gc07_noise.R
In gcplyr: Wrangle and Analyze Growth Curve Data

## ----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)
library(tidyr)

## -----------------------------------------------------------------------------
ex_dat_mrg <- make_example(vignette = 7, example = 1)

# Plot with a log y-axis
ggplot(data = dplyr::filter(ex_dat_mrg, noise == "Yes"),
       aes(x = Time, y = Measurements)) +
  geom_point() +
  geom_line(data = dplyr::filter(ex_dat_mrg, noise == "No"),
            lty = 2, color = "red") +
  facet_wrap(~Well) +
  scale_y_continuous(trans = "log10")

## -----------------------------------------------------------------------------
ex_dat_mrg <- 
  mutate(group_by(ex_dat_mrg, Well, Bacteria_strain, Phage, noise),
         deriv_2 = calc_deriv(x = Time, y = Measurements),
         derivpercap_2 = calc_deriv(x = Time, y = Measurements,
                                   percapita = TRUE, blank = 0))

# Plot derivative
ggplot(data = dplyr::filter(ex_dat_mrg, noise == "Yes"),
       aes(x = Time, y = deriv_2)) +
  geom_point() +
  geom_line(data = dplyr::filter(ex_dat_mrg, noise == "No"),
            lty = 2, color = "red") +
  facet_wrap(~Well, scales = "free_y")

# Plot per-capita derivative
ggplot(data = dplyr::filter(ex_dat_mrg, noise == "Yes"),
       aes(x = Time, y = derivpercap_2)) +
  geom_point() +
  geom_line(data = dplyr::filter(ex_dat_mrg, noise == "No"),
            lty = 2, color = "red") +
  facet_wrap(~Well, scales = "free_y")

## -----------------------------------------------------------------------------
#Plot density
ggplot(data = dplyr::filter(ex_dat_mrg, noise == "Yes"),
       aes(x = Time, y = Measurements)) +
  geom_point() +
  facet_wrap(~Well, scales = "free_y") +
  scale_y_log10()

# Plot per-capita derivative
ggplot(data = dplyr::filter(ex_dat_mrg, noise == "Yes"),
       aes(x = Time, y = derivpercap_2)) +
  geom_point() +
  facet_wrap(~Well, scales = "free_y")

## -----------------------------------------------------------------------------
#Plot density
ggplot(data = dplyr::filter(ex_dat_mrg, noise == "Yes", Measurements > 0.01),
       aes(x = Time, y = Measurements)) +
  geom_point() +
  facet_wrap(~Well, scales = "free_y") +
  geom_hline(yintercept = 0.01, lty = 2) +
  scale_y_log10()

# Plot per-capita derivative
ggplot(data = dplyr::filter(ex_dat_mrg, noise == "Yes", Measurements > 0.01),
       aes(x = Time, y = derivpercap_2)) +
  geom_point() +
  facet_wrap(~Well, scales = "free_y")

## -----------------------------------------------------------------------------
ex_dat_mrg_sum <-
  summarize(group_by(ex_dat_mrg, Well, Bacteria_strain, Phage, noise),
            max_growth_rate = max(derivpercap_2[Measurements > 0.01], 
                                  na.rm = TRUE))
head(ex_dat_mrg_sum)

## -----------------------------------------------------------------------------
ex_dat_mrg <- 
  mutate(group_by(ex_dat_mrg, Well, Bacteria_strain, Phage, noise),
         deriv_5 = calc_deriv(x = Time, y = Measurements,
                            window_width_n = 5),
         derivpercap_5 = calc_deriv(x = Time, y = Measurements,
                                   percapita = TRUE, blank = 0,
                                   window_width_n = 5),
         deriv_9 = calc_deriv(x = Time, y = Measurements,
                            window_width_n = 9),
         derivpercap_9 = calc_deriv(x = Time, y = Measurements,
                                   percapita = TRUE, blank = 0,
                                   window_width_n = 9))
#Reshape our data for plotting purposes
ex_dat_mrg_wide <- 
  pivot_longer(ex_dat_mrg, cols = starts_with("deriv"),
               names_to = c("deriv", "window_width_n"), names_sep = "_")
ex_dat_mrg_wide <- 
  pivot_wider(ex_dat_mrg_wide, names_from = deriv, values_from = value)
                               
#Plot derivative
ggplot(data = dplyr::filter(ex_dat_mrg_wide, noise == "Yes"),
       aes(x = Time, y = deriv)) +
  geom_line(aes(color = window_width_n), linewidth = 0.6) +
  facet_wrap(~Well, scales = "free_y") +
  geom_line(data = dplyr::filter(ex_dat_mrg_wide, 
                                 noise == "No", window_width_n == 2),
            lty = 2, color = "red") +
  scale_color_grey(start = 0.8, end = 0) +
  theme_bw()
       
#Plot per-capita derivative
ggplot(data = dplyr::filter(ex_dat_mrg_wide, noise == "Yes"),
       aes(x = Time, y = derivpercap)) +
  geom_line(aes(color = window_width_n), linewidth = 0.6, alpha = 0.75) +
  facet_wrap(~Well, scales = "free_y") +
  geom_line(data = dplyr::filter(ex_dat_mrg_wide,
                                 noise == "No", window_width_n == 5),
            lty = 2, color = "red") +
  scale_color_grey(start = 0.8, end = 0) +
  ylim(NA, 5) +
  theme_bw()

## -----------------------------------------------------------------------------
ex_dat_mrg <-
  mutate(group_by(ex_dat_mrg, Well, Bacteria_strain, Phage, noise),
         movmed_1 = Measurements,
         movmed_5 = 
           smooth_data(x = Time, y = Measurements,
                       sm_method = "moving-median", window_width_n = 5),
         movmed_9 = 
           smooth_data(x = Time, y = Measurements,
                       sm_method = "moving-median", window_width_n = 9))

#Reshape our data for plotting purposes
ex_dat_mrg_wide <- 
  pivot_longer(ex_dat_mrg, cols = starts_with("movmed"),
               names_prefix = "movmed_", names_to = "window_width_n")

#Plot data
ggplot(data = dplyr::filter(ex_dat_mrg_wide, noise == "Yes"),
       aes(x = Time, y = value)) +
  geom_line(aes(color = window_width_n), linewidth = 0.6, alpha = 0.75) +
  facet_wrap(~Well, scales = "free_y") +
  geom_line(data = dplyr::filter(ex_dat_mrg_wide,
                                 noise == "No", window_width_n == 1),
            lty = 2, color = "red") +
  scale_color_grey(start = 0.8, end = 0) +
  scale_y_log10() +
  ggtitle("moving-median") +
  theme_bw()

## -----------------------------------------------------------------------------
ex_dat_mrg <-
  mutate(group_by(ex_dat_mrg, Well, Bacteria_strain, Phage, noise),
         movavg_1 = Measurements,
         movavg_5 = smooth_data(x = Time, y = Measurements,
              sm_method = "moving-average", window_width_n = 5),
         movavg_9 = smooth_data(x = Time, y = Measurements,
              sm_method = "moving-average", window_width_n = 9))

#Reshape our data for plotting purposes
ex_dat_mrg_wide <- 
  pivot_longer(ex_dat_mrg, cols = starts_with("movavg"),
               names_prefix = "movavg_", names_to = "window_width_n")

#Plot data
ggplot(data = dplyr::filter(ex_dat_mrg_wide, noise == "Yes"),
       aes(x = Time, y = value)) +
  geom_line(aes(color = window_width_n), linewidth = 0.6, alpha = 0.75) +
  facet_wrap(~Well, scales = "free_y") +
  geom_line(data = dplyr::filter(ex_dat_mrg_wide,
                                 noise == "No", window_width_n == 1),
            lty = 2, color = "red") +
  scale_color_grey(start = 0.8, end = 0) +
  scale_y_log10() +
  ggtitle("moving-average") +
  theme_bw()

## -----------------------------------------------------------------------------
ex_dat_mrg <-
  mutate(group_by(ex_dat_mrg, Well, Bacteria_strain, Phage, noise),
         smoothed_no = Measurements,
         sm_med3 = 
           smooth_data(x = Time, y = Measurements,
                       sm_method = "moving-median", window_width_n = 3),
         #Note that for the second round, we're using the 
         #first smoothing as the input y
         smoothed_yes = 
           smooth_data(x = Time, y = sm_med3,
                       sm_method = "moving-average", window_width_n = 3))

#Reshape our data for plotting purposes
ex_dat_mrg_wide <- 
  pivot_longer(ex_dat_mrg, cols = starts_with("smoothed"),
               names_to = "smoothed", names_prefix = "smoothed_")

#Plot data
ggplot(data = dplyr::filter(ex_dat_mrg_wide, noise == "Yes"),
       aes(x = Time, y = value, color = smoothed)) +
  geom_line(linewidth = 0.6, alpha = 0.75) +
  scale_color_grey(start = 0.8, end = 0) +
  facet_wrap(~Well, scales = "free_y") +
  geom_line(data = dplyr::filter(ex_dat_mrg_wide,
                                 noise == "No", smoothed == "no"),
            lty = 2, color = "red") +
  scale_y_log10() +
  ggtitle("median then average smoothing") +
  theme_bw()

## -----------------------------------------------------------------------------
ex_dat_mrg_fortraining <- make_example(vignette = 7, example = 2)

training_results <- 
  reframe(group_by(ex_dat_mrg_fortraining, Bacteria_strain, Phage, Well),
          train_smooth_data(x = Time, y = Measurements, 
                   sm_method = "moving-average",
                   tuneLength = 5))
head(training_results)

## -----------------------------------------------------------------------------
ggplot(data = training_results,
       aes(x = window_width_n, y = RMSE)) +
  geom_point()

## -----------------------------------------------------------------------------
training_results <- 
  reframe(group_by(ex_dat_mrg_fortraining, Bacteria_strain, Phage, Well),
          train_smooth_data(x = Time, y = Measurements, 
                   sm_method = "moving-average",
                   tuneGrid = list(
                     "window_width_n" = c(1, 3, 5, 9, 13))))

ggplot(data = training_results,
       aes(x = window_width_n, y = RMSE)) +
  geom_point()

## -----------------------------------------------------------------------------
# Note here that we're calculating derivatives of the smoothed column generated
#  in the previous section by combining moving median and moving average smoothing
ex_dat_mrg <- 
  mutate(group_by(ex_dat_mrg, Well, Bacteria_strain, Phage, noise),
         smderiv_0 = calc_deriv(x = Time, y = Measurements),
         smderivpercap_0 = calc_deriv(x = Time, y = Measurements,
                                       percapita = TRUE, blank = 0),
         smderiv_3 = calc_deriv(x = Time, y = smoothed_yes, window_width_n = 3),
         smderivpercap_3 = calc_deriv(x = Time, y = smoothed_yes, percapita = TRUE, 
                                    blank = 0, window_width_n = 3))

#Reshape our data for plotting purposes
ex_dat_mrg_wide <- 
  pivot_longer(ex_dat_mrg, cols = starts_with("smderiv"),
               names_to = c("deriv", "window_width_n"), names_sep = "_")
ex_dat_mrg_wide <- 
  pivot_wider(ex_dat_mrg_wide, names_from = deriv, values_from = value)

#Plot derivative
ggplot(data = dplyr::filter(ex_dat_mrg_wide, noise == "Yes"),
       aes(x = Time, y = smderiv, color = window_width_n)) +
  geom_line(linewidth = 0.6, alpha = 0.75) +
  scale_color_grey(start = 0.8, end = 0) +
  facet_wrap(~Well, scales = "free_y") +
  geom_line(data = dplyr::filter(ex_dat_mrg_wide,
                                 noise == "No", window_width_n == 0),
            lty = 2, color = "red") +
  theme_bw()

#Plot per-capita derivative
ggplot(data = dplyr::filter(ex_dat_mrg_wide, noise == "Yes"),
       aes(x = Time, y = smderivpercap, color = window_width_n)) +
  geom_line(linewidth = 0.6, alpha = 0.75) +
  scale_color_grey(start = 0.8, end = 0) +
  facet_wrap(~Well, scales = "free_y") +
  geom_line(data = dplyr::filter(ex_dat_mrg_wide,
                                 noise == "No", window_width_n == 3),
            lty = 2, color = "red") +
  ylim(NA, 5) +
  theme_bw()