R coding Exercise
#View Data
#Load "dslabs" package to manipulate data
library("dslabs")
library("tidyverse")## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.1 ──## ✓ ggplot2 3.3.5 ✓ purrr 0.3.4
## ✓ tibble 3.1.6 ✓ dplyr 1.0.7
## ✓ tidyr 1.1.4 ✓ stringr 1.4.0
## ✓ readr 2.1.2 ✓ forcats 0.5.1## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()#Check out the help file in gapminder
help(gapminder)
#Lets get an overview of the data
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 ...#Now for a summary of the 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#To see what type of object gapminder is, we use the class function
class(gapminder)## [1] "data.frame"#Createign a data subset
#Now let look at only the African countries
africadata <- gapminder[ which(gapminder$continent=="Africa"), ]
#Lets get an overview & summary of the new data
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 ...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#Making two new data frames
imle <- africadata[c("infant_mortality","life_expectancy")]
ple <- africadata[c("population","life_expectancy")]
#Overview of new sets
str(imle)## '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 ...str(ple)## 'data.frame': 2907 obs. of 2 variables:
## $ population : num 11124892 5270844 2431620 524029 4829291 ...
## $ life_expectancy: num 47.5 36 38.3 50.3 35.2 ...#Summary of new sets
summary(imle)## 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 :226summary(ple)## population life_expectancy
## Min. : 41538 Min. :13.20
## 1st Qu.: 1605232 1st Qu.:48.23
## Median : 5570982 Median :53.98
## Mean : 12235961 Mean :54.38
## 3rd Qu.: 13888152 3rd Qu.:60.10
## Max. :182201962 Max. :77.60
## NA's :51#Plotting the variables
#life expectancy vs. infant mortality
ggplot(imle, aes(infant_mortality, life_expectancy)) + geom_point()## Warning: Removed 226 rows containing missing values (geom_point).
#Here we have a negative correlation
#life expectancy vs. population
ggplot(ple, aes(population, life_expectancy)) + geom_point() +scale_x_log10()## Warning: Removed 51 rows containing missing values (geom_point).
#Here we have a loose positive correlation. Notice the "streaks" in the data#Data cleaning
#Use the is.na function to see what data we are missing
africadata$year[is.na(africadata$infant_mortality)]## [1] 1960 1960 1960 1960 1960 1960 1960 1960 1960 1960 1961 1961 1961 1961 1961
## [16] 1961 1961 1961 1961 1961 1961 1961 1961 1961 1961 1961 1961 1962 1962 1962
## [31] 1962 1962 1962 1962 1962 1962 1962 1962 1962 1962 1962 1962 1962 1963 1963
## [46] 1963 1963 1963 1963 1963 1963 1963 1963 1963 1963 1963 1963 1963 1963 1964
## [61] 1964 1964 1964 1964 1964 1964 1964 1964 1964 1964 1964 1964 1964 1964 1965
## [76] 1965 1965 1965 1965 1965 1965 1965 1965 1965 1965 1965 1965 1965 1966 1966
## [91] 1966 1966 1966 1966 1966 1966 1966 1966 1966 1966 1966 1967 1967 1967 1967
## [106] 1967 1967 1967 1967 1967 1967 1967 1968 1968 1968 1968 1968 1968 1968 1968
## [121] 1968 1968 1968 1969 1969 1969 1969 1969 1969 1969 1970 1970 1970 1970 1970
## [136] 1971 1971 1971 1971 1971 1971 1972 1972 1972 1972 1972 1972 1973 1973 1973
## [151] 1973 1973 1973 1974 1974 1974 1974 1974 1975 1975 1975 1975 1975 1976 1976
## [166] 1976 1977 1977 1977 1978 1978 1979 1979 1980 1981 2016 2016 2016 2016 2016
## [181] 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016
## [196] 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016
## [211] 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016
## [226] 2016#Creating a datafram that only includes data from year 2000
newyr <- africadata[africadata$year=="2000", c("country", "year", "infant_mortality", "life_expectancy", "fertility", "population", "gdp", "continent", "region")]
#Check the structure and summary
str(newyr)## '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 ...summary(newyr)## 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#Plotting with new subset
#Plot new dataframe, same variable as before
#Life expectancy vs. Infant Mortality 2000 plot
plot(infant_mortality ~ life_expectancy, data=newyr)
#Life expectancy vs. Population 2000 plot
plot(life_expectancy ~ population, data=newyr, log = "x")
#no relationship
#Lets create fitted lines for the life expectancy outcome
#Life expectancy vs. Infant Mortality 2000
fit1<- lm(formula = life_expectancy ~ infant_mortality, data = newyr)
#Life expectancy vs. Population 2000
fit2<- lm(formula = life_expectancy ~ population, data = newyr)
#summary(fit1)
#summary(fit2)#After looking at P-values for both, it appears that the correlation between Infant Mortality and Life Expectancy has a P-val <0.05 and is a better predictor than Population, which has a P-val >0.05.