This blog is about the improved function, quicksummary in the Dyn4cast package. The function provides quick overview of data and particularly outputting five different means.
Observational study involves procuring large mass of data for analysis and modeling. So, there is always need to have an overview of the data to decide on the appropriate analysis to be undertaken. This is where this function is unique because five different means are computed simultaneously, in spite of the one line code arguments. The five means are:
Arithmetic
Geometric
Harmonic
Quadratic
Cubic.
The basic usage of the codes are:
quicksummary(x, Type, Cut, Up, Down, ci = 0.95)
Arguments
x The data to be summarised. Only numeric data is allowed.
Type The type of data to be summarized. There are two options here 1 or 2, 1 = Continuous and 2 = Likert-type
Cut The cut-off point for Likert-type data
Up The top Likert-type scale, for example, Agree, Constraints etc which would appear in the remark column.
Down The lower Likert-type scale, for example, Disagree, Not a Constraint etc which would appear in the remark column.
ci Confidence interval which is defaults to 0.95.
Let us go!
Up <- "Constraint" Down <- "Not a constraint" sum1 <- quicksummary(x = Quicksummary, Type = 2, Cut = 2.60, Up = Up, Down = Down) # Continuous data x <- select(linearsystems, 1:6) sum2 <- quicksummary(x = x, Type = 1)
Likert-type summaries
General summaries
sum1$Summary Mean SD SE.Mean Nobs Rank Remark Likert scores 1 4.34 1.13 0.11 103 1 Constraint Likert scores 14 3.85 1.35 0.13 103 2 Constraint Likert scores 3 3.49 1.36 0.13 103 3 Constraint Likert scores 10 3.49 1.51 0.15 103 4 Constraint Likert scores 15 3.43 1.38 0.14 103 5 Constraint Likert scores 19 3.43 1.23 0.12 103 6 Constraint Likert scores 17 3.41 1.25 0.12 103 7 Constraint Likert scores 2 3.23 1.57 0.15 103 8 Constraint Likert scores 18 3.23 1.21 0.12 103 9 Constraint Likert scores 4 3.17 1.34 0.13 103 10 Constraint Likert scores 7 3.07 1.32 0.13 103 11 Constraint Likert scores 21 3.07 1.32 0.13 103 12 Constraint Likert scores 26 3.03 1.22 0.12 103 13 Constraint Likert scores 20 2.98 1.18 0.12 103 14 Constraint Likert scores 16 2.94 1.47 0.14 103 15 Constraint Likert scores 22 2.94 1.31 0.13 103 16 Constraint Likert scores 13 2.93 1.37 0.14 103 17 Constraint Likert scores 11 2.89 1.20 0.12 103 18 Constraint Likert scores 25 2.88 1.31 0.13 103 19 Constraint Likert scores 23 2.84 1.48 0.15 103 20 Constraint Likert scores 8 2.83 1.33 0.13 103 21 Constraint Likert scores 6 2.77 1.44 0.14 103 22 Constraint Likert scores 24 2.71 1.30 0.13 103 23 Constraint Likert scores 5 2.67 1.27 0.13 103 24 Constraint Likert scores 9 2.63 1.34 0.13 103 25 Constraint Likert scores 12 2.41 1.26 0.12 103 26 Not a constraint Likert scores 27 2.41 1.35 0.13 103 27 Not a constraint Likert scores 29 0.89 1.78 0.18 103 28 Not a constraint Likert scores 28 0.26 0.83 0.08 103 29 Not a constraint
Means
sum1$Means Arithmetic Geometric Quadratic Harmonic Cubic Likert scores 1 4.34 4.11 4.48 3.74 4.58 Likert scores 2 3.23 2.74 3.59 2.21 3.83 Likert scores 3 3.49 3.13 3.74 2.70 3.92 Likert scores 4 3.17 2.84 3.43 2.48 3.64 Likert scores 5 2.67 2.34 2.95 2.00 3.19 Likert scores 6 2.77 2.37 3.12 1.99 3.39 Likert scores 7 3.07 2.71 3.34 2.31 3.53 Likert scores 8 2.83 2.47 3.12 2.10 3.35 Likert scores 9 2.63 2.29 2.95 1.98 3.22 Likert scores 10 3.49 3.04 3.80 2.50 4.01 Likert scores 11 2.89 2.62 3.13 2.32 3.33 Likert scores 12 2.41 2.08 2.72 1.79 2.98 Likert scores 13 2.93 2.55 3.24 2.14 3.46 Likert scores 14 3.85 3.49 4.08 2.96 4.23 Likert scores 15 3.43 3.07 3.69 2.64 3.89 Likert scores 16 2.94 2.55 3.28 2.18 3.56 Likert scores 17 3.41 3.11 3.63 2.74 3.79 Likert scores 18 3.23 2.93 3.45 2.55 3.61 Likert scores 19 3.43 3.15 3.64 2.80 3.80 Likert scores 20 2.98 2.70 3.20 2.38 3.38 Likert scores 21 3.07 2.73 3.34 2.35 3.55 Likert scores 22 2.94 2.60 3.22 2.22 3.43 Likert scores 23 2.84 2.41 3.20 1.99 3.47 Likert scores 24 2.71 2.37 3.00 2.03 3.24 Likert scores 25 2.88 2.53 3.16 2.15 3.37 Likert scores 26 3.03 2.74 3.26 2.40 3.45 Likert scores 27 2.41 0.00 2.76 0.00 3.03 Likert scores 28 0.26 0.00 0.86 0.00 1.36 Likert scores 29 0.89 0.00 1.98 0.00 2.62
Continous data summaries
General summaries
sum2$Summary MKTcost Age Experience Years spent in formal education Mean 3911.55 38.13 11.78 10.35 SD 2754.19 11.14 4.55 5.19 SE.Mean 275.42 1.11 0.46 0.52 Min 0.00 20.00 2.00 0.00 Median 2950.00 36.50 11.00 12.00 Max 14000.00 68.00 20.00 20.00 Q1 1850.00 30.00 8.75 7.00 Q3 5760.00 45.00 15.00 14.00 Skewness 1.19 0.83 0.38 -0.72 Kurtosis 1.32 0.01 -0.77 -0.42 Nobs 100.00 100.00 100.00 100.00 Household size Years as a cooperative member Mean 8.30 10.16 SD 3.60 3.80 SE.Mean 0.36 0.38 Min 0.00 2.00 Median 8.00 10.00 Max 17.00 20.00 Q1 5.00 7.75 Q3 11.00 12.00 Skewness 0.18 0.64 Kurtosis -0.37 -0.20 Nobs 100.00 100.00
Means
sum2$Means MKTcost Age Experience Years spent in formal education Arithmetic 3911.55 38.13 11.78 10.35 Geometric 0.00 36.64 10.86 0.00 Quadratic 4775.97 39.71 12.62 11.57 Harmonic 0.00 35.26 9.81 0.00 Cubic 5561.65 41.33 13.38 12.25 Household size Years as a cooperative member Arithmetic 8.30 10.16 Geometric 0.00 9.46 Quadratic 9.04 10.84 Harmonic 0.00 8.70 Cubic 9.65 11.49
Welcome to the world of easy Data Science and easy Machine Learning!
Related