Exam # 1: Quantitative Geography Fall 2016
Regina Mendicino
date()## [1] "Fri Oct 07 11:51:57 2016"Due Time: 1:45pm Each question is worth 20 points.
1 The average rainfall (in units of inches) over Florida during the month of June for the years 2001-2012 are in order below.
8.6, 10.3, 11.0, 8.2, 11.8 5.7 5.9 6.9 6.1 5.4 4.9 13.1
- Create a vector object with the above values as elements.
rainfall = c(8.6, 10.3, 11.0, 8.2, 11.8, 5.7, 5.9, 6.9, 6.1, 5.4, 4.9, 13.1)
rainfall## [1] 8.6 10.3 11.0 8.2 11.8 5.7 5.9 6.9 6.1 5.4 4.9 13.1- What is the standard deviation of the June rainfall over these years?
sd(rainfall)## [1] 2.791695- How many Junes had rainfall exceeding 6 inches?
sum(rainfall > 6)## [1] 8- Compute the rainfall departure from the 12-year June mean for each June.
rainfall - mean(rainfall)## [1] 0.44166667 2.14166667 2.84166667 0.04166667 3.64166667
## [6] -2.45833333 -2.25833333 -1.25833333 -2.05833333 -2.75833333
## [11] -3.25833333 4.94166667- What is the average June rainfall for years that are above the 12-year June mean?
rfmean = mean(rainfall)
rfmean## [1] 8.158333mean(rainfall > rfmean)## [1] 0.52 Create a vector containing 100 random numbers generated from a log normal distribution (rlnorm).
rn = rlnorm(100)
rn## [1] 1.9076100 0.8301482 1.7045232 2.6597569 1.8360573 0.9461888
## [7] 0.2829647 2.2141570 0.3967021 1.1304586 0.6316042 0.3326914
## [13] 0.5703098 4.6465236 0.7648833 1.3089663 0.3026619 1.1312956
## [19] 0.9748449 0.2614919 0.7227911 2.4597611 0.8020379 0.3946221
## [25] 0.8659235 0.7447041 0.2392601 4.0023923 0.2422225 0.1574863
## [31] 0.6272363 0.7563500 0.8696872 1.1937493 3.4637999 0.1246687
## [37] 1.0547064 0.7582225 1.3182692 0.9747678 0.3563878 2.8719364
## [43] 3.2247906 1.5603740 3.7926602 5.0947393 1.3913861 2.6263764
## [49] 3.8859923 0.5994645 0.7571952 0.3623959 0.4501997 0.9546799
## [55] 0.5671519 0.8291926 2.3385574 3.5672931 0.4417361 1.2352770
## [61] 1.2078044 0.5288022 0.7558515 0.3294109 1.5075739 1.4782575
## [67] 0.1912092 0.1593220 6.5593225 0.6158932 1.3039234 1.4552446
## [73] 0.4444017 4.1574914 0.8528755 0.7380761 0.6374757 1.3208160
## [79] 0.5317708 0.4617126 0.3688783 0.9526525 4.6065031 3.1644950
## [85] 2.1025942 0.6997556 2.5792749 2.7365112 13.0799535 2.4786776
## [91] 1.2357507 0.6904712 1.5323384 0.9251514 0.8898920 3.2820426
## [97] 0.4080396 1.0019664 0.2145028 0.1317975- Create a density plot from the set of numbers.
plot(rn)
- Compute the mean, median, IQR, 75, 85, and 95th percentile values from the set of numbers.
mean(rn)## [1] 1.518328median(rn)## [1] 0.9356701IQR(rn)## [1] 1.295639quantile(rn, .75)## 75%
## 1.853945quantile(rn, .85)## 85%
## 2.756825quantile(rn, .95)## 95%
## 4.1799423 The data frame carbon (UsingR) contains a list of carbon monoxide levels at three different measuring sites.
library(ggplot2)## Warning: package 'ggplot2' was built under R version 3.2.5library(datasets)
CO2## Plant Type Treatment conc uptake
## 1 Qn1 Quebec nonchilled 95 16.0
## 2 Qn1 Quebec nonchilled 175 30.4
## 3 Qn1 Quebec nonchilled 250 34.8
## 4 Qn1 Quebec nonchilled 350 37.2
## 5 Qn1 Quebec nonchilled 500 35.3
## 6 Qn1 Quebec nonchilled 675 39.2
## 7 Qn1 Quebec nonchilled 1000 39.7
## 8 Qn2 Quebec nonchilled 95 13.6
## 9 Qn2 Quebec nonchilled 175 27.3
## 10 Qn2 Quebec nonchilled 250 37.1
## 11 Qn2 Quebec nonchilled 350 41.8
## 12 Qn2 Quebec nonchilled 500 40.6
## 13 Qn2 Quebec nonchilled 675 41.4
## 14 Qn2 Quebec nonchilled 1000 44.3
## 15 Qn3 Quebec nonchilled 95 16.2
## 16 Qn3 Quebec nonchilled 175 32.4
## 17 Qn3 Quebec nonchilled 250 40.3
## 18 Qn3 Quebec nonchilled 350 42.1
## 19 Qn3 Quebec nonchilled 500 42.9
## 20 Qn3 Quebec nonchilled 675 43.9
## 21 Qn3 Quebec nonchilled 1000 45.5
## 22 Qc1 Quebec chilled 95 14.2
## 23 Qc1 Quebec chilled 175 24.1
## 24 Qc1 Quebec chilled 250 30.3
## 25 Qc1 Quebec chilled 350 34.6
## 26 Qc1 Quebec chilled 500 32.5
## 27 Qc1 Quebec chilled 675 35.4
## 28 Qc1 Quebec chilled 1000 38.7
## 29 Qc2 Quebec chilled 95 9.3
## 30 Qc2 Quebec chilled 175 27.3
## 31 Qc2 Quebec chilled 250 35.0
## 32 Qc2 Quebec chilled 350 38.8
## 33 Qc2 Quebec chilled 500 38.6
## 34 Qc2 Quebec chilled 675 37.5
## 35 Qc2 Quebec chilled 1000 42.4
## 36 Qc3 Quebec chilled 95 15.1
## 37 Qc3 Quebec chilled 175 21.0
## 38 Qc3 Quebec chilled 250 38.1
## 39 Qc3 Quebec chilled 350 34.0
## 40 Qc3 Quebec chilled 500 38.9
## 41 Qc3 Quebec chilled 675 39.6
## 42 Qc3 Quebec chilled 1000 41.4
## 43 Mn1 Mississippi nonchilled 95 10.6
## 44 Mn1 Mississippi nonchilled 175 19.2
## 45 Mn1 Mississippi nonchilled 250 26.2
## 46 Mn1 Mississippi nonchilled 350 30.0
## 47 Mn1 Mississippi nonchilled 500 30.9
## 48 Mn1 Mississippi nonchilled 675 32.4
## 49 Mn1 Mississippi nonchilled 1000 35.5
## 50 Mn2 Mississippi nonchilled 95 12.0
## 51 Mn2 Mississippi nonchilled 175 22.0
## 52 Mn2 Mississippi nonchilled 250 30.6
## 53 Mn2 Mississippi nonchilled 350 31.8
## 54 Mn2 Mississippi nonchilled 500 32.4
## 55 Mn2 Mississippi nonchilled 675 31.1
## 56 Mn2 Mississippi nonchilled 1000 31.5
## 57 Mn3 Mississippi nonchilled 95 11.3
## 58 Mn3 Mississippi nonchilled 175 19.4
## 59 Mn3 Mississippi nonchilled 250 25.8
## 60 Mn3 Mississippi nonchilled 350 27.9
## 61 Mn3 Mississippi nonchilled 500 28.5
## 62 Mn3 Mississippi nonchilled 675 28.1
## 63 Mn3 Mississippi nonchilled 1000 27.8
## 64 Mc1 Mississippi chilled 95 10.5
## 65 Mc1 Mississippi chilled 175 14.9
## 66 Mc1 Mississippi chilled 250 18.1
## 67 Mc1 Mississippi chilled 350 18.9
## 68 Mc1 Mississippi chilled 500 19.5
## 69 Mc1 Mississippi chilled 675 22.2
## 70 Mc1 Mississippi chilled 1000 21.9
## 71 Mc2 Mississippi chilled 95 7.7
## 72 Mc2 Mississippi chilled 175 11.4
## 73 Mc2 Mississippi chilled 250 12.3
## 74 Mc2 Mississippi chilled 350 13.0
## 75 Mc2 Mississippi chilled 500 12.5
## 76 Mc2 Mississippi chilled 675 13.7
## 77 Mc2 Mississippi chilled 1000 14.4
## 78 Mc3 Mississippi chilled 95 10.6
## 79 Mc3 Mississippi chilled 175 18.0
## 80 Mc3 Mississippi chilled 250 17.9
## 81 Mc3 Mississippi chilled 350 17.9
## 82 Mc3 Mississippi chilled 500 17.9
## 83 Mc3 Mississippi chilled 675 18.9
## 84 Mc3 Mississippi chilled 1000 19.9- How many rows and columns does the data frame contain?
NROW(CO2)## [1] 84NCOL(CO2)## [1] 5- List the names of the columns.
colnames(CO2)## [1] "Plant" "Type" "Treatment" "conc" "uptake"- List the first six rows the the data frame.
head(CO2)## Plant Type Treatment conc uptake
## 1 Qn1 Quebec nonchilled 95 16.0
## 2 Qn1 Quebec nonchilled 175 30.4
## 3 Qn1 Quebec nonchilled 250 34.8
## 4 Qn1 Quebec nonchilled 350 37.2
## 5 Qn1 Quebec nonchilled 500 35.3
## 6 Qn1 Quebec nonchilled 675 39.2- Create side-by-side box plots of the monoxide levels from the three sites. Hint: First make the variable ‘site’ a factor variable.g
ggplot(CO2, aes(x = Type, y = uptake)) +
geom_boxplot() 
4 The mtcars dataset contains fuel consumption and automobile design and performance values for 32 automobiles from the 1973–74 models.
mtcars## mpg cyl disp hp drat wt qsec vs am gear carb
## Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4
## Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4
## Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1
## Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1
## Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2
## Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1
## Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4
## Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2
## Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2
## Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4
## Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4
## Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3
## Merc 450SL 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3
## Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3
## Cadillac Fleetwood 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4
## Lincoln Continental 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4
## Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4
## Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1
## Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2
## Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1
## Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1
## Dodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2
## AMC Javelin 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2
## Camaro Z28 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4
## Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2
## Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1
## Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2
## Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2
## Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4
## Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6
## Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8
## Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2What is the average gas mileage (mpg) grouped by the number of carborators (carb)?
What is the correlation (Pearson) between mileage (mpg) and car weight (wt)?
cor(mtcars$mpg, mtcars$wt)## [1] -0.8676594- Create a scatter plot with weight (wt) on the horizontal axis and mileage (mpg) on the vertical axis. Include a best-fit straight line on the plot.
ggplot(mtcars, aes(x = mtcars$wt, y = mtcars$mpg)) +
geom_point() 
5 The dataset wellbeing (UsingR) contains information from 22 European countries related to what makes people happy. The dataset is from the New Economics Foundation’s National Accounts of Well-Being Well being is on a scale from 0 (lowest) to 10 (highest). GDP is gross domestic product per person (US$).
library(UsingR)## Warning: package 'UsingR' was built under R version 3.2.5## Loading required package: MASS## Loading required package: HistData## Warning: package 'HistData' was built under R version 3.2.5## Loading required package: Hmisc## Warning: package 'Hmisc' was built under R version 3.2.5## Loading required package: lattice## Loading required package: survival## Loading required package: Formula## Warning: package 'Formula' was built under R version 3.2.5##
## Attaching package: 'Hmisc'## The following objects are masked from 'package:base':
##
## format.pval, round.POSIXt, trunc.POSIXt, units##
## Attaching package: 'UsingR'## The following object is masked from 'package:survival':
##
## cancerwellbeing## Country Well.being GDP Equality Food.consumption
## 1 Austria 5.37 26171.691 70.85000 3760.36
## 2 Belgium 5.04 24512.413 67.03000 3674.95
## 3 Bulgaria 4.59 2332.363 69.41667 2749.46
## 4 Cyprus 5.12 14719.258 NA 3202.02
## 5 Denmark 5.93 32400.061 75.30000 3404.26
## 6 Estonia 4.80 6570.330 63.50200 3127.11
## 7 Finland 5.39 27495.241 73.12000 3217.47
## 8 France 4.88 23133.351 67.26000 3540.73
## 9 Germany 5.01 24463.846 71.69000 3519.28
## 10 Hungary 4.74 5868.302 71.54400 3463.01
## 11 Ireland 5.44 30669.373 65.72000 3552.36
## 12 Netherlands 5.31 26007.679 69.10000 3217.65
## 13 Norway 5.69 41245.809 74.21000 3421.47
## 14 Poland 4.81 5552.501 65.88333 3394.36
## 15 Portugal 4.89 11716.009 61.55000 3574.31
## 16 Slovakia 4.57 7334.110 72.07167 2904.10
## 17 Slovenia 4.91 12588.108 69.62667 3212.38
## 18 Spain 5.34 16074.462 65.34000 3231.84
## 19 Sweden 5.44 32431.940 75.00000 3111.68
## 20 Switzerland 5.66 37877.182 66.32000 3427.98
## 21 Ukraine 4.39 1037.312 71.77375 3251.21
## 22 United Kingdom 4.98 28913.096 64.03000 3437.22
## Alcohol.consumption Energy.consumption Family Working.hours Work.income
## 1 13.24 2076.2005 1.4054 31.81731 30.46
## 2 10.77 2189.6082 1.7600 30.21154 31.85
## 3 12.44 1260.1506 1.3900 NA NA
## 4 9.26 1912.8753 1.5100 NA NA
## 5 13.37 1940.1139 1.8500 30.26539 35.45
## 6 15.57 1265.0153 1.5580 NA NA
## 7 12.52 4041.7323 1.8400 32.95192 29.90
## 8 13.66 2416.8827 1.9500 30.15577 24.90
## 9 12.81 1717.0774 1.3500 27.55192 34.21
## 10 16.27 1147.4479 1.3100 38.25385 6.29
## 11 14.41 1990.1495 2.0600 31.53846 25.96
## 12 10.05 1506.0806 1.7200 26.75000 32.34
## 13 7.81 7298.4971 1.8620 27.08077 41.05
## 14 13.25 686.9205 1.2800 38.17308 4.99
## 15 14.55 1264.2445 1.3900 33.80769 7.65
## 16 13.33 841.5971 1.2300 33.63462 NA
## 17 15.19 1519.9117 1.3300 NA NA
## 18 11.62 1751.2503 1.3700 31.82115 18.83
## 19 10.10 4602.3431 1.8690 30.30365 31.80
## 20 11.06 2353.3150 1.4300 31.87115 30.67
## 21 15.60 592.6461 1.3000 NA NA
## 22 13.37 1921.6563 1.7700 32.09038 27.10
## Health.spending Military.spending
## 1 15.741705 0.8126331
## 2 14.569664 1.0770664
## 3 11.422118 2.2613561
## 4 6.256808 2.1059625
## 5 16.282838 1.4202110
## 6 10.936640 1.8744255
## 7 12.684467 1.3760444
## 8 16.489788 2.4168083
## 9 17.923930 1.3122866
## 10 11.306848 1.2530279
## 11 16.759486 0.5322577
## 12 16.145314 1.5077302
## 13 17.903669 1.4738621
## 14 9.881070 1.9377735
## 15 14.950302 2.0154748
## 16 13.745857 1.6326807
## 17 13.414136 1.5622485
## 18 15.517984 1.1674735
## 19 13.771747 1.3975303
## 20 18.996177 0.8508913
## 21 8.695101 2.7716469
## 22 15.588776 2.3514279What country has the lowest well-being score?
For countries with GDP greater than 10000 and working hours less than 30, what is the average well-being score? How many countries make up this average?