adding ggplot script

plots_stats.R

+library(readr)
+library(ggplot2)
+library(ggthemes)
+library(foreach)
+library(scales)
+library(dplyr)
+
+csv=c("/home/je/Bureau/lab5/plots/niakhar_2017_radar_toggplot.csv","/home/je/Bureau/lab5/plots/niakhar_2017_radar_notree_toggplot.csv",
+      "/home/je/Bureau/lab5/plots/niakhar_2018_radar_toggplot.csv","/home/je/Bureau/lab5/plots/niakhar_2018_radar_notree_toggplot.csv",
+      "/home/je/Bureau/lab5/plots/nioro_2018_radar_toggplot.csv","/home/je/Bureau/lab5/plots/nioro_2018_radar_notree_toggplot.csv")
+
+foreach(j=1:length(csv)) %do% {
+  df <- read.csv(csv[j], colClasses = c("Date","character","character","Date","Date","double","double"))
+  id<- unique(df$Plot)
+  outName <- unlist(strsplit(csv[j],"/"))[7]
+  outName <- gsub(".csv",".pdf",outName)
+  
+  pdf(paste("/home/je/Bureau/lab5/plots/",outName,sep=""), width = 8.98, height = 5.86, title = "Plot mean")
+  foreach(i=1:length(id)) %do% {
+    plot <- df[which(df$Plot==id[i]),]
+    p <- ggplot(data=plot, aes(x=Date, y=Mean)) + 
+      geom_vline(xintercept = unique(plot$Sowing), colour="grey50", size=1) +
+      geom_vline(xintercept = unique(plot$Harvest), colour="grey50", size=1) +
+      geom_point(aes(color=Band)) +
+      geom_line(aes(color=Band)) +
+      geom_ribbon(aes(ymin=Mean-Std,ymax=Mean+Std,fill=Band),alpha=0.2) + 
+      ggtitle(plot$Plot) + ylab("Value") + theme_bw() + 
+      scale_x_date(name="Time", breaks = date_breaks("months"),labels = date_format("%b")) +
+      theme(axis.text.x=element_text(size=15),
+            axis.text.y=element_text(size=15),
+            axis.title=element_text(size=18),
+            legend.title=element_text(size=18),
+            legend.text=element_text(size=15))
+    print(p)
+  }
+  dev.off()
+}
+
+csv=c("/home/je/Bureau/lab5/plots/niakhar_2017_opt_toggplot.csv","/home/je/Bureau/lab5/plots/niakhar_2017_opt_notree_toggplot.csv",
+      "/home/je/Bureau/lab5/plots/niakhar_2017_opt_gapf_toggplot.csv","/home/je/Bureau/lab5/plots/niakhar_2017_opt_gapf_notree_toggplot.csv",
+      "/home/je/Bureau/lab5/plots/niakhar_2018_opt_toggplot.csv","/home/je/Bureau/lab5/plots/niakhar_2018_opt_notree_toggplot.csv",
+      "/home/je/Bureau/lab5/plots/niakhar_2018_opt_gapf_toggplot.csv","/home/je/Bureau/lab5/plots/niakhar_2018_opt_gapf_notree_toggplot.csv",
+      "/home/je/Bureau/lab5/plots/nioro_2018_opt_toggplot.csv","/home/je/Bureau/lab5/plots/nioro_2018_opt_notree_toggplot.csv",
+      "/home/je/Bureau/lab5/plots/nioro_2018_opt_gapf_toggplot.csv","/home/je/Bureau/lab5/plots/nioro_2018_opt_gapf_notree_toggplot.csv")
+
+foreach(j=1:length(csv)) %do% {
+  df <- read.csv(csv[j], colClasses = c("Date","character","character","Date","Date","double","double"))
+  df <- filter(df, Band %in% c("B2","B3","B4","B8","B5","B6","B7","B8A","B11","B12"))
+  id<- unique(df$Plot)
+  outName <- unlist(strsplit(csv[j],"/"))[7]
+  outName <- gsub(".csv","_Bands.pdf",outName)
+  
+  pdf(paste("/home/je/Bureau/lab5/plots/",outName,sep=""), width = 8.98, height = 5.86, title = "Plot mean")
+  foreach(i=1:length(id)) %do% {
+    plot <- df[which(df$Plot==id[i]),]
+    p <- ggplot(data=plot, aes(x=Date, y=Mean)) + 
+      geom_vline(xintercept = unique(plot$Sowing), colour="grey50", size=1) +
+      geom_vline(xintercept = unique(plot$Harvest), colour="grey50", size=1) +
+      geom_point(aes(color=Band)) +
+      geom_line(aes(color=Band)) +
+      geom_ribbon(aes(ymin=Mean-Std,ymax=Mean+Std,fill=Band),alpha=0.2) + 
+      ggtitle(plot$Plot) + ylab("Value") + theme_bw() + 
+      scale_x_date(name="Time", breaks = date_breaks("months"),labels = date_format("%b")) +
+      theme(axis.text.x=element_text(size=15),
+            axis.text.y=element_text(size=15),
+            axis.title=element_text(size=18),
+            legend.title=element_text(size=18),
+            legend.text=element_text(size=15))
+    print(p)
+  }
+  dev.off()
+}
+
+foreach(j=1:length(csv)) %do% {
+  df <- read.csv(csv[j], colClasses = c("Date","character","character","Date","Date","double","double"))
+  df <- filter(df, Band %in% c("NDVI","NDWI","CIGreen","CIRedEdge","EVI","MSAVI2","GDVI"))
+  id<- unique(df$Plot)
+  outName <- unlist(strsplit(csv[j],"/"))[7]
+  outName <- gsub(".csv","_Indices.pdf",outName)
+  
+  pdf(paste("/home/je/Bureau/lab5/plots/",outName,sep=""), width = 8.98, height = 5.86, title = "Plot mean")
+  foreach(i=1:length(id)) %do% {
+    plot <- df[which(df$Plot==id[i]),]
+    p <- ggplot(data=plot, aes(x=Date, y=Mean)) + 
+      geom_vline(xintercept = unique(plot$Sowing), colour="grey50", size=1) +
+      geom_vline(xintercept = unique(plot$Harvest), colour="grey50", size=1) +
+      geom_point(aes(color=Band)) +
+      geom_line(aes(color=Band)) +
+      geom_ribbon(aes(ymin=Mean-Std,ymax=Mean+Std,fill=Band),alpha=0.2) + 
+      ggtitle(plot$Plot) + ylab("Value") + theme_bw() + 
+      scale_x_date(name="Time", breaks = date_breaks("months"),labels = date_format("%b")) +
+      theme(axis.text.x=element_text(size=15),
+            axis.text.y=element_text(size=15),
+            axis.title=element_text(size=18),
+            legend.title=element_text(size=18),
+            legend.text=element_text(size=15))
+    print(p)
+  }
+  dev.off()
+}
+
+
+
+