Tool: Describe data frame

Tool: Describe data frame

Usage

btw_tool_describe_data_frame(
  data_frame,
  format = c("skim", "glimpse", "print", "json"),
  dims = c(5, 100)
)

Arguments

data_frame

The data frame to describe

format

One of "skim", "glimpse", "print", or "json".

• "skim" is the most information-dense format for describing the data. It uses and returns the same information as skimr::skim() but formatting as a JSON object that describes the dataset.

• To glimpse the data column-by-column, use "glimpse". This is particularly helpful for getting a sense of data frame column names, types, and distributions, when pairings of entries in individual rows aren't particularly important.

• To just print out the data frame, use print().

• To get a json representation of the data, use "json". This is particularly helpful when the pairings among entries in specific rows are important to demonstrate.

dims

The number of rows and columns to show, as a numeric vector of length two. For example, the default dims = c(5, 100) shows the first 5 rows and 100 columns, whereas dims = c(Inf, Inf) would show all of the data.

Value

A character vector containing a representation of the data frame. Will error if the named data frame is not found in the environment.

See also

btw_this.data.frame(), btw_register_tools()

Other Tools: btw_register_tools(), btw_tool_describe_environment(), btw_tool_get_installed_packages(), btw_tool_list_files(), btw_tool_package_docs, btw_tool_read_current_editor(), btw_tool_read_text_file()

Examples

btw_tool_describe_data_frame(mtcars)
#> [1] "```json"                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                   
#> [2] "{\"n_cols\":11,\"n_rows\":32,\"groups\":[],\"class\":\"data.frame\",\"columns\":{\"mpg\":{\"variable\":\"mpg\",\"type\":\"numeric\",\"mean\":20.0906,\"sd\":6.0269,\"p0\":10.4,\"p25\":15.425,\"p50\":19.2,\"p75\":22.8,\"p100\":33.9},\"cyl\":{\"variable\":\"cyl\",\"type\":\"numeric\",\"mean\":6.1875,\"sd\":1.7859,\"p0\":4,\"p25\":4,\"p50\":6,\"p75\":8,\"p100\":8},\"disp\":{\"variable\":\"disp\",\"type\":\"numeric\",\"mean\":230.7219,\"sd\":123.9387,\"p0\":71.1,\"p25\":120.825,\"p50\":196.3,\"p75\":326,\"p100\":472},\"hp\":{\"variable\":\"hp\",\"type\":\"numeric\",\"mean\":146.6875,\"sd\":68.5629,\"p0\":52,\"p25\":96.5,\"p50\":123,\"p75\":180,\"p100\":335},\"drat\":{\"variable\":\"drat\",\"type\":\"numeric\",\"mean\":3.5966,\"sd\":0.5347,\"p0\":2.76,\"p25\":3.08,\"p50\":3.695,\"p75\":3.92,\"p100\":4.93},\"wt\":{\"variable\":\"wt\",\"type\":\"numeric\",\"mean\":3.2172,\"sd\":0.9785,\"p0\":1.513,\"p25\":2.5812,\"p50\":3.325,\"p75\":3.61,\"p100\":5.424},\"qsec\":{\"variable\":\"qsec\",\"type\":\"numeric\",\"mean\":17.8487,\"sd\":1.7869,\"p0\":14.5,\"p25\":16.8925,\"p50\":17.71,\"p75\":18.9,\"p100\":22.9},\"vs\":{\"variable\":\"vs\",\"type\":\"numeric\",\"mean\":0.4375,\"sd\":0.504,\"p0\":0,\"p25\":0,\"p50\":0,\"p75\":1,\"p100\":1},\"am\":{\"variable\":\"am\",\"type\":\"numeric\",\"mean\":0.4062,\"sd\":0.499,\"p0\":0,\"p25\":0,\"p50\":0,\"p75\":1,\"p100\":1},\"gear\":{\"variable\":\"gear\",\"type\":\"numeric\",\"mean\":3.6875,\"sd\":0.7378,\"p0\":3,\"p25\":3,\"p50\":4,\"p75\":4,\"p100\":5},\"carb\":{\"variable\":\"carb\",\"type\":\"numeric\",\"mean\":2.8125,\"sd\":1.6152,\"p0\":1,\"p25\":2,\"p50\":2,\"p75\":4,\"p100\":8}}}"
#> [3] "```"