# Description of datasets

    library(biogrowth)

The biogrowth package includes several datasets to aid in the understanding of its functions. They can be loaded with a call to the function `data()` passing the name of the dataset as an argument.

The dataset `example_cardinal` includes an example of the type of data used for estimating cardinal model parameters. It has three columns: temperature, pH and mu. The two first represent the storage conditions during several static growth experiments, whereas the latter is the specific growth rate observed in those experiments. This dataset is intended to be used for `fit_secondary_growth()`.

    data("example_cardinal")
    head(example_cardinal)
    #>   temperature pH           mu
    #> 1    0.000000  5 9.768505e-04
    #> 2    5.714286  5 2.624919e-03
    #> 3   11.428571  5 0.000000e+00
    #> 4   17.142857  5 1.530706e-04
    #> 5   22.857143  5 2.301817e-05
    #> 6   28.571429  5 3.895598e-04

The datasets `example_dynamic_growth` and `example_env_conditions` describe a dynamic growth experiment, which can be used for the `fit_dynamic_growth()` function. The dataset `example_env_conditions` describes the experimental design; i.e. how the environmental factors vary during the experiment. It has three columns: time (the elapsed time), temperature (the storage temperature) and aw (the water activity).

    data("example_env_conditions")
    head(example_env_conditions)
    #> # A tibble: 3 × 3
    #>    time temperature    aw
    #>   <dbl>       <dbl> <dbl>
    #> 1     0          20  0.99
    #> 2     5          30  0.95
    #> 3    15          35  0.9

The dataset `example_dynamic_growth` illustrates the population size observed during the experiment described by `example_env_conditions`. It has two columns: time (the elapsed time) and logN (the decimal logarithm of the observed population size).

    data("example_dynamic_growth")
    head(example_dynamic_growth)
    #> # A tibble: 6 × 2
    #>    time     logN
    #>   <dbl>    <dbl>
    #> 1 0      0.0670
    #> 2 0.517 -0.00463
    #> 3 1.03  -0.0980
    #> 4 1.55  -0.0986
    #> 5 2.07   0.111
    #> 6 2.59  -0.0465

The dataset `growth_salmonella` contains the growth of Salmonella spp. in broth. It has been retrived from ComBase (ID: B092_10). It has two columns: time (elapsed time) and logN (the decimal logarithm of the observed population size).

    data("growth_salmonella")
    head(growth_salmonella)
    #> # A tibble: 6 × 2
    #>    time  logN
    #>   <dbl> <dbl>
    #> 1  0     3.36
    #> 2  1.95  3.4
    #> 3  2.78  3.44
    #> 4  3.78  3.31
    #> 5  4.8   3.39
    #> 6  5.7   3.65

The datasets `multiple_counts` and `multiple_conditions` simulate several growth experiments performed for the same microorganism under dynamic conditions that vary between experiments. The observed microbial counts are included in `multiple_counts`, which is a list where each element includes the observations of one experiment with two columns: `time` (elapsed time) and `logN` the logarithm of the observed population size.

    data("multiple_counts")
    head(multiple_counts[[1]])
    #>       time        logN
    #> 1 0.000000 -0.20801574
    #> 2 1.666667 -0.03630986
    #> 3 3.333333 -0.29846914
    #> 4 5.000000  0.35029686
    #> 5 6.666667  0.14326140
    #> 6 8.333333 -0.40357904

Then, `multiple_conditions` describes the (dynamic) values of the environmental conditions during the experiment. In this case, the experiment considers the effect of temperature and pH. This is reflected in each entry including a column, `time`, with the elapsed time and two additional columns: `pH` (observed pH) and `temperature` (observed temperature. )

    data("multiple_conditions")
    head(multiple_conditions[[1]])
    #>   time temperature  pH
    #> 1    0          20 6.5
    #> 2   15          30 7.0
    #> 3   40          40 6.5

The dataset `arabian_tractors` includes the number of agricultural tractors in the Arab World according to the World Bank. It is included to show the applicability of `fit_isothermal_growth` for data from other fields.

    data("arabian_tractors")
    head(arabian_tractors)
    #> # A tibble: 6 × 2
    #>    year tractors
    #>   <dbl>    <dbl>
    #> 1  1961    73480
    #> 2  1962    76900
    #> 3  1963    81263
    #> 4  1964    86067
    #> 5  1965    91117
    #> 6  1966    97645