library(tidyverse) ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.2 ──
✔ ggplot2 3.4.0 ✔ purrr 1.0.1
✔ tibble 3.1.8 ✔ dplyr 1.1.0
✔ tidyr 1.3.0 ✔ stringr 1.5.0
✔ readr 2.1.3 ✔ forcats 1.0.0
── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag() masks stats::lag()# assign the url to `github_raw_csv_url`
github_raw_csv_url <- "https://raw.githubusercontent.com/t-emery/sais-susfin_data/main/datasets/blackrock_etf_screener_2022-08-30.csv"
# read in the data, and assign it to the object `blackrock_etf_data`
blackrock_etf_data <- read_csv(github_raw_csv_url)Rows: 393 Columns: 22
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (14): ticker, name, incept_date, net_assets_as_of, asset_class, sub_asse...
dbl (8): gross_expense_ratio_percent, net_expense_ratio_percent, net_assets...
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.mini_blackrock_data <- blackrock_etf_data |>
# group by whether the fund is an ESG fund or not
group_by(is_esg) |>
# take the top 5 from each group, by net assets
slice_max(order_by = net_assets_usd_mn, n = 5) |>
# select the following columns
select(ticker, name, asset_class, sub_asset_class, region, incept_date, net_assets_usd_mn,
msci_weighted_average_carbon_intensity_tons_co2e_m_sales) |>
# rename to `co2_intensity` because the full name is a mouthful, if descriptive.
rename(co2_intensity = msci_weighted_average_carbon_intensity_tons_co2e_m_sales) |>
# always good to ungroup() if you've used a group_by(). We'll discuss later.
ungroup()Adding missing grouping variables: `is_esg`ggplot(data=mini_blackrock_data)+
geom_point(mapping=aes(x=is_esg, y=co2_intensity))+
facet_wrap(~region)
In different regional levels: global and North America, we found the similar trend in the intensity of CO2, considering the type of fund. Roughly, Regular Fund has higher CO2 intensity than ESG Fund.
ggplot(data=mini_blackrock_data)+
geom_point(mapping=aes(x=is_esg, y=net_assets_usd_mn))+
facet_wrap(~region)
In global and North America, we also found the similar trend considering the net assets of ESG Fund and Regular Fund. Regular Fund basically has higher net assets than ESG Fund.
ggplot(data=mini_blackrock_data)+
geom_line(mapping=aes(x=co2_intensity, y=net_assets_usd_mn))
When CO2 intensity is 150, the net assets used reach the highest level, greater than $10 billion.