## ----fig.width=7--------------------------------------------------------------
library(padr)
coffee

## ----fig.width=7, message = FALSE---------------------------------------------
library(ggplot2)
library(dplyr)

coffee |>
  thicken("day") |>
  group_by(time_stamp_day) |>
  summarise(day_amount = sum(amount)) |>
  pad() |>
  fill_by_value() |>
  ggplot(aes(time_stamp_day, day_amount)) +
  geom_line()

## -----------------------------------------------------------------------------
coffee2 <- coffee |> thicken("day")
coffee2$time_stamp |> get_interval()
coffee2$time_stamp_day |> get_interval()

## -----------------------------------------------------------------------------
to_thicken <- data.frame(day_var = as.Date(c(
  "2016-08-12", "2016-08-13",
  "2016-08-26", "2016-08-29"
)))
to_thicken |> thicken(interval = "week")
to_thicken |> thicken(interval = "4 days")

## -----------------------------------------------------------------------------
head(emergency)

## -----------------------------------------------------------------------------
emergency |>
  filter(title == "EMS: OVERDOSE") |>
  thicken("day",
    start_val = as.POSIXct("2015-12-11 08:00:00", tz = "EST"),
    colname = "daystart"
  ) |>
  group_by(daystart) |>
  summarise(nr_od = n()) |>
  head()

## -----------------------------------------------------------------------------
account <- data.frame(
  day = as.Date(c("2016-10-21", "2016-10-23", "2016-10-26")),
  balance = c(304.46, 414.76, 378.98)
)
account |> pad()

## -----------------------------------------------------------------------------
account |>
  pad() |>
  tidyr::fill(balance)

## -----------------------------------------------------------------------------
account |>
  pad("hour", start_val = as.POSIXct("2016-10-20 22:00:00")) |>
  head()

## -----------------------------------------------------------------------------
grouping_df <- data.frame(
  group = rep(c("A", "B"), c(3, 3)),
  date = as.Date(c(
    "2017-10-02", "2017-10-04", "2017-10-06", "2017-10-01",
    "2017-10-03", "2017-10-04"
  )),
  value = rep(2, 6)
)
grouping_df |>
  pad(group = "group")

## -----------------------------------------------------------------------------
grouping_df |>
  group_by(group) |>
  do(pad(.))

## -----------------------------------------------------------------------------
counts <- data.frame(
  x = as.Date(c("2016-11-21", "2016-11-23", "2016-11-24")),
  y = c(2, 4, 4)
) |> pad()

counts |> fill_by_value()
counts |> fill_by_value(value = 42)
counts |> fill_by_function(fun = mean)
counts |> fill_by_prevalent()

## ----fig.width=7--------------------------------------------------------------
emergency |>
  thicken("hour", "h") |>
  count(h) |>
  slice(1:24) |>
  mutate(h_center = center_interval(h)) |>
  ggplot(aes(h_center, n)) +
  geom_bar(stat = "identity")

## ----message=FALSE------------------------------------------------------------
emergency |>
  filter(title == "EMS: HEAD INJURY") |>
  thicken("6 hour", "hour6") |>
  count(hour6) |>
  pad() |>
  fill_by_value() |>
  mutate(
    hour6_fmt =
      format_interval(hour6, start_format = "%Hh", sep = "-")
  ) |>
  ggplot(aes(hour6_fmt, n)) +
  geom_boxplot()