lcolladotor

## For https://docs.google.com/document/d/1iWKnvbn6wGS66rVTRSGkFcJyIKccWTCniN5CWvYuvfw/edit?usp=sharing

## Install R packages if needed
# if (!requireNamespace("remotes", quietly = TRUE)) {
#       install.packages("remotes")
#   }
# remotes::install_cran("ggplot2")
# remotes::install_cran("plotly")
# remotes::install_cran("sessioninfo")
# remotes::install_cran("BiocManager")

lcolladotor

## LIBD rstats club 2020-08-28
## https://docs.google.com/document/d/1bsq-1FfHkgoviECdhlA594T_G-kA2B88JMI5485Z4E4/edit?usp=sharing
library("shiny")

## From https://shiny.rstudio.com/articles/basics.html

# Define UI for app that draws a histogram ----
ui <- fluidPage(
    
    # App title ----

lcolladotor

Chat messages

Hi! Tomorrow is the workshop! ^^ I'm hope that you are excited about it. You can find the materials at http://research.libd.org/recountWorkshop2020/index.html. I'll start the workshop with some slides about the recount2 project & friends, then we'll run some of the code in the workshop (it was originally designed for a 2 hour workshop). I'll finish with a few slides about the future and then we can have a Q & A session, though you are more than welcome to continue asking questions beyond the workshop through the different venues (Bioconductor support site for package questions, GitHub issues for feature requests, Slack for informal questions, etc). See you tomorrow! Best, Leo

The slides are available at https://speakerdeck.com/lcolladotor

Questions and answers:

• "how does scale_count fun work?" As described in the workshop and associated recountWorkflow, it uses the area under the coverage (AUC) and the base-pair coverage counts. In practice, this data is stored in the RangedSummarized

lcolladotor

library("purrr")

# View(mtcars)
.x <- mtcars[[1]]
mean(.x)
map_dbl(mtcars, ~ mean(.x))
map_dbl(mtcars, mean)

# rnorm()
# args(rnorm)

lcolladotor

## From https://docs.google.com/document/d/1CSCPUfHGMoa_QTDf6EUsFMYVTiA5GP8OyeIGCyTVbLI/edit?usp=sharing

if (!requireNamespace("remotes", quietly = TRUE)) {
    install.packages("remotes")
}
remotes::install_cran(
    c(
        "tidyverse", "gganimate", "maps", "knitr", "kableExtra",
        "scales", "lubridate", "RColorBrewer"
    )

lcolladotor

##################### Steps for reproducing this problem ######################

## Create a package
usethis::create_package("~/Desktop/testgit")

## Set up git and say yes to the prompts
usethis::use_git()

## Create a file

lcolladotor

## From https://r-graphics.org/recipe-quick-scatter
plot(mtcars$wt, mtcars$mpg)

library(ggplot2)

ggplot(mtcars, aes(x = wt, y = mpg)) +
  geom_point()

## Check structure of the ggplot object

lcolladotor

$ R CMD build --keep-empty-dirs --no-resave-data regionReport
Loading required package: colorout
* checking for file ‘regionReport/DESCRIPTION’ ... OK
* preparing ‘regionReport’:
* checking DESCRIPTION meta-information ... OK
* installing the package to build vignettes
* creating vignettes ... OK
* checking for LF line-endings in source and make files and shell scripts
* checking for empty or unneeded directories
* building ‘regionReport_1.21.8.tar.gz’

lcolladotor

## Generate the HTML report
report <- derfinderReport(
    prefix = "report", browse = FALSE,
    nBestRegions = 15, makeBestClusters = TRUE, outdir = "html",
    fullCov = list("21" = genomeDataRaw$coverage), optionsStats = optionsStats
)
## Registered S3 method overwritten by 'GGally':
##   method from   
##   +.gg   ggplot2
## Writing 9 Bibtex entries ... OK

lcolladotor

$ java -jar ~/Desktop/bfg-1.13.0.jar --strip-blobs-bigger-than 600K .

Using repo : /Users/lcollado/Dropbox/Code/spatialLIBD/./.git


This repo has been processed by The BFG before! Will prune repo before proceeding - to avoid unnecessary cleaning work on unused objects...
Completed prune of old objects - will now proceed with the main job!

Scanning packfile for large blobs: 4459
Scanning packfile for large blobs completed in 133 ms.