Gráficos con plotly y ggpubr
Cristian Camilo García Barrero
21/9/2020
Integral
y1 = function(x){(x^2)/2} # f(x)
y2 = function(x){2*x}# g(x)
curve(y1, from = 0, to=4, ylim=c(0,12))
curve(y2, from = 0, to=4, add = T, col='red')
x=seq(0,4, l=100)
polygon(rep(x,2),c(y1(x),y2(x)), density = 10, col = 'darkcyan')
text(2,3, 'A')
text(1,4, 'f(x)', pos=3)
text(2,3, 'g(x)', pos=3)
x0 = 2.5
segments(x0,y1(x0),x0,y2(x0), col = 'red')
x0 = 2.6
segments(x0,y1(x0),x0,y2(x0), col = 'red')
text(2.55, y2(2.55), 'dx', pos =3)
\[dA = (y_2-y_1)*dx\] \[A = \int dA = \int (y_2-y_1)\ dx = \int ((2x)-(\frac{x^2}{2}))\ dx\] \[A = \int_{0}^{4} ((2x)-(\frac{x^2}{2}))\ dx = x^2-\frac{x^3}{6}|_{0}^{4} = \frac{16}{3}\]
y1 = function(x){(x^2)/2} # f(x)
y2 = function(x){2*x}# g(x)
curve(y1, from = 0, to=4, ylim=c(0,9))
curve(y2, from = 0, to=4, add = T, col='red')
abline(h = c(0,8), v = c(0,4))
n = 100000
xn = runif(n, 0, 4)
yn = runif(n, 0, 8)
points(xn,yn, pch = 16, cex = 0.1, col=gray(0.5))
curve(y1, from = 0, to=4, add=T, col='yellow')
curve(y2, from = 0, to=4, add = T, col='yellow')
men_y2 = sum(yn<=y2(seq(0,4,l=n)))
men_y1 = sum(yn<=y1(seq(0,4,l=n)))
men_y1;men_y2## [1] 33175## [1] 49968# Puntos dentro de A es la diferencia
puntos_A = men_y2 - men_y1
puntos_A## [1] 16793\[A_32 \rightarrow 100000 Puntos\\ A_x \rightarrow 16708 Puntos\]
areas = c(Ae = puntos_A*32/n, Ar = 16/3)
areas## Ae Ar
## 5.373760 5.333333Area bajo la curva del progreso escalonado de una enfermedad - AUDPS
library(agricolae)
dates<-c(14,21,28) # days
# example 1: evaluation - vector
evaluation<-c(40,80,90)
audps(evaluation,dates)## evaluation
## 1470audps(evaluation,dates,"relative")## evaluation
## 0.7x<-seq(10.5,31.5,7)
y<-c(40,80,90,90)
plot(x,y,"s",ylim=c(0,100),xlim=c(10,32),axes=FALSE,col="red" ,ylab="",xlab="")
title(cex.main=0.8,main="Absolute or Relative AUDPS\nTotal area=(31.5-10.5)*100=2100",
ylab="evaluation",xlab="dates" )
points(x,y,type="h")
z<-c(14,21,28)
points(z,y[-3],col="blue",lty=2,pch=19)
points(z,y[-3],col="blue",lty=2,pch=19)
axis(1,x,pos=0)
axis(2,c(0,40,80,90,100),las=2)
text(dates,evaluation+5,dates,col="blue")
text(14,20,"A = (17.5-10.5)*40",cex=0.8)
text(21,40,"B = (24.5-17.5)*80",cex=0.8)
text(28,60,"C = (31.5-24.5)*90",cex=0.8)
text(14,95,"audps = A+B+C = 1470")
text(14,90,"relative = audps/area = 0.7")
# It calculates audpc absolute
absolute<-audps(evaluation,dates,type="absolute")
print(absolute)## evaluation
## 1470rm(evaluation, dates, absolute)library(agricolae)
dates<-c(14,21,28) # days
# example 1: evaluation - vector
evaluation<-c(40,80,90)
audps(evaluation,dates)## evaluation
## 1470audps(evaluation,dates,"relative")## evaluation
## 0.7mi_audps<- function(t,y){
audps(y,t)
}
tiempos<-seq(7,70,7)
daño<-sort(runif(n=length(tiempos), min=5,80))
mi_audps(tiempos,daño)## evaluation
## 3216.481#################################################
options(digits = 6)
mi_trat_audps<- function(yt){
salida<-c()
t<-unlist(yt[,1])
print(t)
for (i in seq(2,ncol(yt))) {
salida[i]<-mi_audps(t = t, unlist(yt[,i]))
}
salida=salida[-1]
return(salida)
}
library(readxl)
Datos <- read_excel("Datos.xlsx", sheet = "Hoja1")
df_da<-Datos
df_da## # A tibble: 10 x 4
## tiempo D_t1 D_t2 D_t3
## <dbl> <dbl> <dbl> <dbl>
## 1 7 6.20 16.6 6.55
## 2 14 11.5 39.8 7.25
## 3 21 16.4 49.8 13.2
## 4 28 18.2 51.3 46.7
## 5 35 36.6 52.8 49.7
## 6 42 48.8 62.4 62.5
## 7 49 56.8 64.6 67.5
## 8 56 63.8 66.0 68.3
## 9 63 72.2 71.5 69.0
## 10 70 74.4 75.7 76.5mi_trat_audps(df_da)## tiempo1 tiempo2 tiempo3 tiempo4 tiempo5 tiempo6 tiempo7 tiempo8
## 7 14 21 28 35 42 49 56
## tiempo9 tiempo10
## 63 70## [1] 2833.41 3854.11 3269.99audps_trat<-mi_trat_audps(df_da)## tiempo1 tiempo2 tiempo3 tiempo4 tiempo5 tiempo6 tiempo7 tiempo8
## 7 14 21 28 35 42 49 56
## tiempo9 tiempo10
## 63 70df_text<-data.frame(label=round(audps_trat,2), trat=1:3)
dfg<-data.frame(t= rep(df_da$tiempo,3),
y= c(df_da$D_t1,df_da$D_t2,df_da$D_t3),
trat= rep(1:3, each=nrow(df_da)))
library(ggplot2)
ggplot(data=dfg, aes(x=t,y=y))+
geom_line()+
geom_text(data = df_text,
mapping = aes(x=20,y=60,label=label))+
facet_wrap(~trat)
Gráfico con ggpubr
library(ggpubr)
p <- ggboxplot(dfg, x = 'trat', y = 'y',
color = 'trat', palette =c("#00AFBB", "#E7B800", "#FC4E07"),
add = "jitter", main='Diagrama de caja',xlab = 'Tratamientos',
ylab = 'Daño (%)')
p
Gráfico con plotly
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':
##
## layoutg <- ggplot(dfg, aes(x=t,y=y))+
geom_line()+
geom_text(data = df_text,
mapping = aes(x=20,y=60,label=label))+
facet_wrap(~trat)
ggplotly(g)## Warning: `group_by_()` is deprecated as of dplyr 0.7.0.
## Please use `group_by()` instead.
## See vignette('programming') for more help
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_warnings()` to see where this warning was generated.diass<-seq(7,70,7)
dt1<-c(dfg$y[1:10])
dt2<-c(dfg$y[11:20])
dt3<-c(dfg$y[21:30])
dfgt<-data.frame(diass,dt1,dt2,dt3)
fig <- plot_ly(dfgt, x = ~diass, y = ~dt1, name = 'Tratamiento 1', type = 'scatter', mode = 'lines')
fig <- fig %>% add_trace(y = ~dt2, name = 'Tratamiento 2', mode = 'lines+markers')
fig <- fig %>% add_trace(y = ~dt3, name = 'Tratamiento 3', mode = 'lines+markers')
fig <- fig %>% layout(title = "Porcentaje de daño vs Tiempo",
xaxis = list(title = "Dias"),
yaxis = list (title = "Daño (%)"))
fig## Warning: `arrange_()` is deprecated as of dplyr 0.7.0.
## Please use `arrange()` instead.
## See vignette('programming') for more help
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_warnings()` to see where this warning was generated.