mop

Data from lab instruments are often consistently untidy, and can therefore be programmatically tidied. This package seeks to tidy lab data much like how broom tidies statistical objects, with a couple important differences:

1. Each data type must be read in, and has its own read_* function. This can’t really be avoided: there is really no practical way to provide a single function that can accurately and consistently determine the source of a given file.
2. Outputs from tidy_lab are not data.frames. They are instead objects that contain tidy data. This allows for packages to interact with these downstream objects in unique ways depending on the data type (ie, making generic functions that utilize these objects). If you would prefer a tidy data.frame (or more specifically, tibble), run scrub on the object.

Installation

This package can be downloaded from GitHub with:

# install.packages("devtools")
devtools::install_github("KaiAragaki/mop")

Basic Workflow

Using mop generally begins with reading in your lab data with it’s respective function. For example, a nanodrop file:

library(mop)

nano <- system.file("extdata", "nanodrop.csv", package = "mop") |>
  read_nanodrop(nucleotide = "RNA", date = "2021-08-14")

nano
#> <nanodrop[5]>
#> # A tibble: 18 × 24
#>    Date            Sample.Name Nucleic.Acid.ng.… A260.A280 A260.A230  A260  A280
#>    <chr>           <chr>                   <dbl>     <dbl>     <dbl> <dbl> <dbl>
#>  1 8/14/2021 8:26… Sample 1                 420.      2.03      2.29 10.5   5.16
#>  2 8/14/2021 8:27… Sample 2                 450.      2.06      2.26 11.2   5.46
#>  3 8/14/2021 8:28… Sample 3                 498.      2.06      2.28 12.4   6.03
#>  4 8/14/2021 8:28… Sample 4                 449.      2.05      2.25 11.2   5.48
#>  5 8/14/2021 8:29… Sample 5                 474.      2.03      2.29 11.9   5.85
#>  6 8/14/2021 8:29… Sample 6                 543.      2.00      2.17 13.6   6.80
#>  7 8/14/2021 8:30… Sample 7                 483.      2.07      2.24 12.1   5.84
#>  8 8/14/2021 8:31… Sample 8                 588.      2.07      1.97 14.7   7.08
#>  9 8/14/2021 8:31… Sample 9                 490.      2.07      2.25 12.2   5.91
#> 10 8/14/2021 8:32… Sample 10                256.      2.03      2.27  6.40  3.15
#> 11 8/14/2021 8:32… Sample 11                225.      2.03      2.27  5.62  2.77
#> 12 8/14/2021 8:33… Sample 12                429.      2.06      2.26 10.7   5.22
#> 13 8/14/2021 8:35… Sample 13                216.      2.02      2.27  5.39  2.66
#> 14 8/14/2021 8:35… Sample 14                218.      2.03      2.29  5.44  2.68
#> 15 8/14/2021 8:36… Sample 15                206.      2.03      2.24  5.15  2.54
#> 16 8/14/2021 8:37… Sample 16                426.      2.07      2.25 10.7   5.15
#> 17 8/14/2021 8:37… Sample 17                389.      2.06      2.28  9.74  4.73
#> 18 8/14/2021 8:38… Sample 18                560.      2.04      2.19 14.0   6.88
#> # … with 17 more variables: Nucleic.Acid.Factor <dbl>,
#> #   Baseline.Correction..nm. <int>, Baseline.Absorbance <dbl>,
#> #   Corrected..ng.uL. <lgl>, Corrected..CV <lgl>, Impurity.1 <lgl>,
#> #   Impurity.1.A260 <lgl>, Impurity.1..CV <lgl>, Impurity.1.mM <lgl>,
#> #   Impurity.2 <lgl>, Impurity.2.A260 <lgl>, Impurity.2..CV <lgl>,
#> #   Impurity.2.mM <lgl>, Impurity.3 <lgl>, Impurity.3.A260 <lgl>,
#> #   Impurity.3..CV <lgl>, Impurity.3.mM <lgl>
#> # Nucelotide: RNA 
#> # Is tidy: FALSE 
#> # Date: 2021-08-14

Somethings to note:

• nano is currently NOT tidy
• nano is a nanodrop object

Additionally, read_nanodrop will normally try to extract nucleotide and date information from the file name, but were supplied manually here as the file is named to be understandable.

To tidy any lab object, pass it to tidy_lab:

nano_tidy <- tidy_lab(nano)
nano_tidy
#> <nanodrop[5]>
#> # A tibble: 18 × 24
#>    date        sample_name  conc a260_280 a260_230  a260  a280 nucleic_acid_fac…
#>    <chr>       <chr>       <dbl>    <dbl>    <dbl> <dbl> <dbl>             <dbl>
#>  1 2021-08-14… Sample 1     420.     2.03     2.29 10.5   5.16                40
#>  2 2021-08-14… Sample 2     450.     2.06     2.26 11.2   5.46                40
#>  3 2021-08-14… Sample 3     498.     2.06     2.28 12.4   6.03                40
#>  4 2021-08-14… Sample 4     449.     2.05     2.25 11.2   5.48                40
#>  5 2021-08-14… Sample 5     474.     2.03     2.29 11.9   5.85                40
#>  6 2021-08-14… Sample 6     543.     2.00     2.17 13.6   6.80                40
#>  7 2021-08-14… Sample 7     483.     2.07     2.24 12.1   5.84                40
#>  8 2021-08-14… Sample 8     588.     2.07     1.97 14.7   7.08                40
#>  9 2021-08-14… Sample 9     490.     2.07     2.25 12.2   5.91                40
#> 10 2021-08-14… Sample 10    256.     2.03     2.27  6.40  3.15                40
#> 11 2021-08-14… Sample 11    225.     2.03     2.27  5.62  2.77                40
#> 12 2021-08-14… Sample 12    429.     2.06     2.26 10.7   5.22                40
#> 13 2021-08-14… Sample 13    216.     2.02     2.27  5.39  2.66                40
#> 14 2021-08-14… Sample 14    218.     2.03     2.29  5.44  2.68                40
#> 15 2021-08-14… Sample 15    206.     2.03     2.24  5.15  2.54                40
#> 16 2021-08-14… Sample 16    426.     2.07     2.25 10.7   5.15                40
#> 17 2021-08-14… Sample 17    389.     2.06     2.28  9.74  4.73                40
#> 18 2021-08-14… Sample 18    560.     2.04     2.19 14.0   6.88                40
#> # … with 16 more variables: baseline_correction_nm <int>,
#> #   baseline_absorbance <dbl>, corrected_ngul <lgl>, corrected_cv <lgl>,
#> #   impurity_1 <lgl>, impurity_1_a260 <lgl>, impurity_1_cv <lgl>,
#> #   impurity_1_m_m <lgl>, impurity_2 <lgl>, impurity_2_a260 <lgl>,
#> #   impurity_2_cv <lgl>, impurity_2_m_m <lgl>, impurity_3 <lgl>,
#> #   impurity_3_a260 <lgl>, impurity_3_cv <lgl>, impurity_3_m_m <lgl>
#> # Nucelotide: RNA 
#> # Is tidy: TRUE 
#> # Date: 2021-08-14

Of note, our raw data is stored in nano$raw_data.

The operations that make an object tidy vary per object class, and can be found in the object’s documentation (here ?tidy_lab.nanodrop).

Objects are useful as they form a semi-stable language for other functions from this and other packages to operate on. However, it’s often much simpler to interact with data in a flat tibble. This can be done using scrub:

nano_scrub <- scrub(nano_tidy)

nano_scrub[c(1:3, (ncol(nano_scrub)-2):ncol(nano_scrub))]
#> # A tibble: 18 × 6
#>    date                sample_name  conc exp_date   nucleotide is_tidy
#>    <chr>               <chr>       <dbl> <date>     <chr>      <lgl>  
#>  1 2021-08-14 20:26:49 Sample 1     420. 2021-08-14 RNA        TRUE   
#>  2 2021-08-14 20:27:25 Sample 2     450. 2021-08-14 RNA        TRUE   
#>  3 2021-08-14 20:28:07 Sample 3     498. 2021-08-14 RNA        TRUE   
#>  4 2021-08-14 20:28:40 Sample 4     449. 2021-08-14 RNA        TRUE   
#>  5 2021-08-14 20:29:17 Sample 5     474. 2021-08-14 RNA        TRUE   
#>  6 2021-08-14 20:29:54 Sample 6     543. 2021-08-14 RNA        TRUE   
#>  7 2021-08-14 20:30:30 Sample 7     483. 2021-08-14 RNA        TRUE   
#>  8 2021-08-14 20:31:16 Sample 8     588. 2021-08-14 RNA        TRUE   
#>  9 2021-08-14 20:31:50 Sample 9     490. 2021-08-14 RNA        TRUE   
#> 10 2021-08-14 20:32:24 Sample 10    256. 2021-08-14 RNA        TRUE   
#> 11 2021-08-14 20:32:59 Sample 11    225. 2021-08-14 RNA        TRUE   
#> 12 2021-08-14 20:33:50 Sample 12    429. 2021-08-14 RNA        TRUE   
#> 13 2021-08-14 20:35:16 Sample 13    216. 2021-08-14 RNA        TRUE   
#> 14 2021-08-14 20:35:59 Sample 14    218. 2021-08-14 RNA        TRUE   
#> 15 2021-08-14 20:36:34 Sample 15    206. 2021-08-14 RNA        TRUE   
#> 16 2021-08-14 20:37:12 Sample 16    426. 2021-08-14 RNA        TRUE   
#> 17 2021-08-14 20:37:50 Sample 17    389. 2021-08-14 RNA        TRUE   
#> 18 2021-08-14 20:38:21 Sample 18    560. 2021-08-14 RNA        TRUE

Note how meta-data fields have now become individual columns.