In both cases iterating over Quickstats is the main public facing database to find the most relevant agriculture statistics. However, here are the basic steps to install Tableau Public and build and publish the dashboard: After completing this tutorial, you should have a general understanding of: I can imagine many use cases for projects that would use data from the Quick Stats database. These include: R, Python, HTML, and many more. parameters is especially helpful. nassqs_params() provides the parameter names, The waitstaff and restaurant use that number to keep track of your order and bill (Figure 1). To run the script, you click a button in the software program or use a keyboard stroke that tells your computer to start going through the script step by step. want say all county cash rents on irrigated land for every year since Here, code refers to the individual characters (that is, ASCII characters) of the coding language. rnassqs: An R package to access agricultural data via the USDA National Agricultural Statistics Service (USDA-NASS) 'Quick Stats' API. The sample Tableau dashboard is called U.S. It allows you to customize your query by commodity, location, or time period. value. The first line of the code above defines a variable called NASS_API_KEY and assigns it the string of letters and numbers that makes up the NASS Quick Stats API key you received from the NASS. Programmatic access refers to the processes of using computer code to select and download data. The CoA is collected every five years and includes demographics data on farms and ranches (CoA, 2020). It is simple and easy to use, and provides some functions to help navigate the bewildering complexity of some Quick Stats data. multiple variables, geographies, or time frames without having to Griffin, T. W., and J. K. Ward. Because R is accessible to so many people, there is a great deal of collaboration and sharing of R resources, scripts, and knowledge. This is often the fastest method and provides quick feedback on the USDA NASS Quick Stats API usdarnass For more specific information please contact nass@usda.gov or call 1-800-727-9540. Including parameter names in nassqs_params will return a If you have already installed the R package, you can skip to the next step (Section 7.2). Census of Agriculture (CoA). file, and add NASSQS_TOKEN = to the rnassqs citation info - cran.r-project.org Washington and Oregon, you can write state_alpha = c('WA', nc_sweetpotato_data_survey <- filter(nc_sweetpotato_data_sel, source_desc == "SURVEY" & county_name != "OTHER (COMBINED) COUNTIES") It allows you to customize your query by commodity, location, or time period. You can also refer to these software programs as different coding languages because each uses a slightly different coding style (or grammar) to carry out a task. These codes explain why data are missing. R sessions will have the variable set automatically, U.S. National Agricultural Statistics Service (NASS) Summary "The USDA's National Agricultural Statistics Service (NASS) conducts hundreds of surveys every year and prepares reports covering virtually every aspect of U.S. agriculture. But you can change the export path to any other location on your computer that you prefer. parameters. Do do so, you can How to install Tableau Public and learn about it if you want to try it to visualize agricultural data or use it for other projects. Any person using products listed in . Queries that would return more records return an error and will not continue. Please click here to provide feedback for any of the tools on this page. to quickly and easily download new data. Secure .gov websites use HTTPSA Census of Agriculture Top The Census is conducted every 5 years. Accessed online: 01 October 2020. The example Python program shown in the next section will call the Quick Stats with a series of parameters. A&T State University. Create an instance called stats of the c_usda_quick_stats class. api key is in a file, you can use it like this: If you dont want to add the API key to a file or store it in your list with c(). may want to collect the many different categories of acres for every The Cropland Data Layer (CDL) is a product of the USDA National Agricultural Statistics Service (NASS) with the mission "to provide timely, accurate and useful statistics in service to U.S. agriculture" (Johnson and Mueller, 2010, p. 1204). The Quick Stats Database is the most comprehensive tool for accessing agricultural data published by the USDA National Agricultural Statistics Service (NASS). The inputs to this function are 2 and 10 and the output is 12. Its main limitations are 1) it can save visualization projects only to the Tableau Public Server, 2) all visualization projects are visible to anyone in the world, and 3) it can handle only a small number of input data types. Now that youve cleaned the data, you can display them in a plot. which at the time of this writing are. reference_period_desc "Period" - The specic time frame, within a freq_desc. The census collects data on all commodities produced on U.S. farms and ranches, as well as detailed information on expenses, income, and operator characteristics. If all works well, then it should be completed within a few seconds and it will write the specified CSV file to the output folder. function, which uses httr::GET to make an HTTP GET request Combined with an assert from the In the beginning it can be more confusing, and potentially take more Providing Central Access to USDAs Open Research Data, MULTIPOLYGON (((-155.54211 19.08348, -155.68817 18.91619, -155.93665 19.05939, -155.90806 19.33888, -156.07347 19.70294, -156.02368 19.81422, -155.85008 19.97729, -155.91907 20.17395, -155.86108 20.26721, -155.78505 20.2487, -155.40214 20.07975, -155.22452 19.99302, -155.06226 19.8591, -154.80741 19.50871, -154.83147 19.45328, -155.22217 19.23972, -155.54211 19.08348)), ((-156.07926 20.64397, -156.41445 20.57241, -156.58673 20.783, -156.70167 20.8643, -156.71055 20.92676, -156.61258 21.01249, -156.25711 20.91745, -155.99566 20.76404, -156.07926 20.64397)), ((-156.75824 21.17684, -156.78933 21.06873, -157.32521 21.09777, -157.25027 21.21958, 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After it receives the data from the server in CSV format, it will write the data to a file with one record per line. This image shows how working with the NASS Quick Stats API is analogous to ordering food at a restaurant. A&T State University, in all 100 counties and with the Eastern Band of Cherokee organization in the United States. Statistics Service, Washington, D.C. URL: https://quickstats.nass.usda.gov [accessed Feb 2023] . Often 'county', 'state', or 'national', but can include other levels as well", #> [2] "source_desc: Data source. Thsi package is now on CRAN and can be installed through the typical method: install.packages ("usdarnass") Alternatively, the most up-to-date version of the package can be installed with the devtools package. nassqs_param_values(param = ). It accepts a combination of what, where, and when parameters to search for and retrieve the data of interest. Agricultural Chemical Usage - Field Crops and Potatoes NASS national agricultural statistics service (NASS) at the USDA. R is an open source coding language that was first developed in 1991 primarily for conducting statistical analyses and has since been applied to data visualization, website creation, and much more (Peng 2020; Chambers 2020). To cite rnassqs in publications, please use: Potter NA (2019). You can get an API Key here. Then we can make a query. We summarize the specifics of these benefits in Section 5. For example, you will get an error if you write commodity_desc = SWEET POTATO (that is, dropping the ES) or write commodity_desc = sweetpotatoes (that is, with no space and all lowercase letters). Copy BibTeX Tags API reproducibility agriculture economics Altmetrics Markdown badge The core functionality allows the user to query agricultural data from 'Quick Stats' in a reproducible and automated way. N.C. This function replaces spaces and special characters in text with escape codes that can be passed, as part of the full URL, to the Quick Stats web server. You know you want commodity_desc = SWEET POTATOES, agg_level_desc = COUNTY, unit_desc = ACRES, domain_desc = TOTAL, statisticcat_desc = "AREA HARVESTED", and prodn_practice_desc = "ALL PRODUCTION PRACTICES". An official website of the General Services Administration. description of the parameter(s) in question: Documentation on all of the parameters is available at https://quickstats.nass.usda.gov/api#param_define. Section 207(f)(2) of the E-Government Act of 2002 requires federal agencies to develop an inventory of information to be published on their Web sites, establish a schedule for publishing information, make those schedules available for public comment, and post the schedules and priorities on the Web site. Corn stocks down, soybean stocks down from year earlier subset of values for a given query. One way it collects data is through the Census of Agriculture, which surveys all agricultural operations with $1,000 or more of products raised or sold during the census year. lock ( One of the main missions of organizations like the Comprehensive R Archive Network is to curate R packages and make sure their creators have met user-friendly documentation standards. the project, but you have to repeat this process for every new project, PDF usdarnass: USDA NASS Quick Stats API For example, you The query in What R Tools Are Available for Getting NASS Data? its a good idea to check that before running a query. The National Agricultural Statistics Service (NASS) is part of the United States Department of Agriculture. # drop old Value column 2019-67021-29936 from the USDA National Institute of Food and Agriculture. Sys.setenv(NASSQS_TOKEN = . The report shows that, for the 2017 census, Minnesota had 68,822 farm operations covering 25,516,982 acres. NASS Report - USDA Have a specific question for one of our subject experts? You can define this selected data as nc_sweetpotato_data_sel. many different sets of data, and in others your queries may be larger Once the The API response is the food made by the kitchen based on the written order from the customer to the waitstaff. Access Quick Stats Lite . This is less easy because you have to enter (or copy-paste) the key each That is an average of nearly 450 acres per farm operation. As a result, R coders have developed collections of user-friendly R scripts that accomplish themed tasks. assertthat package, you can ensure that your queries are a list of parameters is helpful. Tip: Click on the images to view full-sized and readable versions. Working for Peanuts: Acquiring, Analyzing, and Visualizing Publicly Available Data. Journal of the American Society of Farm Managers and Rural Appraisers, p156-166. The Comprehensive R Archive Network website, Working for Peanuts: Acquiring, Analyzing, and Visualizing Publicly Available Data. Email: askusda@usda.gov You can think of a coding language as a natural language like English, Spanish, or Japanese. Quick Stats Lite provides a more structured approach to get commonly requested statistics from . A script is like a collection of sentences that defines each step of a task. For example, we discuss an R package for downloading datasets from the NASS Quick Stats API in Section 6. N.C. Citation Request - USDA - National Agricultural Statistics Service Homepage Its easiest if you separate this search into two steps. key, you can use it in any of the following ways: In your home directory create or edit the .Renviron NASS Reports Crop Progress (National) Crop Progress & Condition (State) class(nc_sweetpotato_data_survey$Value) You do this by using the str_replace_all( ) function. You can view the timing of these NASS surveys on the calendar and in a summary of these reports. In registering for the key, for which you must provide a valid email address. Create a worksheet that allows the user to select a commodity (corn, soybeans, selected) and view the number of acres planted or harvested from 1997 through 2021. In the example program, the value for api key will be replaced with my API key. First, obtain an API key from the Quick Stats service: https://quickstats.nass.usda.gov/api. NASS_API_KEY <- "ADD YOUR NASS API KEY HERE" National Agricultural Statistics Service (NASS) Quickstats can be found on their website. In R, you would write x <- 1. https://data.nal.usda.gov/dataset/nass-quick-stats. Open Tableau Public Desktop and connect it to the agricultural CSV data file retrieved with the Quick Stats API through the Python program described above. This will create a new In this publication, the word variable refers to whatever is on the left side of the <- character combination. Once your R packages are loaded, you can tell R what your NASS Quick Stats API key is. method is that you dont have to think about the API key for the rest of In the get_data() function of c_usd_quick_stats, create the full URL. they became available in 2008, you can iterate by doing the Now that you have a basic understanding of the data available in the NASS database, you can learn how to reap its benefits in your projects with the NASS Quick Stats API. The following pseudocode describes how the program works: Note the use of the urllib.parse.quote() function in the creation of the parameters string in step 1. 2020. The USDAs National Agricultural Statistics Service (NASS) makes the departments farm agricultural data available to the public on its website through reports, maps, search tools, and its NASS Quick Stats API. DSFW_Peanuts: Analysis of peanut DSFW from USDA-NASS databases. install.packages("rnassqs"). nass_data: Get data from the Quick Stats query In usdarnass: USDA NASS Quick Stats API Description Usage Arguments Value Examples Description Sends query to Quick Stats API from given parameter values. This article will show you how to use Python to retrieve agricultural data with the NASS Quick Stats API. The author. Potter, (2019). (PDF) rnassqs: An R package to access agricultural data via the USDA Here, tidy has a specific meaning: all observations are represented as rows, and all the data categories associated with that observation are represented as columns. A function is another important concept that is helpful to understand while using R and many other coding languages. commitment to diversity. 2020. nassqs_parse function that will process a request object and you risk forgetting to add it to .gitignore. The Comprehensive R Archive Network (CRAN), Weed Management in Nurseries, Landscapes & Christmas Trees, NC How to write a Python program to query the Quick Stats database through the Quick Stats API. developing the query is to use the QuickStats web interface. Corn production data goes back to 1866, just one year after the end of the American Civil War. geographies. AG-903. One way of Once youve installed the R packages, you can load them. The last step in cleaning up the data involves the Value column. In the example shown below, I selected census table 1 Historical Highlights for the state of Minnesota from the 2017 Census of Agriculture. # select the columns of interest Second, you will change entries in each row of the Value column so they are represented as a number, rather than a character. In this case, the task is to request NASS survey data. Quick Stats Lite provides a more structured approach to get commonly requested statistics from our online database. On the other hand, if that person asked you to add 1 and 2, you would know exactly what to do. NASS has also developed Quick Stats Lite search tool to search commodities in its database. # look at the first few lines The Quick Stats Database is the most comprehensive tool for accessing agricultural data published by NASS. You can use many software programs to programmatically access the NASS survey data. How to Develop a Data Analytics Web App in 3 Steps Alan Jones in CodeFile Data Analysis with ChatGPT and Jupyter Notebooks Zach Quinn in Pipeline: A Data Engineering Resource Creating The Dashboard That Got Me A Data Analyst Job Offer Youssef Hosni in Level Up Coding 20 Pandas Functions for 80% of your Data Science Tasks Help Status Writers Blog Potter N (2022). Similar to above, at times it is helpful to make multiple queries and Agricultural Commodity Production by Land Area. For docs and code examples, visit the package web page here . Looking for U.S. government information and services? to automate running your script, since it will stop and ask you to To improve data accessibility and sharing, the NASS developed a "Quick Stats" website where you can select and download data from two of the agency's surveys. Which Software Programs Can Be Used to Programmatically Access NASS Survey Data? ) or https:// means youve safely connected to Some care Before using the API, you will need to request a free API key that your program will include with every call using the API. While I used the free Microsoft Visual Studio Community 2022 integrated development ide (IDE) to write and run the Python program for this tutorial, feel free to use your favorite code editor or IDE. If you are interested in just looking at data from Sampson County, you can use the filter( ) function and define these data as sampson_sweetpotato_data. Healy. For example, you can write a script to access the NASS Quick Stats API and download data. In some cases you may wish to collect You can then visualize the data on a map, manipulate and export the results, or save a link for future use. In this publication we will focus on two large NASS surveys. Within the mutate( ) function you need to remove commas in rows of the Value column that are 1000 acres or more (that is, you want 1000, not 1,000). Accessing data with computer code comes in handy when you want to view data from multiple states, years, crops, and other categories. For example, commodity_desc refers to the commodity description information available in the NASS Quick Stats API and agg_level_desc refers to the aggregate level description of NASS Quick Stats API data. .Renviron, you can enter it in the console in a session. The .gov means its official. It allows you to customize your query by commodity, location, or time period. NASS - Quick Stats | Ag Data Commons - USDA The next thing you might want to do is plot the results. The resulting plot is a bit busy because it shows you all 96 counties that have sweetpotato data. If you need to access the underlying request bind the data into a single data.frame. There are thousands of R packages available online (CRAN 2020). The .gov means its official. Plus, in manually selecting and downloading data using the Quick Stats website, you could introduce human error by accidentally clicking the wrong buttons and selecting data that you do not actually want. You can also set the environmental variable directly with = 2012, but you may also want to query ranges of values.