Visualizing Agricultural Subsidies by Kentucky County

Matt Bogard

11 years ago

[This article was first published on Econometric Sense, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here)

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In this post, I provide results from my first full blown application of R to read, merge, clean, subset, manipulate, analyze and visualize data related to agricultural subsidies by Kentucky counties. This is very similar to the work I do on a daily basis, and was a great test of the capabilities of doing these tasks open source with R. This is not breakthrough research, and doesn’t really even provide any new insights, but does demonstrate that farm subsidies accrue to those that grow the most crops. It does demonstrate R’s usefulness in manipulating data sets and visualization tools.

Data

I extracted CSV files from the USDA Agricultural Statistics service related to the production (2008 data) of three major staple commodities grown in Kentucky and number of farm operations by county in Kentucky. I merged these data sets (via a series of left joins) with data from the Environmental Working Group related to subsidy accounts by Kentucky county. No apparent way was available to extract this data to csv so it was copied, pasted, and cleaned up in excel to produce a csv file. Additional transformations (conversion from character to numeric etc.) were conducted in R.

The data sets were combined through a series of left joins made possible from the ‘merge’ function in R, creating a data set called KyCropsAndSubsidies.

Overlapping Density Charts

The following is a histogram plotting the log acres planted by county with kernal density estimates for three populations: the entire KY county sample, those counties receiving above median subsidies, and those receiving below median subsidies. It can be seen that the density curve for those receiving above median subsidies is to the right of the distributions of the population as a whole, and those receiving below median subsidy amounts (indicating more acres planted by this group of producers)

Bubble Charts

The following is essentially a scatter plot, plotting acres planted in each county by the number of farms in each county. The size of each data point reflects the relative amount of subsidies received by all producers in each county. (note some obvious counties like Warren, Barren, Logan are missing due to missing data). It can be seen that the large circles (those counties receiving the most subsidies) float to the top indicating more acres planted. The number of farms per county seems to be unrelated to acres planed and subsidy amounts.

Maps

The data set KyCropsAndSubsidies was merged with a spatial data frame (by county) which contains data related to county boundaries necessary for creating maps and plotting data by county. The relationship between acres planted and subsidies by county can be seen below. Those counties planting the most acres tend to receive the most in subsidies. It is well known in Kentucky, that most of the row crops are grown in the western part of the state.

Code and References:

# ————————————————————-

# | PROGRAM NAME: Bubble_Crops

# | DATE: 12-4-2010

# | CREATED BY: Matt Bogard

# | PROJECT FILE: /Users/user/Desktop/R Programs

# |————————————————————-

# | PURPOSE: Intitially to create bubble charts to demonstrate

# | allocation

# | of farm subsides to producers by KY county, but expanded to

# | include

# | numerous other visualization tools available using R

# |

# |————————————————————-

# | COMMENTS:

# |

# | 1: Final data set for analysis is read in in the section

# | labeled ‘basic statistical analysis’

# | 2: In analysis section data for acres planted and subsidies

# | are rescaled for

# | maps generated later in the program

# | 3: Code for bubble charts adapted from:

# | http://flowingdata.com/2010/11/23/how-to-make-bubble-charts/

# | 4: Code for spatial analysis and mapping adapted from

# | Harvard Applied Spatial Statistics workshop at:

# |

# |http://www.people.fas.harvard.edu/~zhukov/spatial.html

# | 5:

# |

# |————————————————————-

# | DATA USED:

# |

# | 1) various data sets downloaded from USDA NASS

# |http://www.nass.usda.gov/# |

# | 2) data copied and pasted and formated in excel from EWG

# |subsidy data base: http://www.nass.usda.gov/

# | 3) spatial data frame data used in the Harvard Applied

# |Spatial Statistics workshop at:

# |

# | http://www.people.fas.harvard.edu/~zhukov/spatial.html

# | 4) final data set located in project file:

# |crops_and_subsidies.csv

# |

# |————————————————————-

# | CONTENTS:

# |

# | PART 1: get data

# | PART 2: merge data sets

# | PART 3: recode and aggregate data sets

# | PART 4: basic statisticl analysis

# | PART 5: densisty plots

# | PART 6: bubble charts

# | PART 7: spatial analysis- Maps

# |

# |————————————————————-

# | UPDATES:

# |

# ————————————————————-

setwd(‘/Users/wkuuser/Desktop/R Data Sets’)

rm(list=ls()) # get rid of any existing data

ls() # view open data sets

# *————————————————————-

# |

# | get data

# |

# *————————————————————-

# | get farm # data

# *————————————————————-

farms <- read.csv(“KyFarms.csv”, na.strings=c(“.”, “NA”, “”, “?”), encoding=“UTF-8”)

names(farms)

# get only the most recentl available year (2007)

farms07 <- farms[farms$Year ==“2007”,]

dim(farms07) # n=120

print(farms07)

names(farms07)

# keep only variables you want, convert ‘Value’ to numeric ‘Operations’ and rename county code for common merge key

farms07 <-farms07[c(“Value”,“County.Code”)]

farms07<-transform(farms07, Operations=as.numeric(paste(farms07$Value)))

farms07 <- rename(farms07, c(County.Code=“CoFips”))

dim(farms07)

names(farms07)

farms07

# *————————————————————-

# | get corn data

# *————————————————————-

corn <- read.csv(“CR08.csv”, na.strings=c(“.”, “NA”, “”, “?”), encoding=“UTF-8”)

names(corn)

# subset- get only KY counties data

kycorn <- corn[corn$State ==“Kentucky”,]

dim(kycorn) # n=90

print(kycorn$County)

names(kycorn)

# keep and rename only relevant varaibles

# library(reshape)

kycorn <-kycorn[c(“County”,“Yield”,“Harvested”,“CoFips”,“Planted.All.Purposes”)]

names(kycorn)

kycorn <- rename(kycorn, c(Yield=“cornYield”,Harvested=“cornHarvested”,Planted.All.Purposes=“cornPlanted”))

names(kycorn)

kycorn

# *————————————————————-

# | get soybean data

# *————————————————————-

soybeans <- read.csv(“SB08.csv”, na.strings=c(“.”, “NA”, “”, “?”), encoding=“UTF-8”)

names(soybeans)

# subset- get only KY counties data

kysoybeans <- soybeans[soybeans$State ==“Kentucky”,]

dim(kysoybeans) # n=79

print(kysoybeans$County)

names(kysoybeans)

# keep and rename only relevant varaibles

library(reshape)

kysoybeans <-kysoybeans[c(“County”,“Yield”,“Harvested”,“Planted.All.Purposes”)]

names(kysoybeans)

kysoybeans <- rename(kysoybeans, c(Yield=“soybeanYield”,Harvested=“soybeanHarvested”,Planted.All.Purposes=“soybeanPlanted”))

names(kysoybeans)

kysoybeans

# *————————————————————-

# | get wheat data

# *————————————————————-

wheat <- read.csv(“AW08.csv”, na.strings=c(“.”, “NA”, “”, “?”), encoding=“UTF-8”)

names(wheat)

# subset- get only KY counties data

kywheat <- wheat[wheat$State ==“Kentucky”,]

dim(kywheat) # n= 70

print(kywheat$County)

names(kywheat)

# keep and rename only relevant varaibles

# library(reshape)

kywheat <-kywheat[c(“County”,“Yield”,“Harvested”,“Planted.All.Purposes”)]

names(kywheat)

kywheat <- rename(kywheat, c(Yield=“wheatYield”,Harvested=“wheatHarvested”,Planted.All.Purposes=“wheatPlanted”))

names(kywheat)

# *————————————————————-

# | get EWG subsidy data

# *————————————————————-

kysubsidies <- read.csv(“EWGKYSubsidies.csv”, na.strings=c(“.”, “NA”, “”, “?”), encoding=“UTF-8”)

names(kysubsidies)

kysubsidies

# *————————————————————-

# |

# | merge data sets

# |

# *————————————————————-

# corn and soybeans left join

# note this will keep all corn growing counties (n=90) and add info about soybeans

# this will be the driver file for all subsequent merges, as a result, the staring base

# data is corn growing counties, i.e. the end data set will be based only on counties that

# grow corn in addition to other crops, but as a result counties that do not grow corn will be excluded

# from the analysis

corn_and_soybeans <- merge(kycorn,kysoybeans, by.kycorn=County,by.kysoybeans=County, all=FALSE, all.x=TRUE, all.y=FALSE)

dim(corn_and_soybeans)

names(corn_and_soybeans)

corn_and_soybeans

# all 3 crops- left join with wheat data

allKyCrops <- merge(corn_and_soybeans,kywheat, by.corn_and_soybeans=County,by.kywheat=County, all=FALSE, all.x=TRUE, all.y=FALSE)

dim(allKyCrops)

names(allKyCrops)

# left join with farm operations data on CoFips as key

# since County won’t match due to case differences

farmAndCrops <- merge(allKyCrops,farms07, by.allKyCrops=CoFips,by.farms07=CoFips, all=FALSE, all.x=TRUE, all.y=FALSE)

dim(farmAndCrops)

names(farmAndCrops)

farmAndCrops

# crops and subsidies- left join with subsidy data

KyCropsAndSubsidies <- merge(farmAndCrops,kysubsidies, by.farmAndCrops=County,by.kysubsidies=County, all=FALSE, all.x=TRUE, all.y=FALSE)

dim(KyCropsAndSubsidies)

names(KyCropsAndSubsidies)

# quick report

KyCropsAndSubsidies[ c(“County”,“cornPlanted”, “soybeanPlanted”,“wheatPlanted”,“cornHarvested”,“soybeanHarvested”,“wheatHarvested”,“Operations”,“Subsidy”)]

# *————————————————————-

# |

# | recode and aggregate variables

# |

# *————————————————————-

# recode missing values-acres planted

KyCropsAndSubsidies$SoybeanAcresPlanted <- ifelse (is.na(KyCropsAndSubsidies$soybeanPlanted) == ‘TRUE’, (KyCropsAndSubsidies$SoybeanAcresPlanted <- 0),(KyCropsAndSubsidies$SoybeanAcresPlanted <- KyCropsAndSubsidies$soybeanPlanted))

KyCropsAndSubsidies[ c(“County”,“SoybeanAcresPlanted”)]

KyCropsAndSubsidies$WheatAcresPlanted <- ifelse (is.na(KyCropsAndSubsidies$wheatPlanted) == ‘TRUE’, (KyCropsAndSubsidies$WheatAcresPlanted <- 0),(KyCropsAndSubsidies$WheatAcresPlanted <- KyCropsAndSubsidies$wheatPlanted))

KyCropsAndSubsidies[ c(“County”,“WheatAcresPlanted”)]

# recode missing values – acres harvested

KyCropsAndSubsidies$SoybeanAcresHarvested <- ifelse (is.na(KyCropsAndSubsidies$soybeanHarvested) == ‘TRUE’, (KyCropsAndSubsidies$SoybeanAcresHarvested <- 0),(KyCropsAndSubsidies$SoybeanAcresHarvested <- KyCropsAndSubsidies$soybeanHarvested))

KyCropsAndSubsidies[ c(“County”,“SoybeanAcresHarvested”)]

KyCropsAndSubsidies$WheatAcresHarvested <- ifelse (is.na(KyCropsAndSubsidies$wheatHarvested) == ‘TRUE’, (KyCropsAndSubsidies$WheatAcresHarvested <- 0),(KyCropsAndSubsidies$WheatAcresHarvested <- KyCropsAndSubsidies$wheatHarvested))

KyCropsAndSubsidies[ c(“County”,“WheatAcresHarvested”)]

# aggregations

KyCropsAndSubsidies <- transform(KyCropsAndSubsidies, AcresPlanted = cornPlanted + SoybeanAcresPlanted + WheatAcresPlanted,

AcresHarvested = cornHarvested + SoybeanAcresHarvested + WheatAcresHarvested)

KyCropsAndSubsidies <- transform(KyCropsAndSubsidies, LogAcres =log(AcresPlanted))

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