library(ggplot2)
library(plotly)
data <- read.csv("total-direct-cost-of-atip-directorate-by-institution-2019-2023.csv", header = TRUE, sep = ",")
data$institution <- factor(data$institution, levels=c('ISC', 'CIRNAC'))
p <-ggplot(data, aes(x = year, y = cost, fill = institution)) + geom_bar(stat = "identity", color = "black") + scale_fill_manual(values = c("#DADAEB", "#9E9AC8")) + scale_y_continuous(labels = scales::unit_format(unit = "K", prefix = "$", sep = "", scale = .001)) + labs(fill = "Institution", x = "Year", y = "Direct Cost of ATIP Directorate")
ggplotly(p)

library(ggplot2)
library(plotly)
data <- read.csv("annual-ftes-allocated-to-staffing-and-consultants-in-atip-directorate-2019-2023.csv", header = TRUE, sep = ",")
data$institution <- factor(data$institution, levels=c('ISC', 'CIRNAC'))
p <-ggplot(data, aes(x = year, y = cost, fill = institution)) + geom_bar(stat = "identity", color = "black") + scale_fill_manual(values = c("#DADAEB", "#9E9AC8")) + scale_y_continuous(labels = scales::unit_format(unit = "K", prefix = "$", sep = "", scale = .001)) + labs(fill = "Institution", x = "Year", y = "FTEs")
ggplotly(p)