There is increasing need to make user-friendly and production ready Tables for machine learning data. This function is a simplified 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.
Arguments
- x
The data to be summarised. Only numeric data is allowed.
- Type
The type of data to be summarised. There are two options here 1 or 2, 1 =
Continuousand 2 =Likert-type- Cut
The cut-off point for Likert-type data
- Up
The top Likert-type scale, for example,
Agree,Constraintsetc which would appear in the remark column.- Down
The lower Likert-type scale, for example,
Disagree,Not a Constraintetc which would appear in the remark column.- ci
Confidence interval which is defaults to 0.95.
Value
The function returns a formatted Table of the Quick summary
ANSThe formatted Table of the summary
Examples
# Likert-type data
Up <- "Constraint"
Down <- "Not a constraint"
quicksummary(x = Quicksummary, Type = 2, Cut = 2.60, Up = Up, Down = Down)
#> Mean SD SE.Mean Nobs Rank Remark
#> Likert scores 1 4.34 1.13 0.11 103 1 Constraint
#> Likert scores 14 3.85 1.35 0.13 103 2 Constraint
#> Likert scores 3 3.49 1.36 0.13 103 3 Constraint
#> Likert scores 10 3.49 1.51 0.15 103 4 Constraint
#> Likert scores 15 3.43 1.38 0.14 103 5 Constraint
#> Likert scores 19 3.43 1.23 0.12 103 6 Constraint
#> Likert scores 17 3.41 1.25 0.12 103 7 Constraint
#> Likert scores 2 3.23 1.57 0.15 103 8 Constraint
#> Likert scores 18 3.23 1.21 0.12 103 9 Constraint
#> Likert scores 4 3.17 1.34 0.13 103 10 Constraint
#> Likert scores 7 3.07 1.32 0.13 103 11 Constraint
#> Likert scores 21 3.07 1.32 0.13 103 12 Constraint
#> Likert scores 26 3.03 1.22 0.12 103 13 Constraint
#> Likert scores 20 2.98 1.18 0.12 103 14 Constraint
#> Likert scores 16 2.94 1.47 0.14 103 15 Constraint
#> Likert scores 22 2.94 1.31 0.13 103 16 Constraint
#> Likert scores 13 2.93 1.37 0.14 103 17 Constraint
#> Likert scores 11 2.89 1.20 0.12 103 18 Constraint
#> Likert scores 25 2.88 1.31 0.13 103 19 Constraint
#> Likert scores 23 2.84 1.48 0.15 103 20 Constraint
#> Likert scores 8 2.83 1.33 0.13 103 21 Constraint
#> Likert scores 6 2.77 1.44 0.14 103 22 Constraint
#> Likert scores 24 2.71 1.30 0.13 103 23 Constraint
#> Likert scores 5 2.67 1.27 0.13 103 24 Constraint
#> Likert scores 9 2.63 1.34 0.13 103 25 Constraint
#> Likert scores 12 2.41 1.26 0.12 103 26 Not a constraint
#> Likert scores 27 2.41 1.35 0.13 103 27 Not a constraint
#> Likert scores 29 0.89 1.78 0.18 103 28 Not a constraint
#> Likert scores 28 0.26 0.83 0.08 103 29 Not a constraint
# Continuous data
x <- select(linearsystems, 1:6)
quicksummary(x = x, Type = 1)
#> MKTcost Age Experience Years spent in formal education
#> Mean 3911.6 38.13 11.78 10.35
#> SD 2754.2 11.14 4.55 5.19
#> SE.Mean 275.4 1.11 0.46 0.52
#> Min 0.0 20.00 2.00 0.00
#> Median 2950.0 36.50 11.00 12.00
#> Max 14000.0 68.00 20.00 20.00
#> Q1 1850.0 30.00 8.75 7.00
#> Q3 5760.0 45.00 15.00 14.00
#> Skewness 1.2 0.83 0.38 -0.72
#> Kurtosis 1.3 0.01 -0.77 -0.42
#> Nobs 100.0 100.00 100.00 100.00
#> Household size Years as a cooperative member
#> Mean 8.30 10.16
#> SD 3.60 3.80
#> SE.Mean 0.36 0.38
#> Min 0.00 2.00
#> Median 8.00 10.00
#> Max 17.00 20.00
#> Q1 5.00 7.75
#> Q3 11.00 12.00
#> Skewness 0.18 0.64
#> Kurtosis -0.37 -0.20
#> Nobs 100.00 100.00