library(knitr) opts_chunk$set( fig.pos = "!h", out.extra = "", warning = F, message = F, fig.width = 5, fig.align = "center" ) libs <- c("DailyHRB", "ggplot2") suppressMessages( suppressWarnings(sapply(libs, require, character.only = TRUE)) ) rm(libs)
You can install this pacakge from Github using
if (!"devtools" %in% installed.packages()) { install.pacakges("devtools") } devtools::install_github("HectorRDB/DailyHRB")
Alternatively, you can download it from Github and build it from source. ```{bash, eval = F} git clone git@github.com:HectorRDB/DailyHRB.git R CMD INSTALL DailyHRB
# Templates This package provide two templates that I regularly use. Templates are an *[RStudio](https://www.rstudio.com/)* functionality that I find very useful to speed up project creation. To use those templates, install the package then open RStudio. - __Rmd template__: Click on _New File_ (or the "+" button), select _R Markdown..._ in the pulldown menu. Then, select _From Template_ and select _My Template (DailyHRB)_. - __Project Template__: Click on _New Project (or the second "+" button), select _New Project_. Then, select _Personal Project Template_. To create similar files for yourself, the easiest way is to create your own package. Then, create a __Rmd__ file that you want as template and create a struture similar to the [__int__ folder of the Github repo](https://github.com/HectorRDB/DailyHRB/tree/master/inst). For the project, it ia bit more complex. It relies on the *[project.R](https://github.com/HectorRDB/DailyHRB/blob/master/R/project.R)* file in the __R__ folder and some other files in [__inst__](https://github.com/HectorRDB/DailyHRB/tree/master/inst). I hope to be able to create a tutorial at some point. # Functions ## my_theme I rely a lot on *ggplot2* to quickly plot nice graphs but I don't like the deaults themes. The one I prefer is the *theme_classic* but it still needed some improvements. So I build a *ggplot2* theme. ```r p <- ggplot(data.frame(x = 0:10, y = 0:10 + rnorm(11)), aes(x = x, y = y)) + geom_point() p p + my_theme()
This function loads the packages and install them from CRAN or Bioconductor if needed, using the BiocManager package.
# Example ipak(c("ggplot2", "Biobase"))
This function is useful when analyzing micro-array data, RNA-Seq or scRNA-Seq data. It plots the boxplot of 10 (tunable) random columns from the input matrix. It allows to quickly see whether the data is logged and / or normalized when exploring a new dataset.
data <- matrix(rnbinom(1000 * 100, size = 1000, prob = .999), ncol = 100) checkType(data)
It is a function that I use to clean metadata file, typically from SRA. It removes all columns that only have one unique value, and optionaly all columns that have no value with duplicates.
metaData <- data.frame(SRA = "SRA17CJQ1", ID1 = sample(letters, 12, replace = F), group = c(rep("group1", 4), rep("group2", 4), rep("group3", 4))) metaData$ID2 <- toupper(metaData$ID1) metaData clean(metaData) clean(metaData, unique = F, keep = "ID1")
A sily function I did once when I was bored
plotSmiley()
Just a shorcut, colors()
is equivalent to RColorBrewer::display.brewer.all()
. Setting print = TRUE will also print the names of all palettes in RColorBrewer.
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