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Data description.R
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Data description.R
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#add collaborator
git config --global user.name RasmuSkovOlesen
git config --global user.email rso@ign.ku.dk
#libraries
install.packages("reshape2")
library(reshape2)
library(dplyr)
install.packages("openxlsx")
library(openxlsx)
library(tidyverse)
wave3data <- read_csv('wave3data.csv')
####HISTOGRAMS####
##Histogram over forest cover
qplot(wave3data$forest.ha,
geom="histogram",
main = "Forest cover",
xlab = "Ha",
fill=I("dark blue"),
col=I("green"))
##Histogram over Dietary diversity score
qplot(wave3data$mhdds9,
geom="histogram",
main = "Dietary Diversity Score",
xlab = "DDS value",
fill=I("dark blue"),
col=I("green"))
##Histogram over Dietary diversity score
qplot(wave3data$wealth.score,
geom="histogram",
main = "Wealth of respondents",
xlab = "Wealth Score (1-5)",
fill=I("dark blue"),
col=I("green"))
ggplot(wave3data,
aes(x = mhdds9, fill = sex.head)) +
geom_histogram(binwidth = 1)
ggplot(wave3data,
aes(x = mhdds9, fill = wealth.index)) +
geom_histogram(binwidth = 1)
##Histogram over Forest Patches
qplot(wave3data$forest.patches,
geom="histogram",
main = "Forest Patches",
xlab = "Number of forest patches",
fill=I("dark blue"),
col=I("green"))
##Histogram over Forest Patches
qplot(wave3data$age.head,
geom="histogram",
main = "Age of head of household",
xlab = "Age",
fill=I("dark blue"),
col=I("green"))
##Histogram over Education
wave3data$education <- as.factor(wave3data$education)
#doesn't work
qplot(wave3data$education,
geom="histogram",
main = "Education",
xlab = "Educational level",
fill=I("dark blue"),
col=I("green"))
####PLOTTING####
#Group the data in equal groups
install.packages("Hmisc")
library(Hmisc)
wave3data$ForestCoverGroups <- cut2(wave3data$forest.ha, g=4)
count(wave3data, ForestCoverGroups)
#new group names
levels(wave3data$ForestCoverGroups)<-c("very low cover", "low cover", "medium cover", "high cover" )
#Plot the data
par(mar=c(7,5,1,1))
# Plotting DDS for the 4 groups
boxplot(mhdds9 ~ ForestCoverGroups, data=wave3data,xlab = '',
ylab = 'Dietary diversity score', las=2, col = 2:4)
#Add wealth groups
wave3data$wealth.score<-as.factor(wave3data$wealth.score)
#ggplot
ggplot(wave3data, aes(x=ForestCoverGroups, y=mhdds9, fill=wealth.score)) + geom_boxplot() +
facet_grid(~wealth.score)+ scale_fill_brewer(palette = "RdYlGn")
#ggplot2 - combined
ggplot(wave3data, aes(x=ForestCoverGroups, y=mhdds9, fill=wealth.score)) + geom_boxplot() +
scale_fill_brewer(palette = "RdYlGn")+xlab('Forest cover')+ylab('Dietary diversity score')
## EV BOXPLOTS
#DDScores for different Forest Cover Levels, grouped by Wealth Index
ggplot(wave3data, aes(x=wealth.index, y = mhdds9, fill = ForestCoverGroups)) +
geom_boxplot() + scale_fill_brewer(palette = "RdYlGn")+xlab('Wealth Index') + ylab('Diet Diversity Score')
#Create equal groups for Distance to Market
install.packages("Hmisc")
library(Hmisc)
wave3data$MarketDistanceGroups <- cut2(wave3data$dist.market, g=4)
count(wave3data, MarketDistanceGroups)
levels(wave3data$MarketDistanceGroups)<-c("short distance", "medium distance", "long distance", "very long distance" )
#DDScores for different Forest Cover Levels, grouped by Distance to Market (means in blue)
ggplot(wave3data, aes(x=MarketDistanceGroups, y = mhdds9, fill = ForestCoverGroups)) +
geom_boxplot() + stat_summary(geom = 'point', fun = mean, color = 'blue', position = position_dodge(width = 0.75)) +
scale_fill_brewer(palette = "RdYlGn")+xlab('Market Distance') + ylab('Diet Diversity Score')
#Group the data in equal groups
install.packages("Hmisc")
library(Hmisc)
data$DDSgroups <- cut2(data$mhdds9, g=4)
count(data, DDSgroups)
#new group names
levels(data$DDSgroups)<-c("very low DDS", "low DDS", "medium DDS", "high DDS" )