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✔ forcats 1.0.0 ✔ stringr 1.5.0
✔ ggplot2 3.4.4 ✔ tibble 3.2.1
✔ lubridate 1.9.2 ✔ tidyr 1.3.0
✔ purrr 1.0.2
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library (ggplot2)library (dplyr)library (patchwork)
Warning: package 'patchwork' was built under R version 4.3.2
Warning: package 'esquisse' was built under R version 4.3.2
<- read_csv ("https://raw.githubusercontent.com/t-emery/sais-susfin_data/main/datasets/ishares_etf_screener_as_of_2023-12-27.csv" )
Rows: 424 Columns: 18
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (10): ticker, name, asset_class, sub_asset_class, region, market, locat...
dbl (6): gross_expense_ratio_percent, net_expense_ratio_percent, net_asset...
dttm (2): incept_date, net_assets_as_of
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.
# A tibble: 424 × 18
ticker name incept_date gross_expense_ratio_…¹
<chr> <chr> <dttm> <dbl>
1 IVV iShares Core S&P 500 ETF 2000-05-15 00:00:00 0.03
2 IEFA iShares Core MSCI EAFE ETF 2012-10-18 00:00:00 0.07
3 AGG iShares Core U.S. Aggregat… 2003-09-22 00:00:00 0.03
4 IWF iShares Russell 1000 Growt… 2000-05-22 00:00:00 0.19
5 IJR iShares Core S&P Small-Cap… 2000-05-22 00:00:00 0.06
6 IJH iShares Core S&P Mid-Cap E… 2000-05-22 00:00:00 0.05
7 IEMG iShares Core MSCI Emerging… 2012-10-18 00:00:00 0.09
8 IWM iShares Russell 2000 ETF 2000-05-22 00:00:00 0.19
9 IWD iShares Russell 1000 Value… 2000-05-22 00:00:00 0.19
10 TLT iShares 20+ Year Treasury … 2002-07-22 00:00:00 0.15
# ℹ 414 more rows
# ℹ abbreviated name: ¹gross_expense_ratio_percent
# ℹ 14 more variables: net_expense_ratio_percent <dbl>, net_assets_usd <dbl>,
# net_assets_as_of <dttm>, asset_class <chr>, sub_asset_class <chr>,
# region <chr>, market <chr>, location <chr>, investment_style <chr>,
# msci_esg_fund_rating_aaa_ccc <chr>, msci_esg_quality_score_0_10 <dbl>,
# msci_weighted_average_carbon_intensity_tons_co2e_m_sales <dbl>, …
%>% glimpse ()
Rows: 424
Columns: 18
$ ticker <chr> "IVV", "IEFA"…
$ name <chr> "iShares Core…
$ incept_date <dttm> 2000-05-15, …
$ gross_expense_ratio_percent <dbl> 0.03, 0.07, 0…
$ net_expense_ratio_percent <dbl> 0.03, 0.07, 0…
$ net_assets_usd <dbl> 399312542067,…
$ net_assets_as_of <dttm> 2023-12-27, …
$ asset_class <chr> "Equity", "Eq…
$ sub_asset_class <chr> "Large Cap", …
$ region <chr> "North Americ…
$ market <chr> "Developed", …
$ location <chr> "United State…
$ investment_style <chr> "Index", "Ind…
$ msci_esg_fund_rating_aaa_ccc <chr> "A", "AA", "A…
$ msci_esg_quality_score_0_10 <dbl> 6.6845, 7.519…
$ msci_weighted_average_carbon_intensity_tons_co2e_m_sales <dbl> 106.80, 102.8…
$ msci_esg_percent_coverage <dbl> 99.05, 99.80,…
$ sustainable_classification <chr> NA, NA, NA, N…
%>% distinct (sustainable_classification)
# A tibble: 5 × 1
sustainable_classification
<chr>
1 <NA>
2 Uplift
3 Thematic
4 Impact
5 Screened
%>% group_by (sustainable_classification) %>% count (sort = TRUE )
# A tibble: 5 × 2
# Groups: sustainable_classification [5]
sustainable_classification n
<chr> <int>
1 <NA> 384
2 Uplift 30
3 Thematic 6
4 Screened 3
5 Impact 1
%>% group_by (sustainable_classification) %>% summarize (n_funds = n (), assets_usd = sum (net_assets_usd, na.rm = TRUE )) %>% arrange (assets_usd %>% desc ())
# A tibble: 5 × 3
sustainable_classification n_funds assets_usd
<chr> <int> <dbl>
1 <NA> 384 2.55e12
2 Uplift 30 5.14e10
3 Thematic 6 3.43e 9
4 Screened 3 3.83e 8
5 Impact 1 3.35e 8
<- blackrock_etf_screener %>% mutate (standard_or_esg = if_else (condition = is.na (sustainable_classification),true = "Standard" ,false = "ESG" inception_year = lubridate:: year (incept_date),net_assets_bn_usd = net_assets_usd/ 10 ^ 9 ,.before = everything ()
# A tibble: 424 × 21
standard_or_esg inception_year net_assets_bn_usd ticker name
<chr> <dbl> <dbl> <chr> <chr>
1 Standard 2000 399. IVV iShares Core S&P 500…
2 Standard 2012 107. IEFA iShares Core MSCI EA…
3 Standard 2003 101. AGG iShares Core U.S. Ag…
4 Standard 2000 82.1 IWF iShares Russell 1000…
5 Standard 2000 78.2 IJR iShares Core S&P Sma…
6 Standard 2000 77.1 IJH iShares Core S&P Mid…
7 Standard 2012 73.9 IEMG iShares Core MSCI Em…
8 Standard 2000 67.7 IWM iShares Russell 2000…
9 Standard 2000 55.4 IWD iShares Russell 1000…
10 Standard 2002 52.2 TLT iShares 20+ Year Tre…
# ℹ 414 more rows
# ℹ 16 more variables: incept_date <dttm>, gross_expense_ratio_percent <dbl>,
# net_expense_ratio_percent <dbl>, net_assets_usd <dbl>,
# net_assets_as_of <dttm>, asset_class <chr>, sub_asset_class <chr>,
# region <chr>, market <chr>, location <chr>, investment_style <chr>,
# msci_esg_fund_rating_aaa_ccc <chr>, msci_esg_quality_score_0_10 <dbl>,
# msci_weighted_average_carbon_intensity_tons_co2e_m_sales <dbl>, …
<- blackrock_etf_screener_w_new_features %>% group_by (standard_or_esg) %>% summarize (assets_bn = sum (net_assets_bn_usd, na.rm = TRUE ),n_funds = n ()
# A tibble: 2 × 3
standard_or_esg assets_bn n_funds
<chr> <dbl> <int>
1 ESG 55.6 40
2 Standard 2547. 384
%>% ggplot (aes (x = assets_bn, y = standard_or_esg)) + geom_bar (stat = "identity" )%>% mutate (assets_pct = assets_bn/ sum (assets_bn) * 100 , .after = standard_or_esg)
# A tibble: 2 × 4
standard_or_esg assets_pct assets_bn n_funds
<chr> <dbl> <dbl> <int>
1 ESG 2.14 55.6 40
2 Standard 97.9 2547. 384
%>% ggplot (aes (x = assets_bn, y = standard_or_esg))+ geom_bar (stat = "identity" , fill = "grey40" , alpha = .7 )+ scale_x_continuous (labels = scales:: label_dollar (scale = 1 / 10 ^ 3 , suffix = "tn" )) + labs ( title = "Total Assets: iShares ESG vs Standard Funds" ,subtitle = "ESG comprise a small percentage of total assets." ,x = "Total Assets (USD)" ,y = "Fund Type" ,caption = "I made this \n Data as of xyz" )
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<- blackrock_etf_screener_w_new_features %>% group_by (standard_or_esg, inception_year) %>% count () %>% ungroup ()
# A tibble: 37 × 3
standard_or_esg inception_year n
<chr> <dbl> <int>
1 ESG 2005 1
2 ESG 2006 1
3 ESG 2008 1
4 ESG 2014 1
5 ESG 2016 5
6 ESG 2017 2
7 ESG 2018 3
8 ESG 2019 1
9 ESG 2020 12
10 ESG 2021 5
# ℹ 27 more rows
%>% ggplot (aes (x = inception_year, y = n, fill = standard_or_esg)) + geom_line ()<- etf_formation_esg_vs_standard %>% arrange (standard_or_esg, inception_year) %>% group_by (standard_or_esg) %>% mutate (cumulative_n = cumsum (n)) %>%
# A tibble: 37 × 4
standard_or_esg inception_year n cumulative_n
<chr> <dbl> <int> <int>
1 ESG 2005 1 1
2 ESG 2006 1 2
3 ESG 2008 1 3
4 ESG 2014 1 4
5 ESG 2016 5 9
6 ESG 2017 2 11
7 ESG 2018 3 14
8 ESG 2019 1 15
9 ESG 2020 12 27
10 ESG 2021 5 32
# ℹ 27 more rows
%>% ggplot (aes (x = inception_year, y = cumulative_n, color = standard_or_esg)) + geom_line ()|> group_by (standard_or_esg, asset_class) %>% summarize (max_size = max (net_assets_bn_usd, na.rm = TRUE ),min_size = min (net_assets_bn_usd, na.rm = TRUE ),avg_size = mean (net_assets_bn_usd, na.rm = TRUE ),sd_size = sd (net_assets_bn_usd, na.rm = TRUE ),median_size = median (net_assets_bn_usd, na.rm = TRUE ),percentile_50 = quantile (net_assets_bn_usd, .5 , na.rm = TRUE ),percentile_75 = quantile (net_assets_bn_usd, .75 , na.rm = TRUE ),
`summarise()` has grouped output by 'standard_or_esg'. You can override using
the `.groups` argument.
# A tibble: 9 × 9
# Groups: standard_or_esg [2]
standard_or_esg asset_class max_size min_size avg_size sd_size median_size
<chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
1 ESG Equity 13.4 0.00371 1.74 2.97 0.501
2 ESG Fixed Income 3.56 0.0125 0.954 1.22 0.586
3 ESG Multi Asset 0.0257 0.00527 0.0144 0.00966 0.0132
4 ESG Real Estate 0.00939 0.00939 0.00939 NA 0.00939
5 Standard Commodity 26.5 0.0130 4.48 8.92 0.659
6 Standard Equity 399. 0.000457 8.02 29.9 0.817
7 Standard Fixed Income 101. 0.00247 5.22 12.6 0.423
8 Standard Multi Asset 2.25 0.00271 0.435 0.782 0.00383
9 Standard Real Estate 4.60 0.0403 1.76 1.67 1.41
# ℹ 2 more variables: percentile_50 <dbl>, percentile_75 <dbl>
%>% filter (standard_or_esg == "ESG" ) %>% select (ticker, name, net_assets_bn_usd) %>% mutate (fund_rank = rank (net_assets_bn_usd),fund_percentile = percent_rank (net_assets_bn_usd),fund_decile = ntile (net_assets_bn_usd, n = 10 ),pct_of_total = net_assets_bn_usd/ sum (net_assets_bn_usd, na.rm = TRUE ) * 100 ,pct_of_total_cumulative = cumsum (pct_of_total)
# A tibble: 40 × 8
ticker name net_assets_bn_usd fund_rank fund_percentile fund_decile
<chr> <chr> <dbl> <dbl> <dbl> <int>
1 ESGU iShares ESG A… 13.4 40 1 10
2 ESGD iShares ESG A… 7.64 39 0.974 10
3 SUSA iShares MSCI … 5.37 38 0.949 10
4 DSI iShares MSCI … 4.08 37 0.923 10
5 ESGE iShares ESG A… 4.02 36 0.897 9
6 EAGG iShares ESG A… 3.56 35 0.872 9
7 ICLN iShares Globa… 3.06 34 0.846 9
8 USCL iShares Clima… 2.09 33 0.821 9
9 PABU iShares Paris… 1.54 32 0.795 8
10 LCTU BlackRock U.S… 1.51 31 0.769 8
# ℹ 30 more rows
# ℹ 2 more variables: pct_of_total <dbl>, pct_of_total_cumulative <dbl>
# Homework Problem 1 <- blackrock_etf_screener_w_new_features %>% filter (standard_or_esg == "ESG" )%>% group_by (sustainable_classification)%>% summarize (number_class_funds = n (), big_sust = max (net_assets_bn_usd, na.rm = TRUE ), total_netass_bn = sum (net_assets_bn_usd, na.rm = TRUE ), percent_netass_bn = (big_sust/ total_netass_bn)* 100 )%>% arrange (desc (percent_netass_bn))View (blackrock_only_etf)# Homework Problem 2 <- blackrock_etf_screener_w_new_features %>% filter (standard_or_esg == "ESG" )%>% reframe (mini = head (sort (net_assets_bn_usd), 10 , na.rm = TRUE ))
# A tibble: 10 × 1
mini
<dbl>
1 0.00371
2 0.00412
3 0.00424
4 0.00469
5 0.00527
6 0.00541
7 0.00577
8 0.00749
9 0.00873
10 0.00939
%>% ggplot (aes (x = rank (mini), y = mini, color = "gray20" )) + geom_bar (stat = "identity" ) + labs (title = "10 Smallest ESG Funds" , subtitle = "In terms of assets expressed in USD billions" , x = "ranking of the values from smallest to greatest" , y = "USD bn values" )# Homework Problem 3 <- blackrock_etf_screener_w_new_features%>% filter (standard_or_esg == "ESG" ) %>% group_by (asset_class, sub_asset_class)%>% summarize (bottom_quint = quantile (msci_esg_quality_score_0_10, .2 , na.rm = TRUE ), Assets_p_subc_bn_usd = sum (net_assets_bn_usd), number_o_funds = n ()%>% arrange (desc (bottom_quint))
`summarise()` has grouped output by 'asset_class'. You can override using the
`.groups` argument.
# A tibble: 10 × 5
# Groups: asset_class [4]
asset_class sub_asset_class bottom_quint Assets_p_subc_bn_usd number_o_funds
<chr> <chr> <dbl> <dbl> <int>
1 Equity All Cap 7.10 3.08 5
2 Multi Asset Multi Strategy 7.08 0.0574 4
3 Equity Large Cap 7.08 1.56 3
4 Fixed Income Credit 6.84 2.41 4
5 Equity Large/Mid Cap 6.81 42.5 17
6 Fixed Income Multi Sectors 6.46 4.15 2
7 Real Estate Real Estate Se… 6.02 0.00939 1
8 Equity Mid Cap 5.98 0.137 1
9 Fixed Income High Yield 5.97 0.121 1
10 Equity Small Cap 5.66 1.53 2
# Homework Problem 4 <- blackrock_etf_screener_w_new_features %>% filter (! is.na (msci_esg_fund_rating_aaa_ccc)) %>% filter (! is.na (sustainable_classification)) %>% ggplot (aes (x = sustainable_classification, y = msci_esg_fund_rating_aaa_ccc, fill = sustainable_classification)) + geom_tile () + scale_fill_hue (direction = 1 ) + labs (x = "MSCI ESG Bond Rating" , y = "Sustainability Classification" ,title = "Ratings of ESG Bonds" ,subtitle = "Categorized by Classification" , fill = "Sustainability Classification" + theme_minimal ()<- blackrock_etf_screener_w_new_features %>% filter (! is.na (msci_esg_fund_rating_aaa_ccc)) %>% ggplot () + aes (x = region, y = msci_esg_fund_rating_aaa_ccc, fill = msci_esg_fund_rating_aaa_ccc) + geom_tile () + labs (title = "Available ESG Asset Ratings Per Region" , x = "Rating" , y = "Region" ) + scale_fill_hue (direction = 1 ) + theme_minimal ()<- blackrock_etf_screener_w_new_features %>% filter (! is.na (sustainable_classification)) %>% ggplot () + aes (x = sustainable_classification, y = asset_class, fill = sustainable_classification) + geom_tile () + scale_fill_hue (direction = 1 ) + labs (x = "Sustainability Classification" , y = "Asset Classes" , title = "Sustainability Classification Divided into Asset Classes" , fill = "Sustainability Classification" ) + theme_minimal ()
<- blackrock_etf_screener_w_new_features %>% filter (! is.na (msci_esg_fund_rating_aaa_ccc)) %>% group_by (msci_esg_fund_rating_aaa_ccc, region) %>% count () %>% pivot_wider (names_from = region, values_from = n, values_fill = 0 )View (c)<- blackrock_etf_screener_w_new_features %>% filter (! is.na (msci_esg_fund_rating_aaa_ccc)) %>% filter (! is.na (sustainable_classification)) %>% group_by (msci_esg_fund_rating_aaa_ccc, sustainable_classification) %>% count () %>% pivot_wider (names_from = sustainable_classification, values_from = n, values_fill = 0 )View (d)print ("My focus in this question was linking the MSCI ratings values to regions and sustainability classifications. In graphs A and B, you see the relationships depicted graphically, and in charts c and d, you see the numerical relationships between these variables. Part e shows the relationship between the sustainability classifications and the asset classes" )
[1] "My focus in this question was linking the MSCI ratings values to regions and sustainability classifications. In graphs A and B, you see the relationships depicted graphically, and in charts c and d, you see the numerical relationships between these variables. Part e shows the relationship between the sustainability classifications and the asset classes"
