how to cite usda nass quick stats

rnassqs: An R package to access agricultural data via the USDA National Agricultural Statistics Service (USDA-NASS) 'Quick Stats' API. *In this Extension publication, we will only cover how to use the rnassqs R package. You can then visualize the data on a map, manipulate and export the results, or save a link for future use. Lets say you are going to use the rnassqs package, as mentioned in Section 6. install.packages("tidyverse") In this case, the task is to request NASS survey data. 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. The database allows custom extracts based on commodity, year, and selected counties within a State, or all counties in one or more States. a list of parameters is helpful. Corn stocks down, soybean stocks down from year earlier 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. 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. 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. In registering for the key, for which you must provide a valid email address. The Quick Stats Database is the most comprehensive tool for accessing agricultural data published by the USDA National Agricultural Statistics Service (NASS). There are times when your data look like a 1, but R is really seeing it as an A. 2017 Ag Atlas Maps. Before using the API, you will need to request a free API key that your program will include with every call using the API. If you are using Visual Studio, then set the Startup File to the file run_usda_quick_stats.py. Also note that I wrote this program on a Windows PC, which uses back slashes (\) in file names and folder names. NASS makes it easy for anyone to retrieve most of the data it captures through its Quick Stats database search web page. The report shows that, for the 2017 census, Minnesota had 68,822 farm operations covering 25,516,982 acres. United States Dept. The sample Tableau dashboard is called U.S. After you have completed the steps listed above, run the program. PDF usdarnass: USDA NASS Quick Stats API time, but as you become familiar with the variables and calls of the Corn stocks down, soybean stocks down from year earlier Then you can use it coders would say run the script each time you want to download NASS survey data. Quick Stats Lite provides a more structured approach to get commonly requested statistics from . USDA - National Agricultural Statistics Service - Census of Agriculture However, here are the basic steps to install Tableau Public and build and publish the dashboard: After completing this tutorial, you should have a general understanding of: I can imagine many use cases for projects that would use data from the Quick Stats database. United States Department of Agriculture. Accessed online: 01 October 2020. Suggest a dataset here. Many coders who use R also download and install RStudio along with it. This image shows how working with the NASS Quick Stats API is analogous to ordering food at a restaurant. Source: National Weather Service, www.nws.noaa.gov Drought Monitor, Valid February 21, 2023. example, you can retrieve yields and acres with. The Quick Stats Database is the most comprehensive tool for accessing agricultural data published by NASS. In some environments you can do this with the PIP INSTALL utility. Additionally, the CoA includes data on land use, land ownership, agricultural production practices, income, and expenses at the farm and ranch level. Contact a specialist. 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. 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. multiple variables, geographies, or time frames without having to PDF Released March 18, 2021, by the National Agricultural Statistics Citation Request - USDA - National Agricultural Statistics Service Homepage api key is in a file, you can use it like this: If you dont want to add the API key to a file or store it in your Here we request the number of farm operators Peng, R. D. 2020. parameters. script creates a trail that you can revisit later to see exactly what at least two good reasons to do this: Reproducibility. Statistics by State Explore Statistics By Subject Citation Request Most of the information available from this site is within the public domain. do. The returned data includes all records with year greater than or By setting domain_desc = TOTAL, you will get the total acreage of sweetpotatoes in the county as opposed to the acreage of sweetpotates in the county grown by operators or producers of specific demographic groups that contribute to the total acreage of harvested sweetpotatoes in the county. The .gov means its official. 4:84. The census collects data on all commodities produced on U.S. farms and ranches, as . # look at the first few lines Not all NASS data goes back that far, though. It is best to start by iterating over years, so that if you After you run this code, the output is not something you can see. Here, code refers to the individual characters (that is, ASCII characters) of the coding language. Working for Peanuts: Acquiring, Analyzing, and Visualizing Publicly Available Data. Journal of the American Society of Farm Managers and Rural Appraisers, p156-166. The USDA NASS Quick Stats API provides direct access to the statistical information in the Quick Stats database. nassqs does handles 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. 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 name in parentheses is the name for the same value used in the Quick Stats query tool. You will need this to make an API request later. those queries, append one of the following to the field youd like to You can also write the two steps above as one step, which is shown below. (PDF) rnassqs: An R package to access agricultural data via the USDA 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. You can first use the function mutate( ) to rename the column to harvested_sweetpotatoes_acres. A function is another important concept that is helpful to understand while using R and many other coding languages. An application program interface, or API for short, helps coders access one software program from another. Because R is accessible to so many people, there is a great deal of collaboration and sharing of R resources, scripts, and knowledge. 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. Quick Stats System Updates provides notification of upcoming modifications. Ward, J. K., T. W. Griffin, D. L. Jordan, and G. T. Roberson. In the example shown below, I selected census table 1 Historical Highlights for the state of Minnesota from the 2017 Census of Agriculture. DSFW_Peanuts: Analysis of peanut DSFW from USDA-NASS databases. PDF Texas Crop Progress and Condition Next, you can use the select( ) function again to drop the old Value column. Going back to the restaurant analogy, the API key is akin to your table number at the restaurant. There is no description for this organization, National Agricultural Statistics Service, Department of Agriculture. 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. The National Agricultural Statistics Service (NASS) is part of the United States Department of Agriculture. Finally, you can define your last dataset as nc_sweetpotato_data. Summary rnassqs NASS collects and manages diverse types of agricultural data at the national, state, and county levels. and rnassqs will detect this when querying data. To submit, please register and login first. DRY. Create a worksheet that allows the user to select a commodity (corn, soybeans, selected) and view the number of acres planted or harvested from 1997 through 2021. Grain sorghum (Sorghum bicolor) is one of the most important cereal crops worldwide and is the third largest grain crop grown in the United. Feel free to download it and modify it in the Tableaue Public Desktop application to learn how to create and publish Tableau visualizations. "rnassqs: An 'R' package to access agricultural data via the USDA National Agricultural Statistics Service (USDA-NASS) 'Quick Stats' API." The Journal of Open Source Software. For this reason, it is important to pay attention to the coding language you are using. nc_sweetpotato_data <- select(nc_sweetpotato_data_survey_mutate, -Value) Corn production data goes back to 1866, just one year after the end of the American Civil War. You can change the value of the path name as you would like as well. The USDA-NASS Quick Stats API has a graphic interface here: https://quickstats.nass.usda.gov. and you risk forgetting to add it to .gitignore. It accepts a combination of what, where, and when parameters to search for and retrieve the data of interest. Potter N (2022). Email: askusda@usda.gov rnassqs package and the QuickStats database, youll be able Moreover, some data is collected only at specific Accessed 2023-03-04. Read our 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. 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. Access Data from the NASS Quick Stats API rnassqs - rOpenSci S, R, and Data Science. Proceedings of the ACM on Programming Languages. Now that youve cleaned the data, you can display them in a plot. The API response is the food made by the kitchen based on the written order from the customer to the waitstaff. Be sure to keep this key in a safe place because it is your personal key to the NASS Quick Stats API. USDA National Agricultural Statistics Service Cropland Data - USGS Second, you will use the specific information you defined in nc_sweetpotato_params to make the API query. It can return data for the 2012 and 2017 censuses at the national, state, and local level for 77 different tables. To make this query, you will use the nassqs( ) function with the parameters as an input. An official website of the General Services Administration. Using rnassqs To submit, please register and login first. your .Renviron file and add the key. The USDA Economics, Statistics and Market Information System (ESMIS) contains over 2,100 publications from five agencies of the . This article will show you how to use Python to retrieve agricultural data with the NASS Quick Stats API. The author. For example, in the list of API parameters shown above, the parameter source_desc equates to Program in the Quick Stats query tool. The USDAs National Agricultural Statistics Service (NASS) makes the departments farm agricultural data available to the public on its website through reports, maps, search tools, and its NASS Quick Stats API. Second, you will change entries in each row of the Value column so they are represented as a number, rather than a character. rnassqs tries to help navigate query building with 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. One way it collects data is through the Census of Agriculture, which surveys all agricultural operations with $1,000 or more of products raised or sold during the census year. Skip to 5. However, other parameters are optional. 2017 Census of Agriculture - Census Data Query Tool (CDQT) 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). 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). 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. If you use 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. write_csv(data = nc_sweetpotato_data, path = "Users/your/Desktop/nc_sweetpotato_data_query_on_20201001.csv"). 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). In addition, you wont be able Section 207(f)(2) of the E-Government Act of 2002 requires federal agencies to develop an inventory of information to be published on their Web sites, establish a schedule for publishing information, make those schedules available for public comment, and post the schedules and priorities on the Web site. 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. But you can change the export path to any other location on your computer that you prefer. Providing Central Access to USDAs Open Research Data, MULTIPOLYGON (((-155.54211 19.08348, -155.68817 18.91619, -155.93665 19.05939, -155.90806 19.33888, -156.07347 19.70294, -156.02368 19.81422, -155.85008 19.97729, -155.91907 20.17395, -155.86108 20.26721, -155.78505 20.2487, -155.40214 20.07975, -155.22452 19.99302, -155.06226 19.8591, -154.80741 19.50871, -154.83147 19.45328, -155.22217 19.23972, -155.54211 19.08348)), ((-156.07926 20.64397, -156.41445 20.57241, -156.58673 20.783, -156.70167 20.8643, -156.71055 20.92676, -156.61258 21.01249, -156.25711 20.91745, -155.99566 20.76404, -156.07926 20.64397)), ((-156.75824 21.17684, -156.78933 21.06873, -157.32521 21.09777, -157.25027 21.21958, -156.75824 21.17684)), ((-157.65283 21.32217, -157.70703 21.26442, -157.7786 21.27729, -158.12667 21.31244, -158.2538 21.53919, -158.29265 21.57912, -158.0252 21.71696, -157.94161 21.65272, -157.65283 21.32217)), ((-159.34512 21.982, -159.46372 21.88299, -159.80051 22.06533, -159.74877 22.1382, -159.5962 22.23618, -159.36569 22.21494, -159.34512 21.982)), ((-94.81758 49.38905, -94.64 48.84, -94.32914 48.67074, -93.63087 48.60926, -92.61 48.45, -91.64 48.14, -90.83 48.27, -89.6 48.01, -89.272917 48.019808, -88.378114 48.302918, -87.439793 47.94, -86.461991 47.553338, -85.652363 47.220219, -84.87608 46.900083, -84.779238 46.637102, -84.543749 46.538684, -84.6049 46.4396, -84.3367 46.40877, -84.14212 46.512226, -84.091851 46.275419, -83.890765 46.116927, -83.616131 46.116927, -83.469551 45.994686, -83.592851 45.816894, -82.550925 45.347517, -82.337763 44.44, -82.137642 43.571088, -82.43 42.98, -82.9 42.43, -83.12 42.08, -83.142 41.975681, -83.02981 41.832796, -82.690089 41.675105, -82.439278 41.675105, -81.277747 42.209026, -80.247448 42.3662, -78.939362 42.863611, -78.92 42.965, -79.01 43.27, -79.171674 43.466339, -78.72028 43.625089, -77.737885 43.629056, -76.820034 43.628784, -76.5 44.018459, -76.375 44.09631, -75.31821 44.81645, -74.867 45.00048, -73.34783 45.00738, -71.50506 45.0082, -71.405 45.255, -71.08482 45.30524, -70.66 45.46, -70.305 45.915, -69.99997 46.69307, -69.237216 47.447781, -68.905 47.185, -68.23444 47.35486, -67.79046 47.06636, -67.79134 45.70281, -67.13741 45.13753, -66.96466 44.8097, -68.03252 44.3252, -69.06 43.98, -70.11617 43.68405, -70.645476 43.090238, -70.81489 42.8653, -70.825 42.335, -70.495 41.805, -70.08 41.78, -70.185 42.145, -69.88497 41.92283, -69.96503 41.63717, -70.64 41.475, -71.12039 41.49445, -71.86 41.32, -72.295 41.27, -72.87643 41.22065, -73.71 40.931102, -72.24126 41.11948, -71.945 40.93, -73.345 40.63, -73.982 40.628, -73.952325 40.75075, -74.25671 40.47351, -73.96244 40.42763, -74.17838 39.70926, -74.90604 38.93954, -74.98041 39.1964, -75.20002 39.24845, -75.52805 39.4985, -75.32 38.96, -75.071835 38.782032, -75.05673 38.40412, -75.37747 38.01551, -75.94023 37.21689, -76.03127 37.2566, -75.72205 37.93705, -76.23287 38.319215, -76.35 39.15, -76.542725 38.717615, -76.32933 38.08326, -76.989998 38.239992, -76.30162 37.917945, -76.25874 36.9664, -75.9718 36.89726, -75.86804 36.55125, -75.72749 35.55074, -76.36318 34.80854, -77.397635 34.51201, -78.05496 33.92547, -78.55435 33.86133, -79.06067 33.49395, -79.20357 33.15839, -80.301325 32.509355, -80.86498 32.0333, -81.33629 31.44049, -81.49042 30.72999, -81.31371 30.03552, -80.98 29.18, -80.535585 28.47213, -80.53 28.04, -80.056539 26.88, -80.088015 26.205765, -80.13156 25.816775, -80.38103 25.20616, -80.68 25.08, -81.17213 25.20126, -81.33 25.64, -81.71 25.87, -82.24 26.73, -82.70515 27.49504, -82.85526 27.88624, -82.65 28.55, -82.93 29.1, -83.70959 29.93656, -84.1 30.09, -85.10882 29.63615, -85.28784 29.68612, -85.7731 30.15261, -86.4 30.4, -87.53036 30.27433, -88.41782 30.3849, -89.18049 30.31598, -89.593831 30.159994, -89.413735 29.89419, -89.43 29.48864, -89.21767 29.29108, -89.40823 29.15961, -89.77928 29.30714, -90.15463 29.11743, -90.880225 29.148535, -91.626785 29.677, -92.49906 29.5523, -93.22637 29.78375, -93.84842 29.71363, -94.69 29.48, -95.60026 28.73863, -96.59404 28.30748, -97.14 27.83, -97.37 27.38, -97.38 26.69, -97.33 26.21, -97.14 25.87, -97.53 25.84, -98.24 26.06, -99.02 26.37, -99.3 26.84, -99.52 27.54, -100.11 28.11, -100.45584 28.69612, -100.9576 29.38071, -101.6624 29.7793, -102.48 29.76, -103.11 28.97, -103.94 29.27, -104.45697 29.57196, -104.70575 30.12173, -105.03737 30.64402, -105.63159 31.08383, -106.1429 31.39995, -106.50759 31.75452, -108.24 31.754854, -108.24194 31.34222, -109.035 31.34194, -111.02361 31.33472, -113.30498 32.03914, -114.815 32.52528, -114.72139 32.72083, -115.99135 32.61239, -117.12776 32.53534, -117.295938 33.046225, -117.944 33.621236, -118.410602 33.740909, -118.519895 34.027782, -119.081 34.078, -119.438841 34.348477, -120.36778 34.44711, -120.62286 34.60855, -120.74433 35.15686, -121.71457 36.16153, -122.54747 37.55176, -122.51201 37.78339, -122.95319 38.11371, -123.7272 38.95166, -123.86517 39.76699, -124.39807 40.3132, -124.17886 41.14202, -124.2137 41.99964, -124.53284 42.76599, -124.14214 43.70838, -124.020535 44.615895, -123.89893 45.52341, -124.079635 46.86475, -124.39567 47.72017, -124.68721 48.184433, -124.566101 48.379715, -123.12 48.04, -122.58736 47.096, -122.34 47.36, -122.5 48.18, -122.84 49, -120 49, -117.03121 49, -116.04818 49, -113 49, -110.05 49, -107.05 49, -104.04826 48.99986, -100.65 49, -97.22872 49.0007, -95.15907 49, -95.15609 49.38425, -94.81758 49.38905)), ((-153.006314 57.115842, -154.00509 56.734677, -154.516403 56.992749, -154.670993 57.461196, -153.76278 57.816575, -153.228729 57.968968, -152.564791 57.901427, -152.141147 57.591059, -153.006314 57.115842)), ((-165.579164 59.909987, -166.19277 59.754441, -166.848337 59.941406, -167.455277 60.213069, -166.467792 60.38417, -165.67443 60.293607, -165.579164 59.909987)), ((-171.731657 63.782515, -171.114434 63.592191, -170.491112 63.694975, -169.682505 63.431116, -168.689439 63.297506, -168.771941 63.188598, -169.52944 62.976931, -170.290556 63.194438, -170.671386 63.375822, -171.553063 63.317789, -171.791111 63.405846, -171.731657 63.782515)), ((-155.06779 71.147776, -154.344165 70.696409, -153.900006 70.889989, -152.210006 70.829992, -152.270002 70.600006, -150.739992 70.430017, -149.720003 70.53001, -147.613362 70.214035, -145.68999 70.12001, -144.920011 69.989992, -143.589446 70.152514, -142.07251 69.851938, -140.985988 69.711998, -140.992499 66.000029, -140.99777 60.306397, -140.012998 60.276838, -139.039 60.000007, -138.34089 59.56211, -137.4525 58.905, -136.47972 59.46389, -135.47583 59.78778, -134.945 59.27056, -134.27111 58.86111, -133.355549 58.410285, -132.73042 57.69289, -131.70781 56.55212, -130.00778 55.91583, -129.979994 55.284998, -130.53611 54.802753, -131.085818 55.178906, -131.967211 55.497776, -132.250011 56.369996, -133.539181 57.178887, -134.078063 58.123068, -135.038211 58.187715, -136.628062 58.212209, -137.800006 58.499995, -139.867787 59.537762, -140.825274 59.727517, -142.574444 60.084447, -143.958881 59.99918, -145.925557 60.45861, -147.114374 60.884656, -148.224306 60.672989, -148.018066 59.978329, -148.570823 59.914173, -149.727858 59.705658, -150.608243 59.368211, -151.716393 59.155821, -151.859433 59.744984, -151.409719 60.725803, -150.346941 61.033588, -150.621111 61.284425, -151.895839 60.727198, -152.57833 60.061657, -154.019172 59.350279, -153.287511 58.864728, -154.232492 58.146374, -155.307491 57.727795, -156.308335 57.422774, -156.556097 56.979985, -158.117217 56.463608, -158.433321 55.994154, -159.603327 55.566686, -160.28972 55.643581, -161.223048 55.364735, -162.237766 55.024187, -163.069447 54.689737, -164.785569 54.404173, -164.942226 54.572225, -163.84834 55.039431, -162.870001 55.348043, -161.804175 55.894986, -160.563605 56.008055, -160.07056 56.418055, -158.684443 57.016675, -158.461097 57.216921, -157.72277 57.570001, -157.550274 58.328326, -157.041675 58.918885, -158.194731 58.615802, -158.517218 58.787781, -159.058606 58.424186, -159.711667 58.93139, -159.981289 58.572549, -160.355271 59.071123, -161.355003 58.670838, -161.968894 58.671665, -162.054987 59.266925, -161.874171 59.633621, -162.518059 59.989724, -163.818341 59.798056, -164.662218 60.267484, -165.346388 60.507496, -165.350832 61.073895, -166.121379 61.500019, -165.734452 62.074997, -164.919179 62.633076, -164.562508 63.146378, -163.753332 63.219449, -163.067224 63.059459, -162.260555 63.541936, -161.53445 63.455817, -160.772507 63.766108, -160.958335 64.222799, -161.518068 64.402788, -160.777778 64.788604, -161.391926 64.777235, -162.45305 64.559445, -162.757786 64.338605, -163.546394 64.55916, -164.96083 64.446945, -166.425288 64.686672, -166.845004 65.088896, -168.11056 65.669997, -166.705271 66.088318, -164.47471 66.57666, -163.652512 66.57666, -163.788602 66.077207, -161.677774 66.11612, -162.489715 66.735565, -163.719717 67.116395, -164.430991 67.616338, -165.390287 68.042772, -166.764441 68.358877, -166.204707 68.883031, -164.430811 68.915535, -163.168614 69.371115, -162.930566 69.858062, -161.908897 70.33333, -160.934797 70.44769, -159.039176 70.891642, -158.119723 70.824721, -156.580825 71.357764, -155.06779 71.147776))), USDA National Agricultural Statistics Service, 005:042 - Department of Agriculture - Agricultural Estimates, 005:043 - Department of Agriculture - Census of Agriculture, 005:050 - Department of Agriculture - Commodity Purchases, 005:15 - National Agricultural Statistics Service.