There is increasing need to make user-friendly and production ready Tables for machine learning data. This function is simplified and quick summary; and the output is a formatted table. This is very handy for those who do not have the time to write codes for user-friendly summaries.
Usage
quicksummary(
x,
Type,
Cut = deprecated(),
Up = deprecated(),
Down = deprecated(),
Dig = 2,
ci = 0.95
)![[Deprecated]](figures/lifecycle-deprecated.svg)
Examples
library(tidyverse)
# Likert-type data
quicksummary(x = Quicksummary, Type = 2)
#> $Summary
#> Mean SD SE.Mean Nobs Rank
#> Likert scores 1 4.30 1.10 0.110 103 1
#> Likert scores 14 3.90 1.40 0.130 103 2
#> Likert scores 3 3.50 1.40 0.130 103 3
#> Likert scores 10 3.50 1.50 0.150 103 4
#> Likert scores 15 3.40 1.40 0.140 103 5
#> Likert scores 17 3.40 1.20 0.120 103 6
#> Likert scores 19 3.40 1.20 0.120 103 7
#> Likert scores 2 3.20 1.60 0.150 103 8
#> Likert scores 4 3.20 1.30 0.130 103 9
#> Likert scores 18 3.20 1.20 0.120 103 10
#> Likert scores 7 3.10 1.30 0.130 103 11
#> Likert scores 21 3.10 1.30 0.130 103 12
#> Likert scores 20 3.00 1.20 0.120 103 13
#> Likert scores 26 3.00 1.20 0.120 103 14
#> Likert scores 11 2.90 1.20 0.120 103 15
#> Likert scores 13 2.90 1.40 0.140 103 16
#> Likert scores 16 2.90 1.50 0.140 103 17
#> Likert scores 22 2.90 1.30 0.130 103 18
#> Likert scores 25 2.90 1.30 0.130 103 19
#> Likert scores 6 2.80 1.40 0.140 103 20
#> Likert scores 8 2.80 1.30 0.130 103 21
#> Likert scores 23 2.80 1.50 0.150 103 22
#> Likert scores 5 2.70 1.30 0.130 103 23
#> Likert scores 24 2.70 1.30 0.130 103 24
#> Likert scores 9 2.60 1.30 0.130 103 25
#> Likert scores 12 2.40 1.30 0.120 103 26
#> Likert scores 27 2.40 1.30 0.130 103 27
#> Likert scores 29 0.89 1.80 0.180 103 28
#> Likert scores 28 0.26 0.83 0.082 103 29
#>
#> $Means
#> Arithmetic Geometric Quadratic Harmonic Cubic Nobs
#> Likert scores 1 4.30 4.1 4.50 3.7 1 103
#> Likert scores 2 3.20 2.7 3.60 2.2 1 103
#> Likert scores 3 3.50 3.1 3.70 2.7 1 103
#> Likert scores 4 3.20 2.8 3.40 2.5 1 103
#> Likert scores 5 2.70 2.3 3.00 2.0 1 103
#> Likert scores 6 2.80 2.4 3.10 2.0 1 103
#> Likert scores 7 3.10 2.7 3.30 2.3 1 103
#> Likert scores 8 2.80 2.5 3.10 2.1 1 103
#> Likert scores 9 2.60 2.3 2.90 2.0 1 103
#> Likert scores 10 3.50 3.0 3.80 2.5 1 103
#> Likert scores 11 2.90 2.6 3.10 2.3 1 103
#> Likert scores 12 2.40 2.1 2.70 1.8 1 103
#> Likert scores 13 2.90 2.5 3.20 2.1 1 103
#> Likert scores 14 3.90 3.5 4.10 3.0 1 103
#> Likert scores 15 3.40 3.1 3.70 2.6 1 103
#> Likert scores 16 2.90 2.6 3.30 2.2 1 103
#> Likert scores 17 3.40 3.1 3.60 2.7 1 103
#> Likert scores 18 3.20 2.9 3.50 2.5 1 103
#> Likert scores 19 3.40 3.1 3.60 2.8 1 103
#> Likert scores 20 3.00 2.7 3.20 2.4 1 103
#> Likert scores 21 3.10 2.7 3.30 2.3 1 103
#> Likert scores 22 2.90 2.6 3.20 2.2 1 103
#> Likert scores 23 2.80 2.4 3.20 2.0 1 103
#> Likert scores 24 2.70 2.4 3.00 2.0 1 103
#> Likert scores 25 2.90 2.5 3.20 2.1 1 103
#> Likert scores 26 3.00 2.7 3.30 2.4 1 103
#> Likert scores 27 2.40 0.0 2.80 0.0 1 103
#> Likert scores 28 0.26 0.0 0.86 0.0 1 103
#> Likert scores 29 0.89 0.0 2.00 0.0 1 103
#>
# Continuous data
x <- select(linearsystems, 1:6)
quicksummary(x = x, Type = 1)
#> $Summary
#> MKTcost Age Experience Years spent in formal education
#> Mean 3900.0 3.8e+01 12.00 10.00
#> SD 2800.0 1.1e+01 4.60 5.20
#> SE.Mean 280.0 1.1e+00 0.46 0.52
#> Min 0.0 2.0e+01 2.00 0.00
#> Q1 1800.0 3.0e+01 8.80 7.00
#> Median 3000.0 3.6e+01 11.00 12.00
#> Q3 5800.0 4.5e+01 15.00 14.00
#> Max 14000.0 6.8e+01 20.00 20.00
#> IQR 3900.0 1.5e+01 6.20 7.00
#> Skewness 1.2 8.3e-01 0.38 -0.72
#> Kurtosis 1.3 7.2e-03 -0.77 -0.42
#> Nobs 100.0 1.0e+02 100.00 100.00
#> Household size Years as a cooperative member
#> Mean 8.30 10.00
#> SD 3.60 3.80
#> SE.Mean 0.36 0.38
#> Min 0.00 2.00
#> Q1 5.00 7.80
#> Median 8.00 10.00
#> Q3 11.00 12.00
#> Max 17.00 20.00
#> IQR 6.00 4.20
#> Skewness 0.18 0.64
#> Kurtosis -0.37 -0.20
#> Nobs 100.00 100.00
#>
#> $Means
#> MKTcost Age Experience Years spent in formal education
#> Arithmetic 3900 38 12.0 10
#> Geometric 0 37 11.0 0
#> Quadratic 4800 40 13.0 12
#> Harmonic 0 35 9.8 0
#> Cubic 1 1 1.0 1
#> Nobs 100 100 100.0 100
#> Household size Years as a cooperative member
#> Arithmetic 8.3 10.0
#> Geometric 0.0 9.5
#> Quadratic 9.0 11.0
#> Harmonic 0.0 8.7
#> Cubic 1.0 1.0
#> Nobs 100.0 100.0
#>