Data visualization with the programming language R - published.

Today, I published a piece in The Biochemist about data visualisation with R. I enjoyed writing the piece and it includes some of the code and blog posts that I have written here. 

The code I used to create the figures has all been uploaded on Github. To reproduce the figures you can cut and paste the code from there into a script window on R-Studio. 

Here is a list of pages with code for the figures. 

Figure 1 - R allows reproducible data visualisations. 

• Figure 1A - My first published R data visualization – a cluster dendrogram from a proteomic data set. First published in 2014 and reproduced in 2021 (Alsagaby et al, 2014, https://doi.org/10.1021/pr5002803).
• Figure 1B - A violin plot showing neutralizing antibody titres against five SARS-CoV-2 strains reproduced from shared data and code from Wall et al (2021, https://doi.org/10.1016/S0140-6736(21)01290-3).
• Figure 1C - Phylogenetic tree of human kinase domains inspired by visualization by Manning et al (2002, https://doi.org/10.1126/science.1075762).

Figure 2 - Three most viewed data viz from this blog

• Figure 2A - A volcano plot. 
• Figure 2B - A drug dose–response curve and LD50 calculation.
• Figure 2C - A simple flow cytometry plot.

Figure 3 - Tidy Tuesday data visualisations

• Figure 3A - A volcanic activity time line inspired by @ijeamaka_a
• Figure 3B - Illustrating the importance of numbers in password strength. Across a range of password types, inclusion of numbers increases password strength.
• Figure 3C (Github only) - Showing the proportion of female culprits in Scooby Doo shows from 1960s to 2020s.

Figure 4 - Showcasing drawProteins (below)

• Figure 4A - The Hexsticker for the Bioconductor package drawProteins. (b) 
• Figure 4B (Github only) - Schematic of SARS-CoV S1 protein variants. Protein and variant information from Uniprot (https://www.uniprot.org/uniprot/P0DTC2).

There are lots more scripts on this blog. I hope you find it useful for learning R. 

Do reach out if you need any help or if some of the code doesn't work. Either comment here, contact me through Github or twitter: @brennanpcardiff

Using drawProteins for draw NF-kappaB proteins...

Update, 4 Jan 2018: Following advice from Bioconductor, the package magrittr is not integral to drawProteins so needs to be activated separately to use this script. 

Update,  3 Nov 2017: I've renamed the functions e.g. geom_chains is now draw_chains and added a new function draw_canvas which allows one to draw without the chain, if you want.

Building on what I did previously, more work done on the drawProteins package allows the simplier script below to draw this protein schematic:

SCRIPT START
# devtools::install_github("brennanpincardiff/drawProteins")
library(drawProteins)
library(ggplot2)
library(magrittr)

# accession numbers of five NF-kappaB proteins
"Q04206 Q01201 Q04864 P19838 Q00653" %>%
  drawProteins::get_features() %>%
  drawProteins::feature_to_dataframe() ->
  prot_data

p <- draw_canvas(prot_data)
p <- draw_chains(p, prot_data)
p <- draw_domains(p, prot_data)
p <- draw_repeat(p, prot_data)
p <- draw_motif(p, prot_data)
p <- draw_phospho(p, prot_data, size = 8)

# background and y-axis
p <- p + theme_bw(base_size = 20) +  # white background and change text size
  theme(panel.grid.minor=element_blank(),
        panel.grid.major=element_blank()) +
  theme(axis.ticks = element_blank(),
        axis.text.y = element_blank()) +
  theme(panel.border = element_blank())

# add titles
p <- p + labs(x = "Amino acid number",         # label x-axis
              y = "",  # label y-axis
              title = "Schematic of human NF-kappaB proteins",
              subtitle = "circles = phosphorylation sites\nRHD = Rel Homology Domain\nsource:Uniprot")


# move legend to top
p <- p + theme(legend.position="top") + labs(fill="")

p
SCRIPT END

Resources

• Github repository for the drawProteins package