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Dataset columns and rows structure

Source: R/dataframe_str.R
df_str.Rd

This function lets the user to check quickly the structure of a dataset (data.frame). It returns multiple counters for useful metrics, a plot, and a list of column names for each of the column metrics.

Usage

df_str(df, return = "plot", subtitle = NA, quiet = FALSE)

Arguments

df

Dataframe

return

Character. Return "skimr" for skim report, "numbers" for stats and numbers, "names" for a list with the column names of each of the class types, "plot" for a nice plot with "numbers" output, "distr" for an overall summary plot showing categorical, numeric, and missing values by using plot_df distributions

subtitle

Character. Add subtitle to plot

quiet

Boolean. Keep quiet or show other options available?

Value

Depending on return input and based on your df structure:

  • list with the names of the columns classified by class

  • data.frame with numbers: total values, row, columns, complete rows

  • plot with visualizations

See also

Other Exploratory: corr_cross(), corr_var(), crosstab(), distr(), freqs_df(), freqs_list(), freqs_plot(), freqs(), lasso_vars(), missingness(), plot_cats(), plot_df(), plot_nums(), tree_var()

Examples

Sys.unsetenv("LARES_FONT") # Temporal
data(dft) # Titanic dataset
df_str(dft, "names")
#> Other available 'return' options: 'skimr', 'numbers', 'distr', 'plot'
#> $cols
#>  [1] "PassengerId" "Survived"    "Pclass"      "Sex"         "Age"        
#>  [6] "SibSp"       "Parch"       "Ticket"      "Fare"        "Cabin"      
#> [11] "Embarked"   
#> 
#> $nums
#> [1] "PassengerId" "Age"         "SibSp"       "Parch"       "Fare"       
#> 
#> $char
#> [1] "Ticket"
#> 
#> $factor
#> [1] "Pclass"   "Sex"      "Cabin"    "Embarked"
#> 
#> $logic
#> [1] "Survived"
#> 
#> $time
#> character(0)
#> 
#> $allnas
#> character(0)
#> 
df_str(dft, "numbers", quiet = TRUE)
#>                                  metric counter       p
#> Total.Values               Total.Values    9801  100.00
#> Total.Rows                   Total.Rows     891  100.00
#> Total.Columns             Total.Columns      11  100.00
#> Numeric.Columns         Numeric.Columns       5   45.45
#> Character.Columns     Character.Columns       1    9.09
#> Factor.Columns           Factor.Columns       4   36.36
#> Logical.Columns         Logical.Columns       1    9.09
#> Time.Date.Columns     Time.Date.Columns       0    0.00
#> All.Missing.Columns All.Missing.Columns       0    0.00
#> Missing.Values           Missing.Values     177    1.81
#> Complete.Rows             Complete.Rows     714   80.13
#> Memory.Usage               Memory.Usage  103448 1055.48
df_str(dft, "plot", quiet = TRUE)