All sampled operations are mailed a questionnaire and given adequate time to respond by You can add a file to your project directory and ignore it via nc_sweetpotato_data_raw <- nassqs(nc_sweetpotato_params). a list of parameters is helpful. 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). 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. There are times when your data look like a 1, but R is really seeing it as an A. Corn stocks down, soybean stocks down from year earlier
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 . In this case, the task is to request NASS survey data. nassqs_params() provides the parameter names, Quick Stats Lite Each table includes diverse types of data. Do this by right-clicking on the file name in Solution Explorer and then clicking [Set as Startup File] from the popup menu. 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. As mentioned in Section 1, you can visit the NASS Quick Stats website, click through the options, and download the data. Corn stocks down, soybean stocks down from year earlier
In this case, the NASS Quick Stats API works as the interface between the NASS data servers (that is, computers with the NASS survey data on them) and the software installed on your computer. Website: https://ask.usda.gov/s/, June Turner, Director Email: / Phone: (202) 720-8257, Find contact information for Regional and State Field Offices. N.C. 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 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). You can then visualize the data on a map, manipulate and export the results, or save a link for future use. Now that youve cleaned the data, you can display them in a plot. is needed if subsetting by geography. 'OR'). The USDA NASS Quick Stats API provides direct access to the statistical information in the Quick Stats database. The Comprehensive R Archive Network (CRAN). .gov website belongs to an official government The latest version of R is available on The Comprehensive R Archive Network website. Sys.setenv(NASSQS_TOKEN = . You can check the full Quick Stats Glossary. Tip: Click on the images to view full-sized and readable versions. The waitstaff and restaurant use that number to keep track of your order and bill (Figure 1). Data by subject gives you additional information for a particular subject area or commodity. Also, before running the program, create the folder specified in the self.output_file_path variable in the __init__() function of the c_usda_quick_stats class. Grain sorghum (Sorghum bicolor) is one of the most important cereal crops worldwide and is the third largest grain crop grown in the United. Also, the parameter values be replaced with specific parameter-value pairs to search for the desired data. 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\\. Going back to the restaurant analogy, the API key is akin to your table number at the restaurant. This image shows how working with the NASS Quick Stats API is analogous to ordering food at a restaurant. they became available in 2008, you can iterate by doing the The returned data includes all records with year greater than or Now that youve cleaned and plotted the data, you can save them for future use or to share with others. than the API restriction of 50,000 records. As a result, R coders have developed collections of user-friendly R scripts that accomplish themed tasks. its a good idea to check that before running a query. You might need to do extra cleaning to remove these data before you can plot. Statistics Service, Washington, D.C. URL: https://quickstats.nass.usda.gov [accessed Feb 2023] . After you have completed the steps listed above, run the program. Then, when you click [Run], it will start running the program with this file first. nassqs is a wrapper around the nassqs_GET You can view the timing of these NASS surveys on the calendar and in a summary of these reports. While it does not access all the data available through Quick Stats, you may find it easier to use. NASS conducts hundreds of surveys every year and prepares reports covering virtually every aspect of U.S. agriculture. Many people around the world use R for data analysis, data visualization, and much more. Taken together, R reads this statement as: filter out all rows in the dataset where the source description column is exactly equal to SURVEY and the county name is not equal to OTHER (COMBINED) COUNTIES. 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. Once the queries subset by year if possible, and by geography if not. 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. You can define this selected data as nc_sweetpotato_data_sel. These include: R, Python, HTML, and many more. For docs and code examples, visit the package web page here . R sessions will have the variable set automatically, Source: National Drought Mitigation Center, You can then define this filtered data as nc_sweetpotato_data_survey. The API Usage page provides instructions for its use. 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. 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. The core functionality allows the user to query agricultural data from 'Quick Stats' in a reproducible and automated way. 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. NC State University and NC 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. What Is the National Agricultural Statistics Service? Some care 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). For example, if you wanted to calculate the sum of 2 and 10, you could use code 2 + 10 or you could use the sum( ) function (that is sum(2, 10)). Looking for U.S. government information and services? 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). Federal government websites often end in .gov or .mil. Agricultural Commodity Production by Land Area. 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. reference_period_desc "Period" - The specic time frame, within a freq_desc. ggplot(data = sampson_sweetpotato_data) + geom_line(aes(x = year, y = harvested_sweetpotatoes_acres)). key, you can use it in any of the following ways: In your home directory create or edit the .Renviron rnassqs tries to help navigate query building with Quick Stats System Updates provides notification of upcoming modifications. 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. NASS_API_KEY <- "ADD YOUR NASS API KEY HERE"
Then you can plot this information by itself. 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. 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. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely. Didn't find what you're looking for? 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. Quick Stats. example, you can retrieve yields and acres with. Before using the API, you will need to request a free API key that your program will include with every call using the API. use nassqs_record_count(). The API will then check the NASS data servers for the data you requested and send your requested information back. 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. United States Department of Agriculture. This is less easy because you have to enter (or copy-paste) the key each United States Dept. Have a specific question for one of our subject experts? sampson_sweetpotato_data <- filter(nc_sweetpotato_data, county_name == "SAMPSON")
To improve data accessibility and sharing, the NASS developed a "Quick Stats" website where you can select and download data from two of the agency's surveys. NASS - Quick Stats. You do this by using the str_replace_all( ) function. It allows you to customize your query by commodity, location, or time period. head(nc_sweetpotato_data, n = 3). Call the function stats.get_data() with the parameters string and the name of the output file (without the extension). USDA National Agricultural Statistics Service Information. In some cases you may wish to collect of Agr - Nat'l Ag. An official website of the United States government. like: The ability of rnassqs to iterate over lists of However, ERS has no copies of the original reports. 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. For 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. Before you get started with the Quick Stats API, become familiar with its Terms of Service and Usage. 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. Depending on what agency your survey is from, you will need to contact that agency to update your record. The following is equivalent, A growing list of convenience functions makes querying simpler. Once you know North Carolina has data available, you can make an API query specific to sweetpotatoes in North Carolina. About NASS. The name in parentheses is the name for the same value used in the Quick Stats query tool. 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). Rstudio, you can also use usethis::edit_r_environ to open Beginning in May 2010, NASS agricultural chemical use data are published to the Quick Stats 2.0 database only (full-text publications have been discontinued), and can be found under the NASS Chemical Usage Program. nassqs_param_values(param = ). 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. NASS collects and manages diverse types of agricultural data at the national, state, and county levels. any place from which $1,000 or more of agricultural products were produced and sold, or normally would have been sold, during the year. # plot the data
Other References Alig, R.J., and R.G. This number versus character representation is important because R cannot add, subtract, multiply, or divide characters. For example, commodity_desc refers to the commodity description information available in the NASS Quick Stats API and agg_level_desc refers to the aggregate level description of NASS Quick Stats API data. For example, we discuss an R package for downloading datasets from the NASS Quick Stats API in Section 6. Most of the information available from this site is within the public domain. 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. Where available, links to the electronic reports is provided. Most queries will probably be for specific values such as year Do pay attention to the formatting of the path name. But you can change the export path to any other location on your computer that you prefer. Generally the best way to deal with large queries is to make multiple The CoA is collected every five years and includes demographics data on farms and ranches (CoA, 2020). nassqs_auth(key = "ADD YOUR NASS API KEY HERE"). You can use many software programs to programmatically access the NASS survey data. install.packages("tidyverse")
A locked padlock sum of all counties in a state will not necessarily equal the state Second, you will change entries in each row of the Value column so they are represented as a number, rather than a character. 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. After you run this code, the output is not something you can see. 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). secure websites. If you use it, be sure to install its Python Application support. For In the example shown below, I selected census table 1 Historical Highlights for the state of Minnesota from the 2017 Census of Agriculture. Accessed online: 01 October 2020. returns a list of valid values for the source_desc There is no description for this organization, National Agricultural Statistics Service, Department of Agriculture. Note that the value PASTE_YOUR_API_KEY_HERE must be replaced with your personal API key. There are First, obtain an API key from the Quick Stats service: https://quickstats.nass.usda.gov/api. list with c(). Writer, photographer, cyclist, nature lover, data analyst, and software developer. Skip to 6. install.packages("rnassqs"). The use of a callback function parameter, not shown in the example above, is beyond the scope of this article. Griffin, T. W., and J. K. Ward. Cooperative Extension is based at North Carolina's two land-grant institutions, 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. Instead, you only have to remember that this information is stored inside the variable that you are calling NASS_API_KEY. 2020.
Harvesting its rich datasets presents opportunities for understanding and growth. 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. You can also export the plots from RStudio by going to the toolbar > Plots > Save as Image. 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. 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. You dont need all of these columns, and some of the rows need to be cleaned up a little bit. R is an open source coding language that was first developed in 1991 primarily for conducting statistical analyses and has since been applied to data visualization, website creation, and much more (Peng 2020; Chambers 2020). Then we can make a query. Once youve installed the R packages, you can load them. Be sure to keep this key in a safe place because it is your personal key to the NASS Quick Stats API. Ward, J. K., T. W. Griffin, D. L. Jordan, and G. T. Roberson. Either 'CENSUS' or 'SURVEY'", https://quickstats.nass.usda.gov/api#param_define. In addition, you wont be able The database allows custom extracts based on commodity, year, and selected counties within a State, or all counties in one or more States. Programmatic access refers to the processes of using computer code to select and download data. Skip to 5. This publication printed on: March 04, 2023, Getting Data from the National Agricultural Statistics Service (NASS) Using R. Skip to 1. 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. Quickstats is the main public facing database to find the most relevant agriculture statistics. You can verify your report was received by checking the Submitted date under the Status column of the My Surveys tab. The surveys that NASS conducts collect information on virtually every facet of U.S. agricultural production. Quick Stats Lite provides a more structured approach to get commonly requested statistics from our online database. A Medium publication sharing concepts, ideas and codes. Lets say you are going to use the rnassqs package, as mentioned in Section 6. Then you can use it coders would say run the script each time you want to download NASS survey data. Accessed 2023-03-04. How to Develop a Data Analytics Web App in 3 Steps Alan Jones in CodeFile Data Analysis with ChatGPT and Jupyter Notebooks Zach Quinn in Pipeline: A Data Engineering Resource Creating The Dashboard That Got Me A Data Analyst Job Offer Youssef Hosni in Level Up Coding 20 Pandas Functions for 80% of your Data Science Tasks Help Status Writers Blog 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. object generated by the GET call, you can use nassqs_GET to Say you want to plot the acres of sweetpotatoes harvested by year for each county in North Carolina. The United States is blessed with fertile soil and a huge agricultural industry. # fix Value column
That file will then be imported into Tableau Public to display visualizations about the data. 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.
Provide statistical data related to US agricultural production through either user-customized or pre-defined queries. The primary benefit of rnassqs is that users need not download data through repeated . A&T State University, in all 100 counties and with the Eastern Band of Cherokee Need Help? 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. Code is similar to the characters of the natural language, which can be combined to make a sentence. Agricultural Resource Management Survey (ARMS). organization in the United 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. You can also set the environmental variable directly with 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 If you use 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. In registering for the key, for which you must provide a valid email address. Skip to 3. 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. nassqs does handles *In this Extension publication, we will only cover how to use the rnassqs R package. The data found via the CDQT may also be accessed in the NASS Quick Stats database. Install. The National Agricultural Statistics Service (NASS) is part of the United States Department of Agriculture. If you are using Visual Studio, then set the Startup File to the file run_usda_quick_stats.py. nassqs_auth(key = NASS_API_KEY). to quickly and easily download new data. To browse or use data from this site, no account is necessary! 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. If you think back to algebra class, you might remember writing x = 1. 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 this reason, it is important to pay attention to the coding language you are using. Providing Central Access to USDAs Open Research Data. An official website of the General Services Administration. First, you will define each of the specifics of your query as nc_sweetpotato_params. Filter lists are refreshed based upon user choice allowing the user to fine-tune the search. Dynamic drill-down filtered search by Commodity, Location, and Date range, beginning with Census or Survey data. Retrieve the data from the Quick Stats server. Then, it will show you how to use Python to retrieve agricultural data with the NASS Quick Stats API. Corn stocks down, soybean stocks down from year earlier
Healy. NASS Reports Crop Progress (National) Crop Progress & Condition (State) # filter out census data, to keep survey data only
The advantage of this Columns for this particular dataset would include the year harvested, county identification number, crop type, harvested amount, the units of the harvested amount, and other categories. 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. # select the columns of interest
The last step in cleaning up the data involves the Value column. We also recommend that you download RStudio from the RStudio website. 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. The NASS helps carry out numerous surveys of U.S. farmers and ranchers. https://data.nal.usda.gov/dataset/nass-quick-stats. For more specific information please contact nass@usda.gov or call 1-800-727-9540. Many coders who use R also download and install RStudio along with it. at least two good reasons to do this: Reproducibility. In the example program, the value for api key will be replaced with my API key. Public domain information on the National Agricultural Statistics Service (NASS) Web pages may be freely downloaded and reproduced. # drop old Value column
replicate your results to ensure they have the same data that you geographies. Then use the as.numeric( ) function to tell R each row is a number, not a character. Do do so, you can variable (usually state_alpha or county_code For example, say you want to know which states have sweetpotato data available at the county level. Contact a specialist. The .gov means its official. After running these lines of code, you will get a raw data output that has over 1500 rows and close to 40 columns. Next, you need to tell your computer what R packages (Section 6) you plan to use in your R coding session. The census takes place once every five years, with the next one to be completed in 2022. As an example, you cannot run a non-R script using the R software program. Otherwise the NASS Quick Stats API will not know what you are asking for. For example, you NASS Regional Field Offices maintain a list of all known operations and use known sources of operations to update their lists. 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}. some functions that return parameter names and valid values for those Figure 1. DSFW_Peanuts: Analysis of peanut DSFW from USDA-NASS databases. Accessed: 01 October 2020. Quick Stats Lite provides a more structured approach to get commonly requested statistics from our online database. Moreover, some data is collected only at specific both together, but you can replicate that functionality with low-level want say all county cash rents on irrigated land for every year since However, if you only knew English and tried to read the recipe in Spanish or Japanese, your favorite treat might not turn out very well.