overview.md
In cheapr: Simple Functions to Save Time and Memory

overview

Code
  overview(numeric(), hist = TRUE)
Output
  obs: 0 
  cols: NA

  ----- Numeric -----
    col n_missng p_complt n_unique mean p0 p25 p50 p75 p100 iqr sd hist
  1   x       NA       NA       NA   NA NA  NA  NA  NA   NA  NA NA

Code
  overview(numeric(), hist = FALSE)
Output
  obs: 0 
  cols: NA

  ----- Numeric -----
    col n_missng p_complt n_unique mean p0 p25 p50 p75 p100 iqr sd
  1   x       NA       NA       NA   NA NA  NA  NA  NA   NA  NA NA

Code
  overview(airquality, hist = FALSE)
Output
  obs: 153 
  cols: 6

  ----- Numeric -----
        col n_missng p_complt n_unique   mean   p0    p25   p50    p75  p100
  1   Ozone       37     0.76       67  42.13    1     18  31.5  63.25   168
  2 Solar.R        7     0.95      117 185.93    7 115.75   205 258.75   334
  3    Wind        0        1       31   9.96  1.7    7.4   9.7   11.5  20.7
  4    Temp        0        1       40  77.88   56     72    79     85    97
  5   Month        0        1        5   6.99    5      6     7      8     9
  6     Day        0        1       31   15.8    1      8    16     23    31
       iqr    sd
  1  45.25 32.99
  2    143 90.06
  3    4.1  3.52
  4     13  9.47
  5      2  1.42
  6     15  8.86

Code
  overview(iris, hist = FALSE)
Output
  obs: 150 
  cols: 5

  ----- Numeric -----
             col n_missng p_complt n_unique mean  p0 p25  p50 p75 p100 iqr   sd
  1 Sepal.Length        0        1       35 5.84 4.3 5.1  5.8 6.4  7.9 1.3 0.83
  2  Sepal.Width        0        1       23 3.06   2 2.8    3 3.3  4.4 0.5 0.44
  3 Petal.Length        0        1       43 3.76   1 1.6 4.35 5.1  6.9 3.5 1.77
  4  Petal.Width        0        1       22  1.2 0.1 0.3  1.3 1.8  2.5 1.5 0.76

  ----- Categorical -----
        col n_missng p_complt n_unique n_levels    min       max
  1 Species        0        1        3        3 setosa virginica

Code
  overview(iris2, hist = FALSE)
Output
  obs: 100 
  cols: 7

  ----- Logical -----
      col n_missng p_complt n_true n_false p_true
  1 large        0        1     24      76   0.24

  ----- Numeric -----
             col n_missng p_complt n_unique mean  p0 p25  p50  p75 p100  iqr   sd
  1 Sepal.Length        0        1       28 5.47 4.3   5  5.4  5.9    7  0.9 0.64
  2  Sepal.Width        0        1       23  3.1   2 2.8 3.05  3.4  4.4  0.6 0.48
  3 Petal.Length        0        1       28 2.86   1 1.5 2.45 4.32  5.1 2.83 1.45
  4  Petal.Width        0        1       15 0.79 0.1 0.2  0.8  1.3  1.8  1.1 0.57

  ----- Categorical -----
         col n_missng p_complt n_unique n_levels    min        max
  1  Species        0        1        2        3 setosa versicolor
  2 Species2        0        1        2       NA setosa versicolor

Code
  overview(warpbreaks, hist = FALSE)
Output
  obs: 54 
  cols: 3

  ----- Numeric -----
       col n_missng p_complt n_unique  mean p0   p25 p50 p75 p100   iqr   sd
  1 breaks        0        1       31 28.15 10 18.25  26  34   70 15.75 13.2

  ----- Categorical -----
        col n_missng p_complt n_unique n_levels min max
  1    wool        0        1        2        2   A   B
  2 tension        0        1        3        3   L   H

Code
  overview(ToothGrowth, hist = FALSE)
Output
  obs: 60 
  cols: 3

  ----- Numeric -----
     col n_missng p_complt n_unique  mean  p0   p25   p50   p75 p100  iqr   sd
  1  len        0        1       43 18.81 4.2 13.07 19.25 25.27 33.9 12.2 7.65
  2 dose        0        1        3  1.17 0.5   0.5     1     2    2  1.5 0.63

  ----- Categorical -----
     col n_missng p_complt n_unique n_levels min max
  1 supp        0        1        2        2  OJ  VC

Code
  overview(df)
Output
  obs: 25 
  cols: 3

  ----- Time-Series -----
           col n_missng p_complt n_unique  mean    p0   p25  p50  p75 p100  iqr
  1          y        0        1       25 0.053 -2.53 -0.44 0.24 0.82 1.59 1.26
  2          x        0        1       25 0.053 -2.53 -0.44 0.24 0.82 1.59 1.26
  3 z_Series 1        0        1       25 0.053 -2.53 -0.44 0.24 0.82 1.59 1.26
  4 z_Series 2        0        1       25 0.053 -2.53 -0.44 0.24 0.82 1.59 1.26
  5 z_Series 3        0        1       25 0.053 -2.53 -0.44 0.24 0.82 1.59 1.26
  6 z_Series 4        0        1       25 0.053 -2.53 -0.44 0.24 0.82 1.59 1.26
  7 z_Series 5        0        1       25 0.053 -2.53 -0.44 0.24 0.82 1.59 1.26
      sd  hist grwth_rt
  1 1.15 ▂▃▅▆▇  -14.38%
  2 1.15 ▂▃▅▆▇  -14.38%
  3 1.15 ▂▃▅▆▇  -14.38%
  4 1.15 ▂▃▅▆▇  -14.38%
  5 1.15 ▂▃▅▆▇  -14.38%
  6 1.15 ▂▃▅▆▇  -14.38%
  7 1.15 ▂▃▅▆▇  -14.38%

Code
  overview(ts(matrix(x, ncol = 5)))
Output
  obs: 5 
  cols: 5

  ----- Time-Series -----
         col n_missng p_complt n_unique  mean    p0   p25    p50  p75 p100  iqr
  1 Series 1        0        1        5   0.2 -1.24 -0.36   0.66 0.82 1.14 1.18
  2 Series 2        0        1        5  0.76 -0.39  0.77   0.81  1.3 1.33 0.53
  3 Series 3        0        1        5 -0.67 -2.53  -1.6  -0.11 0.24 0.64 1.84
  4 Series 4        0        1        5  0.21  -0.7 -0.44  0.076 0.53 1.59 0.97
  5 Series 5        0        1        5 -0.24 -2.36 -1.23  0.028 0.86 1.51 2.09
      sd  hist grwth_rt
  1 0.98 ▅▅▁▅▇   -8.04%
  2  0.7 ▅▁▁▇▇  -12.77%
  3 1.34 ▅▅▁▅▇   97.37%
  4 0.91 ▇▅▅▁▅      NA%
  5 1.57 ▅▅▁▅▇      NA%

Code
  overview(EuStockMarkets)
Output
  obs: 1860 
  cols: 4

  ----- Time-Series -----
     col n_missng p_complt n_unique    mean      p0     p25     p50     p75
  1  DAX        0        1     1774 2530.66 1402.34  1744.1 2140.56 2722.37
  2  SMI        0        1     1725 3376.22  1587.4 2165.62 2796.35 3812.43
  3  CAC        0        1     1617 2227.83    1611 1875.15  1992.3 2274.35
  4 FTSE        0        1     1729 3565.64    2281 2843.15  3246.6 3993.57
       p100     iqr      sd  hist grwth_rt
  1 6186.09  978.26 1084.79 ▇▂▂▁▁   0.065%
  2    8412  1646.8 1663.03 ▇▃▁▁▁   0.082%
  3  4388.5   399.2  580.31 ▇▂▁▁▁   0.044%
  4    6179 1150.43  976.72 ▇▇▂▂▂   0.043%