## ----global options, include = FALSE------------------------------------------
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)
knitr::opts_knit$set(root.dir = tempdir())

## ----setup--------------------------------------------------------------------
library(gcplyr)

library(dplyr)
library(ggplot2)

## -----------------------------------------------------------------------------
# This code was previously explained
# Here we're re-running it so it's available for us to work with
example_tidydata <- trans_wide_to_tidy(example_widedata_noiseless,
                                       id_cols = "Time")
ex_dat_mrg <- merge_dfs(example_tidydata, example_design_tidy)
ex_dat_mrg$Well <- 
  factor(ex_dat_mrg$Well,
         levels = paste(rep(LETTERS[1:8], each = 12), 1:12, sep = ""))
#Convert time to hours
ex_dat_mrg$Time <- ex_dat_mrg$Time/3600

## -----------------------------------------------------------------------------
ex_dat_mrg <- mutate(group_by(ex_dat_mrg, Well, Bacteria_strain, Phage),
                     deriv = calc_deriv(x = Time, y = Measurements))

## -----------------------------------------------------------------------------
sample_wells <- c("A1", "F1", "F10", "E11")

# Now let's plot the derivative
ggplot(data = dplyr::filter(ex_dat_mrg, Well %in% sample_wells),
       aes(x = Time, y = deriv)) +
  geom_line() +
  facet_wrap(~Well, scales = "free")

## ----include = FALSE----------------------------------------------------------
# For computational speed, let's just keep the wells we'll be focusing on
#  (this is hidden from readers bc from this point on we never print out
#  the df anyway so there's no difference in the output by filtering here)
ex_dat_mrg <- dplyr::filter(ex_dat_mrg, Well %in% sample_wells)

## -----------------------------------------------------------------------------
ex_dat_mrg <- mutate(group_by(ex_dat_mrg, Well, Bacteria_strain, Phage),
                     deriv_percap = calc_deriv(x = Time, y = Measurements,
                                        percapita = TRUE, blank = 0))

# Now let's plot the per-capita derivative
ggplot(data = dplyr::filter(ex_dat_mrg, Well %in% sample_wells),
       aes(x = Time, y = deriv_percap)) +
  geom_line() +
  facet_wrap(~Well, scales = "free")

## -----------------------------------------------------------------------------
ex_dat_mrg <- mutate(group_by(ex_dat_mrg, Well, Bacteria_strain, Phage),
                     deriv_percap5 = calc_deriv(x = Time, y = Measurements, 
                                        percapita = TRUE, blank = 0,
                                        window_width_n = 5, trans_y = "log"))

# Now let's plot the derivative
ggplot(data = dplyr::filter(ex_dat_mrg, Well %in% sample_wells),
       aes(x = Time, y = deriv_percap5)) +
  geom_line() +
  facet_wrap(~Well, scales = "free")

## -----------------------------------------------------------------------------
ex_dat_mrg <- mutate(group_by(ex_dat_mrg, Well, Bacteria_strain, Phage),
                     deriv_percap5 = calc_deriv(x = Time, y = Measurements, 
                                        percapita = TRUE, blank = 0,
                                        window_width_n = 5, trans_y = "log"),
                     doub_time = doubling_time(y = deriv_percap5))
head(ex_dat_mrg)