knitr::opts_chunk$set(echo = TRUE)

Additionally, some plate readers might output growth curve data in a block shape but in a single file. For instance, the file may contain the block from lines 1 - 8, then an empty line, then the next block from lines 10 - 17, etc. Since read_blocks is vectorized on most of its input arguments, including startrow, startcol, endrow, and endcol, such a layout can be specified by passing a vector of startrows and endrows to read_blocks:

imported_blockdata <- read_blocks(
  files = "example_file.csv",
  startrow = c(1, 10, 19, 28, 37, 46, 55),
  endrow = c(8, 17, 26, 35, 44, 53, 62))

Overview of gcplyr functions

This figure needs updating General overview of gcplyr workflow{#id .class width=7in height=2.45in}

Text graveyard

Additionally, there are broadly two classes of growth curves-related information to be manipulated: numerical spectrophotometric data, and experimental design elements. If reading from files, gcplyr can read both classes of information, transform them, and merge them for subsequent analyses. gcplyr also contains the capability of specifying design elements programmatically, which can then be merged with imported spectrophotometric data.

Once spectrophotometric data and design elements have been combined, this merged measures-designs tidy-shaped dataframe can be used for subsequent pre-processing and analyses.

Note that gcplyr can handle multiple plates throughout this process, using lists which contain the corresponding single-plate data.frames as elements. Once design elements have been merged with these data.frames so that independent plates can be distinguished, multiple-plate lists can be collapsed together into a single tidy-shaped measures-designs dataframe using merge_dfs.



mikeblazanin/gcplyr documentation built on Jan. 18, 2025, 11:40 a.m.