setwd("/Volumes/GoogleDrive/Mi unidad/Agrosavia/Env_muestra/data")
datos<-read.table("testagran.csv", header=T, sep=',')
datos$curva <- factor(datos$curva, levels = c("1", "2"), 
                      labels = c("P3", "P1"))
datos$gen<-as.factor(datos$gen)
datos$curva<-as.factor(datos$curva)
datos$id<-as.factor(datos$id)
datos$muestra<-as.factor(datos$muestra)
datos$dia<-as.factor(datos$dia)
library(ggplot2)
library(Rmisc)
## Loading required package: lattice
## Loading required package: plyr
library(dplyr)
## 
## Attaching package: 'dplyr'
## The following objects are masked from 'package:plyr':
## 
##     arrange, count, desc, failwith, id, mutate, rename, summarise,
##     summarize
## The following objects are masked from 'package:stats':
## 
##     filter, lag
## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union
library(tidyverse)
## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.1 ──
## ✓ tibble  3.1.6     ✓ purrr   0.3.4
## ✓ tidyr   1.1.4     ✓ stringr 1.4.0
## ✓ readr   2.1.1     ✓ forcats 0.5.1
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## x dplyr::arrange()   masks plyr::arrange()
## x purrr::compact()   masks plyr::compact()
## x dplyr::count()     masks plyr::count()
## x dplyr::failwith()  masks plyr::failwith()
## x dplyr::filter()    masks stats::filter()
## x dplyr::id()        masks plyr::id()
## x dplyr::lag()       masks stats::lag()
## x dplyr::mutate()    masks plyr::mutate()
## x dplyr::rename()    masks plyr::rename()
## x dplyr::summarise() masks plyr::summarise()
## x dplyr::summarize() masks plyr::summarize()
library(ggpubr)
## 
## Attaching package: 'ggpubr'
## The following object is masked from 'package:plyr':
## 
##     mutate
library(rstatix)
## 
## Attaching package: 'rstatix'
## The following objects are masked from 'package:plyr':
## 
##     desc, mutate
## The following object is masked from 'package:stats':
## 
##     filter
## Gráficas por réplica y genotipo
datos$dia<-as.numeric(as.character(datos$dia))
##Gráfica por réplica compuesta
pht<- ggplot(datos, aes(x = dia)) +
  facet_grid(curva~gen*muestra) +
  geom_line(aes(y=cd.testa, colour = "cd.testa")) +
  geom_point(aes(y=cd.testa, colour = "cd.testa")) +
  scale_y_continuous(name = expression("Cd (mg*Kg"^"-1)")) +  # Etiqueta de la variable continua
  scale_x_continuous(name = "día", breaks=seq(0,7,1)) + # Etiqueta de los grupos
  theme(legend.position="bottom") +
  theme(axis.line = element_line(colour = "black", # Personalización del tema
                                 size = 0.25)) +
  theme(text = element_text(size = 12))

pht+ geom_line(aes(y = cd.grano, colour="cd.grano")) + geom_point(aes(y=cd.grano, colour = "cd.grano")) + 
  theme(legend.position="bottom")

## Gráfica por genotipo

datos4<-summarySE (datos, measurevar = "cd.testa", groupvars = c("curva", "gen","dia"))
datos5<-summarySE (datos, measurevar = "cd.grano", groupvars = c("curva", "gen","dia"))
datos4$ID <- seq.int(nrow(datos4))
datos5$ID <- seq.int(nrow(datos5))
total2 <- merge (datos4, datos5, by = "ID")


pht2<- ggplot(total2, aes(x = dia.x)) +
  facet_grid(curva.x~gen.x) +
  geom_errorbar(aes(ymin=cd.testa-ci.x, ymax=cd.testa+ci.x, colour="cd.testa"), width=.1) +
  geom_line(aes(y=cd.testa, colour="cd.testa")) +
  geom_point(aes(y=cd.testa, colour="cd.testa")) +
  scale_y_continuous(name = expression("Cd (mg*Kg"^"-1)")) +  # Etiqueta de la variable continua
  scale_x_continuous(name = "día", breaks=seq(0,7,1)) + # Etiqueta de los grupos
  theme(legend.position="bottom") +
  theme(axis.line = element_line(colour = "black", # Personalización del tema
                                 size = 0.25)) +
  theme(text = element_text(size = 15))

pht2+ geom_errorbar(aes(ymin=cd.grano-ci.y, ymax=cd.grano+ci.y, colour="cd.grano"), width=.1) +
  geom_line(aes(y=cd.grano, colour="cd.grano")) +
  geom_point(aes(y=cd.grano, colour = "cd.grano"))