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A Comprehensive Guide to USDA Agricultural Data Analysis

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Chapter 1: Introduction to USDA Data

In recent collaborations, I've delved into agricultural data trends across various states and timeframes. This exploration has led me to valuable sources of production data for specific commodities. Inspired by a generous gift of local honey from friends (which I'm enjoying quite frequently), I became curious about honey production trends. Perhaps I could inform my friend that production is on the rise, making it easier for them to replenish their stock.

Data analysis in agriculture

Section 1.1: Accessing USDA Data via API

The USDA provides an accessible platform for acquiring agricultural statistics, particularly through their API. By utilizing R's 'rnassqs' package, we can effectively query the API and retrieve data based on our specific interests. To start, we need to load the necessary packages for our analysis:

# USDA DATA ANALYSIS

if(!requireNamespace("dplyr", quietly = TRUE)) install.packages("dplyr")

if(!requireNamespace("ggplot2", quietly = TRUE)) install.packages("ggplot2")

if(!requireNamespace("rnassqs", quietly = TRUE)) install.packages("rnassqs")

if(!requireNamespace("purrr", quietly = TRUE)) install.packages("purrr")

Once you have your API key, insert it into the code below, adjust it for your needs, and commence data collection!

nassqs_auth(key = "insertkeyhere")

# Focusing on WYOMING for honey production data over the last two decades

honey_data <- nassqs(commodity_desc = "HONEY", state_name = "WYOMING", year = 2004:2023)

After executing this, we will receive a dataset comprising various columns that detail production levels, regions, and other pertinent variables.

Section 1.2: Preparing the Data for Analysis

To ensure the year variable is appropriately formatted, we must convert it to numeric and aggregate the production data by year for visualization.

# Convert year to numeric for aggregation and plotting

honey_data$year <- as.numeric(as.character(honey_data$year))

# Aggregate honey production data by year

honey_production_yearly <- honey_data %>%

group_by(year) %>%

summarise(total_production = sum(Value, na.rm = TRUE))

honey_production_yearly

After verifying that the yearly data points are valid, we can proceed to visualize the findings.

Chapter 3: Understanding USDA NASS Quarterly Grain Stocks Reports

This video, titled "Understanding USDA NASS Quarterly Grain Stocks Reports: A Comprehensive Overview," provides an in-depth look at the USDA's quarterly grain stock reports, offering valuable insights into agricultural trends and practices.

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