Team-32

Author

Data visionaries

Build a parallel coordinates plot to compare health indicators across multiple countries.

Introduction:

Health indicators are important measures that reflect the overall well-being and development of a country. Comparing multiple indicators helps identify patterns and differences between countries. In this report, a parallel coordinates plot is used to visualize and compare health indicators across multiple countries.

Objective:

To analyze and compare key health indicators such as life expectancy, infant mortality, GDP per capital, and health expenditure using R programming.

Step 1: Load required pacakges

library(GGally)

Warning: package 'GGally' was built under R version 4.5.3

Loading required package: ggplot2

Warning: package 'ggplot2' was built under R version 4.5.3

library(dplyr)

Warning: package 'dplyr' was built under R version 4.5.3


Attaching package: 'dplyr'

The following objects are masked from 'package:stats':

    filter, lag

The following objects are masked from 'package:base':

    intersect, setdiff, setequal, union

library(ggplot2)

Step 2: Load dataset

data <- read.csv(file.choose(), stringsAsFactors = FALSE)

head(data)

  InvoiceNo StockCode                         Description Quantity
1    536365    85123A  WHITE HANGING HEART T-LIGHT HOLDER        6
2    536365     71053                 WHITE METAL LANTERN        6
3    536365    84406B      CREAM CUPID HEARTS COAT HANGER        8
4    536365    84029G KNITTED UNION FLAG HOT WATER BOTTLE        6
5    536365    84029E      RED WOOLLY HOTTIE WHITE HEART.        6
6    536365     22752        SET 7 BABUSHKA NESTING BOXES        2
     InvoiceDate UnitPrice CustomerID        Country
1 12/1/2010 8:26      2.55      17850 United Kingdom
2 12/1/2010 8:26      3.39      17850 United Kingdom
3 12/1/2010 8:26      2.75      17850 United Kingdom
4 12/1/2010 8:26      3.39      17850 United Kingdom
5 12/1/2010 8:26      3.39      17850 United Kingdom
6 12/1/2010 8:26      7.65      17850 United Kingdom

str(data)

'data.frame':   541909 obs. of  8 variables:
 $ InvoiceNo  : chr  "536365" "536365" "536365" "536365" ...
 $ StockCode  : chr  "85123A" "71053" "84406B" "84029G" ...
 $ Description: chr  "WHITE HANGING HEART T-LIGHT HOLDER" "WHITE METAL LANTERN" "CREAM CUPID HEARTS COAT HANGER" "KNITTED UNION FLAG HOT WATER BOTTLE" ...
 $ Quantity   : int  6 6 8 6 6 2 6 6 6 32 ...
 $ InvoiceDate: chr  "12/1/2010 8:26" "12/1/2010 8:26" "12/1/2010 8:26" "12/1/2010 8:26" ...
 $ UnitPrice  : num  2.55 3.39 2.75 3.39 3.39 7.65 4.25 1.85 1.85 1.69 ...
 $ CustomerID : int  17850 17850 17850 17850 17850 17850 17850 17850 17850 13047 ...
 $ Country    : chr  "United Kingdom" "United Kingdom" "United Kingdom" "United Kingdom" ...

colnames(data)

[1] "InvoiceNo"   "StockCode"   "Description" "Quantity"    "InvoiceDate"
[6] "UnitPrice"   "CustomerID"  "Country"

Step 3: Data Preprocessing

# Clean column names
colnames(data) <- trimws(colnames(data))

# Check structure
str(data)

'data.frame':   541909 obs. of  8 variables:
 $ InvoiceNo  : chr  "536365" "536365" "536365" "536365" ...
 $ StockCode  : chr  "85123A" "71053" "84406B" "84029G" ...
 $ Description: chr  "WHITE HANGING HEART T-LIGHT HOLDER" "WHITE METAL LANTERN" "CREAM CUPID HEARTS COAT HANGER" "KNITTED UNION FLAG HOT WATER BOTTLE" ...
 $ Quantity   : int  6 6 8 6 6 2 6 6 6 32 ...
 $ InvoiceDate: chr  "12/1/2010 8:26" "12/1/2010 8:26" "12/1/2010 8:26" "12/1/2010 8:26" ...
 $ UnitPrice  : num  2.55 3.39 2.75 3.39 3.39 7.65 4.25 1.85 1.85 1.69 ...
 $ CustomerID : int  17850 17850 17850 17850 17850 17850 17850 17850 17850 13047 ...
 $ Country    : chr  "United Kingdom" "United Kingdom" "United Kingdom" "United Kingdom" ...

Step 4: Create country-level indicators

# Convert all column names to lowercase
colnames(data) <- tolower(trimws(colnames(data)))

# Now use lowercase safely
country_data <- data %>%
  group_by(country) %>%
  summarise(
    total_quantity = sum(quantity, na.rm = TRUE),
    avg_unitprice = mean(unitprice, na.rm = TRUE),
    total_sales = sum(quantity * unitprice, na.rm = TRUE)
  )

head(country_data)

# A tibble: 6 × 4
  country   total_quantity avg_unitprice total_sales
  <chr>              <int>         <dbl>       <dbl>
1 Australia          83653          3.22     137077.
2 Austria             4827          4.24      10154.
3 Bahrain              260          4.56        548.
4 Belgium            23152          3.64      40911.
5 Brazil               356          4.46       1144.
6 Canada              2763          6.03       3666.

Step 5:Parallel Coordinates Plot

library(GGally)
library(ggplot2)

# Use country_data created in Step 4

# Keep only numeric columns + Country for grouping
plot_data <- country_data

# Create plot
ggparcoord(
  data = plot_data,
  columns = 2:ncol(plot_data),   # numeric columns
  groupColumn = 1,               # Country column
  scale = "uniminmax",
  showPoints = TRUE,
  alphaLines = 0.6
) +
  labs(
    title = "Parallel Coordinates Plot of Country-Level Indicators",
    x = "Indicators",
    y = "Scaled Values"
  ) +
  theme_minimal()

Visualization:

The parallel coordinates plot represents each country as a line across multiple axes, showing indicators like total quantity, average unit price, and total sales. All variables are scaled between 0 and 1 to make comparisons easier, allowing patterns and differences between countries to be visualized clearly in a single graph.

Interpretation:

The plot helps identify trends, clusters, and outliers among countries. Countries with higher lines across all axes show better overall performance, while variations in line patterns indicate different strategies, such as high-volume low-price or low-volume high-price approaches. It also highlights relationships and trade-offs between indicators.