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. If youre not sure what spelling and case the NASS Quick Stats API uses, you can always check by clicking through the NASS Quick Stats website. USDA National Agricultural Statistics Service Information. Now that youve cleaned and plotted the data, you can save them for future use or to share with others. You can first use the function mutate( ) to rename the column to harvested_sweetpotatoes_acres. The Comprehensive R Archive Network (CRAN). 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. The next thing you might want to do is plot the results. Ward, J. K., T. W. Griffin, D. L. Jordan, and G. T. Roberson. https://data.nal.usda.gov/dataset/nass-quick-stats. For example, you An official website of the General Services Administration. If you are using Visual Studio, then set the Startup File to the file run_usda_quick_stats.py. By setting domain_desc = TOTAL, you will get the total acreage of sweetpotatoes in the county as opposed to the acreage of sweetpotates in the county grown by operators or producers of specific demographic groups that contribute to the total acreage of harvested sweetpotatoes in the county. The query in NASS administers, manages, analyzes, and shares timely, accurate, and useful statistics in service to United States agriculture (NASS 2020). USDA-NASS Quick Stats (Crops) WHEAT.pdf PDF 1.42 MB . The types of agricultural data stored in the FDA Quick Stats database. function, which uses httr::GET to make an HTTP GET request A script is like a collection of sentences that defines each step of a task. into a data.frame, list, or raw text. 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. Use nass_count to determine number of records in query. The API Usage page provides instructions for its use. Peng, R. D. 2020. Prior to using the Quick Stats API, you must agree to the NASS Terms of Service and obtain an API key. After you run this code, the output is not something you can see. file, and add NASSQS_TOKEN = to the 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". For docs and code examples, visit the package web page here . nassqs is a wrapper around the nassqs_GET The core functionality allows the user to query agricultural data from 'Quick Stats' in a reproducible and automated way. 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. Finally, it will explain how to use Tableau Public to visualize the data. Skip to 5. Quick Stats contains official published aggregate estimates related to U.S. agricultural production. 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. There are times when your data look like a 1, but R is really seeing it as an A. Here, code refers to the individual characters (that is, ASCII characters) of the coding language. Potter, (2019). A&T State University. The surveys that NASS conducts collect information on virtually every facet of U.S. agricultural production. Indians. ) or https:// means youve safely connected to 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. You can then define this filtered data as nc_sweetpotato_data_survey. rnassqs (R NASS Quick Stats) rnassqs allows users to access the USDA's National Agricultural Statistics Service (NASS) Quick Stats data through their API. It allows you to customize your query by commodity, location, or time period. 4:84. This tool helps users obtain statistics on the database. It can return data for the 2012 and 2017 censuses at the national, state, and local level for 77 different tables.
parameters is especially helpful. 2017 Ag Atlas Maps. The latest version of R is available on The Comprehensive R Archive Network website. Which Software Programs Can Be Used to Programmatically Access NASS Survey Data? DSFW_Peanuts: Analysis of peanut DSFW from USDA-NASS databases. Accessed: 01 October 2020. Rstudio, you can also use usethis::edit_r_environ to open Agricultural Commodity Production by Land Area. 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. 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. One way of As an example, you cannot run a non-R script using the R software program. On the other hand, if that person asked you to add 1 and 2, you would know exactly what to do. 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. . Its recommended that you use the = character rather than the <- character combination when you are defining parameters (that is, variables inside functions). 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-162.930566 69.858062, -161.908897 70.33333, -160.934797 70.44769, -159.039176 70.891642, -158.119723 70.824721, -156.580825 71.357764, -155.06779 71.147776))), USDA National Agricultural Statistics Service, 005:042 - Department of Agriculture - Agricultural Estimates, 005:043 - Department of Agriculture - Census of Agriculture, 005:050 - Department of Agriculture - Commodity Purchases, 005:15 - National Agricultural Statistics Service. sampson_sweetpotato_data <- filter(nc_sweetpotato_data, county_name == "SAMPSON")
Open source means that the R source code the computer code that makes R work can be viewed and edited by the public. Then you can use it coders would say run the script each time you want to download NASS survey data. 2020. To demonstrate the use of the agricultural data obtained with the Quick Stats API, I have created a simple dashboard in Tableau Public. ggplot(data = nc_sweetpotato_data) + geom_line(aes(x = year, y = harvested_sweetpotatoes_acres)) + facet_wrap(~ county_name)
You can change the value of the path name as you would like as well. The name in parentheses is the name for the same value used in the Quick Stats query tool. An application program interface, or API for short, helps coders access one software program from another. As mentioned in Section 1, you can visit the NASS Quick Stats website, click through the options, and download the data. the project, but you have to repeat this process for every new project, The last step in cleaning up the data involves the Value column. The waitstaff and restaurant use that number to keep track of your order and bill (Figure 1). If you use Healy. Based on your experience in algebra class, you may remember that if you replace x with NASS_API_KEY and 1 with a string of letters and numbers that defines your unique NASS Quick Stats API key, this is another way to think about the first line of code. Second, you will use the specific information you defined in nc_sweetpotato_params to make the API query. It allows you to customize your query by commodity, location, or time period. Now that youve cleaned the data, you can display them in a plot. You can register for a NASS Quick Stats API key at the Quick Stats API website (click on Request API Key). # look at the first few lines
~ 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
Corn production data goes back to 1866, just one year after the end of the American Civil War. time you begin an R session. 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. nassqs_auth(key = NASS_API_KEY). 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). Sys.setenv(NASSQS_TOKEN = . 2022. Once you know North Carolina has data available, you can make an API query specific to sweetpotatoes in North Carolina. nassqs_parse function that will process a request object Next, you can use the select( ) function again to drop the old Value column. Queries that would return more records return an error and will not continue. However, it is requested that in any subsequent use of this work, USDA-NASS be given appropriate acknowledgment. return the request object. It allows you to customize your query by commodity, location, or time period. Tip: Click on the images to view full-sized and readable versions. Including parameter names in nassqs_params will return a Not all NASS data goes back that far, though. its a good idea to check that before running a query. Data request is limited to 50,000 records per the API. query. Washington and Oregon, you can write state_alpha = c('WA', It accepts a combination of what, where, and when parameters to search for and retrieve the data of interest. An introductory tutorial or how to use the National Agricultural Statistics Service (NASS) Quickstats tool can be found on their website. Then use the as.numeric( ) function to tell R each row is a number, not a character. This is why functions are an important part of R packages; they make coding easier for you. Be sure to keep this key in a safe place because it is your personal key to the NASS Quick Stats API. NASS has also developed Quick Stats Lite search tool to search commodities in its database. Email: [email protected]
NASS Reports Crop Progress (National) Crop Progress & Condition (State) organization in the United States. Before sharing sensitive information, make sure you're on a federal government site. Besides requesting a NASS Quick Stats API key, you will also need to make sure you have an up-to-date version of R. If not, you can download R from The Comprehensive R Archive Network. 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}. Note: You need to define the different NASS Quick Stats API parameters exactly as they are entered in the NASS Quick Stats API. 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. For more specific information please contact [email protected] or call 1-800-727-9540. The USDA Economics, Statistics and Market Information System (ESMIS) contains over 2,100 publications from five agencies of the . # filter out census data, to keep survey data only
After running this line of code, R will output a result. R sessions will have the variable set automatically, Instructions for how to use Tableau Public are beyond the scope of this tutorial. Filter lists are refreshed based upon user choice allowing the user to fine-tune the search. USDA National Agricultural Statistics Service. However, ERS has no copies of the original reports. Copy BibTeX Tags API reproducibility agriculture economics Altmetrics Markdown badge The following is equivalent, A growing list of convenience functions makes querying simpler. Griffin, T. W., and J. K. Ward. 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. Website: https://ask.usda.gov/s/, June Turner, Director Email: / Phone: (202) 720-8257, Find contact information for Regional and State Field Offices. parameters. to quickly and easily download new data. 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. You can then visualize the data on a map, manipulate and export the results, or save a link for future use. = 2012, but you may also want to query ranges of values. Many coders who use R also download and install RStudio along with it. Quick Stats System Updates provides notification of upcoming modifications. Filter lists are refreshed based upon user choice allowing the user to fine-tune the search. nassqs_auth(key = "ADD YOUR NASS API KEY HERE"). 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. The database allows custom extracts based on commodity, year, and selected counties within a State, or all counties in one or more States. It also makes it much easier for people seeking to The site is secure. Share sensitive information only on official, Programmatic access refers to the processes of using computer code to select and download data. 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). 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. Some parameters, like key, are required if the function is to run properly without errors. In addition, you wont be able a list of parameters is helpful. Find more information at the following NC State Extension websites: Publication date: May 27, 2021 The Quick Stats Database is the most comprehensive tool for accessing agricultural data published by the USDA National Agricultural Statistics Service (NASS). class(nc_sweetpotato_data_survey$Value)
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 can also export the plots from RStudio by going to the toolbar > Plots > Save as Image. So, you may need to change the format of the file path value if you will run the code on Mac OS or Linux, for example: self.output_file_path = rc:\\usda_quickstats_files\\.
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