ggplot2 Summary and Color Recommendation

ggplot(data) + #data frame
geom_bar(aes(x=x, y=value, fill=label), stat = "identity") + #bar plot
facet_grid( ~ type, scales="free_x") + # one panel for one type variable
geom_text(aes(label=value), size = 6, position=position_dodge(width=0.9), vjust=-0.25) + # add value to each bar with proper position
theme(axis.text = element_text(angle = 0, hjust = 1, size=16, colour="black"), # text at ticks can be rotated by angle to avoid overlap labels
      plot.title = element_text(size=22), # title size
      axis.title = element_text(size=16), # axis label size
      panel.border = element_rect(colour = "black", fill=NA, size=0.5),
      panel.grid.major = element_line(colour="lightgrey"), # background grid
      panel.grid.minor = element_blank(),
      panel.background = element_rect(fill="white"), #  white background instead of default grey
      legend.text = element_text(size = 16), # legend text size
      legend.title=element_text(size=16),
      strip.text = element_text(size=16, colour="black"),
      strip.background = element_rect(colour="black")) + 
labs(x="x aixs label", y="y axis label", size=15) + # axis label
ylim(0, 1) + xlim(0, 1) + # lim of x or y
ggtitle("Average Number of Significant Contacts (Transcript Factors) ") # plot title

ggplot(data, aes(x = Type, y = value, fill = Type)) + 
geom_bar(stat = "identity") +
geom_text(aes(label=value), size = 6, position=position_dodge(width=0.9), vjust=-0.25) +
theme(axis.text = element_text(angle = 0, hjust = 1, size = 16, colour = "black"), plot.title= element_text(size=22),
          axis.ticks.x = element_blank(), # no ticks on x
          axis.text.x = element_blank(), # no text on x
          axis.ticks.y = element_blank(),
          axis.text.y = element_blank(),
          axis.title.x = element_blank(), # no label on x
          axis.title = element_text(size=16),
          panel.border = element_rect(colour = "black", fill=NA, size=0.5),
          panel.grid.major = element_line(colour="lightgrey"),
          panel.grid.minor = element_blank(),
          panel.background = element_rect(fill="white"),
          legend.text = element_text(size = 16),
          legend.title=element_text(size=16),
          legend.justification = c(1,1), # move legend into the panel 
          legend.position = c(0.95,0.95), # legend is at the right upper conner
          strip.text = element_text(size=16, colour="black"),
          strip.background = element_rect(colour="black"))

library(virids) # one of my favoriate color package
ggplot(data) + 
  geom_point(aes(x = run, y = delay, size = speed, fill=load), color = "black", shape = 21) + # scatter plot size determined by speed, filled color determined by load, boundary of the cycle point is black.
  scale_fill_viridis(discrete = TRUE) + # discrete value
  geom_text(aes(x = run, y = delay, label = timing), hjust = 0, vjust = 1.5) # add label to each scatter point

ggplot(data) + 
geom_point(aes(x = run, y = delay, fill = load)) + 
geom_smooth(aes(x = run, y = delay, color = load), method = "loess", se = FALSE) # method can be lm for linear regression or loess for smooth regression; group by load will end up with several regressions of each group.
# se = TRUE will give Confidence Interval

p <- ggplot(data, aes(x = dose:brand, y = pcnt)) + 
geom_boxplot(aes(fill = ratio)) + 
stat_summary(fun.y = median, geom = "line", position = "identity", aes(x = dose:brand, y = pcnt_ratio, group = 1), size = 1.6) + # add a thick line connecting the median value of each group of boxplot
ylab(label = "Response (after / before ratio)") + 
xlab(label = "Dose : Brand") + 
guides(fill = guide_legend(title = "ET Ratio")) + # change legend title - to remove legend: guides(fill = FALSE)
theme(axis.text = element_text(angle = 0, hjust = 1, size=16, colour="black"),
      plot.title=element_text(size=16, hjust = 0.5),
      axis.title = element_text(size=16),
      panel.border = element_rect(colour = "black", fill=NA, size=0.5),
      panel.grid.major = element_line(colour="lightgrey"),
      panel.grid.minor = element_blank(),
      panel.background = element_rect(fill="white"),
      legend.text = element_text(size = 16),
      legend.title=element_text(size=16),
      strip.text = element_text(size=16, colour="black"),
      strip.background = element_rect(colour="black"))
ggsave("Keyplot1.pdf", p) # another way to save the plot other than pdf() and dev.off()

ggplot(data) + 
  geom_boxplot(aes(x = factor(Day),y = Proportion, color = Treatment), size =0.8) + # x is factor level by using x = factor(, level = c("")) to determine x order
  stat_smooth(method = "loess", aes(x = factor(Day), y = Proportion, group=Treatment, color = Treatment), se=FALSE, size=0.8) +
  scale_color_viridis(discrete = TRUE) +
  facet_wrap(~Application, nrow = 2) + # divide into several panel by Application with 2 rows.
  theme(legend.position = "bottom", legend.direction = "horizontal") # legend is placed at bottom and direction is horizontal instead of vertical on the right

ggplot(data, aes(logFC, -log2(p))) +
    geom_point(aes(colour = -log2(p))) +
    scale_color_gradient(high = "black", low = "#05D9F6") + # higher value is black but smaller value scatter point is defined as #05D9F6       
    theme(axis.text = element_text(angle = 0, hjust = 1, size=16, colour="black"),
          plot.title=element_text(size=16, hjust = 0.5),
          axis.title = element_text(size=16),
          panel.border = element_rect(colour = "black", fill=NA, size=0.5),
          panel.grid.major = element_line(colour="lightgrey"),
          panel.grid.minor = element_blank(),
          panel.background = element_rect(fill="white"),
          legend.text = element_text(size = 16),
          legend.title=element_text(size=16),
          strip.text = element_text(size=16, colour="black"),
          strip.background = element_rect(colour="black"))

`<img src="{{ site.baseurl}}/images/viridis.png" alt="Drawing" style="width: 400px;"/>`

manhattan(data, logp = FALSE, suggestiveline = F, genomewideline = F , col = plasma(n=7, alpha = 0.4,begin = 0.2, end = 0.9) , ylim = c(-8, 8), cex=0.7) # manhattan plot - scatter plot for genome data with chromosome local

library(RColorBrewer)
ggplot(data, aes(x, y)) +
    stat_binhex(bins = 100) + 
    scale_fill_gradientn(colours = brewer.pal(8, "Spectral"), trans = "log10", labels = trans_format('log10', math_format(10^.x))) +  
    theme(axis.text = element_text(angle = 0, hjust = 1, size=16, colour="black"),
          plot.title=element_text(size=16, hjust = 0.5),
          axis.title = element_text(size=16),
          panel.border = element_rect(colour = "black", fill=NA, size=0.5),
          panel.grid.major = element_line(colour="lightgrey"),
          panel.grid.minor = element_blank(),
          panel.background = element_rect(fill="white"),
          legend.text = element_text(size = 16),
          legend.title=element_text(size=16),
          strip.text = element_text(size=16, colour="black"),
          strip.background = element_rect(colour="black"))

annotation <- data.frame(State = factor(rep(1:6, each = n), 
                          labels = c("State1", "State2", "State3", "State4", "State5", "State6"))) # six states with n samples for each state
anno_colors <- list(State = State)

pheatmap(matrix, 
         color = colorRampPalette(rev(brewer.pal(n = 11, name ="Spectral")))(40),  
         annotation_colors = anno_colors, 
         annotation = annotation, # labels for column groups which will appear in the state legend
         cluster_rows = T, #hierarchical clustering on the rows
         cluster_cols = F, 
         show_rownames= F, 
         show_colnames = F, 
         filename = "heatmap_bound.pdf" #figure saved as pdf
         )

ggplot(data, aes(x = x, fill = label))+ geom_density(alpha = .3) # overlap density with transparent color

ggplot(data, aes(x = improve)) + # density plot with histgram
  geom_density(alpha = 0.3, fill = "#FF6666") + 
  geom_histogram(aes(y = ..density..), color =
 "black", fill = "white", alpha = 0.5) +
  scale_x_continuous(breaks=c(5, 10, 15, 20,25, 30)) +
  labs(size=16) +
  theme(axis.text = element_text(angle = 0, hjust = 1.2, size=16, colour="black"),
          plot.title=element_text(size=20, hjust = 0.5),
          axis.title = element_text(size=18),
          panel.border = element_rect(colour = "black", fill=NA, size=0.5),
          panel.grid.major = element_line(colour="lightgrey"),
          panel.grid.minor = element_blank(),
          panel.background = element_rect(fill="white"),
          legend.text = element_text(size = 16),
          legend.title=element_text(size=16),
          strip.text = element_text(size=18, colour="black"))

xtable(summary(lm(y~x, data = data)))

ggplot(data, aes(x, y)) +
... +
geom_text_repel()