The Data List

The hardware and bandwidth for this mirror is donated by METANET, the Webhosting and Full Service-Cloud Provider.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]metanet.ch.

The Data List

The data_list

This vignette outlines the importance, structure, and creation of the data_list object. You can find much of this info by running ?generate_data_list after loading the metasnf package.

The data_list is the main object used in the metasnf package to store data. It is a named and nested list containing input dataframes (data), the name of that input dataframe (for the user’s reference), the ‘domain’ of that dataframe (the broader source of information that the input dataframe is capturing, determined by user’s domain knowledge), and the type of feature stored in the dataframe (continuous, discrete, ordinal, categorical, or mixed).

Some examples of data_list generation and usage are below:

library(metasnf)

# Preparing some mock data
heart_rate_df <- data.frame(
    patient_id = c("1", "2", "3"),
    var1 = c(0.04, 0.1, 0.3),
    var2 = c(30, 2, 0.3)
)

personality_test_df <- data.frame(
    patient_id = c("1", "2", "3"),
    var3 = c(900, 1990, 373),
    var4 = c(509, 2209, 83)
)

survey_response_df <- data.frame(
    patient_id = c("1", "2", "3"),
    var5 = c(1, 3, 3),
    var6 = c(2, 3, 3)
)

city_df <- data.frame(
    patient_id = c("1", "2", "3"),
    var7 = c("toronto", "montreal", "vancouver")
)

# Generating a data_list explicitly (Name each nested list element):
data_list <- generate_data_list(
    list(
        data = heart_rate_df,
        name = "heart_rate",
        domain = "clinical",
        type = "continuous"
    ),
    list(
        data = personality_test_df,
        name = "personality_test",
        domain = "surveys",
        type = "continuous"
    ),
    list(
        data = survey_response_df,
        name = "survey_response",
        domain = "surveys",
        type = "ordinal"
    ),
    list(
        data = city_df,
        name = "city",
        domain = "location",
        type = "categorical"
    ),
    uid = "patient_id"
)

# Achieving the same result compactly:
data_list <- generate_data_list(
    list(heart_rate_df, "heart_rate", "clinical", "continuous"),
    list(personality_test_df, "personality_test", "surveys", "continuous"),
    list(survey_response_df, "survey_response", "surveys", "ordinal"),
    list(city_df, "city", "location", "categorical"),
    uid = "patient_id"
)

# Printing data_list summaries
summarize_dl(data_list)

##               name        type   domain length width
## 1       heart_rate  continuous clinical      3     3
## 2 personality_test  continuous  surveys      3     3
## 3  survey_response     ordinal  surveys      3     3
## 4             city categorical location      3     2

Depending on your data preprocessing, it may be more convenient to you to assemble the components of your data_list in an automated way and then provide that result to generate_data_list.

For example, your code could have generated a list like the one below:

list_of_lists <- list(
    list(heart_rate_df, "data1", "domain1", "continuous"),
    list(personality_test_df, "data2", "domain2", "continuous")
)

If generate_data_list receives only a single list, it’ll treat that list as containing all the components required to construct a properly formatted data_list:

dl <- generate_data_list(
    list_of_lists,
    uid = "patient_id"
)

summarize_dl(dl)

##    name       type  domain length width
## 1 data1 continuous domain1      3     3
## 2 data2 continuous domain2      3     3

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.