Get start

In the fraping package, the necessary functions for FRAP data analysis are implemented. However, to use it properly, it is also necessary to install the itz package. Both packages are repositories on GitHub, and their installation can be done using the install_github function from the devtools package in R.

devtools::install_github("artitzco/itz")
devtools::install_github("artitzco/fraping")

This way, the libraries will be installed on your system, so every time you start a new project for FRAP data analysis, you only need to import the corresponding libraries:

library(itz)
library(fraping)

Getting Started (optional)

If you want to start using the fraping package right away, you can download a sample dataset using the downloadData function. This function will download a file with FRAP data into the “data” folder within your working directory, which you can use as a starting point to practice the analysis methods described in this manual. Please note that this download is optional, and you can use your own FRAP data if you have it available.

To use the sample data, run the following code in the R command console: fraping::downloadData(). After executing this function, in addition to the “data” folder, you will find a test script called “test.fraping.R” in the root directory. You can use this script as a starting point to explore and analyze FRAP data.

The newDataFrap function creates a base for a new FRAP data group. The function synthesizes the information and returns a list that allows the user to manipulate the data more easily. The supported parameters are the group name and the photobleaching intensity in percentage. For more details, refer to newDataFrap.

To avoid future issues, the group name should follow the following recommendations:

  1. It should not contain spaces.
  2. It should start with a letter.
  3. Only the special characters “.” and “_” are allowed.
A <- newDataFrap("Neurites", 80)
B <- newDataFrap("Secondary.Neurites", 80)
C <- newDataFrap("Dendrites", 80)

Remember to follow these recommendations when creating new FRAP data groups. This will ensure consistency and proper handling of the data in future analyses.