── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag() masks stats::lag()
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Rows: 537 Columns: 14
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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.
Look at the screenshot above. Assign your variables to the correct aesthetics, and choose the correct chart type.
Explore the menus at the bottom of the screen.
Change the x and y axis to log10.
Add a smoother (there is no 45 degree line option in esquisse).
Add descriptive titles and labels. Put your name in the caption.
Change the color palette to a different color palette of your choice.
library(ggplot2)ggplot(blackrock_esg_vs_non_esg_etf) +aes(x = esg_etf, y = standard_etf, colour = sector) +geom_point(shape ="circle", size =1.5) +geom_smooth(method ="loess", se =FALSE, color ="darkgrey") +scale_color_brewer(palette ="Set3", direction =1) +scale_x_continuous(trans ="log10") +scale_y_continuous(trans ="log10") +labs(x ="ESG ETF (ESGU) [log scale]",y ="Standard ETF (IVV) [log scale]",title ="Large Cap American Equities ETFs: ESG vs. Non-ESG",subtitle ="A comparison of the holdings of BlackRock iShares ESGU and IVV",caption ="Linyi Zheng" ) +theme_minimal() +facet_wrap(vars(sector), ncol =3L)
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aesthetics: colour
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call: call
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get_breaks: function
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guide: legend
is_discrete: function
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super: <ggproto object: Class ScaleDiscrete, Scale, gg>
# 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
library(dplyr)library(ggplot2)blackrock_esg_vs_non_esg_etf_long %>%filter(weight >=1& weight <=100) %>%ggplot() +aes(x = weight, y = company_name, colour = fund_type, size = weight) +geom_point(shape ="circle") +scale_color_manual(values =c(`ESG ETF (ESGU)`="green", `Standard ETF (IVV)`="gray")) +labs(x ="Weight", y ="Company Name", title ="Comparation of Weight in ESG and non-ESG", caption ="Linyi Zheng") +theme_minimal()
short reflection: As most data points fall on the lower end of the weight scale, outliers become more noticeable. There are two outliers with extreme weights of approximately 5.8% and 6.9%, which are included in both ESG and non-ESG ETFs. This suggests that the majority of these companies give equal importance to both types of ETFs.
`summarise()` has grouped output by 'sector'. You can override using the
`.groups` argument.
total_counts <- blackrock_esg_vs_non_esg_etf_long %>%group_by(sector) %>%summarise(total_count =n())etf_summary <-merge(total_counts, etf_summary, by ="sector")ggplot(etf_summary, aes(x = sector)) +geom_bar(aes(y = total_count), stat ="identity", fill ="grey") +geom_line(aes(y = proportion_esg *max(total_count)), color ="green", size =1.5) +geom_line(aes(y = proportion_non_esg *max(total_count)), color ="red", size =1.5) +geom_point(aes(y = proportion_esg *max(total_count)), color ="green", size =4) +geom_point(aes(y = proportion_non_esg *max(total_count)), color ="red", size =4) +scale_y_continuous(sec.axis =sec_axis(~ . /max(etf_summary$total_count), name ="Proportion")) +labs(title ="Total Count of ETFs and Proportion of ESG/Non-ESG ETFs Across Sectors", x ="Sector", y ="Total Count of ETFs") +theme_minimal() +theme(axis.text.x =element_text(angle =45, hjust =1))
Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
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This chart shows the amount of ETF across sectors. IT has the most ETFs, and Energy has the least. And within each sector, the proportions of ESG/Non-ESG ETFs across sectors are basically the same.
c2 <- blackrock_esg_vs_non_esg_etf |>ggplot(aes(x = esg_etf, y = standard_etf, color = sector)) +geom_point(size =3) +scale_color_manual(values =c("red", "blue", "green", "orange","purple","cyan", "magenta", "yellow","black", "gray", "brown")) +scale_x_log10() +scale_y_log10()+coord_flip() +theme_minimal() +labs(title ="Comparison of Standard ETFs vs. ESG ETFs",x ="Standard ETF",y ="ESG ETF",color ="Sector") c2
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The graph shows the relationship between ESG ETF and standard ETF among different sectors. There is a subtle linear relationship.
blackrock_esg_vs_non_esg_etf_long %>%filter(weight >=1& weight <=100) %>%ggplot(aes(x =reorder(company_name, -weight), y = weight, fill = fund_type)) +geom_bar(stat ="identity", position =position_dodge(), width =0.7) +scale_fill_manual(values =c(`ESG ETF (ESGU)`="green", `Standard ETF (IVV)`="red")) +labs(x ="Company Name", y ="Weight", title ="Comparison of Company Weight in ESG vs. Non-ESG ETFs",subtitle ="Bar chart showing the weight distribution across companies", caption ="Data visualization by Nadia Xing") +theme_grey() +theme(axis.text.x =element_text(angle =45, hjust =1))
The chart shows the weight distribution in ESG and non-ESG ETFs across companies. We can see different companies have different distributions of ETFs.
