library(ggplot2)

storks <- read.table("storkdata.txt", header=T)

a <- lm(populationThousands ~ storkCount, data=storks)

# summary(a)

# Call:

# lm(formula = populationThousands ~ storkCount, data = storks)

#

# Residuals:

# 1 2 3 4 5 6 7

# -1.3548 -3.2491 3.8830 3.9623 -3.7735 -0.7471 1.2793

#

# Coefficients:

# Estimate Std. Error t value Pr(>|t|)

# (Intercept) 26.04177 5.86058 4.444 0.00674 **

# storkCount 0.19472 0.02898 6.719 0.00111 **

# ---

# Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

#

# Residual standard error: 3.453 on 5 degrees of freedom

# Multiple R-squared: 0.9003, Adjusted R-squared: 0.8803

# F-statistic: 45.14 on 1 and 5 DF, p-value: 0.001107

attach(a)

plot(storks$storkCount, storks$populationThousands)

abline(a, col="red")

setwd("../images")

png(filename="storks.png", res=300, bg="white", type="quartz", pointsize=14, width=6, height=5, units="in")

ggplot(data=storks, aes(x=storkCount,y=populationThousands)) + geom_point() + labs(title="Stork and Human Populations,\nOldenburg Germany, 1930-1936", x="Stork Count", y="Human Population, in Thousands") + geom_abline(data=storks, intercept=a$coefficients[1], slope=a$coefficients[2], color="brown", size=2) +

annotate("text", label="r=0.94\np<0.001", x=145, y=70, size=8, color="black" ) + theme_classic(base_size=16, base_family="sans")

dev.off()