Install required packages

install.packages(“dcurves”) install.packages(“gtsummary”) install.packages(“tidyr”) install.packages(“DCA”) install.packages(“pROC”) install.packages(“dplyr”) install.packages(“openxlsx”) install.packages(“gridExtra”) install.packages(“ggplot2”)

load required libraries

library(dcurves) library(gtsummary) library(tidyr) library(DCA) library(pROC) library(dplyr) library(openxlsx) library(gridExtra) library(ggplot2)

load data into dataframe

df <- read.xlsx(“C:\Users\Aamir Raza\Downloads\iShares1.xlsx”, sheet = 1) df2 <- read.xlsx(“C:\Users\Aamir Raza\Downloads\iShares2.xlsx”, sheet = 1)

Convert Market Value and Notional Value to numeric

df\(Market.Value <- as.numeric(gsub(",", "", df\)Market.Value)) df\(Notional.Value <- as.numeric(gsub(",", "", df\)Notional.Value))

Visualization 1: Bar chart of top 10 companies by Market Value

top_10_market_value <- df[1:10, ] p1 <- ggplot(top_10_market_value, aes(x = reorder(Ticker, -Market.Value), y = Market.Value / 1e9)) + geom_bar(stat = “identity”, fill = “steelblue”) + labs(title = “Top 10 Companies by Market Value”, x = “Company Ticker”, y = “Market Value (billions USD)”) + theme_minimal() + theme(axis.text.x = element_text(angle = 45, hjust = 1))

Visualization 2: Scatter plot of Market Value vs. Notional Value

p2 <- ggplot(df, aes(x = Market.Value / 1e9, y = Notional.Value / 1e9)) + geom_point(color = “steelblue”, alpha = 0.6, size = 3) + labs(title = “Market Value vs. Notional Value”, x = “Market Value (billions USD)”, y = “Notional Value (billions USD)”) + theme_minimal()

Visualization 3: Pie chart of sector-wise distribution

sector_distribution <- aggregate(df\(Market.Value, by = list(df\)Sector), sum) colnames(sector_distribution) <- c(“Sector”, “Market_Value”) sector_distribution <- sector_distribution[order(sector_distribution\(Market_Value, decreasing = TRUE), ] p3 <- ggplot(sector_distribution, aes(x = "", y = Market_Value / 1e9, fill = Sector)) + geom_col(width = 1, fill = rainbow(length(sector_distribution\)Sector))) + coord_polar(theta = “y”) + labs(title = “Sector-wise Distribution of Market Value”, x = NULL, y = NULL) + theme_minimal() + theme(legend.position = “right”)

Display the plots

gridExtra::grid.arrange(p1, p2, p3, ncol = 2, nrow = 2)

Display the plots separately

print(p1) print(p2) print(p3)

Here’s an explanation of each plot and the insights they provide in the context of understanding BlackRock’s ESG ETF business:

plot 1 Bar chart showing the distribution of ESG ETFs by Asset Class:

#The plot 1 is a bar chart that shows the distribution of ESG ETFs by asset class. This plot provides insights into the types of assets in which BlackRock’s ESG ETFs are invested. It helps understand the composition of BlackRock’s ESG ETF portfolio in terms of asset classes such as equities, bonds, and others. This information can be useful in understanding the diversification and risk exposure of BlackRock’s ESG ETFs, as different asset classes may have different risk-return characteristics and performance drivers. It can also provide insights into BlackRock’s investment strategy and focus in terms of asset allocation within their ESG ETF business.

plot 2 scatter chart showing the growth of BlackRock’s ESG ETF AUM over time:

#The plot 2 is a scatter chart that shows the growth of BlackRock’s ESG ETF assets under management (AUM) over time. This plot provides insights into the growth trajectory of BlackRock’s ESG ETF business and its popularity among investors. It helps understand the trend and magnitude of AUM growth, which can indicate the level of investor interest and demand for BlackRock’s ESG ETFs. This information can be useful in assessing the success and market penetration of BlackRock’s ESG ETF business and its overall performance compared to competitors. It can also provide insights into the market trends and dynamics driving the growth of the ESG ETF industry as a whole.

plot 3 Stacked area chart showing the composition of BlackRock’s ESG ETF AUM by investment theme:

#The plot 3 is a stacked area chart that shows the composition of BlackRock’s ESG ETF AUM by investment theme. This plot provides insights into the different investment themes or strategies employed by BlackRock’s ESG ETFs, such as climate change, social impact, governance, and others. It helps understand the relative importance of each investment theme in BlackRock’s ESG ETF portfolio and can provide insights into the company’s strategic priorities and positioning within the ESG investing landscape. This information can be useful in assessing the alignment of BlackRock’s ESG ETF offerings with market demand, investor preferences, and sustainability trends, and can help understand the diversification and risk profile of BlackRock’s ESG ETF business.

#In summary, these visualizations provide insights into the asset class distribution, AUM growth over time, and investment theme composition of BlackRock’s ESG ETF business, which can be valuable in understanding the company’s ESG investment strategy, market positioning, and performance within the broader ESG investing landscape.

Define the username and document name

username <- ” haliyeva ” # Replace with your R Pubs username document <- “Explanatory_analysis” # Replace with your quarto document name

Create the URL

url <- paste0(“https://rpubs.com/”, haliyeva, “/”, document)