UQRUG 56

Overview: TidyTuesday + drop in session
Questions: Getting started, data visualisation, UniProt and API

Topic: TidyTuesday

Today’s dataset, if you want to give it a go: https://github.com/rfordatascience/tidytuesday/blob/master/data/2024/2024-12-17/readme.md

Attendees

Add your name, where you’re from, and why you’re here:

Name	Where are you from?	What brings you here?
Song	Chemical engineering PhD student	learn to use R for data analysis and visualization
Darshi	Library
Stéphane	Library	Here to help!
…

Questions and shared resources

• Getting started with R:

  • Install R: https://cran.r-project.org/

  • Install RStudio: https://posit.co/download/rstudio-desktop/

  • Start coding!

  • Find relevant packages:

    • Task Views: https://cran.r-project.org/web/views/ (as a starting point)

    • Online search

• Joining data from UniProt based on pathway similarity

  • could download tables, or

  • could use API directly: https://www.uniprot.org/help/programmatic_access

    • with httr2 package: https://cran.r-project.org/web/packages/httr2/index.html

  • or could use an existing package to query the database: https://cran.r-project.org/web/packages/UniprotR/index.html

    • note that some dependencies come from Bioconductor: https://www.bioconductor.org/

Today’s script

# comments to document your code
# BiocManager::install(c("Biostrings", "GenomicAlignments", "IRanges"))
library(UniprotR)
#Read Accessions from csv file , Note : Accessions must be in the first column. 
Accessions <-GetAccessionList("https://s3.amazonaws.com/csvpastebin/uploads/9571fa356c67a0c7c95e8431799a051a/Accessions.csv") 
#Get Taxanomy Information 
TaxaObj <- GetNamesTaxa(Accessions) 
#Visualize Chromosomes localization
PlotChromosomeInfo(TaxaObj)
#Visualize protein's gene name as Network 
PlotGenesNetwork(TaxaObj)

##### some starter commands for R ####

#  operations
1 + 1
1 - 2
1 * 10
3 / 2
# objects to store information
one_number <- 42
one_number
one_number * 2
my_result <- one_number / 9
# vectors of any length
dog_ages <- c(1, 2, 7, 4, 3)
dog_ages * 7
# functions
max(dog_ages)
min(dog_ages)
mean(dog_ages)
summary(dog_ages)
sd(dog_ages)

# statistical methods
# for example, lm(), aov()...

# (base R) plotting
plot(dog_ages)
hist(dog_ages)

# people often go straight ggplot2 for data visualisation

#### image as background ####
download.file("https://www.r-project.org/logo/Rlogo.png",
              destfile = "logo.png")
library(png)
r_logo <- readPNG("logo.png")
library(ggpubr)
ggplot(mapping = aes(1:3,1:3)) +
  background_image(r_logo) +
  geom_point()