Load Libraries
library(rvest)
## Loading required package: xml2
library(tidyverse)
## -- Attaching packages ------------------------------------------ tidyverse 1.3.0 --
## v ggplot2 3.3.1 v purrr 0.3.4
## v tibble 3.0.1 v dplyr 1.0.0
## v tidyr 1.1.0 v stringr 1.4.0
## v readr 1.3.1 v forcats 0.5.0
## -- Conflicts --------------------------------------------- tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x readr::guess_encoding() masks rvest::guess_encoding()
## x dplyr::lag() masks stats::lag()
## x purrr::pluck() masks rvest::pluck()
library(ggsci)
library(plotly)
##
## Attaching package: 'plotly'
## The following object is masked from 'package:ggplot2':
##
## last_plot
## The following object is masked from 'package:stats':
##
## filter
## The following object is masked from 'package:graphics':
##
## layout
library(DT)
library(psych)
##
## Attaching package: 'psych'
## The following objects are masked from 'package:ggplot2':
##
## %+%, alpha
library(gridExtra)
##
## Attaching package: 'gridExtra'
## The following object is masked from 'package:dplyr':
##
## combine
library(cowplot)
##
## ********************************************************
## Note: As of version 1.0.0, cowplot does not change the
## default ggplot2 theme anymore. To recover the previous
## behavior, execute:
## theme_set(theme_cowplot())
## ********************************************************
Load Dataset
setwd("C:/Users/Valued Customer/Desktop/Lovebug/Montgomery College/DATA 110/Week 3")
sw_le <- read.csv("SafeWaterLifeExpectancy.csv")
Rename Columns
# Changed column name in dataset
sw_le2 <- read.csv("SafeWaterLifeExpectancy_colrename.csv")
# Added column for continent
sw_le3 <- read.csv("SafeWaterLifeExpectancy_colrename_continent.csv")
head(sw_le3)
## Country.Name Perc..Pop..Safe.Water Average.Life.Expectancy..years. Continent
## 1 Uganda 6.43900 59.50876 Africa
## 2 Ethiopia 10.53542 65.00829 Africa
## 3 Nigeria 19.40221 52.97793 Africa
## 4 Cambodia 24.09879 68.47205 Asia
## 5 Nepal 26.75171 69.86985 Asia
## 6 Ghana 26.86492 62.40724 Africa
dim(sw_le3)
## [1] 81 4
summary(sw_le3)
## Country.Name Perc..Pop..Safe.Water Average.Life.Expectancy..years.
## Length:81 Min. : 6.439 Min. :52.98
## Class :character 1st Qu.: 68.870 1st Qu.:72.22
## Mode :character Median : 91.694 Median :76.64
## Mean : 79.403 Mean :75.68
## 3rd Qu.: 98.024 3rd Qu.:81.39
## Max. :100.000 Max. :83.84
## Continent
## Length:81
## Class :character
## Mode :character
##
##
##
describe(sw_le3)
## Warning in describe(sw_le3): NAs introduced by coercion
## Warning in describe(sw_le3): NAs introduced by coercion
## Warning in FUN(newX[, i], ...): no non-missing arguments to min; returning Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to min; returning Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## vars n mean sd median trimmed mad min
## Country.Name* 1 81 NaN NA NA NaN NA Inf
## Perc..Pop..Safe.Water 2 81 79.40 24.87 91.69 83.84 10.68 6.44
## Average.Life.Expectancy..years. 3 81 75.68 6.55 76.64 76.51 7.04 52.98
## Continent* 4 81 NaN NA NA NaN NA Inf
## max range skew kurtosis se
## Country.Name* -Inf -Inf NA NA NA
## Perc..Pop..Safe.Water 100.00 93.56 -1.30 0.57 2.76
## Average.Life.Expectancy..years. 83.84 30.87 -1.26 1.90 0.73
## Continent* -Inf -Inf NA NA NA
Look at the structure of the data
str(sw_le3)
## 'data.frame': 81 obs. of 4 variables:
## $ Country.Name : chr "Uganda" "Ethiopia" "Nigeria" "Cambodia" ...
## $ Perc..Pop..Safe.Water : num 6.44 10.54 19.4 24.1 26.75 ...
## $ Average.Life.Expectancy..years.: num 59.5 65 53 68.5 69.9 ...
## $ Continent : chr "Africa" "Africa" "Africa" "Asia" ...
Rename Columns
colnames(sw_le2)<- c("country", "wateraccess", "lifeexpectancy")
colnames(sw_le3)<- c("country", "wateraccess", "lifeexpectancy", "continent")
Plots
p1<-sw_le3%>%
ggplot(aes(x=wateraccess, fill= country))+
geom_histogram(position="stack",binwidth=5)+
labs(title = "Clean Water Access by Country")
ggplotly(p1)
p2<-sw_le3%>%
ggplot(aes(x=lifeexpectancy, fill= country))+
geom_histogram(position="stack",binwidth=5)+
labs(title = "Life Expectancy by Country")
ggplotly(p2)
Clean Water Access - Bottom 10 countries
cw_bottom10 <- sw_le2 %>%
filter(wateraccess < 45)
cw_bottom10
## country wateraccess lifeexpectancy
## 1 Uganda 6.43900 59.50876
## 2 Ethiopia 10.53542 65.00829
## 3 Nigeria 19.40221 52.97793
## 4 Cambodia 24.09879 68.47205
## 5 Nepal 26.75171 69.86985
## 6 Ghana 26.86492 62.40724
## 7 Bhutan 34.15480 69.80712
## 8 Pakistan 35.63796 66.33217
## 9 Congo, Rep. 37.00161 64.09073
## 10 Mexico 42.61278 76.88137
cw_bottom10_2 <- sw_le3 %>%
filter(wateraccess < 45)
p3 <- cw_bottom10 %>%
ggplot(aes(x=wateraccess, fill=country)) +
geom_histogram(position="identity", alpha=0.5, binwidth = 5, color = "white")+
scale_fill_discrete(name = "Country")
p3
p4<-cw_bottom10%>%
ggplot(aes(x=wateraccess, fill= country))+
geom_histogram(position="stack",binwidth=2)+
labs(title = "Clean Water Access Bottom 10 Countries")
ggplotly(p4)
p5<-sw_le2 %>%
ggplot()+
geom_point(aes(x=wateraccess,y=lifeexpectancy,size=lifeexpectancy,col=country), alpha=0.72)+
ggtitle("Life Expect. by Water Access")+
xlab("Clean Water Access (% Pop.)")+
ylab("Life Expectancy (years)")
ggplotly(p5)
Too many countries. So separate by bottom and top 10
p6<-cw_bottom10 %>%
ggplot()+
geom_point(aes(x=wateraccess,y=lifeexpectancy,size=lifeexpectancy,col=country), alpha=0.72)+
ggtitle("Life Expectancy by Water Access")
ggplotly(p6)
cw_top10<- sw_le2 %>%
filter(wateraccess > 98.7)
cw_top10
## country wateraccess lifeexpectancy
## 1 Bahrain 98.74000 76.86520
## 2 Ireland 98.87529 81.50244
## 3 Greece 98.89577 81.58780
## 4 United States 99.02272 78.74146
## 5 Germany 99.21542 81.09024
## 6 Israel 99.39000 82.05122
## 7 Cyprus 99.60259 80.29105
## 8 Malta 99.94286 81.94634
## 9 Netherlands 99.95510 81.70732
## 10 Kuwait 100.00000 74.74683
## 11 Liechtenstein 100.00000 82.07317
## 12 New Zealand 100.00000 81.45683
p7<-cw_top10 %>%
ggplot()+
geom_point(aes(x=wateraccess,y=lifeexpectancy,size=lifeexpectancy,col=country), alpha=0.72)+
ggtitle("Life Expectancy by Water Access")
ggplotly(p7)
Overall Plot with Regression Line
sw_le2 %>%
ggplot(aes(wateraccess,lifeexpectancy)) +
geom_point()+
geom_smooth(method=lm,se=T)
## `geom_smooth()` using formula 'y ~ x'
cw_top_bottom <-sw_le2 %>%
filter(wateraccess<50|wateraccess>98.7)
cw_top_bottom
## country wateraccess lifeexpectancy
## 1 Uganda 6.43900 59.50876
## 2 Ethiopia 10.53542 65.00829
## 3 Nigeria 19.40221 52.97793
## 4 Cambodia 24.09879 68.47205
## 5 Nepal 26.75171 69.86985
## 6 Ghana 26.86492 62.40724
## 7 Bhutan 34.15480 69.80712
## 8 Pakistan 35.63796 66.33217
## 9 Congo, Rep. 37.00161 64.09073
## 10 Mexico 42.61278 76.88137
## 11 Cote d'Ivoire 45.80163 53.07824
## 12 Tajikistan 47.40355 71.00712
## 13 Lebanon 47.52393 79.49824
## 14 Bahrain 98.74000 76.86520
## 15 Ireland 98.87529 81.50244
## 16 Greece 98.89577 81.58780
## 17 United States 99.02272 78.74146
## 18 Germany 99.21542 81.09024
## 19 Israel 99.39000 82.05122
## 20 Cyprus 99.60259 80.29105
## 21 Malta 99.94286 81.94634
## 22 Netherlands 99.95510 81.70732
## 23 Kuwait 100.00000 74.74683
## 24 Liechtenstein 100.00000 82.07317
## 25 New Zealand 100.00000 81.45683
Top and Bottom 10 Countries Clean Water Access Combined
p8<-cw_top_bottom %>%
ggplot()+
geom_point(aes(x=wateraccess,y=lifeexpectancy,size=wateraccess,col=country), alpha=0.72)+
ggtitle("Life Expectancy by Water Access")
ggplotly(p8)
boxplot(sw_le2$wateraccess, sw_le2$lifeexpectancy)
plot1 <- ggplot(cw_bottom10, aes(x = wateraccess, y = lifeexpectancy))+
geom_boxplot()+
ggtitle("Bottom 10 Water Access")+
ylab("Life Expectancy (years)")+
xlab("Clean Water Access (% Pop.)")
plot2 <- ggplot(cw_top10, aes(x = wateraccess, y = lifeexpectancy))+
geom_boxplot()+
ggtitle("Top 10 Water Access")+
ylab("Life Expectancy (years)")+
xlab("Clean Water Access (% Pop.)")
plot_grid(plot1, plot2, labels = "AUTO")
## Warning: Continuous x aesthetic -- did you forget aes(group=...)?
## Warning: Continuous x aesthetic -- did you forget aes(group=...)?
# Trying Cowplot - did not make a difference here
plot1 <- ggplot(cw_bottom10, aes(x = wateraccess, y = lifeexpectancy))+
geom_boxplot()+
ggtitle("Bottom 10 Water Access")+
ylab("Life Expectancy (years)")+
xlab("Clean Water Access (% Pop.)")
plot2 <- ggplot(cw_top10, aes(x = wateraccess, y = lifeexpectancy))+
geom_boxplot()+
ggtitle("Top 10 Water Access")+
ylab("Life Expectancy (years)")+
xlab("Clean Water Access (% Pop.)")
cowplot::plot_grid(plot1, plot2, labels = "AUTO")
## Warning: Continuous x aesthetic -- did you forget aes(group=...)?
## Warning: Continuous x aesthetic -- did you forget aes(group=...)?
theme(plot.title = element_text(hjust = 0.5))
## List of 1
## $ plot.title:List of 11
## ..$ family : NULL
## ..$ face : NULL
## ..$ colour : NULL
## ..$ size : NULL
## ..$ hjust : num 0.5
## ..$ vjust : NULL
## ..$ angle : NULL
## ..$ lineheight : NULL
## ..$ margin : NULL
## ..$ debug : NULL
## ..$ inherit.blank: logi FALSE
## ..- attr(*, "class")= chr [1:2] "element_text" "element"
## - attr(*, "class")= chr [1:2] "theme" "gg"
## - attr(*, "complete")= logi FALSE
## - attr(*, "validate")= logi TRUE
cw_top_bottom %>%
ggplot(aes(wateraccess,lifeexpectancy)) +
geom_point()+
geom_smooth(method=lm,se=T)+
ggtitle("Life Expectancy by Clean Water Access")+
theme(plot.title = element_text(hjust = 0.5))+
xlab("Clean Water Access (% Pop.)")+
ylab("Life Expectancy")
## `geom_smooth()` using formula 'y ~ x'
Exploring the middle
cw_middle <- sw_le2 %>%
filter(wateraccess<90 & wateraccess>70)
cw_middle
## country wateraccess lifeexpectancy
## 1 Colombia 71.12262 74.15620
## 2 Azerbaijan 71.51716 71.84520
## 3 Georgia 72.97048 72.96612
## 4 Ecuador 74.36078 76.08151
## 5 Russia 75.51010 70.90854
## 6 Hungary 81.54177 70.90854
## 7 Estonia 81.69058 77.13171
## 8 Latvia 81.93607 74.12439
## 9 Macedonia, FYR 83.48981 75.52432
## 10 Turkmenistan 86.14747 67.64105
## 11 Romania 87.77910 67.64105
## 12 Serbia 88.14379 75.48780
## 13 Oman 88.50000 77.12183
## 14 Bosnia and Herzegovina 88.57549 76.64029
## 15 Montenegro 89.70820 76.86432
## 16 Costa Rica 89.80124 79.60890
p9<-cw_middle %>%
ggplot()+
geom_point(aes(x=wateraccess,y=lifeexpectancy,size=wateraccess,col=country), alpha=0.72)+
ggtitle("Life Expectancy by Water Access")
ggplotly(p9)
cw_middle%>%
ggplot(aes(wateraccess,lifeexpectancy)) +
geom_point()+
geom_smooth(method=lm,se=T)+
ggtitle("Life Expectancy by Clean Water Access")+
theme(plot.title = element_text(hjust = 0.5))+
xlab("Clean Water Access (% Pop.)")+
ylab("Life Expectancy")
## `geom_smooth()` using formula 'y ~ x'
Statistics
m1 <- lm(wateraccess~lifeexpectancy, cw_bottom10)
summary(m1)
##
## Call:
## lm(formula = wateraccess ~ lifeexpectancy, data = cw_bottom10)
##
## Residuals:
## Min 1Q Median 3Q Max
## -15.235 -5.199 3.451 6.129 12.240
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -45.6814 32.9682 -1.386 0.2033
## lifeexpectancy 1.0991 0.5008 2.195 0.0595 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 9.778 on 8 degrees of freedom
## Multiple R-squared: 0.3758, Adjusted R-squared: 0.2978
## F-statistic: 4.816 on 1 and 8 DF, p-value: 0.0595
m2 <- lm(wateraccess~lifeexpectancy, cw_top10)
summary(m2)
##
## Call:
## lm(formula = wateraccess ~ lifeexpectancy, data = cw_top10)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.64583 -0.45755 0.00614 0.46096 0.66550
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 97.52346 5.41611 18.01 5.98e-09 ***
## lifeexpectancy 0.02423 0.06739 0.36 0.727
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.5264 on 10 degrees of freedom
## Multiple R-squared: 0.01276, Adjusted R-squared: -0.08596
## F-statistic: 0.1293 on 1 and 10 DF, p-value: 0.7267
m3 <- lm(wateraccess~lifeexpectancy, cw_middle)
summary(m3)
##
## Call:
## lm(formula = wateraccess ~ lifeexpectancy, data = cw_middle)
##
## Residuals:
## Min 1Q Median 3Q Max
## -10.9823 -5.9208 0.8609 5.3109 8.7943
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 46.5913 38.0029 1.226 0.240
## lifeexpectancy 0.4789 0.5127 0.934 0.366
##
## Residual standard error: 6.894 on 14 degrees of freedom
## Multiple R-squared: 0.05866, Adjusted R-squared: -0.008582
## F-statistic: 0.8724 on 1 and 14 DF, p-value: 0.3661
m4 <- lm(wateraccess~lifeexpectancy, data = sw_le2)
summary(m4)
##
## Call:
## lm(formula = wateraccess ~ lifeexpectancy, data = sw_le2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -43.107 -6.213 2.114 7.393 32.846
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -143.0949 20.5176 -6.974 8.37e-10 ***
## lifeexpectancy 2.9400 0.2701 10.884 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 15.83 on 79 degrees of freedom
## Multiple R-squared: 0.5999, Adjusted R-squared: 0.5949
## F-statistic: 118.5 on 1 and 79 DF, p-value: < 2.2e-16
m5 <- lm(wateraccess~lifeexpectancy, data = sw_le3)
summary(m5)
##
## Call:
## lm(formula = wateraccess ~ lifeexpectancy, data = sw_le3)
##
## Residuals:
## Min 1Q Median 3Q Max
## -43.107 -6.213 2.114 7.393 32.846
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -143.0949 20.5176 -6.974 8.37e-10 ***
## lifeexpectancy 2.9400 0.2701 10.884 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 15.83 on 79 degrees of freedom
## Multiple R-squared: 0.5999, Adjusted R-squared: 0.5949
## F-statistic: 118.5 on 1 and 79 DF, p-value: < 2.2e-16
chisq.test(sw_le2$wateraccess, sw_le2$lifeexpectancy)
## Warning in chisq.test(sw_le2$wateraccess, sw_le2$lifeexpectancy): Chi-squared
## approximation may be incorrect
##
## Pearson's Chi-squared test
##
## data: sw_le2$wateraccess and sw_le2$lifeexpectancy
## X-squared = 5994, df = 5928, p-value = 0.2709
chisq.test(cw_top10$wateraccess, cw_top10$lifeexpectancy)
## Warning in chisq.test(cw_top10$wateraccess, cw_top10$lifeexpectancy): Chi-
## squared approximation may be incorrect
##
## Pearson's Chi-squared test
##
## data: cw_top10$wateraccess and cw_top10$lifeexpectancy
## X-squared = 108, df = 99, p-value = 0.252
chisq.test(cw_bottom10$wateraccess, cw_bottom10$lifeexpectancy)
## Warning in chisq.test(cw_bottom10$wateraccess, cw_bottom10$lifeexpectancy): Chi-
## squared approximation may be incorrect
##
## Pearson's Chi-squared test
##
## data: cw_bottom10$wateraccess and cw_bottom10$lifeexpectancy
## X-squared = 90, df = 81, p-value = 0.2313
chisq.test(cw_middle$wateraccess, cw_middle$lifeexpectancy)
## Warning in chisq.test(cw_middle$wateraccess, cw_middle$lifeexpectancy): Chi-
## squared approximation may be incorrect
##
## Pearson's Chi-squared test
##
## data: cw_middle$wateraccess and cw_middle$lifeexpectancy
## X-squared = 208, df = 195, p-value = 0.249
DS Plot
library("dslabs")
ds_theme_set()
sw_le2 %>% ggplot(aes(x = wateraccess, y = lifeexpectancy, label = country)) +
geom_abline(lty=2, col="darkgrey") +
geom_point(aes(color=country), size = 3) +
xlab("Clean Water Access ") +
ylab("Life Expectancy") +
ggtitle("Clean water access & Life expectancy") +
scale_color_discrete(name="country")
ds_theme_set()
sw_le2 %>% ggplot(aes(x = wateraccess, y = lifeexpectancy, label = country)) +
geom_point(aes(color=country))
ds_theme_set(new = "theme_bw", args = NULL, base_size = 11, bold_title = TRUE)
p11<-sw_le3 %>%
ggplot()+
geom_point(aes(x=wateraccess,y=lifeexpectancy,size=country,col=continent, guides=continent), alpha=0.5)+
ggtitle("Life Expect. by Water Access")+
xlab("Clean Water Access (% Pop.)")+
ylab("Life Expectancy (years)")+
scale_fill_brewer()
## Warning: Ignoring unknown aesthetics: guides
ggplotly(p11)
## Warning: Using size for a discrete variable is not advised.
p12<-sw_le3 %>%
ggplot()+
geom_point(aes(x=wateraccess,y=lifeexpectancy, size=wateraccess, fill=continent), alpha=0.5)+
ggtitle("Life Expect. by Water Access")+
xlab("Clean Water Access (% Pop.)")+
ylab("Life Expectancy (years)")+
scale_fill_brewer()
ggplotly(p12)
p12a<-sw_le3 %>%
ggplot()+
geom_point(aes(x=wateraccess,y=lifeexpectancy, size=wateraccess, fill=continent), alpha=0.5)+
geom_abline()+
ggtitle("Life Expect. by Water Access")+
xlab("Clean Water Access (% Pop.)")+
ylab("Life Expectancy (years)")+
scale_fill_brewer()
ggplotly(p12a)
p13<-sw_le3 %>%
ggplot()+
geom_point(aes(x=wateraccess,y=lifeexpectancy,size=country, fill=continent), alpha=0.5)+
geom_abline(method=m5)+
lims(x=c(0, 100), y = c(0, 100))+
ggtitle("Life Expect. by Water Access")+
xlab("Clean Water Access (% Pop.)")+
ylab("Life Expectancy (years)")+
scale_fill_brewer()
## Warning: Ignoring unknown parameters: method
ggplotly(p13)
## Warning: Using size for a discrete variable is not advised.
p13a<-sw_le3 %>%
ggplot()+
geom_point(aes(x=wateraccess,y=lifeexpectancy,size=country, fill=continent), alpha=0.5)+
geom_jitter(aes(x=wateraccess, y=lifeexpectancy))+
lims(x=c(0, 100), y = c(0, 100))+
ggtitle("Life Expect. by Water Access")+
xlab("Clean Water Access (% Pop.)")+
ylab("Life Expectancy (years)")+
scale_fill_brewer()
ggplotly(p13a)
## Warning: Using size for a discrete variable is not advised.
Final Plot for Analysis:
p13b<-sw_le3 %>%
ggplot()+
geom_point(aes(x=wateraccess,y=lifeexpectancy,size=country, fill=continent), alpha=0.5)+
geom_smooth(aes(x=wateraccess, y=lifeexpectancy),se=FALSE, lwd=0.5, col="black")+
geom_abline(method=lm)+
lims(x=c(0, 100), y = c(0, 100))+
ggtitle("Life Expect. by Water Access")+
xlab("Clean Water Access (% Pop.)")+
ylab("Life Expectancy (years)")+
scale_fill_brewer()
## Warning: Ignoring unknown parameters: method
ggplotly(p13b)
## Warning: Using size for a discrete variable is not advised.
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
Final Plot for Analysis Refined and Zoomed In:
legendtheme <- theme(legend.title = element_text(color = "black", size = 10, face = "bold"), legend.position="bottom")+
theme_replace(legend.title = "Continent")
# guide_legend(title=waiver())
# text='<b> Continent </b>'
# fig <- fig %>% layout(legend=list(title=list(text='<b> Trend </b>')))
# %+replace%
p14<-sw_le3 %>%
ggplot()+
legendtheme+
geom_point(aes(x=wateraccess, y=lifeexpectancy, size=wateraccess, fill=continent, text = paste("Country:", country, '</br>', '</br>Clean Water Access:', wateraccess, '</br>Life Expectancy:', lifeexpectancy)), alpha=0.5)+
geom_smooth(aes(x=wateraccess, y=lifeexpectancy),se=FALSE, lwd=0.5, col="black")+
geom_abline(method=lm)+
ggtitle("Life Expectancy & Clean Water Access")+
theme(plot.title = element_text(hjust = 0.5))+
labs(x="Clean Water Access (% Pop.)", y="Life Expectancy (years)", col="Continent", guide_legend="Continent")+
scale_fill_brewer()
## Warning: Ignoring unknown aesthetics: text
## Warning: Ignoring unknown parameters: method
ggplotly(p14, tooltip = "text")
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'