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than the API restriction of 50,000 records. Some care subset of values for a given query. United States Department of Agriculture. Provide statistical data related to US agricultural production through either user-customized or pre-defined queries. 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. Any person using products listed in . nc_sweetpotato_data <- select(nc_sweetpotato_data_survey_mutate, -Value) rnassqs tries to help navigate query building with There are The United States is blessed with fertile soil and a huge agricultural industry. Tip: Click on the images to view full-sized and readable versions. 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. USDA National Agricultural Statistics Service. You can see whether a column is a character by using the class( ) function on that column (that is, nc_sweetpotato_data_survey$Value where the $ helps you access the Value column in the nc_sweetpotato_data_survey variable). Email: askusda@usda.gov 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. If you use this function on the Value column of nc_sweetpotato_data_survey, R will return character, but you want R to return numeric. In R, you would write x <- 1. However, there are three main reasons that its helpful to use a software program like R to download these data: Currently, there are four R packages available to help access the NASS Quick Stats API (see Section 4). The rnassqs package also has a Information on the query parameters is found at https://quickstats.nass.usda.gov/api#param_define. The census collects data on all commodities produced on U.S. farms and ranches, as . You can then visualize the data on a map, manipulate and export the results, or save a link for future use. 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. In registering for the key, for which you must provide a valid email address. 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. or the like) in lapply. nassqs_params() provides the parameter names, Taken together, R reads this statement as: filter out all rows in the dataset where the source description column is exactly equal to SURVEY and the county name is not equal to OTHER (COMBINED) COUNTIES. You can check by using the nassqs_param_values( ) function. For Why Is it Beneficial to Access NASS Data Programmatically? nc_sweetpotato_data_survey <- filter(nc_sweetpotato_data_sel, source_desc == "SURVEY" & county_name != "OTHER (COMBINED) COUNTIES") In both cases iterating over Agricultural Census since 1997, which you can do with something like. both together, but you can replicate that functionality with low-level they became available in 2008, you can iterate by doing the # filter out census data, to keep survey data only Before you can plot these data, it is best to check and fix their formatting. In this publication we will focus on two large NASS surveys. The waitstaff and restaurant use that number to keep track of your order and bill (Figure 1). The second line of code above uses the nassqs_auth( ) function (Section 4) and takes your NASS_API_KEY variable as the input for the parameter key. In this publication, the word parameter refers to a variable that is defined within a function. The API request is the customers (your) food order, which the waitstaff wrote down on the order notepad. 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. The Quick Stats Database is the most comprehensive tool for accessing agricultural data published by the USDA National Agricultural Statistics Service (NASS). NASS makes it easy for anyone to retrieve most of the data it captures through its Quick Stats database search web page. variable (usually state_alpha or county_code Not all NASS data goes back that far, though. Federal government websites often end in .gov or .mil. reference_period_desc "Period" - The specic time frame, within a freq_desc. Next, you can use the filter( ) function to select data that only come from the NASS survey, as opposed to the census, and represents a single county. Generally the best way to deal with large queries is to make multiple However, the NASS also allows programmatic access to these data via an application program interface as described in Section 2. 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). To put its scale into perspective, in 2021, more than 2 million farms operated on more than 900 million acres (364 million hectares). 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. 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. 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. it. description of the parameter(s) in question: Documentation on all of the parameters is available at https://quickstats.nass.usda.gov/api#param_define. More specifically, the list defines whether NASS data are aggregated at the national, state, or county scale. The National Agricultural Statistics Service (NASS) is part of the United States Department of Agriculture. national agricultural statistics service (NASS) at the USDA. The agency has the distinction of being known as The Fact Finders of U.S. Agriculture due to the abundance of . The <- character combination means the same as the = (that is, equals) character, and R will recognize this. Quick Stats API is the programmatic interface to the National Agricultural Statistics Service's (NASS) online database containing results from the 1997, 2002, 2007, and 2012 Censuses of Agriculture as well as the best source of NASS survey published estimates. It allows you to customize your query by commodity, location, or time period. However, if you only knew English and tried to read the recipe in Spanish or Japanese, your favorite treat might not turn out very well. In the example shown below, I selected census table 1 Historical Highlights for the state of Minnesota from the 2017 Census of Agriculture. API makes it easier to download new data as it is released, and to fetch Sys.setenv(NASSQS_TOKEN = . Here is the format of the base URL that will be used in this articles example: http://quickstats.nass.usda.gov/api/api_GET/?key=api key&{parameter parameter}&format={json | csv | xml}. Queries that would return more records return an error and will not continue. Decode the data Quick Stats data in utf8 format. 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. script creates a trail that you can revisit later to see exactly what class(nc_sweetpotato_data_survey$Value) Moreover, some data is collected only at specific To use a restaurant analogy, you can think of the NASS Quick Stats API as the waitstaff at your favorite restaurant, the NASS data servers as the kitchen, the software on your computer as the waitstaffs order notepad, and the coder as the customer (you) as shown in Figure 1. For most Column or Header Name values, the first value, in lowercase, is the API parameter name, like those shown above. First, you will define each of the specifics of your query as nc_sweetpotato_params. The last step in cleaning up the data involves the Value column. For docs and code examples, visit the package web page here . The resulting plot is a bit busy because it shows you all 96 counties that have sweetpotato data. 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 . Multiple values can be queried at once by including them in a simple rnassqs package and the QuickStats database, youll be able want say all county cash rents on irrigated land for every year since The advantage of this Lock Click the arrow to access Quick Stats. Filter lists are refreshed based upon user choice allowing the user to fine-tune the search. You can also set the environmental variable directly with Census of Agriculture Top The Census is conducted every 5 years. When you are coding, its helpful to add comments so you will remember or so someone you share your script with knows what you were trying to do and why. The QuickStats API offers a bewildering array of fields on which to Potter, (2019). To use a baking analogy, you can think of the script as a recipe for your favorite dessert. If you have already installed the R package, you can skip to the next step (Section 7.2). For DRY. NASS administers, manages, analyzes, and shares timely, accurate, and useful statistics in service to United States agriculture (NASS 2020). query. Additionally, the CoA includes data on land use, land ownership, agricultural production practices, income, and expenses at the farm and ranch level. Now that youve cleaned the data, you can display them in a plot. 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. Working for Peanuts: Acquiring, Analyzing, and Visualizing Publicly Available Data. Journal of the American Society of Farm Managers and Rural Appraisers, p156-166. Some parameters, like key, are required if the function is to run properly without errors. The surveys that NASS conducts collect information on virtually every facet of U.S. agricultural production. = 2012, but you may also want to query ranges of values. Here, code refers to the individual characters (that is, ASCII characters) of the coding language. equal to 2012. By setting statisticcat_desc = "AREA HARVESTED", you will get results for harvest acreage rather than planted acreage. .gitignore if youre using github. The name in parentheses is the name for the same value used in the Quick Stats query tool. Then use the as.numeric( ) function to tell R each row is a number, not a character. Combined with an assert from the Many coders who use R also download and install RStudio along with it. Copy BibTeX Tags API reproducibility agriculture economics Altmetrics Markdown badge The USDA-NASS Quick Stats API has a graphic interface here: https://quickstats.nass.usda.gov. However, it is requested that in any subsequent use of this work, USDA-NASS be given appropriate acknowledgment. If you think back to algebra class, you might remember writing x = 1. You dont need all of these columns, and some of the rows need to be cleaned up a little bit. NASS publications cover a wide range of subjects, from traditional crops, such as corn and wheat, to specialties, such as mushrooms and flowers; from calves born to hogs slaughtered; from agricultural prices to land in farms. You do this by using the str_replace_all( ) function. bind the data into a single data.frame. 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. Writer, photographer, cyclist, nature lover, data analyst, and software developer. Usage 1 2 3 4 5 6 7 8 commitment to diversity. Each parameter is described on the Quick Stats Usage page, in its Quick Stats Columns Definition table, as shown below. 2019. It allows you to customize your query by commodity, location, or time period. 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