getAmountForYearAreaPair(df3, 2022, "Longtermism")
amounts <- c()
for(i in c(1:dim(df4)[1])){
amount <- getAmountForYearAreaPair(df3, df4$year[i], df4$area[i])
amounts <- c(amounts, amount)
}
df4$amount <- amounts
df4$cummulative_amount_for_its_area = sapply(df4$area, function(area) {
return(getAmountForArea(df3, area))
})
## Plotting longtermist funding
title_text="Open Philanthropy allocation by year and cause area"
subtitle_text="restricted to longtermism & GCRs"
palette = "Classic Red-Blue"
direction = -1
open_philanthropy_plot_lt <- ggplot(data=df4, aes(x=year, y=amount, fill=area, group=cummulative_amount_for_its_area))+
geom_bar(stat="identity")+
labs(
title=title_text,
subtitle=subtitle_text,
x=element_blank(),
y=element_blank()
) +
# scale_fill_wsj() +
# scale_fill_tableau(dir =1) +
# scale_fill_tableau(palette, dir=direction) +
# scale_fill_viridis(discrete = TRUE) +
# scale_fill_brewer(palette = "Set2") +
scale_fill_d3( "category20", alpha=0.8) +
# scale_fill_uchicago("dark") +
# scale_fill_startrek() +
scale_y_continuous(labels = scales::dollar_format(scale = 0.000001, suffix = "M"))+
scale_x_continuous(breaks = years)+
theme_tufte() +
theme(
legend.title = element_blank(),
plot.title = element_text(hjust = 0.5),
plot.subtitle = element_text(hjust = 0.5),
legend.position="bottom",
legend.box="vertical",
axis.text.x=element_text(angle=60, hjust=1),
legend.text=element_text(size=7)
) +
geom_text(aes(label=ifelse(amount > 5e6, paste0(round(amount / 1e6, 0), "M"), "")),  size = 2, colour="#f9f9f9", position = position_stack(vjust = 0.5)) +
geom_text(
aes(label = paste0(round(after_stat(y) / 1e6, 0), "M"), group = year),
stat = 'summary', fun = sum, size=2.3, vjust = -0.5
) +
guides(fill=guide_legend(nrow=3,byrow=TRUE))
open_philanthropy_plot_lt <- ggplot(data=df4, aes(x=year, y=amount, fill=area, group=cummulative_amount_for_its_area))+
geom_bar(stat="identity")+
labs(
title=title_text,
subtitle=subtitle_text,
x=element_blank(),
y=element_blank()
) +
# scale_fill_wsj() +
# scale_fill_tableau(dir =1) +
# scale_fill_tableau(palette, dir=direction) +
# scale_fill_viridis(discrete = TRUE) +
# scale_fill_brewer(palette = "Set2") +
scale_fill_d3( "category20", alpha=0.8) +
# scale_fill_uchicago("dark") +
# scale_fill_startrek() +
scale_y_continuous(labels = scales::dollar_format(scale = 0.000001, suffix = "M"))+
scale_x_continuous(breaks = years)+
theme_tufte() +
theme(
legend.title = element_blank(),
legend.text.align = 0,
plot.title = element_text(hjust = 0.5),
plot.subtitle = element_text(hjust = 0.5),
legend.position="bottom",
legend.box="vertical",
axis.text.x=element_text(angle=60, hjust=1),
legend.text=element_text(size=7)
) +
geom_text(aes(label=ifelse(amount > 5e6, paste0(round(amount / 1e6, 0), "M"), "")),  size = 2, colour="#f9f9f9", position = position_stack(vjust = 0.5)) +
geom_text(
aes(label = paste0(round(after_stat(y) / 1e6, 0), "M"), group = year),
stat = 'summary', fun = sum, size=2.3, vjust = -0.5
) +
guides(fill=guide_legend(nrow=3,byrow=TRUE))
open_philanthropy_plot_lt
df3 <- list()
df3$year <- as.vector(sapply(data$Date, getYear))
df3$amount <- as.vector(sapply(data$Amount, parse_number))
df3$amount <- ifelse(is.na(df$amount), 0, df$amount)
df3$area <- as.vector(data$Focus.Area)
df3 <- as.data.frame(df3)
df3$area <- as.vector(data$Focus.Area)
## Including Dustin Moskovitz's wealth
coeff <- 10^7*4
wealth <- c(6, 8, 12, 15, 18, 12, 14, 19, 14)
df2$wealth <- rep(wealth * coeff, num_areas)
make_fortune_plot <- function(show_fortune_legend = FALSE) {
open_philanthropy_plot_with_fortune <- ggplot(data=df2, aes(x=year, y=amount, fill=area, group = cummulative_amount_for_its_area))+
geom_bar(stat="identity")+
geom_point(
aes(x=year, y=wealth), size=2, color="darkblue", shape=4,
show.legend=show_fortune_legend
)+
labs(
title=title_text,
subtitle=subtitle_text,
x=element_blank(),
y=element_blank()
) +
# scale_fill_wsj() +
# scale_fill_tableau(dir =1) +
# scale_fill_tableau(palette, dir=direction) +
# scale_fill_viridis(discrete = TRUE) +
# scale_fill_brewer(palette = "Set2") +
scale_fill_d3( "category20", alpha=0.8) +
# scale_fill_uchicago("dark") +
# scale_fill_startrek() +
scale_y_continuous(
labels = scales::dollar_format(scale = 0.000001, suffix = "M"),
name="OpenPhil donations",
breaks = c(0:5)*10^8,
sec.axis = sec_axis(
~.*1,
name="Dustin Moskovitz's fortune\n(est. Bloomberg)",
breaks = seq(0,20,by=5)*coeff,
labels = c("$0B", "$5B","$10B","$15B", "$20B")
),
limits=c(0,8*10^8)
)+
scale_x_continuous(breaks = years)+
theme_tufte() +
theme(
legend.title = element_blank(),
plot.title = element_text(hjust = 0.5),
plot.subtitle = element_text(hjust = 0.5),
legend.position="bottom",
legend.box="vertical",
axis.text.x=element_text(angle=60, hjust=1),
axis.title.y = element_text(vjust=3, hjust=0.25, size=10),
axis.title.y.right = element_text(vjust=3, hjust=0.5, size=10),
legend.text=element_text(size=8)
) +
guides(fill=guide_legend(nrow=4,byrow=TRUE))
# open_philanthropy_plot_with_fortune
height = 6
width = 5
filename = ifelse(
show_fortune_legend,
"open_philanthropy_plot_with_fortune.png",
"open_philanthropy_plot_with_fortune_clean_labels.png"
)
ggsave(plot=open_philanthropy_plot_with_fortune, filename, width=width, height=height, bg = "white")
}
make_fortune_plot(TRUE)
make_fortune_plot(FALSE)
## Look at the different longtermist areas independently.
longtermism <- c("Biosecurity & Pandemic Preparedness", "Potential Risks from Advanced AI", "Science Supporting Biosecurity and Pandemic Preparedness", "Longtermism")
df3 <- list()
df3$year <- as.vector(sapply(data$Date, getYear))
df3$amount <- as.vector(sapply(data$Amount, parse_number))
df3$amount <- ifelse(is.na(df$amount), 0, df$amount)
df3$area <- as.vector(data$Focus.Area)
df3 <- as.data.frame(df3)
df3$area <- as.vector(data$Focus.Area)
df3 <- df3 %>% dplyr::filter(area %in% longtermism)
df$area <- ifelse(df3$area %in% ea_growth, "EA Community Building", df$area)
## Group area
df3
df3$area <- ifelse(df$area %in% pure_longtermism, "Longtermism", df$area)
pure_longtermism = c("Longtermism")
biorisk = c("Biosecurity & Pandemic Preparedness", "Science Supporting Biosecurity and Pandemic Preparedness")
ai_risk = c( "Potential Risks from Advanced AI")
df3$area <- ifelse(df$area %in% pure_longtermism, "Longtermism", df$area)
df3$area <- ifelse(df$area %in% biorisk, "Biosecurity & Pandemic Preparedness", df$area)
df3$area <- ifelse(df$area %in% ai_risk, "Potential Risks from Advanced AI", df$area)
df3 <- list()
df3$year <- as.vector(sapply(data$Date, getYear))
df3$amount <- as.vector(sapply(data$Amount, parse_number))
df3$amount <- ifelse(is.na(df$amount), 0, df$amount)
df3$area <- as.vector(data$Focus.Area)
df3 <- as.data.frame(df3)
df3$area <- as.vector(data$Focus.Area)
df3 <- df3 %>% dplyr::filter(area %in% longtermism)
pure_longtermism = c("Longtermism")
biorisk = c("Biosecurity & Pandemic Preparedness", "Science Supporting Biosecurity and Pandemic Preparedness")
ai_risk = c( "Potential Risks from Advanced AI")
df3$area <- ifelse(df$area %in% pure_longtermism, "Longtermism", df$area)
df3$area <- ifelse(df$area %in% biorisk, "Biosecurity & Pandemic Preparedness", df$area)
df3 <- list()
df3$year <- as.vector(sapply(data$Date, getYear))
df3$amount <- as.vector(sapply(data$Amount, parse_number))
df3$amount <- ifelse(is.na(df$amount), 0, df$amount)
df3$area <- as.vector(data$Focus.Area)
df3 <- as.data.frame(df3)
df3$area <- as.vector(data$Focus.Area)
df3 <- df3 %>% dplyr::filter(area %in% longtermism)
pure_longtermism = c("Longtermism")
biorisk = c("Biosecurity & Pandemic Preparedness", "Science Supporting Biosecurity and Pandemic Preparedness")
ai_risk = c( "Potential Risks from Advanced AI")
df3$area <- ifelse(df3$area %in% pure_longtermism, "Longtermism", df3$area)
df3$area <- ifelse(df3$area %in% biorisk, "Biosecurity & Pandemic Preparedness", df3$area)
df3$area <- ifelse(df3$area %in% ai_risk, "Potential Risks from Advanced AI", df3$area)
years <- c(2014: 2022) # as.vector(unique(df$year))
num_years <- length(years)
area_names <- longtermism
num_areas <- length(area_names)
df4 <- list()
df4$area <- sort(rep(area_names, num_years))
df4$year <- rep(years, num_areas)
df4 <- as.data.frame(df4)
# View(df4)
getAmountForYearAreaPair(df3, 2022, "Longtermism")
amounts <- c()
for(i in c(1:dim(df4)[1])){
amount <- getAmountForYearAreaPair(df3, df4$year[i], df4$area[i])
amounts <- c(amounts, amount)
}
df4$amount <- amounts
df4$cummulative_amount_for_its_area = sapply(df4$area, function(area) {
return(getAmountForArea(df3, area))
})
## Plotting longtermist funding
title_text="Open Philanthropy allocation by year and cause area"
subtitle_text="restricted to longtermism & GCRs"
palette = "Classic Red-Blue"
direction = -1
open_philanthropy_plot_lt <- ggplot(data=df4, aes(x=year, y=amount, fill=area, group=cummulative_amount_for_its_area))+
geom_bar(stat="identity")+
labs(
title=title_text,
subtitle=subtitle_text,
x=element_blank(),
y=element_blank()
) +
# scale_fill_wsj() +
# scale_fill_tableau(dir =1) +
# scale_fill_tableau(palette, dir=direction) +
# scale_fill_viridis(discrete = TRUE) +
# scale_fill_brewer(palette = "Set2") +
scale_fill_d3( "category20", alpha=0.8) +
# scale_fill_uchicago("dark") +
# scale_fill_startrek() +
scale_y_continuous(labels = scales::dollar_format(scale = 0.000001, suffix = "M"))+
scale_x_continuous(breaks = years)+
theme_tufte() +
theme(
legend.title = element_blank(),
legend.text.align = 0,
plot.title = element_text(hjust = 0.5),
plot.subtitle = element_text(hjust = 0.5),
legend.position="bottom",
legend.box="vertical",
axis.text.x=element_text(angle=60, hjust=1),
legend.text=element_text(size=7)
) +
geom_text(aes(label=ifelse(amount > 5e6, paste0(round(amount / 1e6, 0), "M"), "")),  size = 2, colour="#f9f9f9", position = position_stack(vjust = 0.5)) +
geom_text(
aes(label = paste0(round(after_stat(y) / 1e6, 0), "M"), group = year),
stat = 'summary', fun = sum, size=2.3, vjust = -0.5
) +
guides(fill=guide_legend(nrow=3,byrow=TRUE))
open_philanthropy_plot_lt
longtermism_labels = c(pure_longtermism, biorisk, ai_risk)
open_philanthropy_plot_lt <- ggplot(data=df4, aes(x=year, y=amount, fill=area, group=cummulative_amount_for_its_area))+
geom_bar(stat="identity")+
labs(
title=title_text,
subtitle=subtitle_text,
x=element_blank(),
y=element_blank()
) +
# scale_fill_wsj() +
# scale_fill_tableau(dir =1) +
# scale_fill_tableau(palette, dir=direction) +
# scale_fill_viridis(discrete = TRUE) +
# scale_fill_brewer(palette = "Set2") +
scale_fill_d3( "category20", alpha=0.8) +
# scale_fill_uchicago("dark") +
# scale_fill_startrek() +
scale_y_continuous(labels = scales::dollar_format(scale = 0.000001, suffix = "M"))+
scale_x_continuous(breaks = years)+
theme_tufte() +
theme(
legend.title = element_blank(),
legend.text.align = 0,
plot.title = element_text(hjust = 0.5),
plot.subtitle = element_text(hjust = 0.5),
legend.position="bottom",
legend.box="vertical",
axis.text.x=element_text(angle=60, hjust=1),
legend.text=element_text(size=7)
) +
geom_text(aes(label=ifelse(amount > 5e6, paste0(round(amount / 1e6, 0), "M"), "")),  size = 2, colour="#f9f9f9", position = position_stack(vjust = 0.5)) +
geom_text(
aes(label = paste0(round(after_stat(y) / 1e6, 0), "M"), group = year),
stat = 'summary', fun = sum, size=2.3, vjust = -0.5
) +
guides(fill=guide_legend(nrow=3,byrow=TRUE))
years <- c(2014: 2022) # as.vector(unique(df$year))
num_years <- length(years)
area_names <- longtermism_labels
num_areas <- length(area_names)
df4 <- list()
df4$area <- sort(rep(area_names, num_years))
df4$year <- rep(years, num_areas)
df4 <- as.data.frame(df4)
# View(df4)
getAmountForYearAreaPair(df3, 2022, "Longtermism")
amounts <- c()
for(i in c(1:dim(df4)[1])){
amount <- getAmountForYearAreaPair(df3, df4$year[i], df4$area[i])
amounts <- c(amounts, amount)
}
df4$amount <- amounts
df4$cummulative_amount_for_its_area = sapply(df4$area, function(area) {
return(getAmountForArea(df3, area))
})
## Plotting longtermist funding
title_text="Open Philanthropy allocation by year and cause area"
subtitle_text="restricted to longtermism & GCRs"
palette = "Classic Red-Blue"
direction = -1
open_philanthropy_plot_lt <- ggplot(data=df4, aes(x=year, y=amount, fill=area, group=cummulative_amount_for_its_area))+
geom_bar(stat="identity")+
labs(
title=title_text,
subtitle=subtitle_text,
x=element_blank(),
y=element_blank()
) +
# scale_fill_wsj() +
# scale_fill_tableau(dir =1) +
# scale_fill_tableau(palette, dir=direction) +
# scale_fill_viridis(discrete = TRUE) +
# scale_fill_brewer(palette = "Set2") +
scale_fill_d3( "category20", alpha=0.8) +
# scale_fill_uchicago("dark") +
# scale_fill_startrek() +
scale_y_continuous(labels = scales::dollar_format(scale = 0.000001, suffix = "M"))+
scale_x_continuous(breaks = years)+
theme_tufte() +
theme(
legend.title = element_blank(),
legend.text.align = 0,
plot.title = element_text(hjust = 0.5),
plot.subtitle = element_text(hjust = 0.5),
legend.position="bottom",
legend.box="vertical",
axis.text.x=element_text(angle=60, hjust=1),
legend.text=element_text(size=7)
) +
geom_text(aes(label=ifelse(amount > 5e6, paste0(round(amount / 1e6, 0), "M"), "")),  size = 2, colour="#f9f9f9", position = position_stack(vjust = 0.5)) +
geom_text(
aes(label = paste0(round(after_stat(y) / 1e6, 0), "M"), group = year),
stat = 'summary', fun = sum, size=2.3, vjust = -0.5
) +
guides(fill=guide_legend(nrow=3,byrow=TRUE))
open_philanthropy_plot_lt
## Look at the different longtermist areas independently.
longtermism <- c("Biosecurity & Pandemic Preparedness", "Potential Risks from Advanced AI", "Science Supporting Biosecurity and Pandemic Preparedness", "Longtermism")
df3 <- list()
df3$year <- as.vector(sapply(data$Date, getYear))
df3$amount <- as.vector(sapply(data$Amount, parse_number))
df3$amount <- ifelse(is.na(df$amount), 0, df$amount)
df3$area <- as.vector(data$Focus.Area)
df3 <- as.data.frame(df3)
df3$area <- as.vector(data$Focus.Area)
df3 <- df3 %>% dplyr::filter(area %in% longtermism)
pure_longtermism = c("Longtermism")
biorisk = c("Biosecurity & Pandemic Preparedness", "Science Supporting Biosecurity and Pandemic Preparedness")
ai_risk = c( "Potential Risks from Advanced AI")
longtermism_labels = c(pure_longtermism, biorisk, ai_risk)
df3$area <- ifelse(df3$area %in% pure_longtermism, "Longtermism", df3$area)
df3$area <- ifelse(df3$area %in% biorisk, "Biosecurity & Pandemic Preparedness", df3$area)
df3$area <- ifelse(df3$area %in% ai_risk, "Potential Risks from Advanced AI", df3$area)
years <- c(2014: 2022) # as.vector(unique(df$year))
num_years <- length(years)
area_names <- longtermism_labels
num_areas <- length(area_names)
df4 <- list()
df4$area <- sort(rep(area_names, num_years))
df4$year <- rep(years, num_areas)
df4 <- as.data.frame(df4)
# View(df4)
getAmountForYearAreaPair(df3, 2022, "Longtermism")
amounts <- c()
for(i in c(1:dim(df4)[1])){
amount <- getAmountForYearAreaPair(df3, df4$year[i], df4$area[i])
amounts <- c(amounts, amount)
}
df4$amount <- amounts
df4$cummulative_amount_for_its_area = sapply(df4$area, function(area) {
return(getAmountForArea(df3, area))
})
## Plotting longtermist funding
title_text="Open Philanthropy allocation by year and cause area"
subtitle_text="restricted to longtermism & GCRs"
palette = "Classic Red-Blue"
direction = -1
open_philanthropy_plot_lt <- ggplot(data=df4, aes(x=year, y=amount, fill=area, group=cummulative_amount_for_its_area))+
geom_bar(stat="identity")+
labs(
title=title_text,
subtitle=subtitle_text,
x=element_blank(),
y=element_blank()
) +
# scale_fill_wsj() +
# scale_fill_tableau(dir =1) +
# scale_fill_tableau(palette, dir=direction) +
# scale_fill_viridis(discrete = TRUE) +
# scale_fill_brewer(palette = "Set2") +
scale_fill_d3( "category20", alpha=0.8) +
# scale_fill_uchicago("dark") +
# scale_fill_startrek() +
scale_y_continuous(labels = scales::dollar_format(scale = 0.000001, suffix = "M"))+
scale_x_continuous(breaks = years)+
theme_tufte() +
theme(
legend.title = element_blank(),
legend.text.align = 0,
plot.title = element_text(hjust = 0.5),
plot.subtitle = element_text(hjust = 0.5),
legend.position="bottom",
legend.box="vertical",
axis.text.x=element_text(angle=60, hjust=1),
legend.text=element_text(size=7)
) +
geom_text(aes(label=ifelse(amount > 5e6, paste0(round(amount / 1e6, 0), "M"), "")),  size = 2, colour="#f9f9f9", position = position_stack(vjust = 0.5)) +
geom_text(
aes(label = paste0(round(after_stat(y) / 1e6, 0), "M"), group = year),
stat = 'summary', fun = sum, size=2.3, vjust = -0.5
) +
guides(fill=guide_legend(nrow=3,byrow=TRUE))
open_philanthropy_plot_lt
View(df4)
df4 <- list()
df4$area <- sort(rep(area_names, num_years))
df4 <- list()
df4$area <- sort(rep(area_names, num_years))
df4$area
longtermism_labels
longtermism_labels = c(pure_longtermism, "Biosecurity & Pandemic Preparedness", ai_risk)
df3$area <- ifelse(df3$area %in% pure_longtermism, "Longtermism", df3$area)
df3$area <- ifelse(df3$area %in% biorisk, "Biosecurity & Pandemic Preparedness", df3$area)
df3$area <- ifelse(df3$area %in% ai_risk, "Potential Risks from Advanced AI", df3$area)
years <- c(2014: 2022) # as.vector(unique(df$year))
num_years <- length(years)
area_names <- longtermism_labels
num_areas <- length(area_names)
df4 <- list()
df4$area <- sort(rep(area_names, num_years))
df4$year <- rep(years, num_areas)
df4 <- as.data.frame(df4)
# View(df4)
getAmountForYearAreaPair(df3, 2022, "Longtermism")
amounts <- c()
for(i in c(1:dim(df4)[1])){
amount <- getAmountForYearAreaPair(df3, df4$year[i], df4$area[i])
amounts <- c(amounts, amount)
}
df4$amount <- amounts
df4$cummulative_amount_for_its_area = sapply(df4$area, function(area) {
return(getAmountForArea(df3, area))
})
View(df4)
## Plotting longtermist funding
title_text="Open Philanthropy allocation by year and cause area"
subtitle_text="restricted to longtermism & GCRs"
palette = "Classic Red-Blue"
direction = -1
open_philanthropy_plot_lt <- ggplot(data=df4, aes(x=year, y=amount, fill=area, group=cummulative_amount_for_its_area))+
geom_bar(stat="identity")+
labs(
title=title_text,
subtitle=subtitle_text,
x=element_blank(),
y=element_blank()
) +
# scale_fill_wsj() +
# scale_fill_tableau(dir =1) +
# scale_fill_tableau(palette, dir=direction) +
# scale_fill_viridis(discrete = TRUE) +
# scale_fill_brewer(palette = "Set2") +
scale_fill_d3( "category20", alpha=0.8) +
# scale_fill_uchicago("dark") +
# scale_fill_startrek() +
scale_y_continuous(labels = scales::dollar_format(scale = 0.000001, suffix = "M"))+
scale_x_continuous(breaks = years)+
theme_tufte() +
theme(
legend.title = element_blank(),
legend.text.align = 0,
plot.title = element_text(hjust = 0.5),
plot.subtitle = element_text(hjust = 0.5),
legend.position="bottom",
legend.box="vertical",
axis.text.x=element_text(angle=60, hjust=1),
legend.text=element_text(size=7)
) +
geom_text(aes(label=ifelse(amount > 5e6, paste0(round(amount / 1e6, 0), "M"), "")),  size = 2, colour="#f9f9f9", position = position_stack(vjust = 0.5)) +
geom_text(
aes(label = paste0(round(after_stat(y) / 1e6, 0), "M"), group = year),
stat = 'summary', fun = sum, size=2.3, vjust = -0.5
) +
guides(fill=guide_legend(nrow=3,byrow=TRUE))
open_philanthropy_plot_lt
getwd() ## Working directory on which the file will be saved. Can be changed with setwd("/your/directory")
height = 5
width = 6
## open_philanthropy_plot_lt
ggsave(plot=open_philanthropy_plot_lt, "open_philanthropy_grants_lt_labeled.png", width=width, height=height, bg = "white")