Links[{"label":"gcplyr.html","section":"","type":"","url":"https://cran.r-project.org/web/packages/gcplyr/refman/gcplyr.html"},{"label":"gcplyr.pdf","section":"","type":"","url":"https://cran.r-project.org/web/packages/gcplyr/gcplyr.pdf"},{"label":"Introduction to using gcplyr","section":"","type":"","url":"https://cran.r-project.org/web/packages/gcplyr/vignettes/gc01_gcplyr.pdf"},{"label":"source","section":"","type":"","url":"https://cran.r-project.org/web/packages/gcplyr/vignettes/gc01_gcplyr.Rmd"},{"label":"R code","section":"","type":"","url":"https://cran.r-project.org/web/packages/gcplyr/vignettes/gc01_gcplyr.R"},{"label":"Importing and reshaping data","section":"","type":"","url":"https://cran.r-project.org/web/packages/gcplyr/vignettes/gc02_import_reshape.pdf"},{"label":"source","section":"","type":"","url":"https://cran.r-project.org/web/packages/gcplyr/vignettes/gc02_import_reshape.Rmd"},{"label":"R code","section":"","type":"","url":"https://cran.r-project.org/web/packages/gcplyr/vignettes/gc02_import_reshape.R"},{"label":"Incorporating experimental designs","section":"","type":"","url":"https://cran.r-project.org/web/packages/gcplyr/vignettes/gc03_incorporate_designs.pdf"},{"label":"source","section":"","type":"","url":"https://cran.r-project.org/web/packages/gcplyr/vignettes/gc03_incorporate_designs.Rmd"},{"label":"R code","section":"","type":"","url":"https://cran.r-project.org/web/packages/gcplyr/vignettes/gc03_incorporate_designs.R"},{"label":"Pre-processing and plotting data","section":"","type":"","url":"https://cran.r-project.org/web/packages/gcplyr/vignettes/gc04_preprocess_plot.pdf"},{"label":"source","section":"","type":"","url":"https://cran.r-project.org/web/packages/gcplyr/vignettes/gc04_preprocess_plot.Rmd"},{"label":"R code","section":"","type":"","url":"https://cran.r-project.org/web/packages/gcplyr/vignettes/gc04_preprocess_plot.R"},{"label":"Processing data","section":"","type":"","url":"https://cran.r-project.org/web/packages/gcplyr/vignettes/gc05_process.pdf"},{"label":"source","section":"","type":"","url":"https://cran.r-project.org/web/packages/gcplyr/vignettes/gc05_process.Rmd"},{"label":"R code","section":"","type":"","url":"https://cran.r-project.org/web/packages/gcplyr/vignettes/gc05_process.R"},{"label":"Analyzing data","section":"","type":"","url":"https://cran.r-project.org/web/packages/gcplyr/vignettes/gc06_analyze.pdf"},{"label":"source","section":"","type":"","url":"https://cran.r-project.org/web/packages/gcplyr/vignettes/gc06_analyze.Rmd"},{"label":"R code","section":"","type":"","url":"https://cran.r-project.org/web/packages/gcplyr/vignettes/gc06_analyze.R"}]
TextReference manual: gcplyr.html , gcplyr.pdf Vignettes: Introduction to using gcplyr ( source , R code ) Importing and reshaping data ( source , R code ) Incorporating experimental designs ( source , R code ) Pre-processing and plotting data ( source , R code ) Processing data ( source , R code ) Analyzing data ( source , R code ) Dealing with noise ( source , R code ) Best practices and other tips ( source , R code ) Working with multiple plates ( source , R code ) Using make_design to generate experimental designs ( source , R code )