Assignment 2
Deconstruct, Reconstruct Web Report
Mehak Gupta (S3931990)
Click the Original, Code and Reconstruction tabs to read about the issues and how they were fixed.
Original
Objective
The objective of the visual is to explain mortality in seven countries attributed to COVID - 19. The original data visualisations aims to show number of deaths each day and then simply estimates the average of number of deaths both before and after the day it actually occurred.This then averages it to rolling three days around which it originally occurred. it shows the cumulative total due to which the original plot never falls and keeps rising. The change is the average change on daily basis that is from the day before and the after from which it actually occurred. It helps us monitor deaths on daily basis for all the countries over time (in 2020).
The visualisation chosen had the following three main issues:
On the first look, it plot just shows line emerging of different colors. They all shoot up and get messier. There is a lot of information in the plot which makes it difficult to read. Moreover, when the graph is fully built it gets really cluttered towards the end of graph.
The original plot is also not interactive enough and doesn’t very well represent information for specific point of time. it is not not like you can hover at some on the graph and it actually shows needed information. The time labels of the plot are not understandable, there is no fixed time gap defined or time ratio stated. Time is picked up randomly which makes it difficult analyse the daily death rate
Moreover, we cannot see a trend in the original plot, because the data here is cumulative. it is always increasing. It is hard to analyse as one cannot spot basic trends, seasonality, change in variance, the behavior of data and also intervention points in any. The plot also has a lot comments which can confuse the user. .
Reference - Danny Dorling Diagram and Website by Kirsten McClure. Mortality in seven countries attributed to COVID - 19 from Kirsten McClure website: https://www.dannydorling.org/books/SLOWDOWN/Covid19.html
- Class notes of MATH2270 by Dr. James Baglin
Code
The following code was used to fix the issues identified in the original.
#Load the packages
library(readxl)
library(ggplot2)
library(tidyr)
library(readr)
library(dplyr)
#setting directory
setwd("C:/Users/hp/OneDrive/Desktop/data")
getwd()## [1] "C:/Users/hp/OneDrive/Desktop/data"#Loading the data
Mortality <- read_xlsx("Dataset.xlsx")
#Removed the first column
Mortality <- Mortality[,c(-1)]
head(Mortality)## # A tibble: 6 × 8
## Country Time Cumulative days_after_10 `Deaths/day` X Y
## <chr> <dttm> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 China 2020-01-22 00:00:00 17 0 NA NA NA
## 2 China 2020-01-23 00:00:00 18 1 4.5 NA 8.25
## 3 China 2020-01-24 00:00:00 26 2 12 6.44 10.5
## 4 China 2020-01-25 00:00:00 42 3 15 6.55 21.1
## 5 China 2020-01-26 00:00:00 56 4 20 5.02 23.6
## 6 China 2020-01-27 00:00:00 82 5 37.5 11.3 31.2
## # … with 1 more variable: labels <dttm>
## # ℹ Use `colnames()` to see all variable names#Check for NA values in the dataset "data".
is.na(Mortality) ## Country Time Cumulative days_after_10 Deaths/day X Y labels
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## Country Time Cumulative days_after_10 `Deaths/day` X Y
## <chr> <dttm> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 China 2020-01-22 00:00:00 17 0 NA NA NA
## 2 China 2020-01-23 00:00:00 18 1 4.5 NA 8.25
## 3 China 2020-01-24 00:00:00 26 2 12 6.44 10.5
## 4 China 2020-01-25 00:00:00 42 3 15 6.55 21.1
## 5 China 2020-01-26 00:00:00 56 4 20 5.02 23.6
## 6 China 2020-01-27 00:00:00 82 5 37.5 11.3 31.2
## # … with 1 more variable: labels <dttm>
## # ℹ Use `colnames()` to see all variable names#Check for Categorical variable
Mortality$Country<- as.factor(Mortality$Country)
#Plotting Time vs Average number of deaths per day
a<-ggplot(Mortality, aes(x=Time, y=Y, color = Country,label= round(X,digits = 0))) +
geom_line(aes(colour = Country), linetype =2) +
geom_point(size=1,aes(colour = Country)) +
geom_text(family = "Times New Roman", hjust = -0.2, nudge_x = 0.05, size =3,vjust = 0.75, nudge_y = 0.5,
check_overlap = TRUE,aes(colour = factor(Country))) + scale_colour_discrete(l = 40)
a
p <- a + labs(title = "Mortality in seven countries due to covid-19 (in 2020)",
subtitle = "Submitted by: Mehak Gupta (S3931990)",
x = "Time (2020)",
y = "Average number of deaths per day",
caption = "The labels in the graph represents Increase/Decrease in number of deaths per day")Data Reference
- Danny Dorling Diagram and Website by Kirsten McClure. Mortality in seven countries attributed to COVID - 19 (EXCEL 31st May 2020) from Kirsten McClure website: https://www.dannydorling.org/books/SLOWDOWN/Images/Seven_June_1_7_day_av.xlsx
Reconstruction
The plot now gives information more clearly, it is not messier with all the comments and legends. It shows trend now for all the countries. We can analyse better now with time on x-axis. Moreover, with labels along the plot it gives user a better insight on data at specific point of time.
