diff --git a/Tridimensional barplots in R/barplot_with_dots.R b/Tridimensional barplots in R/barplot_with_dots.R
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+++ b/Tridimensional barplots in R/barplot_with_dots.R
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+mydata <- read.delim("experimental_data.txt", stringsAsFactors = F)
+#strain vector exp1 exp2 exp3
+# 1 wt vector1 0.981 1.011 1.008
+# 2 wt vector2 0.712 0.724 0.818
+# 3 wt vector3 0.135 0.426 0.402
+# 4 wt vector4 0.161 0.426 0.369
+# 5 mutA vector1 1.028 1.089 1.171
+# 6 mutA vector2 0.972 1.096 1.193
+# .....
+
+library(reshape2)
+# convert the table to a "longer" data format, but preserve
+# the dimensions that will be useful for plotting, here by index
+melted <- melt(mydata, id.vars=c(2,1))
+names(melted) <- c("vector", "strain", "experiment", "value")
+# vector strain experiment value
+# 1 vector1 wt exp1 0.981
+# 2 vector2 wt exp1 0.712
+# 3 vector3 wt exp1 0.135
+# 4 vector4 wt exp1 0.161
+# 5 vector1 mutA exp1 1.028
+# 6 vector2 mutA exp1 0.972
+# 7 vector3 mutA exp1 0.141
+# 8 vector4 mutA exp1 0.292
+# .....
+
+melted$strain <- factor(x=melted$strain, levels=c("wt", "mutA", "mutB"))
+# manually setting the "strain" as a factor with the levels in a given order
+# ensures that the plotting of thses categories will be done in this order
+
+library(dplyr)
+# compute mean and standard deviation of values, grouped by vector AND strain
+averages <- melted %>% group_by(vector, strain) %>% summarise(avg=mean(value))
+sds <- melted %>% group_by(vector, strain) %>% summarise(sd=sd(value))
+# create columns that will serve for the range of the error bars
+averages$ymin <- averages$avg-sds$sd
+averages$ymax <- averages$avg+sds$sd
+
+# changing the position of the bars in the barplot by the same amount
+# is crucial to align the dots, error bars and bars.
+# the following value is used three times in the plot, adjust to your liking
+dodge_value <- 0.8
+
+ggplot(data=averages,
+ aes(x=vector, y=avg, color=strain, fill=strain))+
+ geom_col(width=0.7, position = position_dodge(dodge_value))+
+ scale_fill_manual(values = rep(c("gray30", "gray70", "white"), 4))+
+ # this needs to be adjusted and modified according to your needs
+ # for 4 vectors and 3 strains, three shades of gray can be enough
+ scale_color_manual(values = rep("black", 12))+
+ # this parameter ensures that error bars and bar outlines are all black
+ geom_point(data=melted,
+ aes(x=vector, y=value, color=strain),
+ shape=21, fill="white",
+ position=position_jitterdodge(dodge.width=dodge_value, jitter.width = 0.3))+
+ # add the data points. Even if the color is black for all the points, that aesthetic
+ # parameter is required to tell ggplot2 that we want grouping by strain
+ geom_errorbar(data=averages,
+ aes(ymin=ymin, ymax=ymax),
+ position = position_dodge(dodge_value),
+ width=0.2)+
+ # error bars, as SD
+ ylim(0, 1.4)+
+ # axis limits, to be adjusted to the range of the data, or remove to do it automatically
+ # the following parameters affect the plot and are especially useful for the pdf output
+ theme_classic(base_size=10)+
+ theme(text=element_text(size=6, family="ArialMT"), legend.key.size = unit(0.3, 'cm'))
+
+ggsave("mybarplot.pdf", width=8, height=5, units="cm")
diff --git a/Tridimensional barplots in R/experimental_data.txt b/Tridimensional barplots in R/experimental_data.txt
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+strain vector exp1 exp2 exp3
+wt vector1 0.981 1.011 1.008
+wt vector2 0.712 0.724 0.818
+wt vector3 0.135 0.426 0.402
+wt vector4 0.161 0.426 0.369
+mutA vector1 1.028 1.089 1.171
+mutA vector2 0.972 1.096 1.193
+mutA vector3 0.141 0.311 0.385
+mutA vector4 0.292 0.349 0.331
+mutB vector1 1.054 1.078 1.07
+mutB vector2 0.573 0.834 0.683
+mutB vector3 1.063 0.986 0.947
+mutB vector4 0.679 0.804 0.616
diff --git a/Tridimensional barplots in R/initial.png b/Tridimensional barplots in R/initial.png
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diff --git a/Tridimensional barplots in R/mybarplot.pdf b/Tridimensional barplots in R/mybarplot.pdf
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diff --git a/Tridimensional barplots in R/plot_inR.png b/Tridimensional barplots in R/plot_inR.png
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