dataset, retrieves a small preview, and charts one of its fields in base R. It assumes basic R skills; no mapping software or knowledge of the publisher’s data service is required. Run the commands in order. Searching the package’s catalog works offline; retrieving records needs an internet connection. The publisher can change its data, so your results may differ from another person’s or from a later run.

1. Install the package

Install the package from CRAN once:

install.packages("tampaBayOpenData")

Load it each time you start a new R session:

library(tampaBayOpenData)

2. Find a dataset

Search for parks in the package’s checked catalog:

matches <- tbod_search_datasets("parks", source = "checked")
matches[, c("id", "title")]
#> # A tibble: 6 × 2
#>   id                       title                                              
#>   <chr>                    <chr>                                              
#> 1 parks                    Park polygons                                      
#> 2 hillsborough-parks       Hillsborough County parks and recreation properties
#> 3 stpete-parks             St. Petersburg parks                               
#> 4 clearwater-park-buffers  Clearwater park buffers                            
#> 5 pinellas-park-boundaries Pinellas County park boundaries                    
#> 6 pinellas-trail           Pinellas Trail segments

A dataset is a collection of related records published by an organization. Each row in these search results points to one dataset, with a title and an id that you can use to retrieve it. source = "checked" searches the package’s built-in list of datasets whose structures have been inspected. This search does not download park records. Find the hillsborough-parks row; that is the ID we will use for the rest of this tutorial. The list can grow when the package is updated.

3. Download some records

Ask Hillsborough County’s public service for a small preview:

parks <- tbod_get_dataset("hillsborough-parks", limit = 100)

tbod_get_dataset() retrieves public records and puts them in parks, a table you can explore in R. No account or password is needed. limit = 100 requests up to 100 records; if more match, these are only some of them. They are the first records returned by the service, not a random sample. In this source, each row describes a mapped property area managed by the county; several rows can describe the same park. Run this line in your R session to see the publisher’s current records.

4. Explore what you found

Start with three familiar ways to inspect a table:

head(parks)
nrow(parks)
names(parks)

head() shows the first few rows, nrow() tells you how many rows arrived, and names() lists the columns. The publisher supplies these columns. One of them, USES, labels how each property is used. Which labels appear most often in the records you received?

park_uses <- table(parks$USES, useNA = "ifany")
park_uses <- sort(park_uses, decreasing = TRUE)
park_uses

table() counts records for each use label, including a missing-value group if one appears. sort() puts the largest counts first. These counts describe only the returned preview; they do not tell you how many parks of each type exist across the county.

5. Make your first chart

Show the five most common use labels in that same preview:

top_uses <- head(park_uses, 5)
barplot(top_uses, horiz = TRUE, las = 1, cex.names = 0.7,
        col = "steelblue", xlab = "Property records",
        main = "Most common uses in this preview")

Each bar counts property records with one of the publisher’s use labels. The chart is a quick look at the rows you loaded, not an estimate for all county parks. The first 100 records may change, and mapped property outlines do not establish legal boundaries, public access, or opening hours. If the service is temporarily unavailable, try the retrieval again later; the retrieval guide’s troubleshooting section has more help.

What to try next

To work with another dataset, change the search word, choose an id from the results, and inspect names() before counting a column. The discovery guide helps you find more datasets; the retrieval guide covers filters and larger downloads. When you are ready for maps, try the spatial example. For longer real-world walkthroughs, see housing, water quality, and infrastructure.