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, encoding = "latin1")

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" ...

Step 3: Data Preprocessing

# Convert columns to numeric (safe conversion)
data$Quantity <- as.numeric(as.character(data$Quantity))
data$UnitPrice <- as.numeric(as.character(data$UnitPrice))

Step 4: Create country-level indicators

country_data <- data %>%
  group_by(Country) %>%
  summarise(
    Total_Quantity = sum(Quantity, na.rm = TRUE),
    Avg_UnitPrice = mean(UnitPrice, na.rm = TRUE),
    Total_Revenue = sum(Quantity * UnitPrice, na.rm = TRUE),
    Unique_Customers = length(unique(CustomerID))
  )
head(country_data)

# A tibble: 6 × 5
  Country   Total_Quantity Avg_UnitPrice Total_Revenue Unique_Customers
  <chr>              <dbl>         <dbl>         <dbl>            <int>
1 Australia          83653          3.22       137077.                9
2 Austria             4827          4.24        10154.               11
3 Bahrain              260          4.56          548.                3
4 Belgium            23152          3.64        40911.               25
5 Brazil               356          4.46         1144.                1
6 Canada              2763          6.03         3666.                4

Step 5:Remove missing values

country_data <- na.omit(country_data)

Step 6:Parallel Coordinates Plot

library(GGally)

country_data <- as.data.frame(country_data)

ggparcoord(
  data = country_data,
  columns = 2:5,
  groupColumn = 1,
  scale = "uniminmax"
)