Grupo R : RMyP

#####
library(ggplot2)
 
data = structure(list(Trt = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L), .Label = c("A",
"B"), class = "factor"), Clone = structure(c(1L, 2L,
3L, 4L, 5L, 6L, 7L, 8L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L), .Label =
c("1",
"2", "3", "4", "5", "6", "7", "8",
"Mean"), class = "factor"), x = c(-27.5996, -27.5333, -27.0267,
-27.6467, -26.7667, -28.07, -27.4608, -28.1867, -29.3833, -28.2196,
-29.6567, -28.9608, -29.6167, -30.1892, -28.5633, -30.2208),
    x.SE = c(0.3603, 0.3085, 0.3085, 0.3085, 0.3085, 0.3085,
    0.4831, 0.3085, 0.3085, 0.3601, 0.3085, 0.3603, 0.3085, 0.2402,
    0.3085, 0.3686), y = c(98.12, 69.84, 78.47, 68.03, 58.2,
    33.39, 46.57, 65.75, 40.01, 38.23, 34.09, 44.37, 31.92, 39.85,
    34.37, 41.27), y.SE = c(15.32, 12.51, 12.51, 12.51, 12.51,
    12.51, 15.32, 12.51, 12.51, 15.32, 12.51, 15.32, 12.51, 12.51,
    12.51, 15.32)), .Names = c("Trt", "Clone", "x", "x.SE",
"y", "y.SE"), class = "data.frame", row.names = 3:18)
 
# x and ylim for geom_errorbar with standard error
fig.xlim = aes(xmin = x - x.SE, xmax = x + x.SE, height=0)
fig.ylim = aes(ymin=y-y.SE, ymax=y+y.SE, height=0)
 
fig1 = ggplot(data, aes(x, y, shape=factor(Trt), fill=factor(Clone),
size=factor(Clone)) ) +
scale_shape_manual(values=c(23,21), "Treatment") +
scale_fill_brewer(palette="Set1", "Clone") +
theme_bw() +
coord_cartesian(xlim= c(-31,-26), ylim=c(0,120) ) +
scale_y_continuous(limits=c(0,120)) +
scale_x_continuous(limits=c(-31,-26)) +
geom_errorbar(fig.ylim, width=0, size=0.3,colour="black") +
geom_errorbarh(fig.xlim, size=0.3, colour="black") +
geom_point(aes(size=factor(Clone))) +
scale_size_manual(values=c(2,2,4,2,4,4,2,2), "Clone") +
opts(panel.grid.minor=theme_line(colour = NA, size = 0.0),
panel.grid.major=theme_line(colour = NA, size = 0.0),
strip.background = theme_rect(col="black",fill=NA))
fig1

############################ In Grey scale
 
fig2 = ggplot(data, aes(x, y, shape=factor(Trt))) +
scale_shape_manual(values=c(23,21), "Treatment") +
theme_bw() +
coord_cartesian(xlim= c(-31,-26), ylim=c(0,120) ) +
scale_y_continuous(limits=c(0,120)) +
scale_x_continuous(limits=c(-31,-26)) +
geom_errorbar(fig.ylim, width=0, size=0.3,colour="black") +
geom_errorbarh(fig.xlim, size=0.3, colour="black") +
geom_point(size=6, fill="white") +
geom_text(aes(label=Clone),size=4) +
opts(panel.grid.minor=theme_line(colour = NA, size = 0.0),
panel.grid.major=theme_line(colour = NA, size = 0.0),
strip.background = theme_rect(col="black",fill=NA))
 
fig2

#################Grey scale2 y conservar  grid lines
 
fig3 = ggplot(data, aes(x, y, shape=factor(Trt), colour = factor(Trt))) +
  geom_errorbar(fig.ylim, width=0, size=0.3) +
  geom_errorbarh(fig.xlim, size=0.3) +
  geom_point(size=6, fill="white") +
  geom_text(aes(label=Clone),size=3, colour = "grey50") +
  scale_shape_manual(values=c(23,21), "Treatment") +
  scale_colour_grey("Treatment", end = 0.6) +
  theme_bw() 
fig3

 
#introducir tabla de datos 
 
 
   z<- structure(list(Intermoult = structure (c(1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 5L, 6L, 7L,
8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L),.Label = c("Instar1","Instar2","Instar3 ","Instar4","Instar5 ","Instar6","Instar7","Instar8",
"Instar9","Instar10","Instar11","Instar12","Instar13","Instar14" ,"Instar15 ","Instar16"),class = "factor"), 
Way = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L , 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L), 
.Label = c(" Initial 200 larvae", " 80 larvae transferred from the mass culture"), class = "factor"),
Mean = c(8.4,7.6,9.4,11.3,16.7,10.3,17.3,14.2,13.4,14.4,14.2,16.4,19.0,22.3,27.0,NA,16.7,17.0,18.3,15.0,15.6,15.1,14.7,17.6,21.1,18.8,27.0),
SD = c(0.5,1.3,1.0,1.6,2.4,1.7,4.0,3.5,2.1,4.1,2.5,2.2,3.5,3.9,0.0,NA,5.0,6.7,5.8,4.7,4.0,2.6,2.1,3.2,4.5,1.3,0.0),
upp = c(11,19,13,15,20,15,22,22,17,25,19,20,25,28,27,NA,24,33,30,28,26,20,19,23,30,20,27),
low = c(8,6,8,9,13,9,12,10,11,12,10,13,14,19,27,NA,12,10,11,8,11,11,11,13,16,17,27)) ,
class = "data.frame",  ,row.names = c(NA, -27L) )
colnames(z)<- c("Intermoult", "Way", "Mean", "SD", "upper", "lower")
 
###Crear plot
 
library(ggplot2)
 
pdf(file = "intermuda2.pdf", width = 6, height = 6, dpi= 600)    #Guardarlo como pdf
 
 
 
p<-ggplot(z,aes(x=Intermoult,y=Mean,ymin=Mean-SD,ymax=Mean+SD,
             groups=Way,colour=Way))+  
  geom_point(position=position_dodge(width=0.5))+ 
  geom_pointrange(width=0.5,position=position_dodge(width=0.5))+
   labs(x="Intermoult",y="Intermoult Period (Days)")+ theme_bw()+ opts(legend.position = c(0.18,0.6))+ #colocar la leyenda 
 scale_colour_manual("", c(" Initial 200 larvae" = "#5CB0E2", " 80 larvae transferred from the mass culture" = "darkblue"))
 
   p + ylim(c(-1, 30))
 
 
 
 
 #Características del plot#########################
 
last_plot() + opts(panel.grid.minor = theme_line(colour = NA),
panel.grid.major = theme_line(colour = NA),
plot.background =  theme_rect(colour = NA, fill = NA),
axis.title.x = theme_text( face = "bold"),
axis.title.y = theme_text( face = "bold", angle = 90),
legend.key = theme_rect ( colour = NA, fill = NA))
 
 
dev.off
 
#colocar graficos en una página (creamos un nuevo grid y seleccionamos la forma)
library(gridExtra)
grid.newpage()
print(grid.arrange(plot1, plot2, plot3,plot4, plot5, nrow=1, ncol=5))   #ponerlos en columnas y filas

#PARA HACER CURVAS DE LORENZ E INDICES GINI:
#1. Hace falta tener cargado el package "ineq"
library(ineq)
Lorenz <- read.table(file = "C:\\R.gps\\Lorenz.txt",header = TRUE)
names(Lorenz)
summary(Lorenz)
 
#Loop que hizo Inés para hacer las curvas y estimar los indices de las 31 patches de erizo
#y guardar los primeros en un pdf (sin el loop tendría que hacer un código para cada uno de los 31 patches):
giniIndex <- NULL
pdf("GraficosLorentz.pdf", width=11.69, height=8.26)
op <- par(mfrow=c(3,7))
for(i in 1:31){
Mtemp <-  subset(Lorenz,Mancha==as.character(i), select=4)#Columna4=n.celda
plot(Lc(Mtemp[,1]), main=paste("Mancha", i, sep=" "))
giniIndex[i] <- ineq(Mtemp[,1])
}
par(op)
dev.off()
 
#Para guardar los índices:
write(giniIndex,file="clipboard",ncolumns=1)
#o
write.csv(giniIndex,file="ginis.csv")
#Resultó mejor la 2º pq da los datos en una unica columna con el nº 
#de patch asociado
 
##FIGURA DE LOS 3 PATCHES MÁS VISITADOS (7,15Y22):
 
M7<-subset(Lorenz,Mancha=="7")
M15<-subset(Lorenz,Mancha=="15")
M22<-subset(Lorenz,Mancha=="22")
Lc.7<-Lc(M7$n.celda)
Lc.15<-Lc(M15$n.celda)
Lc.22<-Lc(M22$n.celda)
plot(Lc.7,ylab="",xlab="",main="")
lines(Lc.15,lty="dashed")
lines(Lc.22,lty="dotted")

library(nlme) 
fm1 <- lme(distance ~ age*Sex, random = ~ 1 + age | Subject, data = Orthodont) 
 
plot(Orthodont)
newdat <- expand.grid(age=c(8,10,12,14), Sex=c("Male","Female")) 
 
newdat$pred <- predict(fm1, newdat, level = 0)
 
Designmat <- model.matrix(eval(eval(fm1$call$fixed)[-2]), newdat[-3]) 
predvar <- diag(Designmat %*% fm1$varFix %*% t(Designmat)) 
newdat$SE <- sqrt(predvar) 
 
newdat$SE2 <- sqrt(predvar+fm1$sigma^2)
 
library(ggplot2) 
pd <- position_dodge(width=0.4) 
ggplot(newdat,aes(x=age,y=pred,colour=Sex))+ 
   geom_point(position=pd)+ 
  geom_linerange(aes(ymin=pred-2*SE,ymax=pred+2*SE), position=pd)
 
 
## prediction intervals 
ggplot(newdat,aes(x=age,y=pred,colour=Sex))+ 
   geom_point(position=pd)+ 
   geom_linerange(aes(ymin=pred-2*SE2,ymax=pred+2*SE2), position=pd)