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library(aqm)

## 
## Attaching package: 'aqm'

## The following object is masked from 'package:stats':
## 
##     dt

library(ggplot2)
data(sleep)

sleep$logBodyWt <- log10(sleep$BodyWt)
sleep$logBrainWt <- log10(sleep$BrainWt)

dt(sleep)

fig1<-ggplot(sleep,aes(x=logBodyWt,y=logBrainWt)) +geom_point()
fig1

fig1 +geom_smooth()

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

fig1 +geom_smooth(method="lm")

mod<-lm(data=sleep,logBrainWt~logBodyWt)
summary(mod)

## 
## Call:
## lm(formula = logBrainWt ~ logBodyWt, data = sleep)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.74953 -0.22133 -0.02063  0.18531  0.82617 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.92489    0.04670   19.80   <2e-16 ***
## logBodyWt    0.76407    0.03153   24.23   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.3168 on 51 degrees of freedom
## Multiple R-squared:  0.9201, Adjusted R-squared:  0.9185 
## F-statistic: 587.2 on 1 and 51 DF,  p-value: < 2.2e-16

sleep$residuals<-residuals(mod)
sleep$rank<-rank(-sleep$residuals)
dt(sleep)

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

fig1 <- ggplot(sleep,aes(x=logBodyWt,y=logBrainWt, label=Species)) +geom_point()

fig1 + geom_smooth(method="lm") ->fig2
ggplotly(fig2)