COMP3430 DW: Lab 1 Explore - Summary + Describe + Skewness + Show Missing

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See https://datamining.togaware.com/survivor/Exploring_Data.html

Description

COMP3430 DW Lab 1 loads a sample dataset and asks students to explore the dataset: summary, describe, skewness, missing.

Closing Citeria

A collection of R scripts:

• [x] explore_summary.R

• [x] explore_describe.R (incorpoated into summary)

• [x] explore_skewness.R (incorporated into SUMMARY)

• [x] explore_missing.R

• [x] explore correlation

• [x] Explore tab implementation to call upon the different scripts.

• [x] Dataset tab needs to support ignore option.

• [x] Test tab Correlation functionality

Test Case

Data Wrangling Lab 1 Required for Week 3 but must be there end of June

• [x] Load weather data set
• [x] Explore -> Summary -> Summary + Describe + Show Missing
• [x] Explore -> Correlation ->
• [x] Explore -> Summary -> Skewness
• [x] Test -> Correlation + Choose two variables
• [x] Dataset -> Set the role of variables Input/Target/Risk/Ident/Ignore/Weight
• [x] Explore -> Distributions -> Numeric -> Box Plot
• [x] Explore -> Distributions -> Numeric -> Histogram
• [x] Explore -> Distributions -> Numeric -> Cumulative
• [x] Explore -> Distributions -> Numeric -> Benford
• [ ] Explore -> Distributions -> Numeric -> Pairs
• [x] Explore -> Distributions -> Categoric -> Bar Plot
• [x] Explore -> Distributions -> Categoric -> Dot Plot
• [x] Explore -> Distributions -> Categoric -> Mosaic
• [ ] Explore -> Distributions -> Categoric -> Pairs
• [ ] Save plots as PDF or PNG

Details

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Explore -> Summary -> Summary + Describe + Show Missing

#=======================================================================
# Rattle timestamp: 2024-05-31 16:36:48.039319 x86_64-pc-linux-gnu 

# The 'Hmisc' package provides the 'contents' function.

library(Hmisc, quietly=TRUE)

# Obtain a summary of the dataset.

contents(crs$dataset[crs$train, c(crs$input, crs$risk, crs$target)])
summary(crs$dataset[crs$train, c(crs$input, crs$risk, crs$target)])

# The 'Hmisc' package provides the 'describe' function.

library(Hmisc, quietly=TRUE)

# Generate a description of the dataset.

describe(crs$dataset[crs$train, c(crs$input, crs$risk, crs$target)])

# The 'mice' package provides the 'md.pattern' function.

library(mice, quietly=TRUE)

# Generate a summary of the missing values in the dataset.

md.pattern(crs$dataset[,c(crs$input, crs$target)])

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