how to cite usda nass quick stats

However, ERS has no copies of the original reports. # filter out Sampson county data Note that the value PASTE_YOUR_API_KEY_HERE must be replaced with your personal API key. The surveys that NASS conducts collect information on virtually every facet of U.S. agricultural production. Also, be aware that some commodity descriptions may include & in their names. Instructions for how to use Tableau Public are beyond the scope of this tutorial. 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. Receive Email Notifications for New Publications. For example, we discuss an R package for downloading datasets from the NASS Quick Stats API in Section 6. 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). Because R is accessible to so many people, there is a great deal of collaboration and sharing of R resources, scripts, and knowledge. The NASS helps carry out numerous surveys of U.S. farmers and ranchers. 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. 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). 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. for each field as above and iteratively build your query. A list of the valid values for a given field is available via organization in the United States. NASS_API_KEY <- "ADD YOUR NASS API KEY HERE" United States Department of Agriculture. Source: National Drought Mitigation Center, USDA National Agricultural Statistics Service Information. NASS Reports Crop Progress (National) Crop Progress & Condition (State) value. Accessed online: 01 October 2020. like: The ability of rnassqs to iterate over lists of 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. 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. 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. and predecessor agencies, U.S. Department of Agriculture (USDA). rnassqs: An R package to access agricultural data via the USDA National Agricultural Statistics Service (USDA-NASS) 'Quick Stats' API. Production and supplies of food and fiber, prices paid and received by farmers, farm labor and wages, farm finances, chemical use, and changes in the demographics of U.S. producers are only a few examples. An official website of the United States government. Then, it will show you how to use Python to retrieve agricultural data with the NASS Quick Stats API. Indians. Read our Otherwise the NASS Quick Stats API will not know what you are asking for. The Quick Stats Database is the most comprehensive tool for accessing agricultural data published by NASS. nc_sweetpotato_data_raw <- nassqs(nc_sweetpotato_params). lock ( 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. ggplot(data = nc_sweetpotato_data) + geom_line(aes(x = year, y = harvested_sweetpotatoes_acres)) + facet_wrap(~ county_name) These codes explain why data are missing. nassqs does handles In the example program, the value for api key will be replaced with my API key. Finally, you can define your last dataset as nc_sweetpotato_data. Skip to 6. into a data.frame, list, or raw text. You can define this selected data as nc_sweetpotato_data_sel. This number versus character representation is important because R cannot add, subtract, multiply, or divide characters. The API Usage page provides instructions for its use. The USDA-NASS Quick Stats API has a graphic interface here: https://quickstats.nass.usda.gov. It also makes it much easier for people seeking to Many people around the world use R for data analysis, data visualization, and much more. Quick Stats System Updates provides notification of upcoming modifications. 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). Corn stocks down, soybean stocks down from year earlier example. In this case, the task is to request NASS survey data. at least two good reasons to do this: Reproducibility. 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. This publication printed on: March 04, 2023, Getting Data from the National Agricultural Statistics Service (NASS) Using R. Skip to 1. Other References Alig, R.J., and R.G. Information on the query parameters is found at https://quickstats.nass.usda.gov/api#param_define. National Agricultural Statistics Service (NASS) Quickstats can be found on their website. The next thing you might want to do is plot the results. assertthat package, you can ensure that your queries are For Most of the information available from this site is within the public domain. Quick Stats Lite In some environments you can do this with the PIP INSTALL utility. parameters. The Quick Stats Database is the most comprehensive tool for accessing agricultural data published by the USDA National Agricultural Statistics Service (NASS). NC State University and NC For example, you The Cropland Data Layer (CDL) is a product of the USDA National Agricultural Statistics Service (NASS) with the mission "to provide timely, accurate and useful statistics in service to U.S. agriculture" (Johnson and Mueller, 2010, p. 1204). The rnassqs package also has a file, and add NASSQS_TOKEN = to the they became available in 2008, you can iterate by doing 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). 2020. Potter, (2019). U.S. National Agricultural Statistics Service (NASS) Summary "The USDA's National Agricultural Statistics Service (NASS) conducts hundreds of surveys every year and prepares reports covering virtually every aspect of U.S. agriculture. Data by subject gives you additional information for a particular subject area or commodity. script creates a trail that you can revisit later to see exactly what Accessed 2023-03-04. It allows you to customize your query by commodity, location, or time period. sum of all counties in a state will not necessarily equal the state The following is equivalent, A growing list of convenience functions makes querying simpler. Where available, links to the electronic reports is provided. Similar to above, at times it is helpful to make multiple queries and An introductory tutorial or how to use the National Agricultural Statistics Service (NASS) Quickstats tool can be found on their website. Once the By setting statisticcat_desc = "AREA HARVESTED", you will get results for harvest acreage rather than planted acreage. Call 1-888-424-7828 NASS Customer Support is available Monday - Friday, 8am - 5pm CT Please be prepared with your survey name and survey code. nassqs_auth(key = "ADD YOUR NASS API KEY HERE"). Once in the tool please make your selection based on the program, sector, group, and commodity. The QuickStats API offers a bewildering array of fields on which to You can then visualize the data on a map, manipulate and export the results, or save a link for future use. 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. Find more information at the following NC State Extension websites: Publication date: May 27, 2021 You can read more about tidy data and its benefits in the Tidy Data Illustrated Series. Source: National Weather Service, www.nws.noaa.gov Drought Monitor, Valid February 21, 2023. The agency has the distinction of being known as The Fact Finders of U.S. Agriculture due to the abundance of . Here we request the number of farm operators Note: When a line of R code starts with a #, R knows to read this # symbol as a comment and will skip over this line when you run your code. Filter lists are refreshed based upon user choice allowing the user to fine-tune the search. 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). On the site you have the ability to filter based on numerous commodity types. # select the columns of interest Parameters need not be specified in a list and need not be You can view the timing of these NASS surveys on the calendar and in a summary of these reports. To browse or use data from this site, no account is necessary. Have a specific question for one of our subject experts? For example, say you want to know which states have sweetpotato data available at the county level. query. Alternatively, you can query values Create a worksheet that shows the number of acres harvested for top commodities from 1997 through 2021. 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. You can add a file to your project directory and ignore it via Its easiest if you separate this search into two steps. nc_sweetpotato_data <- select(nc_sweetpotato_data_survey_mutate, -Value) bind the data into a single data.frame. year field with the __GE modifier attached to nassqs_param_values(param = ). 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. 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. Some parameters, like key, are required if the function is to run properly without errors. Going back to the restaurant analogy, the API key is akin to your table number at the restaurant. 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. Which Software Programs Can Be Used to Programmatically Access NASS Survey Data? The census collects data on all commodities produced on U.S. farms and ranches, as . Then use the as.numeric( ) function to tell R each row is a number, not a character. those queries, append one of the following to the field youd like to Statistics Service, Washington, D.C. URL: https://quickstats.nass.usda.gov [accessed Feb 2023] . These include: R, Python, HTML, and many more. DSFW_Peanuts: Analysis of peanut DSFW from USDA-NASS databases. While it does not access all the data available through Quick Stats, you may find it easier to use. You can also export the plots from RStudio by going to the toolbar > Plots > Save as Image. Decode the data Quick Stats data in utf8 format. You can define the query output as nc_sweetpotato_data. You can also write the two steps above as one step, which is shown below. Skip to 5. Working for Peanuts: Acquiring, Analyzing, and Visualizing Publicly Available Data. Journal of the American Society of Farm Managers and Rural Appraisers, p156-166. nc_sweetpotato_data_sel <- select(nc_sweetpotato_data_raw, county_name, year, source_desc, Value) 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. For docs and code examples, visit the package web page here . Before sharing sensitive information, make sure you're on a federal government site. R Programming for Data Science. Code is similar to the characters of the natural language, which can be combined to make a sentence. R is also free to download and use. Combined with an assert from the To install packages, use the code below. This article will show you how to use Python to retrieve agricultural data with the NASS Quick Stats API. The author. That file will then be imported into Tableau Public to display visualizations about the data. the project, but you have to repeat this process for every new project, You might need to do extra cleaning to remove these data before you can plot. 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. 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. First, obtain an API key from the Quick Stats service: https://quickstats.nass.usda.gov/api. Here is the most recent United States Summary and State Data (PDF, 27.9 MB), a statistical summary of the Census of Agriculture. 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. All of these reports were produced by Economic Research Service (ERS. 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. That is, the string of letters and numbers that represent your NASS Quick Stats API key is now saved to your R session and you can use it with other rnassqs R package functions. About NASS. Agricultural Resource Management Survey (ARMS). A Medium publication sharing concepts, ideas and codes. install.packages("rnassqs"). To browse or use data from this site, no account is necessary! The <- character combination means the same as the = (that is, equals) character, and R will recognize this. We also recommend that you download RStudio from the RStudio website. install.packages("tidyverse") We summarize the specifics of these benefits in Section 5. This will create a new Next, you can use the select( ) function again to drop the old Value column. You can do this by including the logic statement source_description == SURVEY & county_name != "OTHER (COMBINED) COUNTIES" inside the filter function. 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. You can check by using the nassqs_param_values( ) function. of Agr - Nat'l Ag. After you run this code, the output is not something you can see. A function is another important concept that is helpful to understand while using R and many other coding languages. rnassqs (R NASS Quick Stats) rnassqs allows users to access the USDA's National Agricultural Statistics Service (NASS) Quick Stats data through their API. United States Department of Agriculture. 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. Public domain information on the National Agricultural Statistics Service (NASS) Web pages may be freely downloaded and reproduced. (R coders say you need to load your R packages.) You can do that by running the code below (Section 7.2). Didn't find what you're looking for? # plot the data Reference to products in this publication is not intended to be an endorsement to the exclusion of others which may have similar uses. Additionally, the CoA includes data on land use, land ownership, agricultural production practices, income, and expenses at the farm and ranch level. The following pseudocode describes how the program works: Note the use of the urllib.parse.quote() function in the creation of the parameters string in step 1. Finally, it will explain how to use Tableau Public to visualize the data. capitalized. If you think back to algebra class, you might remember writing x = 1. It is a comprehensive summary of agriculture for the US and for each state. Corn stocks down, soybean stocks down from year earlier equal to 2012. Do do so, you can list with c(). modify: In the above parameter list, year__GE is the You can change the value of the path name as you would like as well. 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. by operation acreage in Oregon in 2012. replicate your results to ensure they have the same data that you In this case, you can use the string of letters and numbers that represents your NASS Quick Stats API key to directly define the key parameter that the function needs to work. Please note that you will need to fill in your NASS Quick Stats API key surrounded by quotation marks. Then we can make a query. In R, you would write x <- 1. The inputs to this function are 2 and 10 and the output is 12. Second, you will change entries in each row of the Value column so they are represented as a number, rather than a character. token API key, default is to use the value stored in .Renviron . Griffin, T. W., and J. K. Ward. In this case, the NC sweetpotato data will be saved to a file called nc_sweetpotato_data_query_on_20201001.csv on your desktop. A script is like a collection of sentences that defines each step of a task. # look at the first few lines head(nc_sweetpotato_data, n = 3). ~ 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 S, R, and Data Science. Proceedings of the ACM on Programming Languages. However, the NASS also allows programmatic access to these data via an application program interface as described in Section 2. Provide statistical data related to US agricultural production through either user-customized or pre-defined queries. However, it is requested that in any subsequent use of this work, USDA-NASS be given appropriate acknowledgment. As an analogy, you can think of R as a plain text editor (such as Notepad), while RStudio is more like Microsoft Word with additional tools and options. If you have already installed the R package, you can skip to the next step (Section 7.2). Copy BibTeX Tags API reproducibility agriculture economics Altmetrics Markdown badge Corn production data goes back to 1866, just one year after the end of the American Civil War. In some cases you may wish to collect For 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. Programmatic access refers to the processes of using computer code to select and download data. Quick Stats contains official published aggregate estimates related to U.S. agricultural production. Data request is limited to 50,000 records per the API. 4:84. It allows you to customize your query by commodity, location, or time period. However, beware that this will be a development version: # install.packages ("devtools") devtools :: install_github ("rdinter . The site is secure. Here are the pairs of parameters and values that it will submit in the API call to retrieve that data: Following is the full encoded URL that the program below creates and sends with the Quick Stats API. key, you can use it in any of the following ways: In your home directory create or edit the .Renviron Quick Stats Lite provides a more structured approach to get commonly requested statistics from our online database. subset of values for a given query. following: Subsetting by geography works similarly, looping over the geography 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. The report shows that, for the 2017 census, Minnesota had 68,822 farm operations covering 25,516,982 acres. Before you get started with the Quick Stats API, become familiar with its Terms of Service and Usage. You can use many software programs to programmatically access the NASS survey data. Lets say you are going to use the rnassqs package, as mentioned in Section 6. 2017 Ag Atlas Maps. It allows you to customize your query by commodity, location, or time period. Data are currently available in the following areas: Pre-defined queries are provided for your convenience. Do this by right-clicking on the file name in Solution Explorer and then clicking [Set as Startup File] from the popup menu. Visit the NASS website for a full library of past and current reports . The National Agricultural Statistics Service (NASS) is part of the United States Department of Agriculture. Most queries will probably be for specific values such as year Its recommended that you use the = character rather than the <- character combination when you are defining parameters (that is, variables inside functions). Washington and Oregon, you can write state_alpha = c('WA', You can think of a coding language as a natural language like English, Spanish, or Japanese. First, you will define each of the specifics of your query as nc_sweetpotato_params. Be sure to keep this key in a safe place because it is your personal key to the NASS Quick Stats API.

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