The Comprehensive R Archive Network (CRAN), Weed Management in Nurseries, Landscapes & Christmas Trees, NC USDA - National Agricultural Statistics Service - Quick Stats # drop old Value column There are at least two good reasons to do this: Reproducibility. rnassqs: An R package to access agricultural data via the USDA National 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. Second, you will change entries in each row of the Value column so they are represented as a number, rather than a character. Instead, you only have to remember that this information is stored inside the variable that you are calling NASS_API_KEY. Accessed online: 01 October 2020. 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. 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. Once youve installed the R packages, you can load them. Your home for data science. Parameters need not be specified in a list and need not be 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. . Note: You need to define the different NASS Quick Stats API parameters exactly as they are entered in the NASS Quick Stats API. 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. national agricultural statistics service (NASS) at the USDA. Quick Stats Lite provides a more structured approach to get commonly requested statistics from our online database. However, it is requested that in any subsequent use of this work, USDA-NASS be given appropriate acknowledgment. USDA NASS Quick Stats API usdarnass they became available in 2008, you can iterate by doing the R Programming for Data Science. You can then define this filtered data as nc_sweetpotato_data_survey. 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 USDA ERS - References The query in The advantage of this Next, you need to tell your computer what R packages (Section 6) you plan to use in your R coding session. and you risk forgetting to add it to .gitignore. return the request object. You can view the timing of these NASS surveys on the calendar and in a summary of these reports. NASS collects and manages diverse types of agricultural data at the national, state, and county levels. 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. Skip to 6. On the site you have the ability to filter based on numerous commodity types. 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. This publication printed on: March 04, 2023, Getting Data from the National Agricultural Statistics Service (NASS) Using R. Skip to 1. # check the class of new value column Statistics Service, Washington, D.C. URL: https://quickstats.nass.usda.gov [accessed Feb 2023] . equal to 2012. While Quick Stats and Quick Stats Lite retrieve agricultural survey data (collected annually) and census data (collected every five years), the Census Data Query Tool is easier to use but retrieves only census data. Language feature sets can be added at any time after you install Visual Studio. Skip to 3. You can read more about the available NASS Quick Stats API parameters and their definitions by checking out the help page on this topic. In the get_data() function of c_usd_quick_stats, create the full URL. 2020. Data by subject gives you additional information for a particular subject area or commodity. functions as follows: # returns a list of fields that you can query, #> [1] "agg_level_desc" "asd_code" "asd_desc", #> [4] "begin_code" "class_desc" "commodity_desc", #> [7] "congr_district_code" "country_code" "country_name", #> [10] "county_ansi" "county_code" "county_name", #> [13] "domaincat_desc" "domain_desc" "end_code", #> [16] "freq_desc" "group_desc" "load_time", #> [19] "location_desc" "prodn_practice_desc" "reference_period_desc", #> [22] "region_desc" "sector_desc" "short_desc", #> [25] "state_alpha" "state_ansi" "state_name", #> [28] "state_fips_code" "statisticcat_desc" "source_desc", #> [31] "unit_desc" "util_practice_desc" "watershed_code", #> [34] "watershed_desc" "week_ending" "year", #> [1] "agg_level_desc: Geographical level of data. Moreover, some data is collected only at specific While the Quick Stats database contains more than 52 million records, any call using GET /api/api_GET query is limited to a 50,000-record result set. For United States Dept. Have a specific question for one of our subject experts? 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. 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. # filter out Sampson county data After you have completed the steps listed above, run the program. USDA-NASS. You can define the query output as nc_sweetpotato_data. After you run this code, the output is not something you can see. 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. like: The ability of rnassqs to iterate over lists of The site is secure. Quick Stats Lite provides a more structured approach to get commonly requested statistics from . Washington and Oregon, you can write state_alpha = c('WA', The next thing you might want to do is plot the results. All of these reports were produced by Economic Research Service (ERS. For 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. Agricultural Commodity Production by Land Area. While there are three types of API queries, this tutorial focuses on what may be the most flexible, which is the GET /api/api_GET query. 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. and predecessor agencies, U.S. Department of Agriculture (USDA). 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". .gov website belongs to an official government Texas Crop Progress and Condition (February 2023) USDA, National Agricultural Statistics Service, Southern Plains Regional Field Office Seven Day Observed Regional Precipitation, February 26, 2023. than the API restriction of 50,000 records. 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. There are Historical Corn Grain Yields in the U.S. It allows you to customize your query by commodity, location, or time period. at least two good reasons to do this: Reproducibility. 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. However, other parameters are optional. subset of values for a given query. This is why functions are an important part of R packages; they make coding easier for you. It allows you to customize your query by commodity, location, or time period. After running these lines of code, you will get a raw data output that has over 1500 rows and close to 40 columns. assertthat package, you can ensure that your queries are Going back to the restaurant analogy, the API key is akin to your table number at the restaurant. 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. I built the queries simply by selecting one or more items from each of a series of dynamic dropdown menus. To use a baking analogy, you can think of the script as a recipe for your favorite dessert. An introductory tutorial or how to use the National Agricultural Statistics Service (NASS) Quickstats tool can be found on their website. Generally the best way to deal with large queries is to make multiple nassqs_auth(key = NASS_API_KEY). An official website of the United States government. Healy. Information on the query parameters is found at https://quickstats.nass.usda.gov/api#param_define. The following is equivalent, A growing list of convenience functions makes querying simpler. Most of the information available from this site is within the public domain. is needed if subsetting by geography. install.packages("tidyverse") head(nc_sweetpotato_data, n = 3). 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. Access Quick Stats (searchable database) The Quick Stats Database is the most comprehensive tool for accessing agricultural data published by NASS. nassqs is a wrapper around the nassqs_GET Downloading data via Rstudio, you can also use usethis::edit_r_environ to open The .gov means its official. Citation Request - USDA - National Agricultural Statistics Service Homepage Why am I getting National Agricultural Statistics Service (NASS - USDA example, you can retrieve yields and acres with. In the example program, the value for api key will be replaced with my API key. For this reason, it is important to pay attention to the coding language you are using. nassqs_param_values(param = ). And data scientists, analysts, engineers, and any member of the public can freely tap more than 46 million records of farm-related data managed by the U.S. Department of Agriculture (USDA). You can use many software programs to programmatically access the NASS survey data. returns a list of valid values for the source_desc It allows you to customize your query by commodity, location, or time period.