Leydet_Lab03
David Leydet
2022-09-07
GEOG6000 Methods of Data Analysis Lab 03
Experiment using R Markup in HTML
Plotting Types
setwd("~/Desktop/University of Utah PhD /Course Work/Fall 2022 Semester/GEOG6000_Data Analysis/lab03") ##Set working directory
##Bar Plots
VAdeaths = read.csv("../datafiles/VAdeaths.csv", row.names = 1)
mycol = heat.colors(5)
barplot(as.matrix(VAdeaths), beside=T, legend = rownames(VAdeaths),
col = mycol,
main = "VA Deaths by Age",
ylab = "Number of Deaths",
xlab = "Location and Sex")
Dot Charts!
VAdeaths.m = as.matrix(VAdeaths)
dotchart(VAdeaths.m)
##Transpose the matrix --> Ask about this in lab...I am still having difficulty conceptualizing the data organizing coding we are using***
##Data Structures
#Data can be numeric, factor, character, or boolean
#Vector is a set a values in one dimension
#Matrix is a 2 dimensional type rows/columns
#Array is like a matrix with more then 2 rows/columns
#Data Frame like csv file
##'as' functions convert data types to use for the function, i.e. as.matrix
dotchart(t(VAdeaths.m)) 
##Transpose for rural/urban category to the age bracket##Plotting Multiple Series with Points
cmp = c(52, 57, 62, 67, 72)
plot (cmp, VAdeaths$Rural.Male, pch = 1, col= 1, ylim = c(0,70),
xlab = "Age Class",
ylab = "Mortalitiy",
main = " Virginia Death Rates")
points(cmp, VAdeaths$Rural.Female, pch = 2, col = 2)
points(cmp, VAdeaths$Urban.Male, pch = 3, col = 3)
points(cmp, VAdeaths$Urban.Female, pch = 4, col = 4)
legend("topleft", legend = c("Rural Male", "Rural Female", "Urban Male", "Urban Female"),
col = c(1, 2, 3, 4), pch = c(1,2,3,4))
##This code concatenated the each group to its own symbol and color on the plot and legend.
##1, 2, 3, 4 was in order of rural male, rural female, urban male, and urban femalePlotting Multiple Series with Lines
ipcc = read.csv("../datafiles/ipccScenario_1900_2100.csv")
plot(ipcc$yrs, ipcc$commitMed,
type = 'l',
lwd = 1,
col = 'orange',
ylim = c(-1.0,3.5),
main = 'IPCC Scenarios',
xlab = 'Years',
ylab = 'Global Temp.')
lines(ipcc$yrs, ipcc$b1Med, lwd = 1, col = "blue")
lines(ipcc$yrs, ipcc$a1bMed, lwd = 1, col = 'green')
lines(ipcc$yrs, ipcc$a2Med, lwd = 1, col = 'red')
###This plot takes multiple data and plots them against a common x axis which is in the first part of the plot and line argument
abline(h = 0, lty = 2)
legend("topleft", legend = c("Commit", "B1", "A1B", "A2"),
lty = 1,
lwd = 1,
col = c('orange', 'blue', 'green', 'red'))
##Experiment with elements of the chart to make it more visually appealingPlotting Polygons
plot(ipcc$yrs, ipcc$commitMed,
type = 'n',
ylim = c(-1.5,1.0),
main = 'Commit Scenario',
xlab = 'Years',
ylab = 'Global Temp.')
##This produces a "blank plot with an x and y axis along with labels
polygon(c(ipcc$yrs,rev(ipcc$yrs)),c(ipcc$commitLo,rev(ipcc$commitHi)),
col = 'orange')
##How does the "rev" function work??****
##Need to reverse it in order to give it its total set of vertices instead of going back to the beginning (think drawing as opposed to typewriter)
lines(ipcc$yrs, ipcc$commitMed,
lwd = 2,
col = 'black')
Polygon Fill with Shading Lines
plot(ipcc$yrs, ipcc$commitMed,
type = 'n',
ylim = c(-1.5,1.0))
polygon(c(ipcc$yrs,rev(ipcc$yrs)),c(ipcc$commitLo,rev(ipcc$commitHi)),
col = 'orange',
density = 20,
angle = 145)
lines(ipcc$yrs, ipcc$commitMed,
lwd = 2,
col = 'black')
Plotting Images
volcano = read.table("../datafiles/volcanodem.txt")
volcano = as.matrix(volcano)
##Where do the the V"n" columns come from? Matrix transition?
z = 2 * volcano ##Exaggerate the relief
x = 10 * (1:nrow(z))
y = 10 * (1:ncol(z))
##Not sure how we are organizing the data here.
image(x, y, z)
Image with better color scheme:
image(x, y, z, col = terrain.colors(100))
Contour Plot:
contour(x, y, z, nlevel = 20)
##Can this be used for isopleths???Perspective Plot:
persp(x, y, z,theta = 210, phi = 15, scale = FALSE)
##Neat!Shaded Perspective Plot
persp(x, y, z,
theta = 130,
phi = 30,
scale = FALSE,
col = 'green3',
ltheta = -120,
shade = 0.75,
border = NA,
box = FALSE)
##ltheta controls the light angle
##shade controls the diffusion of the lighting
#phi is the vertical angle
#theta is the horizontal view anglepdf('volcano.pdf')
image(x, y, z,
col = terrain.colors(100),
main = "Maunga Whau DEM")
dev.off()## quartz_off_screen
## 2##Export to .pdfSimple Linear Regression
regrex = read.csv("../datafiles/regrex.csv")
summary(regrex)## y x
## Min. : 2.281 Min. : 0.8347
## 1st Qu.: 3.830 1st Qu.: 3.6437
## Median : 7.006 Median : 9.8750
## Mean : 6.971 Mean :10.0678
## 3rd Qu.: 9.257 3rd Qu.:14.4428
## Max. :15.000 Max. :25.0000plot( y ~ x, data=regrex)
cor.test(regrex$x, regrex$y)##
## Pearson's product-moment correlation
##
## data: regrex$x and regrex$y
## t = 19.194, df = 28, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.9250459 0.9829233
## sample estimates:
## cor
## 0.9640353ex1.lm = lm( y ~ x, data = regrex)
ex1.lm##
## Call:
## lm(formula = y ~ x, data = regrex)
##
## Coefficients:
## (Intercept) x
## 2.2481 0.4691anova(ex1.lm)## Analysis of Variance Table
##
## Response: y
## Df Sum Sq Mean Sq F value Pr(>F)
## x 1 283.947 283.947 368.4 < 2.2e-16 ***
## Residuals 28 21.581 0.771
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1##Is this appropriate given only two variables?summary(ex1.lm)##
## Call:
## lm(formula = y ~ x, data = regrex)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.66121 -0.53286 -0.02869 0.50436 2.36786
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.24814 0.29365 7.656 2.44e-08 ***
## x 0.46906 0.02444 19.194 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8779 on 28 degrees of freedom
## Multiple R-squared: 0.9294, Adjusted R-squared: 0.9268
## F-statistic: 368.4 on 1 and 28 DF, p-value: < 2.2e-16This model seems to be a good fit based on the residuals distribution, p-value, and adjusted r-squared
Plotting the regression with a line
plot( y ~ x, data = regrex,
main = "Linear Regression Example",
xlab = "X",
ylab = "Y",
col = "blue",
pch = 2)
abline(ex1.lm, col = "red")
Model Standard Errors
predict(ex1.lm, level = 0.95, interval = "conf")## fit lwr upr
## 1 6.868031 6.539515 7.196547
## 2 7.475842 7.143110 7.808574
## 3 4.650164 4.238912 5.061416
## 4 6.051534 5.708864 6.394204
## 5 3.917958 3.455424 4.380491
## 6 3.180779 2.659831 3.701728
## 7 9.533712 9.106359 9.961065
## 8 5.775585 5.423353 6.127816
## 9 8.824490 8.441149 9.207830
## 10 5.776711 5.424523 6.128899
## 11 8.968492 8.576998 9.359986
## 12 4.075140 3.624265 4.526015
## 13 3.125618 2.600085 3.651150
## 14 2.639669 2.072716 3.206622
## 15 10.851355 10.322830 11.379880
## 16 3.028897 2.495266 3.562528
## 17 10.249360 9.769518 10.729202
## 18 6.581106 6.250152 6.912059
## 19 12.098451 11.460251 12.736650
## 20 3.903276 3.439638 4.366914
## 21 13.974700 13.158269 14.791131
## 22 6.892235 6.563795 7.220674
## 23 10.503170 10.003231 11.003109
## 24 8.758258 8.378518 9.137998
## 25 7.346381 7.015607 7.677155
## 26 8.169491 7.817107 8.521876
## 27 2.704024 2.142656 3.265393
## 28 9.040821 8.645070 9.436572
## 29 3.270323 2.756759 3.783888
## 30 10.881328 10.350293 11.412363##What am I looking at?
##Fit = predicted value
##lwr = lower interval
##upr = upper interval -> In this case 95%?newx = data.frame(x = -1:30) ##New data frame with values x from -1 to 30
newy = predict(ex1.lm, newdata = newx,
level = 0.95,
interval = "conf") ##prediction of our new y values with our new data newx
str(newy)## num [1:32, 1:3] 1.78 2.25 2.72 3.19 3.66 ...
## - attr(*, "dimnames")=List of 2
## ..$ : chr [1:32] "1" "2" "3" "4" ...
## ..$ : chr [1:3] "fit" "lwr" "upr"plot( y ~ x, data=regrex,
pch = 16,
main = 'Model Confidence Intervals')
lines(newx$x, newy[,"fit"], col = 2) ##predicted values of x...or fit?
lines(newx$x, newy[,"lwr"], col = 3, lty = 2) ##lower confidence interval?
lines(newx$x, newy[,"upr"], col = 3, lty = 2) ##upper confidence interval?
Residual Plots
ex1.res = residuals (ex1.lm) ##creating a variable for the residuals
hist(ex1.res)
plot(ex1.lm, which = 1)
##Question on thisplot(ex1.lm, which = 2)
plot(ex1.lm, which = 4)
shapiro.test(ex1.res)##
## Shapiro-Wilk normality test
##
## data: ex1.res
## W = 0.97685, p-value = 0.7371#The null hypothesis is that the data are normally distributed. Here we get a high p-value, and no evidence to reject the null.GG PLot Example During Lab (September 8, 2022)
library(ggplot2)
library(ggthemes)
head(ipcc)## yrs commitLo commitMed commitHi b1Lo b1Med b1Hi a1bLo
## 1 1900 -1.249695 -0.7738950 -0.4706987 -1.297196 -0.728424 -0.2830675 -1.297196
## 2 1901 -1.271248 -0.7549440 -0.4823416 -1.331284 -0.817597 -0.3107170 -1.331284
## 3 1902 -1.277435 -0.7278140 -0.4359170 -1.277878 -0.772888 -0.3653695 -1.277878
## 4 1903 -1.432007 -0.9645080 -0.5018959 -1.448410 -0.979095 -0.4230190 -1.444413
## 5 1904 -1.475003 -0.9552915 -0.3693616 -1.477845 -0.990509 -0.3251775 -1.473847
## 6 1905 -1.259312 -0.8682560 -0.3919640 -1.275238 -0.878296 -0.3458400 -1.271241
## a1bMed a1bHi a2Lo a2Med a2Hi
## 1 -0.728424 -0.4105530 -1.260944 -0.728424 -0.4130732
## 2 -0.817597 -0.4692840 -1.282025 -0.817597 -0.4438598
## 3 -0.772888 -0.5234835 -1.285516 -0.773956 -0.4910338
## 4 -0.979095 -0.4545900 -1.461756 -0.979095 -0.4747374
## 5 -0.990509 -0.3253635 -1.499365 -1.022706 -0.3149656
## 6 -0.878296 -0.4089970 -1.289893 -0.894043 -0.3495362##need to put it into long format data
plot_df = data.frame(
yrs = rep(ipcc$yrs, 4), #replicate 4 columns by year
cilo = c(ipcc$commitLo, ipcc$b1Lo, ipcc$a1bLo, ipcc$a2Lo),
cihi = c(ipcc$commitHi, ipcc$b1Hi, ipcc$a1bHi, ipcc$a2Hi),
med = c(ipcc$commitMed, ipcc$b1Med, ipcc$a1bMed, ipcc$a2Med),
scenario = rep(c('Commit', 'b1', 'a1b', 'a2'), each = nrow(ipcc))
)
plot_df## yrs cilo cihi med scenario
## 1 1900 -1.249695250 -0.470698750 -0.7738950 Commit
## 2 1901 -1.271247750 -0.482341625 -0.7549440 Commit
## 3 1902 -1.277435375 -0.435917000 -0.7278140 Commit
## 4 1903 -1.432007375 -0.501895875 -0.9645080 Commit
## 5 1904 -1.475002625 -0.369361625 -0.9552915 Commit
## 6 1905 -1.259312125 -0.391964000 -0.8682560 Commit
## 7 1906 -1.193469875 -0.528411750 -0.8206175 Commit
## 8 1907 -1.213142375 -0.503215375 -0.7650755 Commit
## 9 1908 -1.382938250 -0.376731375 -0.7171630 Commit
## 10 1909 -1.346119000 -0.346671750 -0.7363120 Commit
## 11 1910 -1.208850625 -0.463943250 -0.6988675 Commit
## 12 1911 -1.299510875 -0.477496625 -0.7300565 Commit
## 13 1912 -1.253853000 -0.459686125 -0.7516785 Commit
## 14 1913 -1.170108875 -0.541373875 -0.7887725 Commit
## 15 1914 -1.172077250 -0.563056500 -0.8452145 Commit
## 16 1915 -1.143642250 -0.470172875 -0.7923430 Commit
## 17 1916 -1.125194250 -0.363517500 -0.7026675 Commit
## 18 1917 -1.294612875 -0.505202875 -0.6505735 Commit
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## 700 1999 -0.335894600 0.089648800 -0.0219110 a2
## 701 2000 0.000000000 0.000000000 0.0000000 a2
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## 718 2017 0.036895800 0.553503600 0.2387090 a2
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## 720 2019 0.137732200 0.669976400 0.2983090 a2
## 721 2020 0.046166400 0.628155400 0.3509830 a2
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## 789 2088 2.152380600 3.275634600 2.7221980 a2
## 790 2089 2.207800800 3.484905800 2.8735050 a2
## 791 2090 2.185162600 3.476220600 2.8899540 a2
## 792 2091 2.212127800 3.584191600 2.8329780 a2
## 793 2092 2.265704400 3.598578000 3.0452880 a2
## 794 2093 2.314471400 3.612701600 3.0615840 a2
## 795 2094 2.351654400 3.706927400 3.0638730 a2
## 796 2095 2.360620200 3.637304800 3.1381230 a2
## 797 2096 2.369805600 3.885998600 3.2000740 a2
## 798 2097 2.411359000 3.916290200 3.2280890 a2
## 799 2098 2.435931800 3.928204400 3.2879640 a2
## 800 2099 2.428711200 3.978564200 3.3472590 a2ggplot(plot_df, aes(x = yrs, y=med, col = scenario)) +
geom_line(size = 1.25) +
theme_fivethirtyeight()
Simon’s Code
a2_ipcc <- subset(plot_df, scenario == "a2")
ggplot(a2_ipcc, aes(x = yrs)) +
geom_ribbon(aes(ymin = cilo, ymax = cihi), alpha = 0.5,
col = NA, fill = 'skyblue') +
geom_line(aes(y = med), size = 1.25) +
theme_bw()
ggplot(plot_df, aes(x = yrs, col = scenario, fill = scenario)) +
geom_ribbon(aes(ymin = cilo, ymax = cihi), alpha = 0.25) +
geom_line(aes(y = med), size = 1.25) +
theme_bw()
ggplot(plot_df, aes(x = yrs, col = scenario, fill = scenario)) +
geom_ribbon(aes(ymin = cilo, ymax = cihi), alpha = 0.25) +
geom_line(aes(y = med), size = 1.25) +
theme_bw() +
facet_wrap(~ scenario)
##Model Confidence Interval versus Prediction Model Confidence Interval
##We are 95% sure that the model line will fall between the confidence interval lines
##Prediction interval