# profrep

# Using profrep

This vignette documents the most common use cases and patterns for the profrep package.

## Introduction

To use profrep, you will have to start with lab work! As described on the home page, one of the most common measurements that stress physiologists take is blood samples for corticosterone quantification during a stress response.

## Basic Use: From a CSV

First, we will install the package from CRAN:

    library(profrep)

Next, we will load in a data set from a csv. This would take the form of `data <- read.csv(<path_to_csv>)`. Instead of loading in new data, this vignette will make use of one of the three example data sets provided with `profrep`. We shall use the synthetic data created for this purpose, which consists of 11 individuals, which had four replicates sampled at 4 different times.

To load in, we call the data from the `profrep` package:

    synthetic_data <- profrep::synthetic_data_four_point
    print(synthetic_data)
    #>    Animal TIME     A  B  C  D
    #> 1       A    0  9.00 10 11 12
    #> 2       A   15 12.00 20 25 30
    #> 3       A   30 16.00 30 40 50
    #> 4       A   45 20.00 40 55 70
    #> 5       B    0  9.00 11 13 15
    #> 6       B   15 27.00 30 33 36
    #> 7       B   30 30.00 33 36 39
    #> 8       B   45 10.00 13 16 19
    #> 9       C    0  9.00 11 13 15
    #> 10      C   15 22.00 28 34 40
    #> 11      C   30 27.00 35 41 47
    #> 12      C   45  7.00 20 32 40
    #> 13      D    0 12.00 16 19 24
    #> 14      D   15 50.00 54 58 62
    #> 15      D   30 20.00 24 28 32
    #> 16      D   45 46.00 50 54 58
    #> 17      E    0 12.00 13 14 15
    #> 18      E   15 34.00 36 38 40
    #> 19      E   30 39.00 41 43 45
    #> 20      E   45 44.00 46 48 50
    #> 21      F    0  0.01 13 14 15
    #> 22      F   15 23.00 36 38 40
    #> 23      F   30 28.00 41 43 45
    #> 24      F   45 33.00 46 48 50
    #> 25      G    0  2.00 16 20 24
    #> 26      G   15 40.00 54 58 62
    #> 27      G   30 10.00 24 28 32
    #> 28      G   45 38.00 50 54 58
    #> 29      H    0  5.00 20 35 50
    #> 30      H   15 25.00 38 45 56
    #> 31      H   30 46.00 52 57 63
    #> 32      H   45 67.00 68 69 70
    #> 33      I    0  6.00 14 22 30
    #> 34      I   15 16.00 24 32 40
    #> 35      I   30 28.00 36 44 52
    #> 36      I   45 42.00 50 58 66
    #> 37      J    0  6.00 14 22 30
    #> 38      J   15 16.00 24 32 40
    #> 39      J   30 28.00 36 44 52
    #> 40      J   45 66.00 50 58 42
    #> 41      K    0  6.00 14 22 30
    #> 42      K   15 24.00 40 32 16
    #> 43      K   30 44.00 28 36 52
    #> 44      K   45 42.00 66 58 50

Here, we see that there are no missing replicates for any individual at any time. What clean data!

Since we know how many trials we performed, we can easily perform the profile repeatability calculation with

    pr_score_df <- profrep::profrep(df=synthetic_data, n_timepoints=4)

If we wanted logs to be displayed in the terminal, we would have changed the default verbosity factor from `FALSE` to `TRUE` with `verbose = True`.

The results of the calculation are below:

    print(pr_score_df)
    #>    individual n_crossings max_variance ave_variance base_score final_score rank
    #> 1           E           0         6.67         5.42      12.10      0.9925    1
    #> 2           B           0        15.00        12.92      27.95      0.9912    2
    #> 3           D           0        26.67        26.40      53.12      0.9887    3
    #> 4           F           0        58.92        56.59     115.62      0.9790    4
    #> 5           G           0        91.67        87.42     179.27      0.9611    5
    #> 6           I           0       106.67       106.67     213.55      0.9461    6
    #> 7           J           5       106.67       106.67     277.33      0.9026    7
    #> 8           C           0       207.58        86.81     294.81      0.8861    8
    #> 9           K          15       106.67       106.67     384.00      0.7613    9
    #> 10          H           0       375.00       149.42     525.17      0.4374   10
    #> 11          A           0       456.25       181.88     639.04      0.1993   11

We see that the individual `E` is the highest-ranking individual, with a profile-repeatability score of 0.9925!