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ropensci / tidyqpcr

quantitative PCR analysis in the tidyverse
https://docs.ropensci.org/tidyqpcr/
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Check install via R-universe #190

Closed ewallace closed 4 months ago

ewallace commented 2 years ago

You can add this installation method to your package README install.packages("", repos = "https://ropensci.r-universe.dev") thanks to R-universe.

from https://github.com/ropensci/software-review/issues/470#issuecomment-1152058920

@DimmestP and I checked this. It downloads the package fine but in my hands didn't download the dependencies need.

ramiromagno commented 1 year ago

Hi Edward,

I tried on Ubuntu 20.04 with no packages installed and {tidyqpcr} installed just fine with all its dependencies.

platform       x86_64-pc-linux-gnu         
arch           x86_64                      
os             linux-gnu                   
system         x86_64, linux-gnu           
status                                     
major          4                           
minor          2.1                         
year           2022                        
month          06                          
day            23                          
svn rev        82513                       
language       R                           
version.string R version 4.2.1 (2022-06-23)
nickname       Funny-Looking Kid

Here are the commands used:

options(
  repos = c(ropensci = 'https://ropensci.r-universe.dev',
            CRAN = 'https://cloud.r-project.org')
)

install.packages('tidyqpcr')

Quick test:

library(tidyqpcr)
#> Loading required package: tibble

packageVersion("tidyqpcr")
#> [1] '1.0'

rowkey4 <- tibble(well_row = LETTERS[1:4],
                  target_id = c("ACT1", "BFG2", "CDC19", "DED1"))

colkey3 <- tibble(
  well_col = 1:3,
  sample_id = c("rep1", "rep2", "rep3"),
  prep_type = "+RT"
)

create_blank_plate(well_row = LETTERS[1:4], well_col = 1:3)
#> # A tibble: 12 × 3
#>    well  well_row well_col
#>    <chr> <fct>    <fct>   
#>  1 A1    A        1       
#>  2 A2    A        2       
#>  3 A3    A        3       
#>  4 B1    B        1       
#>  5 B2    B        2       
#>  6 B3    B        3       
#>  7 C1    C        1       
#>  8 C2    C        2       
#>  9 C3    C        3       
#> 10 D1    D        1       
#> 11 D2    D        2       
#> 12 D3    D        3

plate_plan12 <- label_plate_rowcol(
  plate = create_blank_plate(well_row = LETTERS[1:4], well_col = 1:3),
  rowkey = rowkey4,
  colkey = colkey3
)
#> coercing well_col to a factor with levels from plate$well_col
#> coercing well_row to a factor with levels from plate$well_row

display_plate_qpcr(plate_plan12)
ewallace commented 4 months ago

Thanks @ramiromagno - this worked so closing the issue.