Discussion_12
Gabe Abreu
11/12/2020
Discussion 12
This data set was downloaded from Kaggle and sourced from the World Health Organization. I want to examine variables that are positively correlated with life expectancy.
LE_data <- read.csv('Life Expectancy Data.csv')
head(LE_data)## Country Year Status Life.expectancy Adult.Mortality infant.deaths
## 1 Afghanistan 2015 Developing 65.0 263 62
## 2 Afghanistan 2014 Developing 59.9 271 64
## 3 Afghanistan 2013 Developing 59.9 268 66
## 4 Afghanistan 2012 Developing 59.5 272 69
## 5 Afghanistan 2011 Developing 59.2 275 71
## 6 Afghanistan 2010 Developing 58.8 279 74
## Alcohol percentage.expenditure Hepatitis.B Measles BMI under.five.deaths
## 1 0.01 71.279624 65 1154 19.1 83
## 2 0.01 73.523582 62 492 18.6 86
## 3 0.01 73.219243 64 430 18.1 89
## 4 0.01 78.184215 67 2787 17.6 93
## 5 0.01 7.097109 68 3013 17.2 97
## 6 0.01 79.679367 66 1989 16.7 102
## Polio Total.expenditure Diphtheria HIV.AIDS GDP Population
## 1 6 8.16 65 0.1 584.25921 33736494
## 2 58 8.18 62 0.1 612.69651 327582
## 3 62 8.13 64 0.1 631.74498 31731688
## 4 67 8.52 67 0.1 669.95900 3696958
## 5 68 7.87 68 0.1 63.53723 2978599
## 6 66 9.20 66 0.1 553.32894 2883167
## thinness..1.19.years thinness.5.9.years Income.composition.of.resources
## 1 17.2 17.3 0.479
## 2 17.5 17.5 0.476
## 3 17.7 17.7 0.470
## 4 17.9 18.0 0.463
## 5 18.2 18.2 0.454
## 6 18.4 18.4 0.448
## Schooling
## 1 10.1
## 2 10.0
## 3 9.9
## 4 9.8
## 5 9.5
## 6 9.2summary(LE_data)## Country Year Status Life.expectancy
## Afghanistan : 16 Min. :2000 Developed : 512 Min. :36.30
## Albania : 16 1st Qu.:2004 Developing:2426 1st Qu.:63.10
## Algeria : 16 Median :2008 Median :72.10
## Angola : 16 Mean :2008 Mean :69.22
## Antigua and Barbuda: 16 3rd Qu.:2012 3rd Qu.:75.70
## Argentina : 16 Max. :2015 Max. :89.00
## (Other) :2842 NA's :10
## Adult.Mortality infant.deaths Alcohol percentage.expenditure
## Min. : 1.0 Min. : 0.0 Min. : 0.0100 Min. : 0.000
## 1st Qu.: 74.0 1st Qu.: 0.0 1st Qu.: 0.8775 1st Qu.: 4.685
## Median :144.0 Median : 3.0 Median : 3.7550 Median : 64.913
## Mean :164.8 Mean : 30.3 Mean : 4.6029 Mean : 738.251
## 3rd Qu.:228.0 3rd Qu.: 22.0 3rd Qu.: 7.7025 3rd Qu.: 441.534
## Max. :723.0 Max. :1800.0 Max. :17.8700 Max. :19479.912
## NA's :10 NA's :194
## Hepatitis.B Measles BMI under.five.deaths
## Min. : 1.00 Min. : 0.0 Min. : 1.00 Min. : 0.00
## 1st Qu.:77.00 1st Qu.: 0.0 1st Qu.:19.30 1st Qu.: 0.00
## Median :92.00 Median : 17.0 Median :43.50 Median : 4.00
## Mean :80.94 Mean : 2419.6 Mean :38.32 Mean : 42.04
## 3rd Qu.:97.00 3rd Qu.: 360.2 3rd Qu.:56.20 3rd Qu.: 28.00
## Max. :99.00 Max. :212183.0 Max. :87.30 Max. :2500.00
## NA's :553 NA's :34
## Polio Total.expenditure Diphtheria HIV.AIDS
## Min. : 3.00 Min. : 0.370 Min. : 2.00 Min. : 0.100
## 1st Qu.:78.00 1st Qu.: 4.260 1st Qu.:78.00 1st Qu.: 0.100
## Median :93.00 Median : 5.755 Median :93.00 Median : 0.100
## Mean :82.55 Mean : 5.938 Mean :82.32 Mean : 1.742
## 3rd Qu.:97.00 3rd Qu.: 7.492 3rd Qu.:97.00 3rd Qu.: 0.800
## Max. :99.00 Max. :17.600 Max. :99.00 Max. :50.600
## NA's :19 NA's :226 NA's :19
## GDP Population thinness..1.19.years
## Min. : 1.68 Min. :3.400e+01 Min. : 0.10
## 1st Qu.: 463.94 1st Qu.:1.958e+05 1st Qu.: 1.60
## Median : 1766.95 Median :1.387e+06 Median : 3.30
## Mean : 7483.16 Mean :1.275e+07 Mean : 4.84
## 3rd Qu.: 5910.81 3rd Qu.:7.420e+06 3rd Qu.: 7.20
## Max. :119172.74 Max. :1.294e+09 Max. :27.70
## NA's :448 NA's :652 NA's :34
## thinness.5.9.years Income.composition.of.resources Schooling
## Min. : 0.10 Min. :0.0000 Min. : 0.00
## 1st Qu.: 1.50 1st Qu.:0.4930 1st Qu.:10.10
## Median : 3.30 Median :0.6770 Median :12.30
## Mean : 4.87 Mean :0.6276 Mean :11.99
## 3rd Qu.: 7.20 3rd Qu.:0.7790 3rd Qu.:14.30
## Max. :28.60 Max. :0.9480 Max. :20.70
## NA's :34 NA's :167 NA's :163flattenCorrMatrix <- function(cormat, pmat) {
ut <- upper.tri(cormat)
data.frame(
row = rownames(cormat)[row(cormat)[ut]],
column = rownames(cormat)[col(cormat)[ut]],
cor =(cormat)[ut],
p = pmat[ut]
)
}
LE_data_df <- LE_data[ ,4:length(LE_data)]
LE_data_df.cor = cor(LE_data_df, method = c("spearman"))
newCorr <- rcorr(as.matrix(LE_data_df))
flattenCorrMatrix(newCorr$r, newCorr$P)## row column
## 1 Life.expectancy Adult.Mortality
## 2 Life.expectancy infant.deaths
## 3 Adult.Mortality infant.deaths
## 4 Life.expectancy Alcohol
## 5 Adult.Mortality Alcohol
## 6 infant.deaths Alcohol
## 7 Life.expectancy percentage.expenditure
## 8 Adult.Mortality percentage.expenditure
## 9 infant.deaths percentage.expenditure
## 10 Alcohol percentage.expenditure
## 11 Life.expectancy Hepatitis.B
## 12 Adult.Mortality Hepatitis.B
## 13 infant.deaths Hepatitis.B
## 14 Alcohol Hepatitis.B
## 15 percentage.expenditure Hepatitis.B
## 16 Life.expectancy Measles
## 17 Adult.Mortality Measles
## 18 infant.deaths Measles
## 19 Alcohol Measles
## 20 percentage.expenditure Measles
## 21 Hepatitis.B Measles
## 22 Life.expectancy BMI
## 23 Adult.Mortality BMI
## 24 infant.deaths BMI
## 25 Alcohol BMI
## 26 percentage.expenditure BMI
## 27 Hepatitis.B BMI
## 28 Measles BMI
## 29 Life.expectancy under.five.deaths
## 30 Adult.Mortality under.five.deaths
## 31 infant.deaths under.five.deaths
## 32 Alcohol under.five.deaths
## 33 percentage.expenditure under.five.deaths
## 34 Hepatitis.B under.five.deaths
## 35 Measles under.five.deaths
## 36 BMI under.five.deaths
## 37 Life.expectancy Polio
## 38 Adult.Mortality Polio
## 39 infant.deaths Polio
## 40 Alcohol Polio
## 41 percentage.expenditure Polio
## 42 Hepatitis.B Polio
## 43 Measles Polio
## 44 BMI Polio
## 45 under.five.deaths Polio
## 46 Life.expectancy Total.expenditure
## 47 Adult.Mortality Total.expenditure
## 48 infant.deaths Total.expenditure
## 49 Alcohol Total.expenditure
## 50 percentage.expenditure Total.expenditure
## 51 Hepatitis.B Total.expenditure
## 52 Measles Total.expenditure
## 53 BMI Total.expenditure
## 54 under.five.deaths Total.expenditure
## 55 Polio Total.expenditure
## 56 Life.expectancy Diphtheria
## 57 Adult.Mortality Diphtheria
## 58 infant.deaths Diphtheria
## 59 Alcohol Diphtheria
## 60 percentage.expenditure Diphtheria
## 61 Hepatitis.B Diphtheria
## 62 Measles Diphtheria
## 63 BMI Diphtheria
## 64 under.five.deaths Diphtheria
## 65 Polio Diphtheria
## 66 Total.expenditure Diphtheria
## 67 Life.expectancy HIV.AIDS
## 68 Adult.Mortality HIV.AIDS
## 69 infant.deaths HIV.AIDS
## 70 Alcohol HIV.AIDS
## 71 percentage.expenditure HIV.AIDS
## 72 Hepatitis.B HIV.AIDS
## 73 Measles HIV.AIDS
## 74 BMI HIV.AIDS
## 75 under.five.deaths HIV.AIDS
## 76 Polio HIV.AIDS
## 77 Total.expenditure HIV.AIDS
## 78 Diphtheria HIV.AIDS
## 79 Life.expectancy GDP
## 80 Adult.Mortality GDP
## 81 infant.deaths GDP
## 82 Alcohol GDP
## 83 percentage.expenditure GDP
## 84 Hepatitis.B GDP
## 85 Measles GDP
## 86 BMI GDP
## 87 under.five.deaths GDP
## 88 Polio GDP
## 89 Total.expenditure GDP
## 90 Diphtheria GDP
## 91 HIV.AIDS GDP
## 92 Life.expectancy Population
## 93 Adult.Mortality Population
## 94 infant.deaths Population
## 95 Alcohol Population
## 96 percentage.expenditure Population
## 97 Hepatitis.B Population
## 98 Measles Population
## 99 BMI Population
## 100 under.five.deaths Population
## 101 Polio Population
## 102 Total.expenditure Population
## 103 Diphtheria Population
## 104 HIV.AIDS Population
## 105 GDP Population
## 106 Life.expectancy thinness..1.19.years
## 107 Adult.Mortality thinness..1.19.years
## 108 infant.deaths thinness..1.19.years
## 109 Alcohol thinness..1.19.years
## 110 percentage.expenditure thinness..1.19.years
## 111 Hepatitis.B thinness..1.19.years
## 112 Measles thinness..1.19.years
## 113 BMI thinness..1.19.years
## 114 under.five.deaths thinness..1.19.years
## 115 Polio thinness..1.19.years
## 116 Total.expenditure thinness..1.19.years
## 117 Diphtheria thinness..1.19.years
## 118 HIV.AIDS thinness..1.19.years
## 119 GDP thinness..1.19.years
## 120 Population thinness..1.19.years
## 121 Life.expectancy thinness.5.9.years
## 122 Adult.Mortality thinness.5.9.years
## 123 infant.deaths thinness.5.9.years
## 124 Alcohol thinness.5.9.years
## 125 percentage.expenditure thinness.5.9.years
## 126 Hepatitis.B thinness.5.9.years
## 127 Measles thinness.5.9.years
## 128 BMI thinness.5.9.years
## 129 under.five.deaths thinness.5.9.years
## 130 Polio thinness.5.9.years
## 131 Total.expenditure thinness.5.9.years
## 132 Diphtheria thinness.5.9.years
## 133 HIV.AIDS thinness.5.9.years
## 134 GDP thinness.5.9.years
## 135 Population thinness.5.9.years
## 136 thinness..1.19.years thinness.5.9.years
## 137 Life.expectancy Income.composition.of.resources
## 138 Adult.Mortality Income.composition.of.resources
## 139 infant.deaths Income.composition.of.resources
## 140 Alcohol Income.composition.of.resources
## 141 percentage.expenditure Income.composition.of.resources
## 142 Hepatitis.B Income.composition.of.resources
## 143 Measles Income.composition.of.resources
## 144 BMI Income.composition.of.resources
## 145 under.five.deaths Income.composition.of.resources
## 146 Polio Income.composition.of.resources
## 147 Total.expenditure Income.composition.of.resources
## 148 Diphtheria Income.composition.of.resources
## 149 HIV.AIDS Income.composition.of.resources
## 150 GDP Income.composition.of.resources
## 151 Population Income.composition.of.resources
## 152 thinness..1.19.years Income.composition.of.resources
## 153 thinness.5.9.years Income.composition.of.resources
## 154 Life.expectancy Schooling
## 155 Adult.Mortality Schooling
## 156 infant.deaths Schooling
## 157 Alcohol Schooling
## 158 percentage.expenditure Schooling
## 159 Hepatitis.B Schooling
## 160 Measles Schooling
## 161 BMI Schooling
## 162 under.five.deaths Schooling
## 163 Polio Schooling
## 164 Total.expenditure Schooling
## 165 Diphtheria Schooling
## 166 HIV.AIDS Schooling
## 167 GDP Schooling
## 168 Population Schooling
## 169 thinness..1.19.years Schooling
## 170 thinness.5.9.years Schooling
## 171 Income.composition.of.resources Schooling
## cor p
## 1 -0.696359314 0.000000e+00
## 2 -0.196557177 0.000000e+00
## 3 0.078756012 1.986753e-05
## 4 0.404876761 0.000000e+00
## 5 -0.195848196 0.000000e+00
## 6 -0.115637677 1.239093e-09
## 7 0.381863503 0.000000e+00
## 8 -0.242859528 0.000000e+00
## 9 -0.085612222 3.367554e-06
## 10 0.341285313 0.000000e+00
## 11 0.256761948 0.000000e+00
## 12 -0.162476325 1.776357e-15
## 13 -0.223566281 0.000000e+00
## 14 0.087548711 3.706709e-05
## 15 0.016273693 4.269710e-01
## 16 -0.157585804 0.000000e+00
## 17 0.031176412 9.166560e-02
## 18 0.501128342 0.000000e+00
## 19 -0.051826674 6.618874e-03
## 20 -0.056595677 2.149003e-03
## 21 -0.120529372 3.533331e-09
## 22 0.567693548 0.000000e+00
## 23 -0.387016784 0.000000e+00
## 24 -0.227278888 0.000000e+00
## 25 0.330408460 0.000000e+00
## 26 0.228699753 0.000000e+00
## 27 0.150379532 1.834088e-13
## 28 -0.175977063 0.000000e+00
## 29 -0.222529116 0.000000e+00
## 30 0.094146127 3.334215e-07
## 31 0.996628882 0.000000e+00
## 32 -0.112370397 3.583787e-09
## 33 -0.087852306 1.850661e-06
## 34 -0.233126251 0.000000e+00
## 35 0.507808707 0.000000e+00
## 36 -0.237668522 0.000000e+00
## 37 0.465555806 0.000000e+00
## 38 -0.274822815 0.000000e+00
## 39 -0.170688559 0.000000e+00
## 40 0.221733797 0.000000e+00
## 41 0.147259463 1.332268e-15
## 42 0.486170773 0.000000e+00
## 43 -0.136166014 1.489919e-13
## 44 0.284568764 0.000000e+00
## 45 -0.188720213 0.000000e+00
## 46 0.218086374 0.000000e+00
## 47 -0.115280689 1.859234e-09
## 48 -0.128616342 1.782818e-11
## 49 0.296941560 0.000000e+00
## 50 0.174419689 0.000000e+00
## 51 0.058280304 6.297607e-03
## 52 -0.106240588 2.925670e-08
## 53 0.242502604 0.000000e+00
## 54 -0.130148312 1.020739e-11
## 55 0.137330249 7.398526e-13
## 56 0.479494864 0.000000e+00
## 57 -0.275131358 0.000000e+00
## 58 -0.175171496 0.000000e+00
## 59 0.222020171 0.000000e+00
## 60 0.143624426 6.217249e-15
## 61 0.611494949 0.000000e+00
## 62 -0.141881938 1.332268e-14
## 63 0.283147336 0.000000e+00
## 64 -0.195668288 0.000000e+00
## 65 0.673553321 0.000000e+00
## 66 0.152753524 1.332268e-15
## 67 -0.556556253 0.000000e+00
## 68 0.523820508 0.000000e+00
## 69 0.025231318 1.715448e-01
## 70 -0.048844563 1.049749e-02
## 71 -0.097856819 1.068527e-07
## 72 -0.112675448 3.441238e-08
## 73 0.030898718 9.403233e-02
## 74 -0.243716531 0.000000e+00
## 75 0.038061512 3.911929e-02
## 76 -0.159559542 0.000000e+00
## 77 -0.001388836 9.423688e-01
## 78 -0.164860095 0.000000e+00
## 79 0.461455193 0.000000e+00
## 80 -0.296049318 0.000000e+00
## 81 -0.108427363 5.834695e-08
## 82 0.354712086 0.000000e+00
## 83 0.899372641 0.000000e+00
## 84 0.083903212 1.634528e-04
## 85 -0.076466053 1.338173e-04
## 86 0.301557394 0.000000e+00
## 87 -0.112081253 2.050190e-08
## 88 0.211975566 0.000000e+00
## 89 0.138364222 1.940448e-11
## 90 0.200665557 0.000000e+00
## 91 -0.136490819 7.947643e-12
## 92 -0.021538108 3.035322e-01
## 93 -0.013646972 5.144795e-01
## 94 0.556801332 0.000000e+00
## 95 -0.035252342 1.038404e-01
## 96 -0.025661888 2.200179e-01
## 97 -0.123320952 1.485790e-07
## 98 0.265966087 0.000000e+00
## 99 -0.072301023 5.921647e-04
## 100 0.544422649 0.000000e+00
## 101 -0.038540248 6.655204e-02
## 102 -0.079661838 2.322268e-04
## 103 -0.028443781 1.757934e-01
## 104 -0.027854290 1.830894e-01
## 105 -0.028269671 1.787417e-01
## 106 -0.477183192 0.000000e+00
## 107 0.302903787 0.000000e+00
## 108 0.465710883 0.000000e+00
## 109 -0.428795257 0.000000e+00
## 110 -0.251368631 0.000000e+00
## 111 -0.120429205 4.044164e-09
## 112 0.224808308 0.000000e+00
## 113 -0.532024750 0.000000e+00
## 114 0.467789051 0.000000e+00
## 115 -0.221823439 0.000000e+00
## 116 -0.277100607 0.000000e+00
## 117 -0.229518256 0.000000e+00
## 118 0.204063588 0.000000e+00
## 119 -0.285697168 0.000000e+00
## 120 0.253943752 0.000000e+00
## 121 -0.471583605 0.000000e+00
## 122 0.308457270 0.000000e+00
## 123 0.471350139 0.000000e+00
## 124 -0.417413629 0.000000e+00
## 125 -0.252904587 0.000000e+00
## 126 -0.124959929 1.025443e-09
## 127 0.221072200 0.000000e+00
## 128 -0.538910573 0.000000e+00
## 129 0.472262827 0.000000e+00
## 130 -0.222591763 0.000000e+00
## 131 -0.283773605 0.000000e+00
## 132 -0.222742797 0.000000e+00
## 133 0.207283246 0.000000e+00
## 134 -0.290539012 0.000000e+00
## 135 0.251402968 0.000000e+00
## 136 0.939101992 0.000000e+00
## 137 0.724775979 0.000000e+00
## 138 -0.457625638 0.000000e+00
## 139 -0.145139259 1.620926e-14
## 140 0.450039706 0.000000e+00
## 141 0.381952444 0.000000e+00
## 142 0.199549238 0.000000e+00
## 143 -0.129568176 7.578160e-12
## 144 0.508773746 0.000000e+00
## 145 -0.163304792 0.000000e+00
## 146 0.381077721 0.000000e+00
## 147 0.166682045 0.000000e+00
## 148 0.401455803 0.000000e+00
## 149 -0.249519497 0.000000e+00
## 150 0.460341479 0.000000e+00
## 151 -0.008734836 6.764449e-01
## 152 -0.422429087 0.000000e+00
## 153 -0.411053256 0.000000e+00
## 154 0.751975463 0.000000e+00
## 155 -0.454611932 0.000000e+00
## 156 -0.193719751 0.000000e+00
## 157 0.547378379 0.000000e+00
## 158 0.389687148 0.000000e+00
## 159 0.231116546 0.000000e+00
## 160 -0.137224530 3.874678e-13
## 161 0.546961042 0.000000e+00
## 162 -0.209373285 0.000000e+00
## 163 0.417866408 0.000000e+00
## 164 0.246384299 0.000000e+00
## 165 0.425332298 0.000000e+00
## 166 -0.220428716 0.000000e+00
## 167 0.448272829 0.000000e+00
## 168 -0.031667638 1.301149e-01
## 169 -0.471651570 0.000000e+00
## 170 -0.460631702 0.000000e+00
## 171 0.800092420 0.000000e+00We can see that life expectancy and schooling have a high correlation and a statistically significant p value.
Including Plots
corrplot(newCorr$r, type="upper", order="hclust",
p.mat = newCorr$P, sig.level = 0.05, insig = "blank")
chart.Correlation(LE_data_df, histograme=TRUE, pch=19)
palette = colorRampPalette(c("green", "white", "red")) (10)
heatmap(x = newCorr$r, scale="column", col = palette, symm = TRUE)
Schooling and life expectancy are positively correlated. Let’s create a linear regression model and later evaluate it.
lif <- LE_data_df$Life.expectancy
sch <- LE_data_df$Schooling
lif_sch_lm <- lm(sch ~ lif)
summary(lif_sch_lm)##
## Call:
## lm(formula = sch ~ lif)
##
## Residuals:
## Min 1Q Median 3Q Max
## -13.4111 -1.1027 0.0164 1.2696 6.1774
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -6.643467 0.313559 -21.19 <2e-16 ***
## lif 0.268828 0.004481 59.99 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.206 on 2766 degrees of freedom
## (170 observations deleted due to missingness)
## Multiple R-squared: 0.5655, Adjusted R-squared: 0.5653
## F-statistic: 3599 on 1 and 2766 DF, p-value: < 2.2e-16ggplot(data = LE_data_df, aes(x = Schooling, y = Life.expectancy)) +
geom_point() +
stat_smooth(method = "lm", se = FALSE)## Warning: Removed 170 rows containing non-finite values (stat_smooth).## Warning: Removed 170 rows containing missing values (geom_point).
Examining the Residuals
life_exp.res = resid(lif_sch_lm)
plot(fitted(lif_sch_lm), life_exp.res)
abline(0,0)
hist(life_exp.res)
qqnorm(resid(lif_sch_lm))
qqline(resid(lif_sch_lm))