#Let's begin by opening the appropiate window for graphics
motif()
#Now bring the file and store it into a variable called 'x'
x_read.table("final",header=T)
#Let's store every column separately
tim_x$TIM
nim_x$NIM
pim_x$PIM
nwbr_x$NWBR
inc_x$INC
sr90_x$SR90
cs137_x$CS137
nb_x$NB
#4 graphics per page
par(mfrow=c(2,2))
#1st histogram
hist(sr90,xlab="Strontium-90 content of milk",
ylab="Percentage",main="SR90")
#2nd histogram
#Let's create another variable called logarithm of sr90
lsr90_log(sr90)
hist(lsr90,xlab="Logarithm of Strontium-90 content of milk",
ylab="Percentage",main="logSR90")
#3rd graphic
plot(inc,tim,ylab="Total Infant Mortality",
xlab="Per Capita Income",main="TIM vs INC",pch="o")
#4th graphic
#Let's create another variable called logarithm of nwbr
lnwbr_log(nwbr)
plot(lnwbr,tim,xlab="Logarithm of the Percent of non-white births",
ylab="Total Infant Mortality",main="TIM vs log(NWBR)",pch="o")

#Now we can print these graphs and continue with another four

#5th graphic
plot(lsr90,tim,xlab="Logarithm of the Strontium-90 content of milk",
ylab="Total Infant Mortality",main="TIM vs log(SR90)",pch="o")
#6th graphic: The first variable contains the Infant Mortality of
#1961-1970. The second variable contains the log of the SR90 
#from 1960-1969
tima_tim[c(49:528)]
lsr90a_lsr90[c(1:480)]
plot(lsr90a,tima,
xlab="Logarithm of the Strontium-90 content of milk",
ylab="Total Infant Mortality of the following year",
main="Lagged SR90",pch="o")
#7th graphic, same idea
inc63_inc[c(145:192)]
inc60_inc[c(1:48)]
inca_(inc63-inc60)
plot(inc60,inca,pty="s",ylim=range(inc60)-mean(range(inc60)),
xlab="Per Capita Income 1960",ylab="Per Capita Income 1963-1960",
main="Income 63 -Income 60 vs Income 60",pch="o")
#8th graphic, same idea
tim63_tim[c(145:192)]
tim60_tim[c(1:48)]
tima_(tim63-tim60)
sr903_sr90[c(145:192)]
sr900_sr90[c(1:48)]
sr90a_(sr903-sr900)
plot(sr90a,tima,pty="s",ylim=range(sr90a)-mean(range(sr90a)),
ylab="Total Infant Mortality 63 - 60",
xlab="Strontium-90 content of milk 63 - 60",
main="TIM 63-60 vs SR90 63 - 60",pch="o")

#9th and 10th graphic better in one page each
par(mfrow=c(2,1))
#9th graphic, time series in one plot
times_c(1960,1961,1962,1963,1964,1965,1966,1967,1968,1969,1970)
plot(times,c(tim[1],tim[49],tim[97],tim[145],tim[193],tim[241],
tim[289],tim[337],tim[385],tim[433],tim[481]),type="l",
ylim=c(15,45),xlab="year",
ylab="Total Infant Mortality for each state",
main="TIM vs year for all states")
#this command draws a line for between each value of time for every
#state
#See that we can do this because the values are ordered so that
#by adding 48 to any value we get the same value for the following
#year for the same state. By taking the first plot outside the for loop
#we ensure that we are printing everything in the same graph.
for(w in 2:48){lines(times,c(tim[w],tim[w+48],tim[w+96],tim[w+144],
tim[w+192],tim[w+240],tim[w+288],tim[w+336],tim[w+384],tim[w+432],
tim[w+480]))}
#10th graphic, equal
plot(times,c(sr90[1],sr90[49],sr90[97],sr90[145],sr90[193],sr90[241],
sr90[289],sr90[337],sr90[385],sr90[433],sr90[481]),type="l",
ylim=c(0,55),xlab="year",ylab="Strontium-90 content of milk",
main="SR90 vs year for all states")
for(ww in 2:48){lines(times,c(sr90[ww],sr90[ww+48],sr90[ww+96],
sr90[ww+144],sr90[ww+192],sr90[ww+240],sr90[ww+288],sr90[ww+336],
sr90[ww+384],sr90[ww+432],sr90[ww+480]))}