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Explore Billing Data

Load libraries

library(sixtyfour)
library(dplyr)
library(ggplot2)
library(lubridate)

Get data for the past approximately 13 months

start_date <- today() - months(13)
my_data <-
  aws_billing(date_start = start_date)

Simple plot of RDS spend through time

rds_by_day <-
  my_data %>%
  filter(
    id == "blended",
    service == "Amazon Relational Database Service"
  ) %>%
  mutate(date = as.Date(date))

ggplot(rds_by_day, aes(date, cost)) +
  geom_col() +
  scale_x_date(date_breaks = "10 days", date_labels =  "%b %d") +
  theme_grey(base_size = 16)

Plot showing AWS cost by day for the RDS service. There's a big peak in early Feburary 2024, and a few smaller peaks in mid to late February.

Plot of all types with cost greater than zero though time

all_by_day <-
  my_data %>%
  filter(id == "blended") %>%
  group_by(service) %>%
  filter(sum(cost) > 0)  %>%
  mutate(date = as.Date(date))

ggplot(all_by_day, aes(date, cost)) +
  geom_col(aes(fill = acronym)) +
  scale_x_date(date_breaks = "10 days", date_labels =  "%b %d") +
  theme_grey(base_size = 16)

Plot showing AWS cost by day for many AWS services, including CE, RDS, SEC< Tax, and VPC. There's a big peak in early Feburary 2024, and steady spend in mid to late February and early March.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.