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neurohazardous · Aug 11, 2021 · 06fdba8 · 06fdba8
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commit 06fdba8
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diff --git a/6_Plotting_results_with_ggplot.R b/6_Plotting_results_with_ggplot.R
@@ -89,3 +89,239 @@ ggplot(data=df_1, aes(x=feat, y=value, fill=feat)) + geom_bar(stat = "identity",
 # Second Component 
 ggplot(data=df_2, aes(x=feat, y=value, fill=feat)) + geom_bar(stat = "identity", width = 1) +theme_minimal() + theme(text = element_text(size=40), legend.title=element_blank(), legend.position = c(-1.7, -1.2)) + labs(y="Design Salience", x="",title="") + coord_flip() +   geom_errorbar(aes(ymin=lower, ymax=upper), width=.2) + scale_fill_manual(values=cbbPalette)
 
+
+############################### scatter plot of all fingerprinting  #######################################
+rm(df, df2, data_plot)
+df=read.csv("Desktop/OMEGA_Fingerprinting_data_two_session_for_plot.csv", header = TRUE)
+df$ACC=as.numeric(df$ACC)*100
+df$band= factor(df$band,levels = c("Fullband", "Delta", "Theta", "Alpha", "Beta", "Gamma", "High Gamma"))
+df$band=plyr::mapvalues(df$band, unique(df$band), c("Delta", "Theta", "Alpha", "Beta", "Gamma", "High\nGamma", "Broadband"))
+df$dataset=plyr::mapvalues(df$dataset, unique(df$dataset), c("   Dataset 1 -> 2", "   Dataset 2 -> 1"))
+
+data_plot=df[,1:4]
+df=read.csv("Desktop/OMEGA_Fingerprint_data_for_plot.csv", header = TRUE)
+df$ACC=as.numeric(df$ACC)*100
+df$band= factor(df$band,levels = c("Fullband", "Delta", "Theta", "Alpha", "Beta", "Gamma", "High Gamma"))
+df$band=plyr::mapvalues(df$band, unique(df$band), c("Delta", "Theta", "Alpha", "Beta", "Gamma", "High\nGamma", "Broadband"))
+df$dataset=plyr::mapvalues(df$dataset, unique(df$dataset), c("   Dataset 1 -> 2", "   Dataset 2 -> 1"))
+
+data_plot=rbind(data_plot,df[,1:4])
+
+df=read.csv("Desktop/OMEGA_Fingerprinting_data_short_for_plot.csv", header = TRUE, stringsAsFactors = FALSE)
+df$ACC=as.numeric(df$ACC)*100
+df$band= factor(df$band,levels = c("Fullband", "Delta", "Theta", "Alpha", "Beta", "Gamma", "High Gamma"))
+df$band=plyr::mapvalues(df$band, unique(df$band), c("Delta", "Theta", "Alpha", "Beta", "Gamma", "High\nGamma", "Broadband"))
+df$dataset[df$dataset=="d" & df$Dataset_parent=="D1D2"] = "   Dataset 1 -> 2"
+df$dataset[df$dataset=="t" & df$Dataset_parent=="D1D2"] = "   Dataset 2 -> 1"
+df$dataset[df$dataset=="d" & df$Dataset_parent=="D1D3"] = "   Dataset 1 -> 3"
+df$dataset[df$dataset=="t" & df$Dataset_parent=="D1D3"] = "   Dataset 3 -> 1"
+df$dataset[df$dataset=="d" & df$Dataset_parent=="D2D3"] = "   Dataset 2 -> 3"
+df$dataset[df$dataset=="t" & df$Dataset_parent=="D2D3"] = "   Dataset 3 -> 2"
+
+data_plot=rbind(data_plot,df[,2:5])
+
+df=read.csv("Desktop/OMEGA_short_review_complete_full.csv", header = TRUE, stringsAsFactors = FALSE)
+df2=read.csv("Desktop/OMEGA_fingerprinting_reviewer_short_2session.csv", header = TRUE, stringsAsFactors = FALSE)
+df=rbind(df,df2[,1:5])
+df$ACC=as.numeric(df$ACC)*100
+df$band= factor(df$band,levels = c("Fullband", "Delta", "Theta", "Alpha", "Beta", "Gamma", "High Gamma"))
+df$band=plyr::mapvalues(df$band, unique(df$band), c("Delta", "Theta", "Alpha", "Beta", "Gamma", "High\nGamma", "Broadband"))
+df$dataset[df$dataset=="d" & df$Dataset_parent=="D1D2"] = "   identify dataset 2 from 1"
+df$dataset[df$dataset=="t" & df$Dataset_parent=="D1D2"] = "   identify dataset 1 from 2"
+df$dataset[df$dataset=="d" & df$Dataset_parent=="D1D3"] = "   identify dataset 2 from 1"
+df$dataset[df$dataset=="t" & df$Dataset_parent=="D1D3"] = "   identify dataset 1 from 2"
+df$dataset[df$dataset=="d" & df$Dataset_parent=="D2D3"] = "   identify dataset 2 from 1"
+df$dataset[df$dataset=="t" & df$Dataset_parent=="D2D3"] = "   identify dataset 1 from 2"
+
+data_plot=rbind(data_plot,df[,2:5])
+data_plot$dataset[data_plot$band=='High   Gamma']="High Gamma"
+
+
+cbbPalette <- c( "#6683f8","#f483a1", "#F0E442", "#0072B2", "#D55E00", "#CC79A7", "#f3b2bf")
+pd <- position_dodge(-0.6)
+jitter <- position_jitter(width = 0.01, height = 0)
+ggplot(data=data_plot, aes(x=band, y=ACC, colour=Method, width=.5)) +  geom_jitter( position= position_jitterdodge(jitter.width=0.1,dodge.width=-0.6), size=4 )+ stat_summary(fun.data = mean_se, geom = "errorbar", size=2.5, width=0.5, position = pd) +
+  stat_summary(fun.data = "mean_cl_boot", geom = "point", size= 10, position = pd)  +theme_minimal() + scale_fill_grey() + labs(y="Accuracy (%)", x="",title="") +  theme(text = element_text(size=40), legend.title=element_blank()) + scale_color_manual(values=cbbPalette)
+
+dodge <- position_dodge(width = 0.6)
+ggplot(data=data_plot, aes(x=band, y=ACC, colour=Method, width=.5)) +  geom_jitter(position= position_jitterdodge(jitter.width=0.3, dodge.width=0.7), size=4, alpha=0.5 ) + stat_summary(fun.data = mean_se, geom = "errorbar", size=2.5, width=0, position = dodge)  +
+  stat_summary(fun.data = "mean_cl_boot", geom = "point", size= 10, position = dodge)  +theme_minimal() + labs(y="Accuracy (%)", x="",title="") +  theme(text = element_text(size=40), legend.title=element_blank()) + scale_color_manual(values=cbbPalette) 
+
+
+data_plot %>% group_by(band) %>% summarize(ACC1=mean(ACC))
+data_plot %>% group_by(band, Method) %>% summarize(ACC1=mean(ACC))
+
+
+
+########################################################
+# plotting brains with ggseg
+########################################################
+library(ggseg)
+library(dplyr)
+library(ggplot2)
+library(RColorBrewer)
+someData <- tibble(
+  region = rep(c("transverse temporal", "insula",
+                 "precentral","superior parietal"), 2), 
+  p = sample(seq(0.5,1,.01), 8),
+  groups = c(rep("g1", 4), rep("g2", 4))
+)
+
+
+df=read.csv('~/Desktop/McGill/python_codes/PSD_ICC_orig_atlas.csv', header = FALSE, stringsAsFactors = FALSE)
+colnames(df)[1]='region'
+df$hemi[] =''
+df$hemi[seq(2,68,2)] ='right'
+df$hemi[seq(1,67,2)] ='left'
+#df$region[seq(1,68,2)]=paste('lh_', df$region[seq(1,68,2)], sep='')
+#df$region[seq(2,68,2)]=paste('rh_', df$region[seq(2,68,2)], sep='')
+
+# delta
+someData= tibble(df$region, rowMeans(df[,2:9]), df$hemi)
+colnames(someData)[2]='p'
+colnames(someData)[1]='region'
+colnames(someData)[3]='hemi'
+someData$p[someData$p<0.4]=0.4
+someData$p[someData$p>0.8]=0.8
+ggplot(someData) +
+  geom_brain(atlas = dk, 
+             position = position_brain(hemi ~ side),
+             aes(fill = p)) +
+  scale_fill_distiller(palette = "RdBu"
+                       , limits = c(0.4, 0.8))+
+  #viridis::scale_fill_viridis(option = 'magma',limits=c(0.4, 0.8 ))+
+  #scale_fill_gradient2(low="white", mid="#EC352F", high="#6C1917", 
+  #                     midpoint=0.6,   
+  #                     limits=c(0.4, 0.8 ))+
+  theme_void() +
+  labs(title = "delta")
+#> merging atlas and data by 'region'
+ggsave('~/Desktop/figure_DRAFTS/new_topo/ICC_Delta_m', device = "pdf")
+
+
+
+# theta
+someData= tibble(df$region, rowMeans(df[,10:17]), df$hemi)
+colnames(someData)[2]='p'
+colnames(someData)[1]='region'
+colnames(someData)[3]='hemi'
+someData$p[someData$p<0.4]=0.4
+someData$p[someData$p>0.8]=0.8
+ggplot(someData) +
+  geom_brain(atlas = dk, 
+             position = position_brain(hemi ~ side),
+             aes(fill = p)) +
+  #viridis::scale_fill_viridis(option = 'magma',limits=c(0.4, 0.8 ))+
+  scale_fill_distiller(palette = "RdBu"
+                       , limits = c(0.4, 0.8))+
+  # scale_fill_gradient2(low="white", mid="#EC352F", high="#6C1917", 
+  #                      midpoint=0.6,   
+  #                      limits=c(0.4, 0.8 ))+
+  theme_void() +
+  labs(title = "theta")
+#> merging atlas and data by 'region'
+ggsave('~/Desktop/figure_DRAFTS/new_topo/ICC_Theta_m', device = "pdf")
+
+
+
+# alpha
+someData= tibble(df$region, rowMeans(df[,18:27]),df$hemi)
+colnames(someData)[2]='p'
+colnames(someData)[1]='region'
+colnames(someData)[3]='hemi'
+someData$p[someData$p<0.4]=0.4
+someData$p[someData$p>0.8]=0.8
+ggplot(someData) +
+  geom_brain(atlas = dk, 
+             position = position_brain(hemi ~ side),
+             aes(fill = p)) +
+  #viridis::scale_fill_viridis(option = 'magma',limits=c(0.4, 0.8 ))+
+  scale_fill_distiller(palette = "RdBu"
+                       , limits = c(0.4, 0.8))+
+  #scale_fill_gradient(brewer.pal(n = 2, name = "Reds")) +
+  # scale_fill_gradient2(low="white", mid="#EC352F", high="#6C1917", 
+  #                      midpoint=0.6,   
+  #                      limits=c(0.4, 0.8 ))+
+  theme_void() +
+  labs(title = "Alpha")
+#> merging atlas and data by 'region'
+ggsave('~/Desktop/figure_DRAFTS/new_topo/ICC_Alpha_m', device = "pdf")
+
+
+# beta
+someData= tibble(df$region, rowMeans(df[,28:61]),df$hemi)
+colnames(someData)[2]='p'
+colnames(someData)[1]='region'
+colnames(someData)[3]='hemi'
+someData$p[someData$p<0.4]=0.4
+someData$p[someData$p>0.8]=0.8
+ggplot(someData) +
+  geom_brain(atlas = dk, 
+             position = position_brain(hemi ~ side),
+             aes(fill = p)) +
+  #viridis::scale_fill_viridis(option = 'magma',limits=c(0.4, 0.8 ))+
+  scale_fill_distiller(palette = "RdBu"
+                       , limits = c(0.4, 0.8))+
+  #scale_fill_gradient(brewer.pal(n = 2, name = "Reds")) +
+  # scale_fill_gradient2(low="white", mid="#EC352F", high="#6C1917", 
+  #                      midpoint=0.6,   
+  #                      limits=c(0.4, 0.8 ))+
+  theme_void() +
+  labs(title = "Beta")
+#> merging atlas and data by 'region'
+ggsave('~/Desktop/figure_DRAFTS/new_topo/ICC_Beta_rb', device = "pdf")
+
+
+
+# gamma
+someData= tibble(df$region, rowMeans(df[,62:101]),df$hemi)
+colnames(someData)[2]='p'
+colnames(someData)[1]='region'
+colnames(someData)[3]='hemi'
+someData$p[someData$p<0.4]=0.4
+someData$p[someData$p>0.8]=0.8
+ggplot(someData) +
+  geom_brain(atlas = dk, 
+             position = position_brain(hemi ~ side),
+             aes(fill = p)) +
+  scale_fill_distiller(palette = "RdBu"
+                       , limits = c(0.4, 0.8))+
+  #viridis::scale_fill_viridis(option = 'magma',limits=c(0.4, 0.8 ))+
+  #scale_fill_gradient(brewer.pal(n = 2, name = "Reds")) +
+  # scale_fill_gradient2(low="white", mid="#EC352F", high="#6C1917", 
+  #                      midpoint=0.6,   
+  #                      limits=c(0.4, 0.8 ))+
+  theme_void() +
+  labs(title = "Gamma")
+#> merging atlas and data by 'region'
+ggsave('~/Desktop/figure_DRAFTS/new_topo/ICC_Gamma_m', device = "pdf")
+
+
+
+# Hgamma
+someData= tibble(df$region, rowMeans(df[,102:302]),df$hemi)
+colnames(someData)[2]='p'
+colnames(someData)[1]='region'
+colnames(someData)[3]='hemi'
+someData$p[someData$p<0.4]=0.4
+someData$p[someData$p>0.8]=0.8
+ggplot(someData) +
+  geom_brain(atlas = dk, 
+             position = position_brain(hemi ~ side),
+             aes(fill = p)) +
+  scale_fill_distiller(palette = "RdBu"
+                       , limits = c(0.4, 0.8))+
+  #viridis::scale_fill_viridis(option = 'magma',limits=c(0.4, 0.8 ))+
+  #scale_fill_gradient(brewer.pal(n = 2, name = "Reds")) +
+  # scale_fill_gradient2(low="white", mid="#EC352F", high="#6C1917", 
+  #                      midpoint=0.6,   
+  #                      limits=c(0.4, 0.8 ))+
+  theme_void() +
+  labs(title = "High Gamma")
+#> merging atlas and data by 'region'
+ggsave('~/Desktop/figure_DRAFTS/new_topo/ICC_HighGamma_rb', device = "pdf")
+
+
+
+
+