Trims vector and dataframe objects — eda

Removes records from either tail-ends of a sorted dataset. Trimming can be performed by number of records (specify the num = option) or by quantiles (specify the prop= option).

eda_trim Trims a vector
eda_trim_df Trims a data frame
eda_ltrim Left-trims a vector
eda_rtrim Right-trims a vector
eda_ltrim_df Left-trims a dataframe
eda_rtrim_df Right-trims a dataframe

Usage

eda_trim(x, prop = 0.05, num = 0)

eda_trim_df(dat, x, prop = 0.05, num = 0)

eda_ltrim(x, prop = 0.05, num = 0)

eda_ltrim_df(dat, x, prop = 0.05, num = 0)

eda_rtrim(x, prop = 0.05, num = 0)

eda_rtrim_df(dat, x, prop = 0.05, num = 0)

Arguments

x: Vector of values (if trimming a vector) or the column whose values are used to trim a dataframe (applies to *_df functions only)
prop: Fraction of values to trim
num: Number of values to trim
dat: Dataframe (applies to *_df functions only)

Value

Returns the same data type as the input (i.e. vector or dataframe)

Details

The input dataset does not need to be sorted (sorting is performed in the functions).
If num is set to zero, then the function will assume that the trimming is to be done by fraction (defined by the prop parameter).
If eda_trim or eda_trim_df functions are called, the num and prop values apply to each tail. For example, if num = 5 then the 5 smallest AND 5 largest values are removed from the data.
NA values must be stripped from the input vector or column elements before running the trim functions.
Elements are returned sorted on the trimmed elements.

Examples


# Trim a vector by 10% (i.e. 10% of the smallest and 10% of the largest
# values)
eda_trim( mtcars[,1], prop=0.1)
#>  [1] 14.7 15.0 15.2 15.2 15.5 15.8 16.4 17.3 17.8 18.1 18.7 19.2 19.2 19.7 21.0
#> [16] 21.0 21.4 21.4 21.5 22.8 22.8 24.4 26.0 27.3

# Trim a data frame by 10% using the mpg column(i.e. 10% of the smallest
# and 10% of the largest mpg values)
eda_trim_df( mtcars, mpg, prop=0.1)
#>                    mpg cyl  disp  hp drat    wt  qsec vs am gear carb
#> Chrysler Imperial 14.7   8 440.0 230 3.23 5.345 17.42  0  0    3    4
#> Maserati Bora     15.0   8 301.0 335 3.54 3.570 14.60  0  1    5    8
#> Merc 450SLC       15.2   8 275.8 180 3.07 3.780 18.00  0  0    3    3
#> AMC Javelin       15.2   8 304.0 150 3.15 3.435 17.30  0  0    3    2
#> Dodge Challenger  15.5   8 318.0 150 2.76 3.520 16.87  0  0    3    2
#> Ford Pantera L    15.8   8 351.0 264 4.22 3.170 14.50  0  1    5    4
#> Merc 450SE        16.4   8 275.8 180 3.07 4.070 17.40  0  0    3    3
#> Merc 450SL        17.3   8 275.8 180 3.07 3.730 17.60  0  0    3    3
#> Merc 280C         17.8   6 167.6 123 3.92 3.440 18.90  1  0    4    4
#> Valiant           18.1   6 225.0 105 2.76 3.460 20.22  1  0    3    1
#> Hornet Sportabout 18.7   8 360.0 175 3.15 3.440 17.02  0  0    3    2
#> Merc 280          19.2   6 167.6 123 3.92 3.440 18.30  1  0    4    4
#> Pontiac Firebird  19.2   8 400.0 175 3.08 3.845 17.05  0  0    3    2
#> Ferrari Dino      19.7   6 145.0 175 3.62 2.770 15.50  0  1    5    6
#> Mazda RX4         21.0   6 160.0 110 3.90 2.620 16.46  0  1    4    4
#> Mazda RX4 Wag     21.0   6 160.0 110 3.90 2.875 17.02  0  1    4    4
#> Hornet 4 Drive    21.4   6 258.0 110 3.08 3.215 19.44  1  0    3    1
#> Volvo 142E        21.4   4 121.0 109 4.11 2.780 18.60  1  1    4    2
#> Toyota Corona     21.5   4 120.1  97 3.70 2.465 20.01  1  0    3    1
#> Datsun 710        22.8   4 108.0  93 3.85 2.320 18.61  1  1    4    1
#> Merc 230          22.8   4 140.8  95 3.92 3.150 22.90  1  0    4    2
#> Merc 240D         24.4   4 146.7  62 3.69 3.190 20.00  1  0    4    2
#> Porsche 914-2     26.0   4 120.3  91 4.43 2.140 16.70  0  1    5    2
#> Fiat X1-9         27.3   4  79.0  66 4.08 1.935 18.90  1  1    4    1