Warning: package 'dslabs' was built under R version 4.5.2
# only run the next command interactively, not in a script# help(gapminder) # get an overview of data structurestr(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 datasummary(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 isclass(gapminder)
[1] "data.frame"
africandata <-subset(gapminder, continent %in%"Africa") #subsetting data for african countries# get an overview of data structurestr(africandata)
'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 datasummary(africandata)
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
# determine the type of object class(africandata)
[1] "data.frame"
infant_mortality_data <-data.frame(africandata$infant_mortality, africandata$life_expectancy) #making a dataframe for infant mortalitypopulation_data <-data.frame(africandata$population, africandata$life_expectancy) #making a dataframe for infant mortality
# get an overview of data structurestr(infant_mortality_data)
'data.frame': 2907 obs. of 2 variables:
$ africandata.infant_mortality: num 148 208 187 116 161 ...
$ africandata.life_expectancy : num 47.5 36 38.3 50.3 35.2 ...
# get a summary of datasummary(infant_mortality_data)
africandata.infant_mortality africandata.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
# get an overview of data structurestr(population_data)
'data.frame': 2907 obs. of 2 variables:
$ africandata.population : num 11124892 5270844 2431620 524029 4829291 ...
$ africandata.life_expectancy: num 47.5 36 38.3 50.3 35.2 ...
# get a summary of datasummary(population_data)
africandata.population africandata.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
#ploting infant mortality vs life expectancyplot(infant_mortality_data$africandata.infant_mortality, infant_mortality_data$africandata.life_expectancy)
#ploting population vs life expectancyplot(log(population_data$africandata.population, base =2), population_data$africandata.life_expectancy)
# In both plots, streak patterns occur because each country contributes multiple times across different years and they tend to grow.
africandata_year2000 <-subset(africandata, year %in%2000)# get an overview of data structurestr(africandata_year2000)
'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 datasummary(africandata_year2000)
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
#replotting only year 2000plot(africandata_year2000$infant_mortality, africandata_year2000$life_expectancy)
plot(log(africandata_year2000$population, base =2), africandata_year2000$life_expectancy)
#lm base on year 2000fit1 <-lm(life_expectancy ~ infant_mortality, data = africandata_year2000)summary(fit1)
Call:
lm(formula = life_expectancy ~ infant_mortality, data = africandata_year2000)
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
fit2 <-lm(life_expectancy ~ population, data = africandata_year2000)summary(fit2)
Call:
lm(formula = life_expectancy ~ population, data = africandata_year2000)
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
#This section contributed by Riley Herber.#More Data Exploration#Analysis of Concentrations of the three main greenhouse gases carbon dioxide, methane and nitrous oxide from 1-2000 CE.#summary(greenhouse_gases)data <- greenhouse_gases#creates a data.frame object for each gas including year and concentrationsCO2 <- greenhouse_gases[greenhouse_gases$gas =="CO2", ]CH4 <- greenhouse_gases[greenhouse_gases$gas =="CH4", ]N2O <- greenhouse_gases[greenhouse_gases$gas =="N2O", ]# set up an empty plot using all data to get axis limitsplot( greenhouse_gases$year, greenhouse_gases$concentration,type ="n", xlab ="Year",ylab ="Concentration",main ="Greenhouse Gas Concentrations Over Time")# add lines for each gaslines(CO2$year, CO2$concentration, col ="blue", lwd =2)lines(CH4$year, CH4$concentration, col ="green", lwd =2)lines(N2O$year, N2O$concentration, col ="red", lwd =2)# add legendlegend("topleft",legend =c("CO2", "CH4", "N2O"),col =c("blue", "green", "red"),lwd =2)
#ChatGPT was used to help me learn how to do multi-panel figure (code below)par(mfrow =c(3, 1)) # plot with 3 rows, 1 columnplot(CO2$year, CO2$concentration, type ="l",main ="CO2", xlab ="Year", ylab ="Concentration")plot(CH4$year, CH4$concentration, type ="l",main ="CH4", xlab ="Year", ylab ="Concentration")plot(N2O$year, N2O$concentration, type ="l",main ="N2O", xlab ="Year", ylab ="Concentration")
par(mfrow =c(1, 1)) # reset layoutfit3 <-lm(concentration ~ year * gas, data = greenhouse_gases)summary(fit3)
Call:
lm(formula = concentration ~ year * gas, data = greenhouse_gases)
Residuals:
Min 1Q Median 3Q Max
-128.95 -6.83 -1.70 3.33 868.77
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 558.37400 15.44941 36.142 < 2e-16 ***
year 0.13813 0.01328 10.401 < 2e-16 ***
gasCO2 -285.06103 21.84876 -13.047 < 2e-16 ***
gasN2O -297.83861 21.84876 -13.632 < 2e-16 ***
year:gasCO2 -0.12940 0.01878 -6.890 3.39e-11 ***
year:gasN2O -0.13027 0.01878 -6.936 2.56e-11 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 76.67 on 294 degrees of freedom
Multiple R-squared: 0.879, Adjusted R-squared: 0.877
F-statistic: 427.2 on 5 and 294 DF, p-value: < 2.2e-16
Analysis of greenhouse gas concentrations from 1–2000 CE revealed significant temporal trends that differed by gas type. Methane (CH4) exhibited a strong positive increase over time (CH4 slope = 0.138 per year and p < 0.001), whereas carbon dioxide (CO2) and nitrous oxide (N2O) had much lower baseline concentrations and minimal growth (CO2 adjusted slope = 0.009 per year; N2O adjusted slope = 0.008 per year; both p < 0.001). The interaction between year and gas was highly significant, indicating that the rate of increase varied across gases. Overall, the model explained 88% of the variance in concentrations (adjusted R-squared = 0.877), highlighting that CH4 has risen much more rapidly than CO2 or N2O over the past two millennia.
