Working with Data Frames
working-with-dataframes.RmdOverview
One of the key features of osp.snapshots is the ability to convert complex nested snapshot data into structured data frames that are easy to work with in R. This vignette demonstrates how to extract, convert, and analyze snapshot data using standard R data analysis tools.
Converting Building Blocks to Data Frames
Each building block type has a corresponding get_*_dfs()
function that converts the data to structured data frames.
Converting Individuals
Individual data is converted into multiple related data frames:
individual_dfs <- get_individuals_dfs(snapshot)
names(individual_dfs)
#> [1] "individuals" "individuals_parameters"
#> [3] "individuals_expressions"Let’s examine each data frame:
# Basic characteristics
head(individual_dfs$individuals)
#> # A tibble: 2 × 17
#> individual_id name seed species population gender age age_unit
#> <chr> <chr> <int> <chr> <chr> <chr> <dbl> <chr>
#> 1 European (P-gp modified… Euro… 1.72e7 Human European_… MALE 30 year(s)
#> 2 Korean (Yu 2004 study) Kore… 5.30e7 Human Asian_Tan… MALE 23.3 year(s)
#> # ℹ 9 more variables: gestational_age <dbl>, gestational_age_unit <chr>,
#> # weight <dbl>, weight_unit <chr>, height <dbl>, height_unit <chr>,
#> # disease_state <chr>, calculation_methods <glue>,
#> # disease_state_parameters <chr>
# Parameters
head(individual_dfs$individuals_parameters)
#> # A tibble: 2 × 7
#> individual_id path value unit source description source_id
#> <chr> <chr> <dbl> <chr> <chr> <chr> <int>
#> 1 European (P-gp modified, CYP3A… Orga… 1 NA Unkno… NA NA
#> 2 Korean (Yu 2004 study) Orga… 1 NA Unkno… NA NA
# Expression profiles
head(individual_dfs$individuals_expressions)
#> # A tibble: 6 × 2
#> individual_id profile
#> <chr> <chr>
#> 1 European (P-gp modified, CYP3A4 36 h) CYP3A4|Human|European (P-gp modified, C…
#> 2 European (P-gp modified, CYP3A4 36 h) AADAC|Human|European (P-gp modified, CY…
#> 3 European (P-gp modified, CYP3A4 36 h) P-gp|Human|European (P-gp modified, CYP…
#> 4 European (P-gp modified, CYP3A4 36 h) OATP1B1|Human|European (P-gp modified, …
#> 5 European (P-gp modified, CYP3A4 36 h) ATP1A2|Human|European (P-gp modified, C…
#> 6 European (P-gp modified, CYP3A4 36 h) UGT1A4|Human|European (P-gp modified, C…Converting Single Individual
You can also convert a single individual to data frames:
# Get the first individual
individual <- snapshot$individuals[[1]]
single_individual_df <- individual$to_df()
names(single_individual_df)
#> [1] "individuals" "individuals_parameters"
#> [3] "individuals_expressions"
head(single_individual_df$individuals)
#> # A tibble: 1 × 17
#> individual_id name seed species population gender age age_unit
#> <chr> <chr> <int> <chr> <chr> <chr> <dbl> <chr>
#> 1 European (P-gp modified… Euro… 1.72e7 Human European_… MALE 30 year(s)
#> # ℹ 9 more variables: gestational_age <dbl>, gestational_age_unit <chr>,
#> # weight <dbl>, weight_unit <chr>, height <dbl>, height_unit <chr>,
#> # disease_state <chr>, calculation_methods <glue>,
#> # disease_state_parameters <chr>Converting Compounds
compounds_df <- get_compounds_dfs(snapshot)
head(compounds_df)
#> # A tibble: 6 × 8
#> compound category type parameter value unit data_source source
#> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
#> 1 Midazolam physicochemical_prop… lipo… Optimized 2.89… Log … NA Param…
#> 2 Midazolam physicochemical_prop… frac… Gertz et… 0.031 NA NA Param…
#> 3 Midazolam physicochemical_prop… mole… NA 325.… g/mol NA NA
#> 4 Midazolam physicochemical_prop… halo… Cl 1 NA NA NA
#> 5 Midazolam physicochemical_prop… halo… F 1 NA NA NA
#> 6 Midazolam physicochemical_prop… pKa base 6.2 NA NA NAConverting Formulations
formulations_dfs <- get_formulations_dfs(snapshot)
names(formulations_dfs)
#> [1] "formulations" "formulations_parameters"
# Basic formulation info
head(formulations_dfs$formulations)
#> # A tibble: 2 × 4
#> formulation_id name formulation formulation_type
#> <chr> <chr> <chr> <chr>
#> 1 Oral solution Oral solution Formulation_Dissolved Dissolved
#> 2 Tablet (Dormicum) Tablet (Dormicum) Formulation_Tablet_Weibu… Weibull
# Formulation parameters
head(formulations_dfs$parameters)
#> NULLConverting Populations
populations_dfs <- get_populations_dfs(snapshot)
names(populations_dfs)
#> [1] "populations" "populations_parameters"
head(populations_dfs$populations)
#> # A tibble: 0 × 22
#> # ℹ 22 variables: population_id <chr>, name <chr>, seed <int>,
#> # number_of_individuals <int>, proportion_of_females <dbl>,
#> # source_population <chr>, individual_name <chr>, age_min <dbl>,
#> # age_max <dbl>, age_unit <chr>, weight_min <dbl>, weight_max <dbl>,
#> # weight_unit <chr>, height_min <dbl>, height_max <dbl>, height_unit <chr>,
#> # bmi_min <dbl>, bmi_max <dbl>, bmi_unit <chr>, egfr_min <dbl>,
#> # egfr_max <dbl>, egfr_unit <chr>Converting Observed Data
Observed data conversion creates a flat data frame perfect for analysis:
obs_data_df <- get_observed_data_dfs(snapshot)
head(obs_data_df)
#> # A tibble: 6 × 30
#> name xValues yValues yErrorValues xDimension xUnit yDimension yUnit
#> <chr> <dbl> <dbl> <dbl> <chr> <chr> <chr> <chr>
#> 1 Ahonen 1995 - … 0.5 0.0156 NA Time h Concentra… mg/l
#> 2 Ahonen 1995 - … 1 0.0280 NA Time h Concentra… mg/l
#> 3 Ahonen 1995 - … 1.5 0.0235 NA Time h Concentra… mg/l
#> 4 Ahonen 1995 - … 2 0.0185 NA Time h Concentra… mg/l
#> 5 Ahonen 1995 - … 3 0.0118 NA Time h Concentra… mg/l
#> 6 Ahonen 1995 - … 4 0.00848 NA Time h Concentra… mg/l
#> # ℹ 22 more variables: yErrorType <chr>, yErrorUnit <chr>, molWeight <dbl>,
#> # lloq <dbl>, `DB Version` <chr>, ID <chr>, `Study Id` <chr>,
#> # Reference <chr>, Source <chr>, Grouping <chr>, `Data type` <chr>, N <chr>,
#> # Molecule <chr>, Species <chr>, Organ <chr>, Compartment <chr>, Route <chr>,
#> # Dose <chr>, `Times of Administration [h]` <chr>, Formulation <chr>,
#> # `Food state` <chr>, Comment <chr>Next Steps
- Learn how to create new building blocks in
vignette("creating-building-blocks") - Explore the full API in the function reference