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-04The 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 0.9The 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 -0.0465The 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 3.65The 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 -0.40357904Then, 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 6.5The 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.