how to cite usda nass quick stats

If you need to access the underlying request You can also export the plots from RStudio by going to the toolbar > Plots > Save as Image. Website: https://ask.usda.gov/s/, June Turner, Director Email: / Phone: (202) 720-8257, Find contact information for Regional and State Field Offices. 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 use nassqs_record_count(). Cooperative Extension prohibits discrimination and harassment regardless of age, color, disability, family and marital status, gender identity, national origin, political beliefs, race, religion, sex (including pregnancy), sexual orientation and veteran status. R Programming for Data Science. Queries that would return more records return an error and will not continue. 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. the project, but you have to repeat this process for every new project, It allows you to customize your query by commodity, location, or time period. developing the query is to use the QuickStats web interface. The .gov means its official. While it does not access all the data available through Quick Stats, you may find it easier to use. An official website of the United States government. Its recommended that you use the = character rather than the <- character combination when you are defining parameters (that is, variables inside functions). The last step in cleaning up the data involves the Value column. 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). The surveys that NASS conducts collect information on virtually every facet of U.S. agricultural production. To put its scale into perspective, in 2021, more than 2 million farms operated on more than 900 million acres (364 million hectares). Quick Stats. nc_sweetpotato_data_survey <- filter(nc_sweetpotato_data_sel, source_desc == "SURVEY" & county_name != "OTHER (COMBINED) COUNTIES") United States Department of Agriculture. nassqs_params() provides the parameter names, The download data files contain planted and harvested area, yield per acre and production. 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. those queries, append one of the following to the field youd like to 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. Federal government websites often end in .gov or .mil. Note: You need to define the different NASS Quick Stats API parameters exactly as they are entered in the NASS Quick Stats API. of Agr - Nat'l Ag. Each parameter is described on the Quick Stats Usage page, in its Quick Stats Columns Definition table, as shown below. valid before attempting to access the data: Once youve built a query, running it is easy: Putting all of the above together, we have a script that looks 2022. than the API restriction of 50,000 records. RStudio is another open-source software that makes it easier to code in R. The latest version of RStudio is available at the RStudio website. County level data are also available via Quick Stats. Combined with an assert from the The latest version of R is available on The Comprehensive R Archive Network website. If you have already installed the R package, you can skip to the next step (Section 7.2). Email: askusda@usda.gov replicate your results to ensure they have the same data that you Open source means that the R source code the computer code that makes R work can be viewed and edited by the public. Other References Alig, R.J., and R.G. its a good idea to check that before running a query. # select the columns of interest Here we request the number of farm operators The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely. Skip to 3. All sampled operations are mailed a questionnaire and given adequate time to respond by Once youve installed the R packages, you can load them. If you use Visual Studio, you can install them through the IDEs menu by following these instructions from Microsoft. Writer, photographer, cyclist, nature lover, data analyst, and software developer. 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. The Python program that calls the NASS Quick Stats API to retrieve agricultural data includes these two code modules (files): Scroll down to see the code from the two modules. United States Department of Agriculture. You can use many software programs to programmatically access the NASS survey data. This is less easy because you have to enter (or copy-paste) the key each 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. Agricultural Resource Management Survey (ARMS). returns a list of valid values for the source_desc Accessed online: 01 October 2020. value. The sample Tableau dashboard is called U.S. (R coders say you need to load your R packages.) You can do that by running the code below (Section 7.2). Generally the best way to deal with large queries is to make multiple USDA-NASS. The USDA Economics, Statistics and Market Information System (ESMIS) contains over 2,100 publications from five agencies of the . 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. 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. NASS_API_KEY <- "ADD YOUR NASS API KEY HERE" NASS - Quick Stats. 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. and predecessor agencies, U.S. Department of Agriculture (USDA). Also note that I wrote this program on a Windows PC, which uses back slashes (\) in file names and folder names. nc_sweetpotato_data_sel <- select(nc_sweetpotato_data_raw, county_name, year, source_desc, Value) Now you have a dataset that is easier to work with. These codes explain why data are missing. 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. Once the As mentioned in Section 4, RStudio provides a user-friendly way to interact with R. If this is your first time using a particular R package or if you have forgotten whether you installed an R package, you first need to install it on your computer by downloading it from the Comprehensive R Archive Network (Section 4). Using rnassqs Nicholas A Potter 2022-03-10. rnassqs is a package to access the QuickStats API from national agricultural statistics service (NASS) at the USDA. 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. Many people around the world use R for data analysis, data visualization, and much 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. However, it is requested that in any subsequent use of this work, USDA-NASS be given appropriate acknowledgment. Do this by right-clicking on the file name in Solution Explorer and then clicking [Set as Startup File] from the popup menu. To improve data accessibility and sharing, the NASS developed a Quick Stats website where you can select and download data from two of the agencys surveys. class(nc_sweetpotato_data_survey$Value) Data by subject gives you additional information for a particular subject area or commodity. 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. This will call its initializer (__init__()) function, which sets the API key, the base URL for the Quick Stats API, and the name of the folder where the class will write the output CSV file that contains agricultural data. Quick Stats Lite Before you make a specific API query, its best to see whether the data are even available for a particular combination of parameters. 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. Corn stocks down, soybean stocks down from year earlier Thsi package is now on CRAN and can be installed through the typical method: install.packages ("usdarnass") Alternatively, the most up-to-date version of the package can be installed with the devtools package. Use nass_count to determine number of records in query. The API only returns queries that return 50,000 or less records, so The API request is the customers (your) food order, which the waitstaff wrote down on the order notepad. However, the NASS also allows programmatic access to these data via an application program interface as described in Section 2. the end takes the form of a list of parameters that looks like. If you are using Visual Studio, then set the Startup File to the file run_usda_quick_stats.py. In the example program, the value for api key will be replaced with my API key. 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 For example, if youd like data from both 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. United States Dept. For request. Statistics Service, Washington, D.C. URL: https://quickstats.nass.usda.gov [accessed Feb 2023] . 1987. write_csv(data = nc_sweetpotato_data, path = "Users/your/Desktop/nc_sweetpotato_data_query_on_20201001.csv"). Statistics by State Explore Statistics By Subject Citation Request Most of the information available from this site is within the public domain. The agency has the distinction of being known as The Fact Finders of U.S. Agriculture due to the abundance of . Access Quick Stats (searchable database) The Quick Stats Database is the most comprehensive tool for accessing agricultural data published by NASS. 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. Some parameters, like key, are required if the function is to run properly without errors. Next, you can define parameters of interest. 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. This publication printed on: March 04, 2023, Getting Data from the National Agricultural Statistics Service (NASS) Using R. Skip to 1. Filter lists are refreshed based upon user choice allowing the user to fine-tune the search. The site is secure. 2020. Each table includes diverse types of data. 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 All of these reports were produced by Economic Research Service (ERS. USDA National Agricultural Statistics Service Information. Also, the parameter values be replaced with specific parameter-value pairs to search for the desired data. Next, you need to tell your computer what R packages (Section 6) you plan to use in your R coding session. For example, if someone asked you to add A and B, you would be confused. head(nc_sweetpotato_data, n = 3). The NASS helps carry out numerous surveys of U.S. farmers and ranchers. ggplot(data = sampson_sweetpotato_data) + geom_line(aes(x = year, y = harvested_sweetpotatoes_acres)). Information on the query parameters is found at https://quickstats.nass.usda.gov/api#param_define. Once in the tool please make your selection based on the program, sector, group, and commodity. assertthat package, you can ensure that your queries are It is a comprehensive summary of agriculture for the US and for each state. 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. 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). Click the arrow to access Quick Stats. They are (1) the Agriculture Resource Management Survey (ARMS) and (2) the Census of Agriculture (CoA). 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. You can verify your report was received by checking the Submitted date under the Status column of the My Surveys tab. NASS makes it easy for anyone to retrieve most of the data it captures through its Quick Stats database search web page. I built the queries simply by selecting one or more items from each of a series of dynamic dropdown menus. # filter out census data, to keep survey data only You can use the ggplot( ) function along with your nc_sweetpotato_data variable to do this. Open Tableau Public Desktop and connect it to the agricultural CSV data file retrieved with the Quick Stats API through the Python program described above. The returned data includes all records with year greater than or 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\\. sum of all counties in a state will not necessarily equal the state To make this query, you will use the nassqs( ) function with the parameters as an input. After you run this code, the output is not something you can see. Source: National Drought Mitigation Center, 2019. In some cases you may wish to collect There is no description for this organization, National Agricultural Statistics Service, Department of Agriculture. Do pay attention to the formatting of the path name. nc_sweetpotato_data <- select(nc_sweetpotato_data_survey_mutate, -Value) Then use the as.numeric( ) function to tell R each row is a number, not a character. your .Renviron file and add the key. Skip to 5. As mentioned in Section 1, you can visit the NASS Quick Stats website, click through the options, and download the data. # check the class of Value column However, beware that this will be a development version: # install.packages ("devtools") devtools :: install_github ("rdinter . It allows you to customize your query by commodity, location, or time period. Washington and Oregon, you can write state_alpha = c('WA', 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). 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. A function is another important concept that is helpful to understand while using R and many other coding languages. A Medium publication sharing concepts, ideas and codes. For Read our Plus, in manually selecting and downloading data using the Quick Stats website, you could introduce human error by accidentally clicking the wrong buttons and selecting data that you do not actually want. Visit the NASS website for a full library of past and current reports . U.S. Department of Agriculture, National Agricultural Statistics Service (NASS). class(nc_sweetpotato_data$harvested_sweetpotatoes_acres) the QuickStats API requires authentication. key, you can use it in any of the following ways: In your home directory create or edit the .Renviron Due to suppression of data, the commitment to diversity. USDA-NASS Quick Stats (Crops) WHEAT.pdf PDF 1.42 MB . Potter N (2022). If you use it, be sure to install its Python Application support. You might need to do extra cleaning to remove these data before you can plot. As a result, R coders have developed collections of user-friendly R scripts that accomplish themed tasks. Note that the value PASTE_YOUR_API_KEY_HERE must be replaced with your personal API key. Journal of Open Source Software , 4(43 . parameters. Website: https://ask.usda.gov/s/, June Turner, Director Email: / Phone: (202) 720-8257, Find contact information for Regional and State Field Offices. One way of Moreover, some data is collected only at specific Because R is accessible to so many people, there is a great deal of collaboration and sharing of R resources, scripts, and knowledge. Public domain information on the National Agricultural Statistics Service (NASS) Web pages may be freely downloaded and reproduced. Decode the data Quick Stats data in utf8 format. 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". Potter, (2019). .gov website belongs to an official government manually click through the QuickStats tool for each data The Quick Stats Database is the most comprehensive tool for accessing agricultural data published by NASS. file, and add NASSQS_TOKEN = to the Quick Stats contains official published aggregate estimates related to U.S. agricultural production. On the site you have the ability to filter based on numerous commodity types. Dynamic drill-down filtered search by Commodity, Location, and Date range, beginning with Census or Survey 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 . Secure .gov websites use HTTPSA In the example below, we describe how you can use the software program R to write and run a script that will download NASS survey data. rnassqs package and the QuickStats database, youll be able What Is the National Agricultural Statistics Service? they became available in 2008, you can iterate by doing the it. Note: In some cases, the Value column will have letter codes instead of numbers. We also recommend that you download RStudio from the RStudio website. organization in the United States. 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 automate running your script, since it will stop and ask you to 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).