Assignment 2

Deconstruct, Reconstruct Web Report

Objective

Explain the objective of the original data visualization and the targetted audience.
- objective this data visualization was created to for users to find out what brands in 2020 were considered the most valuable according to the data provided

• target audience the target audience is towards competitors and readers who are interested in company data and valuations of major companies and their rankings in the world.

The visualization chosen had the following three main issues:

• visual bombardment plotting the top 100 brands has made the data unreadable and confusing as there are too many colors and smaller brands are difficult to look at. by only listing the top 20 companies we can reduce the noise of the data visualization

• Visual choice the data visualization chosen is very difficult to read therefore being counterproductive to its objective of showing the top brands as they have been separated into four different groups making it difficult for readers to compare one brand to another. the colors could also throw off the readers as there is so many categories that are not necessary.

• area and size it can be seen that the size of data is important as the smaller brands cannot be recognized due to how small they are. it also shows inconsistencies as only the bigger brands have their worth and name shown due to their size. brands like apple and Google look the exact same even though there is a 40 billion dollar difference. Reference

most valuable brands in 2020. (2020). Visualizing the Most Valuable Brands in the World in 2020. Retrieved April 22,2023, from howmuch.net: https://howmuch.net/articles/top-100-most-valuable-brands-2020

Code

The following code was used to fix the issues identified in the original.

library(ggplot2)
library(readr)
library(dplyr)
company_data <- read_csv("brandirectory-ranking-data-global-2020.csv")
company_data <- company_data[c(1:20),c(1,2,4)]
company_data$`Brand Value ($M)`<- as.character(company_data$`Brand Value ($M)`)
company_data$`Brand Value ($M)`<- as.numeric(company_data$`Brand Value ($M)`)
company_billion <- company_data %>% mutate(Billion_dollar = `Brand Value ($M)`/1000) 
round(company_billion$Billion_dollar,digits = 2)

##  [1] 220.79 188.51 140.52 117.07  94.49  80.79  79.80  77.52  69.04  65.08
## [11]  65.04  63.69  62.60  59.10  58.08  56.96  56.12  54.66  54.15  50.63

company_billion

## # A tibble: 20 × 4
##    Brand                      Position `Brand Value ($M)` Billion_dollar
##    <chr>                         <dbl>              <dbl>          <dbl>
##  1 Amazon                            1            220791.          221. 
##  2 Google                            2            188512.          189. 
##  3 Apple                             3            140524.          141. 
##  4 Microsoft                         4            117072.          117. 
##  5 Samsung Group                     5             94494            94.5
##  6 ICBC                              6             80791.           80.8
##  7 Facebook                          7             79804.           79.8
##  8 Walmart                           8             77520.           77.5
##  9 Ping An                           9             69041            69.0
## 10 Huawei                           10             65084.           65.1
## 11 Mercedes-Benz                    11             65041            65.0
## 12 Verizon                          12             63692.           63.7
## 13 China Construction Bank          13             62602.           62.6
## 14 AT&T                             14             59103.           59.1
## 15 Toyota                           15             58076            58.1
## 16 State Grid                       16             56965.           57.0
## 17 Disney                           17             56123.           56.1
## 18 Agricultural Bank Of China       18             54658.           54.7
## 19 WeChat                           19             54146.           54.1
## 20 Bank of China                    20             50630.           50.6

Data Reference

• top 500 global brands 2020. (2020). TOP 500 GLOBAL BRANDS 2020 RANKING retrieved Retrieved April 22,2023, from BrandFinance Brandictory :https://brandirectory.com/rankings/global/2020/table

Reconstruction