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. Working for Peanuts: Acquiring, Analyzing, and Visualizing Publicly Available Data. Journal of the American Society of Farm Managers and Rural Appraisers, p156-166. The advantage of this Quick Stats Lite provides a more structured approach to get commonly requested statistics from our online database. 1987. Here we request the number of farm operators some functions that return parameter names and valid values for those nc_sweetpotato_data <- select(nc_sweetpotato_data_survey_mutate, -Value) First, obtain an API key from the Quick Stats service: https://quickstats.nass.usda.gov/api. nc_sweetpotato_data_survey_mutate <- mutate(nc_sweetpotato_data_survey, harvested_sweetpotatoes_acres = as.numeric(str_replace_all(string = Value, pattern = ",", replacement = ""))) To install packages, use the code below. like: The ability of rnassqs to iterate over lists of parameters. To use a baking analogy, you can think of the script as a recipe for your favorite dessert. An API request occurs when you programmatically send a data query from software on your computer (for example, R, Section 4) to the API for some NASS survey data that you want. The latest version of R is available on The Comprehensive R Archive Network website. you downloaded. What R Tools Are Available for Getting NASS Data? Remember to request your personal Quick Stats API key and paste it into the value for self.api_key in the __init__() function in the c_usda_quick_stats class. Each table includes diverse types of data. An introductory tutorial or how to use the National Agricultural Statistics Service (NASS) Quickstats tool can be found on their website. You do this by using the str_replace_all( ) function. United States Department of Agriculture. Also, be aware that some commodity descriptions may include & in their names. may want to collect the many different categories of acres for every token API key, default is to use the value stored in .Renviron . Combined with an assert from the Once in the tool please make your selection based on the program, sector, group, and commodity. Scripts allow coders to easily repeat tasks on their computers. This tool helps users obtain statistics on the database. NASS - Quick Stats Quick Stats database Back to dataset Quick Stats database Dynamic drill-down filtered search by Commodity, Location, and Date range, beginning with Census or Survey data. It allows you to customize your query by commodity, location, or time period. All of these reports were produced by Economic Research Service (ERS. Then you can use it coders would say run the script each time you want to download NASS survey data. You can check the full Quick Stats Glossary. Here, code refers to the individual characters (that is, ASCII characters) of the coding language. The returned data includes all records with year greater than or Access Quick Stats Lite . The data include the total crops and cropping practices for each county, and breakouts for irrigated and non-irrigated practices for many crops, for selected States. 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. ~ 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 In this example shown below, I used Quick Stats to build a query to retrieve the number of acres of corn harvested in the US from 2000 through 2021. Federal government websites often end in .gov or .mil. That is an average of nearly 450 acres per farm operation. API makes it easier to download new data as it is released, and to fetch a list of parameters is helpful. reference_period_desc "Period" - The specic time frame, within a freq_desc. Install. Before coding, you have to request an API access key from the NASS. rnassqs package and the QuickStats database, youll be able return the request object. class(nc_sweetpotato_data_survey$Value) These collections of R scripts are known as R packages. The ARMS is collected each year and includes data on agricultural production practices, agricultural resource use, and the economic well-being of farmers and ranchers (ARMS 2020). Do pay attention to the formatting of the path name. Read our multiple variables, geographies, or time frames without having to Language feature sets can be added at any time after you install Visual Studio. nc_sweetpotato_data_survey <- filter(nc_sweetpotato_data_sel, source_desc == "SURVEY" & county_name != "OTHER (COMBINED) COUNTIES") install.packages("tidyverse") Before sharing sensitive information, make sure you're on a federal government site. Winter Wheat Seedings up for 2023, 12/13/22 NASS to publish milk production data in updated data dissemination format, 11/28/22 USDA-NASS Crop Progress report delayed until Nov. 29, 10/28/22 NASS reinstates Cost of Pollination survey, 09/06/22 NASS to review acreage information, 09/01/22 USDA NASS reschedules 2021 Conservation Practice Adoption Motivations data highlights release, 05/06/22 Respond Now to the 2022 Census of Agriculture, 08/05/20 The NASS Mission: We do it for you, 04/11/19 2017 Census of Agriculture Highlight Series Farms and Land in Farms, 04/11/19 2017 Census of Agriculture Highlight Series Economics, 04/11/19 2017 Census of Agriculture Highlight Series Demographics, 02/08/23 Crop Production (February 2023), 01/31/23 Cattle & Sheep and Goats (January 2023), 12/23/22 Quarterly Hogs and Pigs (December 2022), 12/15/22 2021 Certified Organics (December 2022), Talking About NASS - A guide for partners and stakeholders, USDA and NASS Anti-Harassment Policy Statement, REE Reasonable Accommodations and Personal Assistance Services, Safeguarding America's Agricultural Statistics Report and Video, Agriculture Counts - The Founding and Evolution of the National Agricultural Statistics Service 1957-2007, Hours: 7:30 a.m. - 4:00 p.m. Eastern Time Monday - Friday, except federal holidays Toll-Free: (800) 727-9540, Hours: 9:00 a.m. - 5:30 p.m. Eastern Time Monday - Friday, except federal holidays Toll-Free: (833) One-USDA Do this by right-clicking on the file name in Solution Explorer and then clicking [Set as Startup File] from the popup menu. at least two good reasons to do this: Reproducibility. The Comprehensive R Archive Network website, Working for Peanuts: Acquiring, Analyzing, and Visualizing Publicly Available Data. In this publication, the word variable refers to whatever is on the left side of the <- character combination. A list of the valid values for a given field is available via Before using the API, you will need to request a free API key that your program will include with every call using the API. # look at the first few lines . 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. The API will then check the NASS data servers for the data you requested and send your requested information back. Dont repeat yourself. The .gov means its official. United States Department of Agriculture. The next thing you might want to do is plot the results. Also, before running the program, create the folder specified in the self.output_file_path variable in the __init__() function of the c_usda_quick_stats class. It allows you to customize your query by commodity, location, or time period. The National Agricultural Statistics Service (NASS) is part of the United States Department of Agriculture. In some cases you may wish to collect A script is like a collection of sentences that defines each step of a task. request. 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. NASS Reports Crop Progress (National) Crop Progress & Condition (State) Open source means that the R source code the computer code that makes R work can be viewed and edited by the public. To make this query, you will use the nassqs( ) function with the parameters as an input. Note: You need to define the different NASS Quick Stats API parameters exactly as they are entered in the NASS Quick Stats API. queries subset by year if possible, and by geography if not. County level data are also available via Quick Stats. Agricultural Resource Management Survey (ARMS). Now that youve cleaned and plotted the data, you can save them for future use or to share with others. script creates a trail that you can revisit later to see exactly what You can read more about the available NASS Quick Stats API parameters and their definitions by checking out the help page on this topic. Accessed: 01 October 2020. The API Usage page provides instructions for its use. function, which uses httr::GET to make an HTTP GET request United States Dept. nassqs_param_values(param = ). Other References Alig, R.J., and R.G. A locked padlock Dynamic drill-down filtered search by Commodity, Location, and Date range, beginning with Census or Survey data. The inputs to this function are 2 and 10 and the output is 12. Based on this result, it looks like there are 47 states with sweetpotato data available at the county level, and North Carolina is one of them. parameters is especially helpful. head(nc_sweetpotato_data, n = 3). The use of a callback function parameter, not shown in the example above, is beyond the scope of this article. The Quick Stats Database is the most comprehensive tool for accessing agricultural data published by NASS. Next, you need to tell your computer what R packages (Section 6) you plan to use in your R coding session. 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. Quick Stats Lite provides a more structured approach to get commonly requested statistics from our online database. nassqs is a wrapper around the nassqs_GET After running these lines of code, you will get a raw data output that has over 1500 rows and close to 40 columns. Instead, you only have to remember that this information is stored inside the variable that you are calling NASS_API_KEY. Beginning in May 2010, NASS agricultural chemical use data are published to the Quick Stats 2.0 database only (full-text publications have been discontinued), and can be found under the NASS Chemical Usage Program. Here is the most recent United States Summary and State Data (PDF, 27.9 MB), a statistical summary of the Census of Agriculture. If you use it, be sure to install its Python Application support. As a result, R coders have developed collections of user-friendly R scripts that accomplish themed tasks. The primary benefit of rnassqs is that users need not download data through repeated . Not all NASS data goes back that far, though. DSFW_Peanuts: Analysis of peanut DSFW from USDA-NASS databases. USDA-NASS. The CoA is collected every five years and includes demographics data on farms and ranches (CoA, 2020). Data by subject gives you additional information for a particular subject area or commodity. 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. Call the function stats.get_data() with the parameters string and the name of the output file (without the extension). The NASS helps carry out numerous surveys of U.S. farmers and ranchers. and rnassqs will detect this when querying data. 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". The rnassqs R package provides a simple interface for accessing the United States Department of Agriculture National Agricultural Statistics Service (USDA-NASS) 'Quick Stats' API. Generally the best way to deal with large queries is to make multiple Lock In this example, the sum function is doing a task that you can easily code by using the + sign, but it might not always be easy for you to code up the calculations and analyses done by a function. The agency has the distinction of being known as The Fact Finders of U.S. Agriculture due to the abundance of . 2019-67021-29936 from the USDA National Institute of Food and Agriculture. Lets say you are going to use the rnassqs package, as mentioned in Section 6. Winter Wheat Seedings up for 2023, NASS to publish milk production data in updated data dissemination format, USDA-NASS Crop Progress report delayed until Nov. 29, NASS reinstates Cost of Pollination survey, USDA NASS reschedules 2021 Conservation Practice Adoption Motivations data highlights release, Respond Now to the 2022 Census of Agriculture, 2017 Census of Agriculture Highlight Series Farms and Land in Farms, 2017 Census of Agriculture Highlight Series Economics, 2017 Census of Agriculture Highlight Series Demographics, NASS Climate Adaptation and Resilience Plan, Statement of Commitment to Scientific Integrity, USDA and NASS Civil Rights Policy Statement, Civil Rights Accountability Policy and Procedures, Contact information for NASS Civil Rights Office, International Conference on Agricultural Statistics, Agricultural Statistics: A Historical Timeline, As We Recall: The Growth of Agricultural Estimates, 1933-1961, Safeguarding America's Agricultural Statistics Report, Application Programming Interfaces (APIs), Economics, Statistics and Market Information System (ESMIS). USDA-NASS Quick Stats (Crops) WHEAT.pdf PDF 1.42 MB . Most of the information available from this site is within the public domain. 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. You can also make small changes to the script to download new types of data. Any opinions, findings, conclusions, or recommendations expressed in this publication are those of the authors and do not necessarily reflect the view of the U.S. Department of Agriculture. It allows you to customize your query by commodity, location, or time period. 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 . Some parameters, like key, are required if the function is to run properly without errors. The API response is the food made by the kitchen based on the written order from the customer to the waitstaff. Peng, R. D. 2020. The types of agricultural data stored in the FDA Quick Stats database. The Quick Stats Database is the most comprehensive tool for accessing agricultural data published by NASS. Agricultural Census since 1997, which you can do with something like. Coding is a lot easier when you use variables because it means you dont have to remember the specific string of letters and numbers that defines your unique NASS Quick Stats API key. query. To demonstrate the use of the agricultural data obtained with the Quick Stats API, I have created a simple dashboard in Tableau Public. For The USDA-NASS Quick Stats API has a graphic interface here: https://quickstats.nass.usda.gov. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely. If you download NASS data without using computer code, you may find that it takes a long time to manually select each dataset you want from the Quick Stats website. Each parameter is described on the Quick Stats Usage page, in its Quick Stats Columns Definition table, as shown below. For example, say you want to know which states have sweetpotato data available at the county level. Statistics Service, Washington, D.C. URL: https://quickstats.nass.usda.gov [accessed Feb 2023] . Access Quick Stats (searchable database) The Quick Stats Database is the most comprehensive tool for accessing agricultural data published by NASS. However, the NASS also allows programmatic access to these data via an application program interface as described in Section 2. commitment to diversity. Also, the parameter values be replaced with specific parameter-value pairs to search for the desired data. After you have completed the steps listed above, run the program. We also recommend that you download RStudio from the RStudio website. The == character combination tells R that this is a logic test for exactly equal, the & character is a logic test for AND, and the != character combination is a logic test for not equal. However, it is requested that in any subsequent use of this work, USDA-NASS be given appropriate acknowledgment. You can define the query output as nc_sweetpotato_data. http://quickstats.nass.usda.gov/api/api_GET/?key=PASTE_YOUR_API_KEY_HERE&source_desc=SURVEY§or_desc%3DFARMS%20%26%20LANDS%20%26%20ASSETS&commodity_desc%3DFARM%20OPERATIONS&statisticcat_desc%3DAREA%20OPERATED&unit_desc=ACRES&freq_desc=ANNUAL&reference_period_desc=YEAR&year__GE=1997&agg_level_desc=NATIONAL&state_name%3DUS%20TOTAL&format=CSV. The program will use the API to retrieve the number of acres used for each commodity (a crop, such as corn or soybeans), on a national level, from 1997 through 2021. 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. Receive Email Notifications for New Publications. You might need to do extra cleaning to remove these data before you can plot. NASS develops these estimates from data collected through: Dynamic drill-down filtered search by Commodity, Location, and Date range, (dataset) USDA National Agricultural Statistics Service (2017). If you have already installed the R package, you can skip to the next step (Section 7.2). However, other parameters are optional. 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. A Medium publication sharing concepts, ideas and codes. For example, if you wanted to calculate the sum of 2 and 10, you could use code 2 + 10 or you could use the sum( ) function (that is sum(2, 10)). Grain sorghum (Sorghum bicolor) is one of the most important cereal crops worldwide and is the third largest grain crop grown in the United. After running this line of code, R will output a result. 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. Corn production data goes back to 1866, just one year after the end of the American Civil War. Have a specific question for one of our subject experts? Data are currently available in the following areas: Pre-defined queries are provided for your convenience. 2017 Ag Atlas Maps. Retrieve the data from the Quick Stats server. Once you know North Carolina has data available, you can make an API query specific to sweetpotatoes in North Carolina. Do do so, you can The USDA NASS Quick Stats API provides direct access to the statistical information in the Quick Stats database.