R Coding Exercise

Loading libraries

library("dslabs")
library("dplyr") 

Attaching package: 'dplyr'

The following objects are masked from 'package:stats':

    filter, lag

The following objects are masked from 'package:base':

    intersect, setdiff, setequal, union

library("ggplot2")

###look at help file for gapminder data

help(gapminder)

###get an overview of data structure

str(gapminder)

'data.frame':   10545 obs. of  9 variables:
 $ country         : Factor w/ 185 levels "Albania","Algeria",..: 1 2 3 4 5 6 7 8 9 10 ...
 $ year            : int  1960 1960 1960 1960 1960 1960 1960 1960 1960 1960 ...
 $ infant_mortality: num  115.4 148.2 208 NA 59.9 ...
 $ life_expectancy : num  62.9 47.5 36 63 65.4 ...
 $ fertility       : num  6.19 7.65 7.32 4.43 3.11 4.55 4.82 3.45 2.7 5.57 ...
 $ population      : num  1636054 11124892 5270844 54681 20619075 ...
 $ gdp             : num  NA 1.38e+10 NA NA 1.08e+11 ...
 $ continent       : Factor w/ 5 levels "Africa","Americas",..: 4 1 1 2 2 3 2 5 4 3 ...
 $ region          : Factor w/ 22 levels "Australia and New Zealand",..: 19 11 10 2 15 21 2 1 22 21 ...

###get a summary of data

summary(gapminder)

                country           year      infant_mortality life_expectancy
 Albania            :   57   Min.   :1960   Min.   :  1.50   Min.   :13.20  
 Algeria            :   57   1st Qu.:1974   1st Qu.: 16.00   1st Qu.:57.50  
 Angola             :   57   Median :1988   Median : 41.50   Median :67.54  
 Antigua and Barbuda:   57   Mean   :1988   Mean   : 55.31   Mean   :64.81  
 Argentina          :   57   3rd Qu.:2002   3rd Qu.: 85.10   3rd Qu.:73.00  
 Armenia            :   57   Max.   :2016   Max.   :276.90   Max.   :83.90  
 (Other)            :10203                  NA's   :1453                    
   fertility       population             gdp               continent   
 Min.   :0.840   Min.   :3.124e+04   Min.   :4.040e+07   Africa  :2907  
 1st Qu.:2.200   1st Qu.:1.333e+06   1st Qu.:1.846e+09   Americas:2052  
 Median :3.750   Median :5.009e+06   Median :7.794e+09   Asia    :2679  
 Mean   :4.084   Mean   :2.701e+07   Mean   :1.480e+11   Europe  :2223  
 3rd Qu.:6.000   3rd Qu.:1.523e+07   3rd Qu.:5.540e+10   Oceania : 684  
 Max.   :9.220   Max.   :1.376e+09   Max.   :1.174e+13                  
 NA's   :187     NA's   :185         NA's   :2972                       
             region    
 Western Asia   :1026  
 Eastern Africa : 912  
 Western Africa : 912  
 Caribbean      : 741  
 South America  : 684  
 Southern Europe: 684  
 (Other)        :5586  

determine the type of object gapminder is

class(gapminder)

[1] "data.frame"

###Creating a dataset for only African countries

africadata <- gapminder %>% filter(continent == "Africa")

###Get an overview of data strucure

str(africadata)

'data.frame':   2907 obs. of  9 variables:
 $ country         : Factor w/ 185 levels "Albania","Algeria",..: 2 3 18 22 26 27 29 31 32 33 ...
 $ year            : int  1960 1960 1960 1960 1960 1960 1960 1960 1960 1960 ...
 $ infant_mortality: num  148 208 187 116 161 ...
 $ life_expectancy : num  47.5 36 38.3 50.3 35.2 ...
 $ fertility       : num  7.65 7.32 6.28 6.62 6.29 6.95 5.65 6.89 5.84 6.25 ...
 $ population      : num  11124892 5270844 2431620 524029 4829291 ...
 $ gdp             : num  1.38e+10 NA 6.22e+08 1.24e+08 5.97e+08 ...
 $ continent       : Factor w/ 5 levels "Africa","Americas",..: 1 1 1 1 1 1 1 1 1 1 ...
 $ region          : Factor w/ 22 levels "Australia and New Zealand",..: 11 10 20 17 20 5 10 20 10 10 ...

###Get a summary of data

summary(africadata)

         country          year      infant_mortality life_expectancy
 Algeria     :  57   Min.   :1960   Min.   : 11.40   Min.   :13.20  
 Angola      :  57   1st Qu.:1974   1st Qu.: 62.20   1st Qu.:48.23  
 Benin       :  57   Median :1988   Median : 93.40   Median :53.98  
 Botswana    :  57   Mean   :1988   Mean   : 95.12   Mean   :54.38  
 Burkina Faso:  57   3rd Qu.:2002   3rd Qu.:124.70   3rd Qu.:60.10  
 Burundi     :  57   Max.   :2016   Max.   :237.40   Max.   :77.60  
 (Other)     :2565                  NA's   :226                     
   fertility       population             gdp               continent   
 Min.   :1.500   Min.   :    41538   Min.   :4.659e+07   Africa  :2907  
 1st Qu.:5.160   1st Qu.:  1605232   1st Qu.:8.373e+08   Americas:   0  
 Median :6.160   Median :  5570982   Median :2.448e+09   Asia    :   0  
 Mean   :5.851   Mean   : 12235961   Mean   :9.346e+09   Europe  :   0  
 3rd Qu.:6.860   3rd Qu.: 13888152   3rd Qu.:6.552e+09   Oceania :   0  
 Max.   :8.450   Max.   :182201962   Max.   :1.935e+11                  
 NA's   :51      NA's   :51          NA's   :637                        
                       region   
 Eastern Africa           :912  
 Western Africa           :912  
 Middle Africa            :456  
 Northern Africa          :342  
 Southern Africa          :285  
 Australia and New Zealand:  0  
 (Other)                  :  0  

Create a new object from africa data with only infant_mortality and life_expectancy

africadata_v1 <- africadata[,3:4]

###Get an overview of data structure

str(africadata_v1)

'data.frame':   2907 obs. of  2 variables:
 $ infant_mortality: num  148 208 187 116 161 ...
 $ life_expectancy : num  47.5 36 38.3 50.3 35.2 ...

Get summary of data

summary(africadata_v1)

 infant_mortality life_expectancy
 Min.   : 11.40   Min.   :13.20  
 1st Qu.: 62.20   1st Qu.:48.23  
 Median : 93.40   Median :53.98  
 Mean   : 95.12   Mean   :54.38  
 3rd Qu.:124.70   3rd Qu.:60.10  
 Max.   :237.40   Max.   :77.60  
 NA's   :226                     

View(africadata) ### Create another object from africadata with only population and life expectancy

africadata_v2 <- africadata[,c(4,6)]

Get an overview of data structure

str(africadata_v2)

'data.frame':   2907 obs. of  2 variables:
 $ life_expectancy: num  47.5 36 38.3 50.3 35.2 ...
 $ population     : num  11124892 5270844 2431620 524029 4829291 ...

Summary of data

summary(africadata_v2)

 life_expectancy   population       
 Min.   :13.20   Min.   :    41538  
 1st Qu.:48.23   1st Qu.:  1605232  
 Median :53.98   Median :  5570982  
 Mean   :54.38   Mean   : 12235961  
 3rd Qu.:60.10   3rd Qu.: 13888152  
 Max.   :77.60   Max.   :182201962  
                 NA's   :51         

Plot infant mortality and life expectancy

ggplot(africadata_v1, aes(x=life_expectancy, y = infant_mortality)) +
geom_point()

Warning: Removed 226 rows containing missing values (`geom_point()`).

Plot infant mortality and Population size

ggplot(africadata_v2, aes(x=life_expectancy, y = population)) +
geom_point()

Warning: Removed 51 rows containing missing values (`geom_point()`).

Getting the missing records for infant mortality

is.na(africadata$infant_mortality)

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New object excluding missing values

africadata_2000 <- africadata %>% filter(year == 2000)

Check structure of the data

str(africadata_2000)

'data.frame':   51 obs. of  9 variables:
 $ country         : Factor w/ 185 levels "Albania","Algeria",..: 2 3 18 22 26 27 29 31 32 33 ...
 $ year            : int  2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 ...
 $ infant_mortality: num  33.9 128.3 89.3 52.4 96.2 ...
 $ life_expectancy : num  73.3 52.3 57.2 47.6 52.6 46.7 54.3 68.4 45.3 51.5 ...
 $ fertility       : num  2.51 6.84 5.98 3.41 6.59 7.06 5.62 3.7 5.45 7.35 ...
 $ population      : num  31183658 15058638 6949366 1736579 11607944 ...
 $ gdp             : num  5.48e+10 9.13e+09 2.25e+09 5.63e+09 2.61e+09 ...
 $ continent       : Factor w/ 5 levels "Africa","Americas",..: 1 1 1 1 1 1 1 1 1 1 ...
 $ region          : Factor w/ 22 levels "Australia and New Zealand",..: 11 10 20 17 20 5 10 20 10 10 ...

Get a summary of the data

summary(africadata_2000)

         country        year      infant_mortality life_expectancy
 Algeria     : 1   Min.   :2000   Min.   : 12.30   Min.   :37.60  
 Angola      : 1   1st Qu.:2000   1st Qu.: 60.80   1st Qu.:51.75  
 Benin       : 1   Median :2000   Median : 80.30   Median :54.30  
 Botswana    : 1   Mean   :2000   Mean   : 78.93   Mean   :56.36  
 Burkina Faso: 1   3rd Qu.:2000   3rd Qu.:103.30   3rd Qu.:60.00  
 Burundi     : 1   Max.   :2000   Max.   :143.30   Max.   :75.00  
 (Other)     :45                                                  
   fertility       population             gdp               continent 
 Min.   :1.990   Min.   :    81154   Min.   :2.019e+08   Africa  :51  
 1st Qu.:4.150   1st Qu.:  2304687   1st Qu.:1.274e+09   Americas: 0  
 Median :5.550   Median :  8799165   Median :3.238e+09   Asia    : 0  
 Mean   :5.156   Mean   : 15659800   Mean   :1.155e+10   Europe  : 0  
 3rd Qu.:5.960   3rd Qu.: 17391242   3rd Qu.:8.654e+09   Oceania : 0  
 Max.   :7.730   Max.   :122876723   Max.   :1.329e+11                
                                                                      
                       region  
 Eastern Africa           :16  
 Western Africa           :16  
 Middle Africa            : 8  
 Northern Africa          : 6  
 Southern Africa          : 5  
 Australia and New Zealand: 0  
 (Other)                  : 0  

Ploting life expectancy and infant mortality for the year 2000

ggplot(africadata_2000, aes(x=life_expectancy, y = infant_mortality)) +
geom_point()

Ploting life expectancy and population size for the year 2000

ggplot(africadata_2000, aes(x=life_expectancy, y = population)) +
geom_point()

Fitting a linear regression: using infant mortality as a predictor

fit1 <- lm(life_expectancy ~ infant_mortality, data = africadata_2000)

Print result

summary(fit1)

Call:
lm(formula = life_expectancy ~ infant_mortality, data = africadata_2000)

Residuals:
     Min       1Q   Median       3Q      Max 
-22.6651  -3.7087   0.9914   4.0408   8.6817 

Coefficients:
                 Estimate Std. Error t value Pr(>|t|)    
(Intercept)      71.29331    2.42611  29.386  < 2e-16 ***
infant_mortality -0.18916    0.02869  -6.594 2.83e-08 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 6.221 on 49 degrees of freedom
Multiple R-squared:  0.4701,    Adjusted R-squared:  0.4593 
F-statistic: 43.48 on 1 and 49 DF,  p-value: 2.826e-08

Using population size as a predictor

fit2 <- lm(life_expectancy ~ population, data = africadata_2000)

###Print result

summary(fit2)

Call:
lm(formula = life_expectancy ~ population, data = africadata_2000)

Residuals:
    Min      1Q  Median      3Q     Max 
-18.429  -4.602  -2.568   3.800  18.802 

Coefficients:
             Estimate Std. Error t value Pr(>|t|)    
(Intercept) 5.593e+01  1.468e+00  38.097   <2e-16 ***
population  2.756e-08  5.459e-08   0.505    0.616    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 8.524 on 49 degrees of freedom
Multiple R-squared:  0.005176,  Adjusted R-squared:  -0.01513 
F-statistic: 0.2549 on 1 and 49 DF,  p-value: 0.6159

Kelly Hatfield’s Contributions

This section is added by Kelly Hatfield.

Assessing the Impact of region in the relationship between life expectancy and mortality in year 2000

First, we will recreate the visualization of the Africa data in 2000 but adding in color to differentiate the differences in region.

#load packages

library(dslabs) 
library("dplyr") 
library("ggplot2") 

africadata <- gapminder %>% filter(continent == "Africa")
#reset africa dataset for year 2000 only
africadata_2000 = subset(africadata, year==2000)

#More Plotting #Infant Mortality and Life Expectancy in 2000;
ggplot(africadata_2000, aes(x=infant_mortality, y=life_expectancy, color=region)) +geom_point()

Next we will run a simple linear regression model to assess the mean life expectancy in each of the regions

#Box Plots of Life Expectancy by Region for the years 2000;

box=ggplot(africadata_2000, aes(x=region, y=life_expectancy, color=region)) + geom_boxplot()

box + geom_jitter(shape=16, position=position_jitter(0.2))

#Simple linear regression model for year 2000; 

fit3 = lm (life_expectancy~factor(region), data=africadata_2000)

summary(fit3)

Call:
lm(formula = life_expectancy ~ factor(region), data = africadata_2000)

Residuals:
    Min      1Q  Median      3Q     Max 
-16.056  -4.138  -0.500   3.013  17.744 

Coefficients:
                              Estimate Std. Error t value Pr(>|t|)    
(Intercept)                    53.6563     1.6203  33.115  < 2e-16 ***
factor(region)Middle Africa    -0.8562     2.8065  -0.305    0.762    
factor(region)Northern Africa  17.4604     3.1026   5.628 1.04e-06 ***
factor(region)Southern Africa  -2.1563     3.3206  -0.649    0.519    
factor(region)Western Africa    3.1812     2.2915   1.388    0.172    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 6.481 on 46 degrees of freedom
Multiple R-squared:  0.4601,    Adjusted R-squared:  0.4131 
F-statistic:   9.8 on 4 and 46 DF,  p-value: 8.074e-06

This shows us that there are significant variability in the mean life expectancy by region in the year 2000.

Next, we will assess region and infant mortality in the same model.

#MLR
adjfit =lm(life_expectancy~infant_mortality+factor(region), data=africadata_2000)

summary(adjfit)

Call:
lm(formula = life_expectancy ~ infant_mortality + factor(region), 
    data = africadata_2000)

Residuals:
     Min       1Q   Median       3Q      Max 
-19.1675  -2.4682   0.6053   2.2362   7.8495 

Coefficients:
                              Estimate Std. Error t value Pr(>|t|)    
(Intercept)                   66.95785    2.36426  28.321  < 2e-16 ***
infant_mortality              -0.17479    0.02694  -6.488 5.87e-08 ***
factor(region)Middle Africa    2.96949    2.12310   1.399  0.16877    
factor(region)Northern Africa 10.89992    2.47117   4.411 6.35e-05 ***
factor(region)Southern Africa -4.13489    2.43244  -1.700  0.09605 .  
factor(region)Western Africa   5.92329    1.71809   3.448  0.00124 ** 
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 4.71 on 45 degrees of freedom
Multiple R-squared:  0.721, Adjusted R-squared:  0.6901 
F-statistic: 23.26 on 5 and 45 DF,  p-value: 1.828e-11

From this model we can interpret that when adjusting for region in Africa, infant mortality is still linearly associated with life expectancy (p<0.001). The parameter estimate tells us that for each one-unit increase in infant mortality life expectancy declines by 0.18 years.