Simple-plots-in-R/Simple line plot with error bars at master · FEMLab/Simple-plots-in-R · GitHub

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## Simple line plot with error bars

## for error bars used:
## http://docs.ggplot2.org/0.9.3.1/geom_errorbar.html

# Load ggplot

library(ggplot2)

# read in data change sep as appropriate ('\t' = tab separated)

pH <-read.table("LinePlot with error bars_data.txt", header = TRUE, sep='\t')

# here is what data looks like

pH

   Week     pH Treatment   variance
1     0 5.5800      Zero 0.08366600
2     2 5.2450      Zero 0.02661453
3     4 5.0000      Zero 0.15785014
4     5 4.9025      Zero 0.07824907
5     6 4.9000      Zero 0.06164414
6     7 4.8500      Zero 0.15774452
7     0 5.6275      High 0.05121442
8     2 5.2875      High 0.05359960
9     4 5.1675      High 0.07203876
10    5 4.8900      High 0.04020779
11    6 5.1425      High 0.03750000
12    7 5.1725      High 0.07846177


# Define the top and bottom of the errorbars using + & - variance variable

limits <- aes(ymax = pH + variance, ymin=pH - variance)

# now make plot (called j) of pH against week with colour of points changing according to Treatment


j <- ggplot(data=pH, aes(x=Week,y=pH, group=Treatment, colour=Treatment)) +
  geom_line() +
  geom_point(size=4, shape=21) +
  ## add error bars
  geom_errorbar(limits, width=0.25)

j # print plot

## change from default colours:

## For colour plot using scale_color_brewer using code below instead of scale_colour_grey()
## change palette from "Set1" if you want to change colours
## See http://docs.ggplot2.org/current/scale_brewer.html
## rename plot as j2

j2 <- j + scale_color_brewer(palette="Set1") +
	theme_bw() + # remove grey background of plot
	theme(text = element_text(size=20, color = "black"))

j2 # print plot and have a look

## now add axis labels and title
j3 <- j2 +  labs(title = "pH change with trial time in High & Zero Plots")

## Editing title to make space beneath it and to change size and font face

j3 + theme(plot.title = element_text(size=20, face="bold", vjust=2))

j3 # print plot

## if you are interested in the variance of the samples (eg differences in replicates at each x axis point)
## then maybe scatterplot could be better way to represent data