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# the URL of our data on GitHubgithub_url <-"https://raw.githubusercontent.com/t-emery/sais-susfin_data/main/datasets/etf_comparison-2022-10-03.csv"# read the data from GitHubblackrock_esg_vs_non_esg_etf <- github_url |>read_csv() |># select the four columns we will use in our anlaysis hereselect(company_name:standard_etf)
Rows: 537 Columns: 14
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (4): ticker, company_name, sector, esg_uw_ow
dbl (7): esg_etf, standard_etf, esg_tilt, esg_tilt_z_score, esg_tilt_rank, e...
lgl (3): in_esg_only, in_standard_only, in_on_index_only
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
# recreate the chartggplot(blackrock_esg_vs_non_esg_etf) +aes(x = esg_etf, y = standard_etf, colour = sector) +geom_point(shape ="circle", size =1.5) +geom_smooth(span =0.75) +scale_color_viridis_d(option ="inferno", direction =1) +scale_x_continuous(trans ="log10") +scale_y_continuous(trans ="log10") +labs(x ="ESG ETF (ESGU)",y ="Standard ETF (IVV)",title ="Large Cap American Equities ETFs: ESG vs. Non-ESG",subtitle ="A comparison of the holdings of BlackRock iShares ESGU and IVV",caption ="Boyuan Gao" ) +theme_minimal() +facet_wrap(vars(sector))
Warning: Transformation introduced infinite values in continuous x-axis
Warning: Transformation introduced infinite values in continuous y-axis
Warning: Transformation introduced infinite values in continuous x-axis
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`geom_smooth()` using method = 'loess' and formula = 'y ~ x'
# change data from wide to longblackrock_esg_vs_non_esg_etf_long <- blackrock_esg_vs_non_esg_etf |>pivot_longer(cols =contains("etf"), names_to ="fund_type", values_to ="weight") |>mutate(fund_type =case_when(fund_type =="esg_etf"~"ESG ETF (ESGU)", fund_type =="standard_etf"~"Standard ETF (IVV)"))# reload datablackrock_esg_vs_non_esg_etf_long
# A tibble: 1,074 × 4
company_name sector fund_type weight
<chr> <chr> <chr> <dbl>
1 PRUDENTIAL FINANCIAL INC Financials ESG ETF (ESGU) 0.537
2 PRUDENTIAL FINANCIAL INC Financials Standard ETF (IV… 0.106
3 GENERAL MILLS INC Consumer Staples ESG ETF (ESGU) 0.552
4 GENERAL MILLS INC Consumer Staples Standard ETF (IV… 0.151
5 KELLOGG Consumer Staples ESG ETF (ESGU) 0.453
6 KELLOGG Consumer Staples Standard ETF (IV… 0.0592
7 AUTOMATIC DATA PROCESSING INC Information Technology ESG ETF (ESGU) 0.649
8 AUTOMATIC DATA PROCESSING INC Information Technology Standard ETF (IV… 0.312
9 ECOLAB INC Materials ESG ETF (ESGU) 0.441
10 ECOLAB INC Materials Standard ETF (IV… 0.118
# ℹ 1,064 more rows
# create the chartblackrock_esg_vs_non_esg_etf_long %>%# limit the weight variable to companies over a 1% weightfilter(weight >=1L & weight <=7L) %>%ggplot() +aes(x = weight,y = company_name,colour = fund_type,size = weight ) +geom_point(shape ="circle") +# change the color for ESG fund points to green, and the non-ESG fund to greyscale_color_manual(values =c(`ESG ETF (ESGU)`="#AAEB7C",`Standard ETF (IVV)`="#797678") ) +labs(x ="Weight (%)",y ="Company Name",title ="Companies with High Weight in ESG and Non-ESG ETFs",caption ="Boyuan Gao",color ="ESG vs. Non ESG",size ="Weight" ) +theme_minimal()
Reflection: Most of the companies with a high weight in ESG and Non-ESG ETFs are leading tech companies, i.e. Apple, Microsoft, Alphabet, Amazon, etc. This could be that these tech companies have shown significant revenue and earnings growth, and hence a larger presence in the indices.
Problem 3
Make your own charts with esquisse.
Question 1: What kind of companies are excluded from the ESG fund?
Companies excluded from ESGU are primarily from certain key sectors. Out of the 226 companies that are included in the IVV index but excluded from the ESGU index, the predominant sectors are Information Technology, Industrials, Financials, and Consumer Discretionary. These four sectors collectively constitute half of the excluded companies.
# filter companies with 0 weight in the ESG fundblackrock_esg_excluded <- blackrock_esg_vs_non_esg_etf |>filter(esg_etf ==0)blackrock_esg_excluded
# A tibble: 226 × 4
company_name sector esg_etf standard_etf
<chr> <chr> <dbl> <dbl>
1 DISH NETWORK CORP Communication 0 0.0113
2 RALPH LAUREN CORP Consumer Discretiona… 0 0.0120
3 VORNADO REALTY TRUST REIT Real Estate 0 0.0127
4 MOHAWK INDUSTRIES INC Consumer Discretiona… 0 0.0155
5 NORWEGIAN CRUISE LINE HOLDINGS LTD Consumer Discretiona… 0 0.0157
6 ALASKA AIR GROUP INC Industrials 0 0.0170
7 NEWELL BRANDS INC Consumer Discretiona… 0 0.0174
8 DXC TECHNOLOGY Information Technolo… 0 0.0188
9 ORGANON Health Care 0 0.0196
10 DENTSPLY SIRONA INC Health Care 0 0.0196
# ℹ 216 more rows
# create the chartggplot(blackrock_esg_excluded) +aes(x = sector, fill = sector) +geom_bar() +scale_fill_hue(direction =1) +labs(x ="Sector",y ="Number of Companies",title ="Companies Excluded from the ESG Fund",subtitle ="Distribution by sector",caption ="Boyuan Gao" ) +coord_flip() +theme_minimal()
Question 2: What kind of companies are excluded from the Non-ESG fund?
Companies excluded from IVV are primarily from the Information Technology sector. Out of the 35 companies that are included in the ESGU index but excluded from the IVV index, 15 are tech companies. This indicates a significant sectoral focus or bias in the exclusion criteria.
# filter companies with 0 weight in the Non-ESG fundblackrock_non_esg_excluded <- blackrock_esg_vs_non_esg_etf |>filter(standard_etf ==0)blackrock_non_esg_excluded
# A tibble: 35 × 4
company_name sector esg_etf standard_etf
<chr> <chr> <dbl> <dbl>
1 CHENIERE ENERGY INC Energy 0.274 0
2 BUNGE LTD Consumer Staples 0.199 0
3 VAIL RESORTS INC Consumer Discretionary 0.173 0
4 UBER TECHNOLOGIES INC Industrials 0.170 0
5 INSULET CORP Health Care 0.149 0
6 MERCADOLIBRE INC Consumer Discretionary 0.145 0
7 SNOWFLAKE Information Technology 0.142 0
8 VMWARE CLASS A INC Information Technology 0.134 0
9 MARVELL TECHNOLOGY INC Information Technology 0.130 0
10 HORIZON THERAPEUTICS PUBLIC PLC Health Care 0.121 0
# ℹ 25 more rows
# create the chartggplot(blackrock_non_esg_excluded) +aes(x = sector, fill = sector) +geom_bar() +scale_fill_hue(direction =1) +labs(x ="Sector",y ="Number of Companies",title ="Company Excluded from the Non-ESG Fund",subtitle ="Distribution by sector",caption ="Boyuan Gao" ) +coord_flip() +theme_minimal()
I added a linear trend (red) to the scatterplot along with a 45 degree line (black). Observations above the 45 degree line indicates a higher weight in the standard ETF fund (IVV). Note that the linear trend line is below the 45 degree line, which means that many companies included in the IVV has a higher weight in the ESGU fund.
ggplot(blackrock_esg_vs_non_esg_etf) +aes(x = esg_etf, y = standard_etf) +geom_point(shape ="circle", size =1.5, aes(color = sector)) +# add a linear trendgeom_smooth(method = lm, color ="RED", se =FALSE) +# add a 45 degree linegeom_abline() +scale_x_continuous(trans ="log10") +scale_y_continuous(trans ="log10") +labs(x ="ESG ETF (ESGU)",y ="Standard ETF (IVV)",caption ="Boyuan Gao" )
Warning: Transformation introduced infinite values in continuous x-axis
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Warning: Transformation introduced infinite values in continuous x-axis
Warning: Transformation introduced infinite values in continuous y-axis
Make a new chart from the ggplot2 Extensions Gallery.
On top of Chart 2 in Problem 4, I added labels to each trended lines using an extension from ggplot2 Extensions Gallery. Although the lines are a bit cramped up for our data set, it is a good tool to indicate what each line represents.
Skipping install of 'geomtextpath' from a github remote, the SHA1 (d6a9a9a6) has not changed since last install.
Use `force = TRUE` to force installation
library(geomtextpath)ggplot(blackrock_esg_vs_non_esg_etf) +aes(x = esg_etf, y = standard_etf, color = sector) +geom_point(shape ="circle", size =1.5) +geom_smooth() +geom_labelsmooth(aes(label = sector), text_smoothing =30, fill ="#F6F6FF", alpha =0.6, method ="loess", formula = y ~x, size =3, linewidth =1, boxlinewidth =0.3) +scale_x_continuous(trans ="log10") +scale_y_continuous(trans ="log10")
Warning: Transformation introduced infinite values in continuous x-axis
Warning: Transformation introduced infinite values in continuous y-axis
Warning: Transformation introduced infinite values in continuous x-axis
Warning: Transformation introduced infinite values in continuous y-axis
Warning: Transformation introduced infinite values in continuous x-axis
Warning: Transformation introduced infinite values in continuous y-axis
`geom_smooth()` using method = 'loess' and formula = 'y ~ x'
