Exploring a data frame

The purpose of the qacBase package is to provide functions for descriptive statistics, data management, and data visualization. As a part of this package, the contents function produces a series of informational tables that allow for users to have a comprehensive understanding of their dataset of choice as well as the each of the quantitative and categorical variables featured in their dataset. Graphical functions, such as barcharts, histograms, and densities provide succinct visualizations of the variables in a data frame.

Example Dataset: Motor Trend Magazine Review of Cars

How can you quickly become familiar with the data in a data frame? We’ll use the cars74 data frame as an example. This data frame contains information on the characteristics of 32 cars in 1974.

data(cars74)

Data description with `contents()`

contents(cars74)
#> 
#> The data frame cars74 has 32 observations and 12 variables.
#> 
#> Overall
#>  pos varname type      n_unique n_miss pct_miss
#>   1  auto    character 32       0      0%      
#>   2  mpg     numeric   25       0      0%      
#>   3  cyl     ordered    3       0      0%      
#>   4  disp    numeric   27       0      0%      
#>   5  hp      numeric   22       0      0%      
#>   6  drat    numeric   22       0      0%      
#>   7  wt      numeric   29       0      0%      
#>   8  qsec    numeric   30       0      0%      
#>   9  vs      factor     2       0      0%      
#>  10  am      factor     2       0      0%      
#>  11  gear    ordered    3       0      0%      
#>  12  carb    ordered    6       0      0%      
#> 
#> Numeric Variables
#>       n   mean     sd skew   min    p25 median    p75    max
#> mpg  32  20.09   6.03 0.61 10.40  15.43  19.20  22.80  33.90
#> disp 32 230.72 123.94 0.38 71.10 120.83 196.30 326.00 472.00
#> hp   32 146.69  68.56 0.73 52.00  96.50 123.00 180.00 335.00
#> drat 32   3.60   0.53 0.27  2.76   3.08   3.70   3.92   4.93
#> wt   32   3.22   0.98 0.42  1.51   2.58   3.33   3.61   5.42
#> qsec 32  17.85   1.79 0.37 14.50  16.89  17.71  18.90  22.90
#> 
#> Categorical Variables
#>  variable level              n  pct 
#>  auto     AMC Javelin         1 0.03
#>           Cadillac Fleetwood  1 0.03
#>           Camaro Z28          1 0.03
#>           Chrysler Imperial   1 0.03
#>           Datsun 710          1 0.03
#>           Dodge Challenger    1 0.03
#>           Duster 360          1 0.03
#>           Ferrari Dino        1 0.03
#>           Fiat 128            1 0.03
#>           Fiat X1-9           1 0.03
#>           (22 more levels)   22 0.69
#>  cyl      cyl4               11 0.34
#>           cyl6                7 0.22
#>           cyl8               14 0.44
#>  vs       V-shaped           18 0.56
#>           straight           14 0.44
#>  am       automatic          19 0.59
#>           manual             13 0.41
#>  gear     gears3             15 0.47
#>           gears4             12 0.38
#>           gears5              5 0.16
#>  carb     carb1               7 0.22
#>           carb2              10 0.31
#>           carb3               3 0.09
#>           carb4              10 0.31
#>           carb6               1 0.03
#>           carb8               1 0.03

As shown above, the contents function produced three tables, one that provides an overall summary of the data and two tables that break down the quantitative and categorical variables of the dataset respectively.

The overall table describes each of the variables in the data frame, listing their column position, variable name, the variable type, number of unique values, the number of missing values, and the corresponding percentage of missing values.

The quantitative table provides information for each of the quantitative variables in the data frame, listing the variable name, the number of non-missing values, and a series of summary statistics including the mean, standard deviation, skewness, minimum, 25%tile, median, 75%tile, and maximum.

The categorical table provides information for each of the categorical variables in the data frame, listing the variable name, the specific levels of each variable, and the number of observations for each level, and percentage distribution of each variable level. By default, up to 10 levels of a categorical variable are displayed (but you can increase this by adding the option maxcat = #, where # is the number of levels to display).

If the dataset only contains quantitative or categorical, then only the overall summary table and the relevant variable table would be printed.

Visualizing the data frame

The df_plot function provides a single graph displaying the variables, their types, and the percent of values present (or missing).

df_plot(cars74)

Visualizing the categorical variables

The barcharts function plots the distribution of each of the categorical variables in the data frame.

barcharts(cars74)
#> The following variable had more than 20 levels and were not graphed:
#>  auto

Visualizing the quantitative variables

The histograms and densities functions plot the distribution of each of the quantitative variables in the data frame.

histograms(cars74)