Diane has published a similar plot in the journal, Anaerobe.
Here is the plot made in ggplot2:
The plot is made without the data reformatting that I did in the previous example. Each line is added separately, scaled and text added and positioned manually. The is no legend.
The curves are smoother because there is lots more data points.
The secondary axis on the right hand side of the plot is in response to a question from Diane and is created using this code:
p + scale_y_continuous(sec.axis = sec_axis(~./4,
The tilda "~./4" part of this code modifies the numbers on the secondary axis. In this case, it divides them by four. We also supply a name for the axis.
The data is downloaded from Github and is supplied with Diane's permission - thanks Diane.
Here is the script that makes it and the intermediate plots:
# Hatziioanou, D, Mayer MJ, Duncan, SH, Flint, HJ & Arjan Narbad, A
# (2013) Anaerobe 23:5-8
link <- "https://raw.githubusercontent.com/brennanpincardiff/RforBiochemists/master/data/20100212roseburiab.xls"
download.file(url=link, destfile="file.xls", mode="wb")
data_2 <- read_excel("file.xls", skip = 2)
colnames(data_2) <- c("vol_ml", "UV_mAU", "Cond_ml_11_1", "conduct_mS_cm",
"Cond_11_%_ml", "percent_%", "Conc_11_ml", "percentB_1", "percentB_2",
"Pressure_ml", "Pressure_ MPa", "Flow_ml", "Flow_ml_min", "Temp_ml",
# published data is column 2 vs column 7
ggplot(data_2, aes(x = Conc_11_ml, y = UV_mAU)) + geom_line()
# Warning message - missing values
# row 602 gives a volume of almost 20 mls so limit the plot to that
# subset the data - just 602 rows
ggplot(data_2[1:602,], aes(x = Conc_11_ml, y = UV_mAU)) + geom_line()
# output looks good
# very much like in the paper.
# manual overlay and text in lots of layers to create a nice graph
# make a canvas and set the x axis
p <- ggplot(data_2[1:602,], aes(x = Conc_11_ml))
# add our first line with y = UV which is a measure of protein
p <- p + geom_line(aes(y = UV_mAU))
# add our second line with %B - this is a chromatography concept
# we change the scale of the line by multiplying by 10
# define colour as red
p <- p + geom_line(aes(y = percentB_2*10), colour = "red")
# add conductivity as our third line in blue
# we change the scale of the line by multiplying by 4
# define colour as blue
p <- p + geom_line(aes(y = conduct_mS_cm*4), colour = "blue")
# add coloured text to explain the lines
# x and y define the places the text arrive and label gives the text
p <- p + geom_text(mapping = aes(x = 2, y = 400, label = "UV (mAU)"))
p <- p + geom_text(mapping = aes(x = 4, y = 50, label = "0% B"), colour = "red")
p <- p + geom_text(mapping = aes(x = 17.5, y = 1050, label = "100% B"), colour = "red")
p <- p + geom_text(mapping = aes(x = 7, y = 950, label = "Conductivity (mS/cm)"), colour = "blue")
# this gives the secondary axis on the right hand side
# the function sec_axis() with a correction factor to change the number
# in this case the correction factor is "/4" meaning divide by four
# we multiplied by four when we plotted the blue line.
# we also give the scale a name - "Conductivity" and units.
p <- p + scale_y_continuous(sec.axis = sec_axis(~./4,
# add a theme to change the background
p <- p + theme_bw()
# add labels, titles with a source for the data...
p <- p + labs(x = "Volume (ml)",
y = "[Protein] (UV mAU)",
title = "Chromatogram of FPLC purification",
subtitle = "Source: Hatziioanou et al, (2013) Anaerobe 23:5e8")
- First chromatography example made with model data with data format changed prior to plotting
- ggplot documentation for scale_y_continuous()
- Documentation on secondary axis in ggplot2
- Citation for data: Hatziioanou, D, Mayer MJ, Duncan, SH, Flint, HJ & Arjan Narbad, A (2013) Anaerobe 23:5-8