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. 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 You might need to do extra cleaning to remove these data before you can plot. Agricultural Commodity Production by Land Area. The latest version of R is available on The Comprehensive R Archive Network website.
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. 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. 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\\. many different sets of data, and in others your queries may be larger Note that the value PASTE_YOUR_API_KEY_HERE must be replaced with your personal API key. You can also export the plots from RStudio by going to the toolbar > Plots > Save as Image. USDA National Agricultural Statistics Service Information. There are The .gov means its official. The waitstaff and restaurant use that number to keep track of your order and bill (Figure 1). nassqs_auth(key = NASS_API_KEY). Quick Stats Lite provides a more structured approach to get commonly requested statistics from our online database. Before using the API, you will need to request a free API key that your program will include with every call using the API. Tableau Public is a free version of the commercial Tableau data visualization tool. NASS collects and manages diverse types of agricultural data at the national, state, and county levels. commitment to diversity. Quick Stats Lite provides a more structured approach to get commonly requested statistics from . rnassqs: An R package to access agricultural data via the USDA National Agricultural Statistics Service (USDA-NASS) 'Quick Stats' API. # drop old Value column
Filter lists are refreshed based upon user choice allowing the user to fine-tune the search. its a good idea to check that before running a query. First, you will define each of the specifics of your query as nc_sweetpotato_params. Once you have a Access Quick Stats Lite . 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". Receive Email Notifications for New Publications. Agricultural Resource Management Survey (ARMS). What Is the National Agricultural Statistics Service? Corn stocks down, soybean stocks down from year earlier
Data request is limited to 50,000 records per the API. ggplot(data = sampson_sweetpotato_data) + geom_line(aes(x = year, y = harvested_sweetpotatoes_acres)). Create a worksheet that shows the number of acres harvested for top commodities from 1997 through 2021. year field with the __GE modifier attached to 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. 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. To browse or use data from this site, no account is necessary. NASS Regional Field Offices maintain a list of all known operations and use known sources of operations to update their lists. It allows you to customize your query by commodity, location, or time period. You can then visualize the data on a map, manipulate and export the results, or save a link for future use. Accessed online: 01 October 2020. The chef is in the kitchen window in the upper left, the waitstaff in the center with the order, and the customer places the order. If you use Visual Studio, you can install them through the IDEs menu by following these instructions from Microsoft. For example, say you want to know which states have sweetpotato data available at the county level. Accessed online: 01 October 2020. The United States is blessed with fertile soil and a huge agricultural industry. want say all county cash rents on irrigated land for every year since Potter N (2022). NC State University and NC the .gov website. Quick Stats System Updates provides notification of upcoming modifications. nassqs_parse function that will process a request object In fact, you can use the API to retrieve the same data available through the Quick Stats search tool and the Census Data Query Tool, both of which are described above. Each language has its own unique way of representing meaning, using these characters and its own grammatical rules for combining these characters. 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). object generated by the GET call, you can use nassqs_GET to Agricultural Resource Management Survey (ARMS). N.C. You can read more about the available NASS Quick Stats API parameters and their definitions by checking out the help page on this topic. After you have completed the steps listed above, run the program. head(nc_sweetpotato_data, n = 3). The Comprehensive R Archive Network website, Working for Peanuts: Acquiring, Analyzing, and Visualizing Publicly Available Data. 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. An official website of the United States government. Accessed: 01 October 2020. 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. Next, you can use the select( ) function again to drop the old Value column. Say you want to plot the acres of sweetpotatoes harvested by year for each county in North Carolina. If you think back to algebra class, you might remember writing x = 1. Where available, links to the electronic reports is provided. Do pay attention to the formatting of the path name. The <- character combination means the same as the = (that is, equals) character, and R will recognize this. token API key, default is to use the value stored in .Renviron . queries subset by year if possible, and by geography if not. by operation acreage in Oregon in 2012. However, the NASS also allows programmatic access to these data via an application program interface as described in Section 2. nassqs does handles Most queries will probably be for specific values such as year The county data includes totals for the Agricultural Statistics Districts (county groupings) and the State. NASS Reports Crop Progress (National) Crop Progress & Condition (State) Do do so, you can It is a comprehensive summary of agriculture for the US and for each state. Some care If you need to access the underlying request Quick Stats. modify: In the above parameter list, year__GE is the 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. 2019. ~ 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
You can change the value of the path name as you would like as well. manually click through the QuickStats tool for each data A&T State University, in all 100 counties and with the Eastern Band of Cherokee = 2012, but you may also want to query ranges of values. to the Quick Stats API. assertthat package, you can ensure that your queries are
method is that you dont have to think about the API key for the rest of Potter, (2019). R sessions will have the variable set automatically, Email: askusda@usda.gov
4:84. The Comprehensive R Archive Network (CRAN). Then we can make a query. Source: National Weather Service, www.nws.noaa.gov Drought Monitor, Valid February 21, 2023. Data are currently available in the following areas: Pre-defined queries are provided for your convenience. For For example, we discuss an R package for downloading datasets from the NASS Quick Stats API in Section 6. All sampled operations are mailed a questionnaire and given adequate time to respond by There is no description for this organization, National Agricultural Statistics Service, Department of Agriculture. 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. system environmental variable when you start a new R 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. Census of Agriculture (CoA). a list of parameters is helpful. It allows you to customize your query by commodity, location, or time period. NASS develops these estimates from data collected through: Dynamic drill-down filtered search by Commodity, Location, and Date range, (dataset) USDA National Agricultural Statistics Service (2017). Healy. time you begin an R session. 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. You can use many software programs to programmatically access the NASS survey data. They are (1) the Agriculture Resource Management Survey (ARMS) and (2) the Census of Agriculture (CoA). To submit, please register and login first. If the survey is from USDA National Agricultural Statistics Service (NASS), y ou can make a note on the front page and explain that you no longer farm, no longer own the property, or if the property is farmed by someone else. While I used the free Microsoft Visual Studio Community 2022 integrated development ide (IDE) to write and run the Python program for this tutorial, feel free to use your favorite code editor or IDE. *In this Extension publication, we will only cover how to use the rnassqs R package. It allows you to customize your query by commodity, location, or time period. In the get_data() function of c_usd_quick_stats, create the full URL. Each table includes diverse types of data. All of these reports were produced by Economic Research Service (ERS. Providing Central Access to USDAs Open Research Data. You do this by using the str_replace_all( ) function. do. Its recommended that you use the = character rather than the <- character combination when you are defining parameters (that is, variables inside functions). Prior to using the Quick Stats API, you must agree to the NASS Terms of Service and obtain an API key. 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. nassqs_params() provides the parameter names, 2020. These codes explain why data are missing. Federal government websites often end in .gov or .mil. You can get an API Key here. organization in the United States. Before coding, you have to request an API access key from the NASS. 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. Quick Stats is the National Agricultural Statistics Service's (NASS) online, self-service tool to access complete results from the 1997, 2002, 2007, and 2012 Censuses of Agriculture as well as the best source of NASS survey published estimates. 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. 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. your .Renviron file and add the key. Quick Stats contains official published aggregate estimates related to U.S. agricultural production. Information on the query parameters is found at https://quickstats.nass.usda.gov/api#param_define. 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. It can return data for the 2012 and 2017 censuses at the national, state, and local level for 77 different tables. Then you can use it coders would say run the script each time you want to download NASS survey data. This publication printed on: March 04, 2023, Getting Data from the National Agricultural Statistics Service (NASS) Using R. Skip to 1. Any person using products listed in . 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. It accepts a combination of what, where, and when parameters to search for and retrieve the data of interest. function, which uses httr::GET to make an HTTP GET request To demonstrate the use of the agricultural data obtained with the Quick Stats API, I have created a simple dashboard in Tableau Public. The core functionality allows the user to query agricultural data from 'Quick Stats' in a reproducible and automated way. A script is like a collection of sentences that defines each step of a task. The primary benefit of rnassqs is that users need not download data through repeated .
There are thousands of R packages available online (CRAN 2020). 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. Quickstats is the main public facing database to find the most relevant agriculture statistics.
Running the script is similar to your pulling out the recipe and working through the steps when you want to make this dessert. Ward, J. K., T. W. Griffin, D. L. Jordan, and G. T. Roberson. Use nass_count to determine number of records in query. As a result, R coders have developed collections of user-friendly R scripts that accomplish themed tasks. The API only returns queries that return 50,000 or less records, so 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. and predecessor agencies, U.S. Department of Agriculture (USDA). If you are using Visual Studio, then set the Startup File to the file run_usda_quick_stats.py. than the API restriction of 50,000 records. An official website of the United States government. In addition, you wont be able County level data are also available via Quick Stats. into a data.frame, list, or raw text. 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 . The USDA NASS Quick Stats API provides direct access to the statistical information in the Quick Stats database. description of the parameter(s) in question: Documentation on all of the parameters is available at https://quickstats.nass.usda.gov/api#param_define. The ARMS is collected each year and includes data on agricultural production practices, agricultural resource use, and the economic well-being of farmers and ranchers (ARMS 2020). After it receives the data from the server in CSV format, it will write the data to a file with one record per line. and rnassqs will detect this when querying data. lock ( N.C. Grain sorghum (Sorghum bicolor) is one of the most important cereal crops worldwide and is the third largest grain crop grown in the United. ) or https:// means youve safely connected to 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
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. you downloaded. In the example shown below, I selected census table 1 Historical Highlights for the state of Minnesota from the 2017 Census of Agriculture. Moreover, some data is collected only at specific 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. nc_sweetpotato_data_sel <- select(nc_sweetpotato_data_raw, county_name, year, source_desc, Value)
Once the For more specific information please contact nass@usda.gov or call 1-800-727-9540. Lets say you are going to use the rnassqs package, as mentioned in Section 6. nc_sweetpotato_data_raw <- nassqs(nc_sweetpotato_params). Now that youve cleaned and plotted the data, you can save them for future use or to share with others. You can see a full list of NASS parameters that are available and their exact names by running the following line of code. Rstudio, you can also use usethis::edit_r_environ to open The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely. Dont repeat yourself. # check the class of Value column
A script includes a collection of code that, when taken together, defines a series of steps the coder wants his or her computer to carry out. There are times when your data look like a 1, but R is really seeing it as an A. Read our Public domain information on the National Agricultural Statistics Service (NASS) Web pages may be freely downloaded and reproduced. Before you can plot these data, it is best to check and fix their formatting. API makes it easier to download new data as it is released, and to fetch they became available in 2008, you can iterate by doing the Reference to products in this publication is not intended to be an endorsement to the exclusion of others which may have similar uses.
In this case, the NC sweetpotato data will be saved to a file called nc_sweetpotato_data_query_on_20201001.csv on your desktop. parameters is especially helpful. nc_sweetpotato_data_survey_mutate <- mutate(nc_sweetpotato_data_survey, harvested_sweetpotatoes_acres = as.numeric(str_replace_all(string = Value, pattern = ",", replacement = "")))
In the example program, the value for api key will be replaced with my API key. 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. It is best to start by iterating over years, so that if you Including parameter names in nassqs_params will return a 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. These include: R, Python, HTML, and many more. 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. Multiple values can be queried at once by including them in a simple 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. This is why functions are an important part of R packages; they make coding easier for you. Due to suppression of data, the To make this query, you will use the nassqs( ) function with the parameters as an input. Then use the as.numeric( ) function to tell R each row is a number, not a character. There are R packages to do linear modeling (such as the lm R package), make pretty plots (such as the ggplot2 R package), and many more. To run the script, you click a button in the software program or use a keyboard stroke that tells your computer to start going through the script step by step. Parameters need not be specified in a list and need not be Also, be aware that some commodity descriptions may include & in their names. What R Tools Are Available for Getting NASS Data? .gov website belongs to an official government The API response is the food made by the kitchen based on the written order from the customer to the waitstaff. The example Python program shown in the next section will call the Quick Stats with a series of parameters. The query in file. # plot Sampson county data
Install. The CoA is collected every five years and includes demographics data on farms and ranches (CoA, 2020). 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. The last step in cleaning up the data involves the Value column. example, you can retrieve yields and acres with. Please click here to provide feedback for any of the tools on this page. It allows you to customize your query by commodity, location, or time period. You will need this to make an API request later. 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. . 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. However, the NASS also allows programmatic access to these data via an application program interface as described in Section 2. AG-903. You can then visualize the data on a map, manipulate and export the results, or save a link for future use. You can add a file to your project directory and ignore it via Additionally, the CoA includes data on land use, land ownership, agricultural production practices, income, and expenses at the farm and ranch level. class(nc_sweetpotato_data$harvested_sweetpotatoes_acres)
Sign Up: https://rruntsch.medium.com/membership, install them through the IDEs menu by following these instructions from Microsoft, Year__GE = 1997 (all years greater than or equal to 1997). 2017 Census of Agriculture - Census Data Query Tool, QuickStats Parameter Definitions and Operators, Agricultural Statistics Districts (ASD) zipped (.zip) ESRI shapefile format for download, https://data.nal.usda.gov/dataset/nass-quick-stats, National Agricultural Library Thesaurus Term, hundreds of sample surveys conducted each year covering virtually every aspect of U.S. agriculture, the Census of Agriculture conducted every five years providing state- and county-level aggregates. rnassqs: Access the NASS 'Quick Stats' API. NASS makes it easy for anyone to retrieve most of the data it captures through its Quick Stats database search web page. This will create a new Corn production data goes back to 1866, just one year after the end of the American Civil War. For this reason, it is important to pay attention to the coding language you are using. Generally the best way to deal with large queries is to make multiple Not all NASS data goes back that far, though. 1987. It also makes it much easier for people seeking to Now you have a dataset that is easier to work with. Language feature sets can be added at any time after you install Visual Studio. These collections of R scripts are known as R packages. Journal of Open Source Software , 4(43 . Skip to 6. You are also going to use the tidyverse package, which is called a meta-package because it is a package of packages that helps you work with your datasets easily and keep them tidy.. In file run_usda_quick_stats.py create the parameters variable that contains parameter and value pairs to select data from the Quick Stats database. # check the class of new value column
Copy BibTeX Tags API reproducibility agriculture economics Altmetrics Markdown badge nc_sweetpotato_data <- select(nc_sweetpotato_data_survey_mutate, -Value)
the end takes the form of a list of parameters that looks like. 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).