README.md

In bishun945/DAMATO: Data Management Tools

DAMATO

Current version: 0.0.8

The goal of DAMATO is to automatically assess the MS spreadsheets recording in situ data by:

• Format check includes sheetname check, sampleID check, base info check, etc.;
• Generate a skeleton by the pre-defined spreadsheet format (easy to use when start to data collection);
• Produce assessment figures includes parameters comparison, relationships between AOPs (or IOPs) and OACs, geolocation map and so on.

The present built-in spreadsheet format only supports the lab of Prof. Yunmei Li in Nanjing Normal University. If you want to customize the spreadsheet format, you can consult Prof. Li for more details via [e-mail] and cc to [e-mail].

Installation

You can install the released version of DAMATO from My Github with:

install.packages("devtools") # install devtools require
devtools::install_github('bishun945/DAMATO')

Supported spread formats

run data(DAMATO::dataset_format_1) to see more details about one of supported formats.

Format check

If you are getting start to data collection, use generage function like this:

library(DAMATO)
Generate_ref_spread()

Then a MS spreadsheet file will be created in fold spreads in your working directory. Just add your records to this file. Once you finish your collection, use Check functions to make sure the format is okay.

fn  = file.choose() # select your spreadsheet file
res_1 = Check_sheet_name(fn)
res_2 = Check_sample_id(res_1)
res_3 = Check_base_info(res_2)
res_4 = Check_geo_location(res_3)

The above Check functions are merged to one main function which could be more convenient for users. But it is okay if you want to check a specific item, such as sheet name or sample id.

It is better to use %>% for these successional functions. res = Check_sheet_name(fn) %>% Check_sample_id()

res = Check_format(fn)

Quatliy check (coming soon)

When all format stuff are passed, the more important thing is to check the quality of data set you collect. Use Check_param_quality() and Check_spectra_quality() to inspect (interactively to some extent) the quality and flag them by qualtiy_flag().

res_5 = Check_param_quality(res_4) # coming soon
res_6 = Check_spectra_quality(res_5) # coming soon
res_7 = Quatliy_flag(res_6) # coming soon

Example steps by steps

First thing to do

The first thing for DAMATO is creating a formatted spread if you have to type your paper records in PC. Try run the following function:

Generate_ref_spread()

Then you will get a supper cool, formatted, and version controlled spread in your current working directory named spread.

Now let us type our valuable data in it.

Check_sheet_name

Once you have a spread need to be check, please use Check_* functions to check their format. Basically, if you have typed your data in the generated spread by running Generate_ref_spread, the format would be pretty well.

fn = "/foo/bar/foobar.xlsx"
tmp <- Check_sheet_name(fn, excel_option = "readxl")

The result is shown as follows:

These warning messages mean the colnames in [base] sheet has some carriage returns or newlines. But this does not matter for the follow-up process. Thus, the number of warnings is 1.

Check_sample_id - Error

fn = "/foo/bar/foobar.xlsx"
tmp <- Check_sheet_name(fn, excel_option = "readxl") %>%
  Check_sample_id()

These messages mean that the first columns of head have been replaced by SampleID for matching the [base] sheet. However, in the [apc] sheet, several samples (masked) have been found in error SampleID format. Check the raw excel file and try to re-submit! The final message of Sample ID status: presents Error which means there is unable to do the follow-up process.

Check_sample_id - Pass

fn = "/foo/bar/foobar_revised.xlsx"
tmp <- Check_sheet_name(fn, excel_option = "readxl") %>%
  Check_sample_id()

Once you have revised those SampleID with inappropriate format, the check function will return the Pass information which means you can do next step now.

Check_base_info - Error

fn = "/foo/bar/foobar_revised.xlsx"
tmp <- Check_sheet_name(fn, excel_option = "readxl") %>%
  Check_sample_id() %>%
  Check_base_info()

> Okay, in this check result, you gonna find some interesting features that if colnames of the [base] sheet to be checked cannot match the designed format, i.e., dataset_format = 1, then Check_base_info would return warnings and errors indicating which colnames could not be found or have possible matchings (by means of the fuzzy matching).

It is recommended that you could manually inspect your spread and check the colnames in the [base] sheet following the guides/suggestions from Try to find the matched pattern (just guess) .... If there are some required colnames but do not exsit in the current spread, just add/insert an empty column with those colnames.

And don’t remember those are just guess ;)

Check_base_info - Pass

fn = "/foo/bar/foobar_revised_2.xlsx"
tmp <- Check_sheet_name(fn, excel_option = "readxl") %>%
  Check_sample_id() %>%
  Check_base_info()

Boom! After your second revision, the format in this spread has been passed now.

Return of Check_* functions

Once you finish all format check functions, i.e., Check_sheet_name, Check_sample_id, and Check_base_info, the final result of those functions will return a list that includes many elements that could be further used by upcoming functions or features as well as your own decision.

The Status_* in that list all present Pass which means the list has been checked by the three corresponding functions.

The data.frame of this list, namely dt, is restored data from the spread in the filename fn but with formatted data.frame.

bishun945/DAMATO documentation built on Dec. 19, 2021, 9:48 a.m.