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2020 Candidate Climate Leadership Scores
Name Party State Public Engagement Press Releases Bills Sponsored Bills Cosponsored Caucuses Website Total Senator 2020 Hopeful
Whitehouse, Sheldon D RI 30 7 7 7 10 10 71 Whitehouse, Sheldon (D-RI) No
Schatz, Brian D HI 24 5 14 8 4 10 65 Schatz, Brian (D-HI) No
Markey, Ed D MA 20 8 11 6 10 10 65 Markey, Ed (D-MA) No
Sanders, Bernie I VT 17 7 15 4 10 10 63 Sanders, Bernie (I-VT) Yes
Boxer, Barbara D CA 22 4 6 4 10 10 56 Boxer, Barbara (D-CA) No
Cardin, Ben D MD 20 6 0 1 10 10 47 Cardin, Ben (D-MD) No
King, Angus I ME 25 6 0 1 10 5 47 King, Angus (I-ME) No
Cantwell, Maria D WA 19 5 0 3 10 10 47 Cantwell, Maria (D-WA) No
Menendez, Robert D NJ 17 4 0 5 10 10 46 Menendez, Robert (D-NJ) No
Merkley, Jeff D OR 10 4 6 5 10 10 45 Merkley, Jeff (D-OR) Yes
Booker, Cory D NJ 18 4 0 0 10 10 42 Booker, Cory (D-NJ) Yes
Feinstein, Dianne D CA 15 4 6 3 0 10 38 Feinstein, Dianne (D-CA) No
Udall, Tom D NM 12 5 7 -1 10 5 38 Udall, Tom (D-NM) No
Coons, Christopher D DE 14 4 3 6 0 10 37 Coons, Christopher (D-DE) No
Gillibrand, Kirsten D NY 10 4 1 0 10 10 35 Gillibrand, Kirsten (D-NY) Yes
Shaheen, Jeanne D NH 7 5 0 2 10 10 34 Shaheen, Jeanne (D-NH) No
Heinrich, Martin D NM 10 3 0 -1 10 10 32 Heinrich, Martin (D-NM) No
Reid, Harry D NV 15 5 0 2 0 10 32 Reid, Harry (D-NV) No
Murphy, Chris D CT 0 5 0 2 10 10 27 Murphy, Chris (D-CT) No
Leahy, Patrick D VT 14 4 0 4 0 5 27 Leahy, Patrick (D-VT) No
Reed, Jack D RI 9 2 4 1 0 10 26 Reed, Jack (D-RI) No
Nelson, Bill D FL 15 2 7 1 0 0 25 Nelson, Bill (D-FL) No
Warren, Elizabeth D MA 10 2 0 3 10 0 25 Warren, Elizabeth (D-MA) Yes
Wyden, Ron D OR 10 4 4 1 0 5 24 Wyden, Ron (D-OR) No
Carper, Thomas D DE 5 2 3 2 0 10 22 Carper, Thomas (D-DE) No
Hirono, Maizie D HI 12 4 0 1 0 5 22 Hirono, Maizie (D-HI) No
Blumenthal, Richard D CT 3 2 1 0 10 5 21 Blumenthal, Richard (D-CT) No
Schumer, Chuck D NY 14 3 0 4 0 0 21 Schumer, Chuck (D-NY) No
Mikulski, Barbara D MD 14 4 0 0 0 0 18 Mikulski, Barbara (D-MD) No
Stabenow, Debbie D MI 7 4 0 2 0 5 18 Stabenow, Debbie (D-MI) No
Franken, Al D MN 7 3 0 2 0 5 17 Franken, Al (D-MN) No
Brown, Sherrod D OH 10 0 0 1 0 5 16 Brown, Sherrod (D-OH) Yes
Klobuchar, Amy D MN -5 2 1 0 10 5 13 Klobuchar, Amy (D-MN) Yes
Peters, Gary D MI 7 4 0 0 0 0 11 Peters, Gary (D-MI) No
Bennet, Michael D CO -6 4 4 3 0 5 10 Bennet, Michael (D-CO) No
Durbin, Richard D IL -5 3 0 2 0 10 10 Durbin, Richard (D-IL) No
Collins, Susan R ME 0 1 1 -1 0 5 6 Collins, Susan (R-ME) No
Baldwin, Tammy D WI 0 4 0 1 0 0 5 Baldwin, Tammy (D-WI) No
Kaine, Tim D VA -4 -1 0 2 0 5 2 Kaine, Tim (D-VA) No
Ayotte, Kelly R NH 3 1 0 -3 0 0 1 Ayotte, Kelly (R-NH) No
Grassley, Chuck R IA 0 -4 0 -4 0 5 -3 Grassley, Chuck (R-IA) No
Casey, Bob D PA -7 0 0 4 0 0 -3 Casey, Bob (D-PA) No
Warner, Mark D VA -7 -3 4 -1 0 0 -7 Warner, Mark (D-VA) No
Tester, Jon D MT -10 -2 0 -4 0 0 -16 Tester, Jon (D-MT) No
Donnelly, Joe D IN -10 -2 0 0 0 -5 -17 Donnelly, Joe (D-IN) No
Heitkamp, Heidi D ND -20 -5 6 0 1 -5 -23 Heitkamp, Heidi (D-ND) No
Manchin, Joe D WV -17 -6 0 2 0 -5 -26 Manchin, Joe (D-WV) No
McCaskill, Claire D MO -20 -3 -4 -2 0 0 -29 McCaskill, Claire (D-MO) No
---
title: "R Notebook"
output:
html_document:
df_print: paged
---
```{r setup}
library(tidyverse)
library(plotly)
library(ggrepel)
dat <- read_csv("Climate Hawks Vote 2015 Scores.csv")
```
```{r}
dat %>%
select_if(is.numeric) %>%
as.matrix %>%
cov %>%
as.data.frame() %>%
rownames_to_column("var1") %>%
gather(var2, value, -var1) %>%
mutate(type = ifelse(var1 == var2, "var", "cov")) ->
varcov
varcov %>%
arrange(type, desc(value))
```
Component with most variance: Public Engagement
```{r}
varcov %>%
filter(var1 == "Public Engagement", type == "cov") %>%
arrange(value)
```
High variance components least correlated with public engagment: caucuses
```{r}
dat %>%
ggplot(aes(y = `Public Engagement`, x = Caucuses)) +
geom_point()
```
Despite high vairance, Caucses is nearly dichotomous. Probbaly better to bin and place on a qualitative mapping.
```{r}
dat %>%
ggplot(aes(y = `Public Engagement`, x = `Bills Cosponsored`)) +
geom_point()
```
```{r}
dat %>%
ggplot(aes(y = `Public Engagement`, x = `Bills Sponsored`)) +
geom_point()
```
Public Engagement versus Bills **Co**sponsored gives the most varied distribution - will use these for the positional mappings.
```{r}
bgc <- "#111111"
bg <- element_rect(color = bgc, fill = bgc)
dat2 <- dat %>%
mutate(Caucuses =`levels<-`(
Hmisc::cut2(Caucuses, c(0, 1, 10, 11)),
c("None", "Some", "All")
))
base_plot <- dat2 %>%
ggplot(aes(y = `Public Engagement`, x = `Bills Cosponsored`,
label = Name)) +
geom_point(aes(color = Website, size = `Bills Sponsored`,
shape = Caucuses),
# position = position_jitter(0.1, 0.1, 100),
fill = "grey80", stroke = 1.5) +
scale_shape_manual("Caucus\nMembership", values = c(1, 21, 19)) +
scale_color_gradientn("Website Score",
colors = RColorBrewer::brewer.pal(5, "RdYlBu"),
limits = c(-10, 10)) +
labs(
x = "Bill Cosponsorship Score", y = "Public Engagement Score",
title = "Climate Leadership Scores",
subtitle = "for potential 2020 Democratic candidates",
caption = "data from Climate Hawks Vote 2015 Senate scorecards"
) +
theme(
panel.background = bg,
text = element_text(color = "white"),
plot.background = bg,
legend.background = bg,
legend.key = bg,
panel.grid = element_blank(),
legend.margin = margin(0, 0, 0, 0),
axis.text = element_text(color = "white"),
axis.ticks = element_line(color = "white")
)
static_plot <- base_plot +
geom_label_repel(data = filter(dat, `2020 Hopeful` == "Yes"),
# position = position_jitter(0.1, 0.1, 100),
hjust = 1.15, vjust = "middle", alpha = 0.9,
segment.color = "grey80",
direction="y", seed = 100) +
scale_size("Bill Sponsorship Score", limits = c(-5, 15),
breaks = c(-5, 5, 15)) +
guides(
shape = guide_legend(
override.aes = list(color = c("#ffffbf"), size = 4)
),
size = guide_legend(
override.aes = list(color = c("#ffffbf"))
)
) +
theme(text = element_text(size = 16, color = "white"))
static_plot
```
```{r}
size <- 5
ggsave("climate scores.png", static_plot, height = size, width = size * 1.618)
```
```{r}
# fix legend label being placed incorrectly - remove and add manually
interactive_plot <- plotly::ggplotly(base_plot +
guides(size = guide_legend(""),
shape = guide_legend(""))) %>%
add_annotations(text="Caucus\nMembership",
xref="paper", yref="paper",
x=1.03, xanchor="left",
y=0.35, yanchor="bottom",
legendtitle=TRUE, showarrow=FALSE)
# remove excess padding
interactive_plot$sizingPolicy$padding <- "0"
# highlight 2020 contenders
# (can't use data index because data is split by symbol)
c2020 <- dat$Name[dat$`2020 Hopeful` == "Yes"] %>%
paste(collapse = "|")
interactive_plot$x$data <- lapply(interactive_plot$x$data, function (trc) {
if (length(trc$text) == 0) { return(trc) }
sel <- grep(c2020, trc$text)
trc$selectedpoints <- sel - 1 # zero-based js indexing
trc
})
htmlwidgets::saveWidget(
interactive_plot, "index.html", libdir = "lib",
title = "2020 Candidate Climate Leadership Scores",
selfcontained = FALSE, background = bgc
)
interactive_plot
```
Abandoned concepts and other scratch work below:
```{r}
rank_order <- dat$Name[order(dat$Total)]
dat %>%
ggplot(aes(y = `Public Engagement`, x = Name,
color = `2020 Hopeful`)) +
geom_point() +
scale_x_discrete(limits = rank_order) +
coord_flip()
```
```{r}
table(dat$`Bills Sponsored`)
```
```{r}
table(dat$`Website`)
```
```{r}
Hmisc::cut2(dat$Caucuses, c(0, 1, 10, 11)) %>%
table
```
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