The waitstaff and restaurant use that number to keep track of your order and bill (Figure 1). The United States is blessed with fertile soil and a huge agricultural industry. Scripts allow coders to easily repeat tasks on their computers. This article will show you how to use Python to retrieve agricultural data with the NASS Quick Stats API. The author. Otherwise the NASS Quick Stats API will not know what you are asking for. You can define the query output as nc_sweetpotato_data. .gitignore if youre using github. 2020. 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. DSFW_Peanuts: Analysis of peanut DSFW from USDA-NASS databases. Accessed: 01 October 2020. Corn stocks down, soybean stocks down from year earlier function, which uses httr::GET to make an HTTP GET request Before sharing sensitive information, make sure you're on a federal government site. 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 The example Python program shown in the next section will call the Quick Stats with a series of parameters. There are R packages to do linear modeling (such as the lm R package), make pretty plots (such as the ggplot2 R package), and many more. rnassqs (R NASS Quick Stats) rnassqs allows users to access the USDA's National Agricultural Statistics Service (NASS) Quick Stats data through their API. If you are using Visual Studio, then set the Startup File to the file run_usda_quick_stats.py. As an example, one year of corn harvest data for a particular county in the United States would represent one row, and a second year would represent another row. 'OR'). Access Quick Stats Lite . In some cases you may wish to collect ~ Providing Timely, Accurate and Useful Statistics in Service to U.S. Agriculture ~, County and District Geographic Boundaries, Crop Condition and Soil Moisture Analytics, Agricultural Statistics Board Corrections, Still time to respond to the 2022 Census of Agriculture, USDA to follow up with producers who have not yet responded, Still time to respond to the 2022 Puerto Rico Census of Agriculture, USDA to follow-up with producers who have not yet responded (Puerto Rico - English), 2022 Census of Agriculture due next week Feb. 6, Corn and soybean production down in 2022, USDA reports After you run this code, the output is not something you can see. many different sets of data, and in others your queries may be larger Official websites use .govA You can also make small changes to the script to download new types of data. The download data files contain planted and harvested area, yield per acre and production. Website: https://ask.usda.gov/s/, June Turner, Director Email: / Phone: (202) 720-8257, Find contact information for Regional and State Field Offices. The following are some of the types of data it stores and makes available: NASS makes data available through CSV and PDF files, charts and maps, a searchable database, pre-defined queries, and the Quick Stats API. You can use the select( ) function to keep the following columns: Value (acres of sweetpotatoes harvested), county_name (the name of the county), source_desc (whether data are coming from the NASS census or NASS survey), and year (the year of the data). In this case, youre wondering about the states with data, so set param = state_alpha. Instructions for how to use Tableau Public are beyond the scope of this tutorial. 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). The resulting plot is a bit busy because it shows you all 96 counties that have sweetpotato data. Open source means that the R source code the computer code that makes R work can be viewed and edited by the public. 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). Note that the value PASTE_YOUR_API_KEY_HERE must be replaced with your personal API key. To use a baking analogy, you can think of the script as a recipe for your favorite dessert. downloading the data via an R script creates a trail that you can revisit later to see exactly what you downloaded.It also makes it much easier for people seeking to . 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. Dont repeat yourself. want say all county cash rents on irrigated land for every year since it. Rstudio, you can also use usethis::edit_r_environ to open for each field as above and iteratively build your query. United States Department of Agriculture. The API will then check the NASS data servers for the data you requested and send your requested information back. For 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. Including parameter names in nassqs_params will return a You can read more about the available NASS Quick Stats API parameters and their definitions by checking out the help page on this topic. your .Renviron file and add the key. geographies. Retrieve the data from the Quick Stats server. 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. This image shows how working with the NASS Quick Stats API is analogous to ordering food at a restaurant. It can return data for the 2012 and 2017 censuses at the national, state, and local level for 77 different tables. write_csv(data = nc_sweetpotato_data, path = "Users/your/Desktop/nc_sweetpotato_data_query_on_20201001.csv"). NASS_API_KEY <- "ADD YOUR NASS API KEY HERE" Title USDA NASS Quick Stats API Version 0.1.0 Description An alternative for downloading various United States Department of Agriculture (USDA) data from <https://quickstats.nass.usda.gov/> through R. . https://www.nass.usda.gov/Education_and_Outreach/Understanding_Statistics/index.php, https://www.nass.usda.gov/Surveys/Guide_to_NASS_Surveys/Census_of_Agriculture/index.php, https://project-open-data.cio.gov/v1.1/schema/catalog.jsonld, https://project-open-data.cio.gov/v1.1/schema, https://project-open-data.cio.gov/v1.1/schema/catalog.json, https://www.agcensus.usda.gov/Publications/2012/Full_Report/Volume_1,_Chapter_1_US/usappxa.pdf,https://www.agcensus.usda.gov/Publications/2007/Full_Report/Volume_1,_Chapter_1_US/usappxa.pdf, https://creativecommons.org/publicdomain/zero/1.0/, https://www.nass.usda.gov/Education_and_Outreach/Understanding_Statistics/index.php, https://www.nass.usda.gov/Surveys/Guide_to_NASS_Surveys/Census_of_Agriculture/index.php. Copy BibTeX Tags API reproducibility agriculture economics Altmetrics Markdown badge Once your R packages are loaded, you can tell R what your NASS Quick Stats API key is. Need Help? Suggest a dataset here. The site is secure. nc_sweetpotato_data_survey <- filter(nc_sweetpotato_data_sel, source_desc == "SURVEY" & county_name != "OTHER (COMBINED) COUNTIES") nassqs is a wrapper around the nassqs_GET Agricultural Resource Management Survey (ARMS). 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. N.C. Similar to above, at times it is helpful to make multiple queries and All sampled operations are mailed a questionnaire and given adequate time to respond by Many people around the world use R for data analysis, data visualization, and much more. 2020. NC State University and NC For more specific information please contact [email protected] or call 1-800-727-9540. NASS Regional Field Offices maintain a list of all known operations and use known sources of operations to update their lists. Have a specific question for one of our subject experts? Corn stocks down, soybean stocks down from year earlier With the Quick Stats application programming interface (API), you can use a programming language, such as Python, to retrieve data from the Quick Stats database. DSFW_Peanuts: Analysis of peanut DSFW from USDA-NASS databases. Dynamic drill-down filtered search by Commodity, Location, and Date range, beginning with Census or Survey data. Working for Peanuts: Acquiring, Analyzing, and Visualizing Publicly Available Data. Journal of the American Society of Farm Managers and Rural Appraisers, p156-166. Filter lists are refreshed based upon user choice allowing the user to fine-tune the search. Quick Stats Lite provides a more structured approach to get commonly requested statistics from our online database. Quick Stats. # fix Value column you downloaded. Read our assertthat package, you can ensure that your queries are You can get an API Key here. Now that youve cleaned the data, you can display them in a plot. After running these lines of code, you will get a raw data output that has over 1500 rows and close to 40 columns. As an analogy, you can think of R as a plain text editor (such as Notepad), while RStudio is more like Microsoft Word with additional tools and options. All of these reports were produced by Economic Research Service (ERS. The agency has the distinction of being known as The Fact Finders of U.S. Agriculture due to the abundance of . Before coding, you have to request an API access key from the NASS. Tableau Public is a free version of the commercial Tableau data visualization tool. Any person using products listed in . The .gov means its official. In some environments you can do this with the PIP INSTALL utility. is needed if subsetting by geography. Second, you will use the specific information you defined in nc_sweetpotato_params to make the API query. There are # select the columns of interest Finally, it will explain how to use Tableau Public to visualize the data. Census of Agriculture Top The Census is conducted every 5 years. provide an api key. Statistics Service, Washington, D.C. URL: https://quickstats.nass.usda.gov [accessed Feb 2023] . Accessed online: 01 October 2020. In registering for the key, for which you must provide a valid email address. You can register for a NASS Quick Stats API key at the Quick Stats API website (click on Request API Key). Columns for this particular dataset would include the year harvested, county identification number, crop type, harvested amount, the units of the harvested amount, and other categories. file. There are at least two good reasons to do this: Reproducibility. It allows you to customize your query by commodity, location, or time period. # check the class of Value column For example, in the list of API parameters shown above, the parameter source_desc equates to Program in the Quick Stats query tool. To submit, please register and login first. You can verify your report was received by checking the Submitted date under the Status column of the My Surveys tab. time, but as you become familiar with the variables and calls of the Its very easy to export data stored in nc_sweetpotato_data or sampson_sweetpotato_data as a comma-separated variable file (.CSV) in R. To do this, you can use the write_csv( ) function. 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. You can then visualize the data on a map, manipulate and export the results, or save a link for future use. The Census Data Query Tool (CDQT) is a web based tool that is available to access and download table level data from the Census of Agriculture Volume 1 publication. Your home for data science. In addition, you wont be able at least two good reasons to do this: Reproducibility. Receive Email Notifications for New Publications. You can then visualize the data on a map, manipulate and export the results as an output file compatible for updating databases and spreadsheets, or save a link for future use. Here are the two Python modules that retrieve agricultural data with the Quick Stats API: To run the program, you will need to install the Python requests and urllib packages. request. This example in Section 7.8 represents a path name for a Mac computer, but a PC path to the desktop might look more like C:\Users\your\Desktop\nc_sweetpotato_data_query_on_20201001.csv. Read our Either 'CENSUS' or 'SURVEY'", https://quickstats.nass.usda.gov/api#param_define. The information on this page (the dataset metadata) is also available in these formats: The Quick Stats Database is the most comprehensive tool for accessing agricultural data published by the USDA National Agricultural Statistics Service (NASS). Skip to 3. Combined with an assert from the By setting prodn_practice_desc = "ALL PRODUCTION PRACTICES", you will get results for all production practices rather than those that specifically use irrigation, for example. 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. For example, a (D) value denotes data that are being withheld to avoid disclosing data for individual operations according to the creators of the NASS Quick Stats API. R Programming for Data Science. You can read more about tidy data and its benefits in the Tidy Data Illustrated Series. An official website of the United States government. Healy. Special Tabulations and Restricted Microdata, 02/15/23 Still time to respond to the 2022 Census of Agriculture, USDA to follow up with producers who have not yet responded, 02/15/23 Still time to respond to the 2022 Puerto Rico Census of Agriculture, USDA to follow-up with producers who have not yet responded (Puerto Rico - English), 01/31/23 United States cattle inventory down 3%, 01/30/23 2022 Census of Agriculture due next week Feb. 6, 01/12/23 Corn and soybean production down in 2022, USDA reports The Quick Stats Database is the most comprehensive tool for accessing agricultural data published by NASS. they became available in 2008, you can iterate by doing the 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, 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005:050 - Department of Agriculture - Commodity Purchases, 005:15 - National Agricultural Statistics Service. There are times when your data look like a 1, but R is really seeing it as an A. returns a list of valid values for the source_desc Here, code refers to the individual characters (that is, ASCII characters) of the coding language.