library(tidyverse)
library(cowplot)
library(broom) 
library(modelr) 
library(viridis)
library(lubridate)
library(hms)
library(knitr)
library(kableExtra)

knitr::opts_chunk$set(tidy.opts=list(width.cutoff=60),tidy=TRUE, echo = TRUE, message=FALSE, warning=FALSE, fig.align="center")

source("../../tools/echem_processing_tools.R")
source("../../tools/plotting_tools.R")

theme_set(theme_1())

idaA_tran1 = "../data/biofilm_A/tran_1/"
idaA_tranEtBr_2 = "../data/biofilm_A/tranEtBr_2/"
idaA_tranEtBr_3 = "../data/biofilm_A/tranEtBr_3/"

idaB_tran1 = "../data/biofilm_B/tran_1/"
idaB_tranEtBr_2 = "../data/biofilm_B/tranEtBr_2/"
idaB_tranEtBr_3 = "../data/biofilm_B/tranEtBr_3/"

data_cols <- c("E", "i1", "i2")

swv_skip_rows = 18

gc_skip_rows = 21

All SWVs

# Add 'reactor' to file name so it is parsed into column
filename_cols = c("biofilm", "reactor", "reactor_num", "condition", 
    "echem", "rep")

swv_idaA_tran1_names <- dir(path = idaA_tran1, pattern = "[swv]+.+[txt]$") %>% 
    paste("A_transfer_1_pbsPBS", ., sep = "_")
swv_idaA_tranEtBr_2_names <- dir(path = idaA_tranEtBr_2, pattern = "[swv]+.+[txt]$") %>% 
    paste("A_transfer_2_pbsEtBr", ., sep = "_")
swv_idaA_tranEtBr_3_names <- dir(path = idaA_tranEtBr_3, pattern = "[swv]+.+[txt]$") %>% 
    paste("A_transfer_3_etbrEtBr", ., sep = "_")

swv_idaB_tran1_names <- dir(path = idaB_tran1, pattern = "[swv]+.+[txt]$") %>% 
    paste("B_transfer_1_pbsPBS", ., sep = "_")
swv_idaB_tranEtBr_2_names <- dir(path = idaB_tranEtBr_2, pattern = "[swv]+.+[txt]$") %>% 
    paste("B_transfer_2_pbsEtBr", ., sep = "_")
swv_idaB_tranEtBr_3_names <- dir(path = idaB_tranEtBr_3, pattern = "[swv]+.+[txt]$") %>% 
    paste("B_transfer_3_etbrEtBr", ., sep = "_")

# Add correct paths separate from filenames
swv_idaA_tran1_paths <- dir(path = idaA_tran1, pattern = "[swv]+.+[txt]$") %>% 
    paste(idaA_tran1, ., sep = "")
swv_idaA_tranEtBr_2_paths <- dir(path = idaA_tranEtBr_2, pattern = "[swv]+.+[txt]$") %>% 
    paste(idaA_tranEtBr_2, ., sep = "")
swv_idaA_tranEtBr_3_paths <- dir(path = idaA_tranEtBr_3, pattern = "[swv]+.+[txt]$") %>% 
    paste(idaA_tranEtBr_3, ., sep = "")

swv_idaB_tran1_paths <- dir(path = idaB_tran1, pattern = "[swv]+.+[txt]$") %>% 
    paste(idaB_tran1, ., sep = "")
swv_idaB_tranEtBr_2_paths <- dir(path = idaB_tranEtBr_2, pattern = "[swv]+.+[txt]$") %>% 
    paste(idaB_tranEtBr_2, ., sep = "")
swv_idaB_tranEtBr_3_paths <- dir(path = idaB_tranEtBr_3, pattern = "[swv]+.+[txt]$") %>% 
    paste(idaB_tranEtBr_3, ., sep = "")


# Combine all SWVs into single vector
swv_names <- c(swv_idaA_tran1_names, swv_idaA_tranEtBr_2_names, 
    swv_idaA_tranEtBr_3_names, swv_idaB_tran1_names, swv_idaB_tranEtBr_2_names, 
    swv_idaB_tranEtBr_3_names)

swv_paths <- c(swv_idaA_tran1_paths, swv_idaA_tranEtBr_2_paths, 
    swv_idaA_tranEtBr_3_paths, swv_idaB_tran1_paths, swv_idaB_tranEtBr_2_paths, 
    swv_idaB_tranEtBr_3_paths)



# Read in all SWVs with one function call
swv_data <- echem_import_to_df(filenames = swv_names, file_paths = swv_paths, 
    data_cols = data_cols, skip_rows = swv_skip_rows, filename_cols = filename_cols, 
    rep = T, PHZadded = F) %>% mutate(rep = rep - 1)


swv_data %>% head() %>% kable() %>% kable_styling()

biofilm	reactor	reactor_num	condition	echem	minutes	E	electrode	current
A	transfer	1	pbsPBS	swv	940.4167	0.099	i1	2e-07
A	transfer	1	pbsPBS	swv	940.4167	0.098	i1	2e-07
A	transfer	1	pbsPBS	swv	940.4167	0.097	i1	2e-07
A	transfer	1	pbsPBS	swv	940.4167	0.096	i1	1e-07
A	transfer	1	pbsPBS	swv	940.4167	0.095	i1	0e+00
A	transfer	1	pbsPBS	swv	940.4167	0.094	i1	1e-07

ggplot(swv_data %>% filter(condition == "pbsPBS"), aes(x = E, 
    y = current, color = rep, group = rep)) + geom_path() + facet_wrap(biofilm ~ 
    electrode, scales = "free") + scale_x_reverse() + labs(title = "Transfer 1: PBS to PBS")

ggplot(swv_data %>% filter(condition == "pbsEtBr"), aes(x = E, 
    y = current, color = rep, group = rep)) + geom_path() + facet_wrap(biofilm ~ 
    electrode, scales = "free") + scale_x_reverse() + labs(title = "Transfer 2: PBS to EtBr")

ggplot(swv_data %>% filter(condition == "etbrEtBr"), aes(x = E, 
    y = current, color = rep, group = rep)) + geom_path() + facet_wrap(biofilm ~ 
    electrode, scales = "free") + scale_x_reverse() + labs(title = "Transfer 3: EtBr to EtBr")

Probably need to smooth these SWVs if possible. Should look something like this:

ggplot(swv_data %>% filter(electrode == "i1"), aes(x = E, y = current, 
    color = rep, group = rep)) + geom_point(alpha = 0.5) + geom_smooth(span = 0.1, 
    se = F) + facet_wrap(biofilm ~ condition, scales = "free") + 
    scale_x_reverse()

Let’s do it with loess()

smooth_loess <- function(df) {
    loess(current ~ E, data = df, span = 0.1)
}

swv_data_smooth <- swv_data %>% # filter(biofilm == 'A' & condition == 'pbsPBS' & electrode
# == 'i1' & rep == 1) %>%
group_by(biofilm, condition, electrode, rep) %>% nest() %>% mutate(loess_mod = map(data, 
    smooth_loess)) %>% mutate(preds = map2(data, loess_mod, add_predictions)) %>% 
    unnest(preds)


ggplot(swv_data_smooth %>% filter(electrode == "i1"), aes(x = E, 
    y = current, color = rep, group = rep)) + geom_point(alpha = 0.5) + 
    geom_path(aes(y = pred)) + facet_wrap(biofilm ~ condition, 
    scales = "free") + scale_x_reverse()

Looks good. Here’s what the max current data looks like unsmoothed:

swv_max <- swv_data %>% group_by(biofilm, condition) %>% mutate(min_time = min(minutes)) %>% 
    mutate(norm_time = minutes - min_time) %>% group_by(biofilm, 
    condition, rep, electrode) %>% filter(E > -0.4 & E < -0.2) %>% 
    mutate(max_current = max(abs(current)), ) %>% filter(abs(current) == 
    max_current)

ggplot(swv_data %>% filter(electrode == "i1"), aes(x = E, y = abs(current), 
    color = rep, group = rep)) + geom_path() + geom_point(data = swv_max %>% 
    filter(electrode == "i1"), color = "red") + scale_x_reverse() + 
    facet_wrap(biofilm ~ condition)

# ggplot(swv_max %>% filter(electrode == 'i1'), aes(x =
# minutes, y = max_current, color = condition)) +
# geom_point() + facet_wrap(~biofilm, scales = 'free')

ggplot(swv_max %>% filter(electrode == "i1"), aes(x = norm_time, 
    y = max_current, color = condition)) + geom_point() + facet_wrap(~biofilm, 
    scales = "free")

And here’s what it looks like smoothed:

swv_smooth_max <- swv_data_smooth %>% group_by(biofilm, condition) %>% 
    mutate(min_time = min(minutes)) %>% mutate(norm_time = minutes - 
    min_time) %>% group_by(biofilm, condition, rep, electrode) %>% 
    filter(E > -0.35 & E < -0.2) %>% mutate(max_current = max(abs(pred))) %>% 
    filter(abs(pred) == max_current)

ggplot(swv_data_smooth %>% filter(electrode == "i1"), aes(x = E, 
    y = abs(pred), color = rep, group = rep)) + geom_path() + 
    geom_point(data = swv_smooth_max %>% filter(electrode == 
        "i1"), aes(y = max_current), color = "red") + scale_x_reverse() + 
    facet_wrap(biofilm ~ condition)

ggplot(swv_smooth_max %>% filter(electrode == "i1"), aes(x = norm_time, 
    y = max_current, color = condition)) + geom_point() + facet_wrap(~biofilm, 
    scales = "free")

Looks good, I’m going to used this smoothed data from here on.

All GCs

# Add 'reactor' to file name so it is parsed into column
filename_cols = c("biofilm", "reactor", "reactor_num", "condition", 
    "echem", "rep")

gc_idaA_tran1_names <- dir(path = idaA_tran1, pattern = "[gc]+.+[txt]$") %>% 
    paste("A_transfer_1_pbsPBS", ., sep = "_")
gc_idaA_tranEtBr_2_names <- dir(path = idaA_tranEtBr_2, pattern = "[gc]+.+[txt]$") %>% 
    paste("A_transfer_2_pbsEtBr", ., sep = "_")
gc_idaA_tranEtBr_3_names <- dir(path = idaA_tranEtBr_3, pattern = "[gc]+.+[txt]$") %>% 
    paste("A_transfer_3_etbrEtBr", ., sep = "_")

gc_idaB_tran1_names <- dir(path = idaB_tran1, pattern = "[gc]+.+[txt]$") %>% 
    paste("B_transfer_1_pbsPBS", ., sep = "_")
gc_idaB_tranEtBr_2_names <- dir(path = idaB_tranEtBr_2, pattern = "[gc]+.+[txt]$") %>% 
    paste("B_transfer_2_pbsEtBr", ., sep = "_")
gc_idaB_tranEtBr_3_names <- dir(path = idaB_tranEtBr_3, pattern = "[gc]+.+[txt]$") %>% 
    paste("B_transfer_3_etbrEtBr", ., sep = "_")

# Add correct paths separate from filenames
gc_idaA_tran1_paths <- dir(path = idaA_tran1, pattern = "[gc]+.+[txt]$") %>% 
    paste(idaA_tran1, ., sep = "")
gc_idaA_tranEtBr_2_paths <- dir(path = idaA_tranEtBr_2, pattern = "[gc]+.+[txt]$") %>% 
    paste(idaA_tranEtBr_2, ., sep = "")
gc_idaA_tranEtBr_3_paths <- dir(path = idaA_tranEtBr_3, pattern = "[gc]+.+[txt]$") %>% 
    paste(idaA_tranEtBr_3, ., sep = "")

gc_idaB_tran1_paths <- dir(path = idaB_tran1, pattern = "[gc]+.+[txt]$") %>% 
    paste(idaB_tran1, ., sep = "")
gc_idaB_tranEtBr_2_paths <- dir(path = idaB_tranEtBr_2, pattern = "[gc]+.+[txt]$") %>% 
    paste(idaB_tranEtBr_2, ., sep = "")
gc_idaB_tranEtBr_3_paths <- dir(path = idaB_tranEtBr_3, pattern = "[gc]+.+[txt]$") %>% 
    paste(idaB_tranEtBr_3, ., sep = "")


# Combine all gcs into single vector
gc_names <- c(gc_idaA_tran1_names, gc_idaA_tranEtBr_2_names, 
    gc_idaA_tranEtBr_3_names, gc_idaB_tran1_names, gc_idaB_tranEtBr_2_names, 
    gc_idaB_tranEtBr_3_names)

gc_paths <- c(gc_idaA_tran1_paths, gc_idaA_tranEtBr_2_paths, 
    gc_idaA_tranEtBr_3_paths, gc_idaB_tran1_paths, gc_idaB_tranEtBr_2_paths, 
    gc_idaB_tranEtBr_3_paths)



# Read in all gcs with one function call
gc_data <- echem_import_to_df(filenames = gc_names, file_paths = gc_paths, 
    data_cols = data_cols, skip_rows = gc_skip_rows, filename_cols = filename_cols, 
    rep = T, PHZadded = F)

gc_data %>% head() %>% kable() %>% kable_styling()

biofilm	reactor	reactor_num	condition	echem	rep	minutes	E	electrode
A	transfer	1	pbsPBS	gc	1	942.7333	0.000	i1
A	transfer	1	pbsPBS	gc	1	942.7333	-0.001	i1
A	transfer	1	pbsPBS	gc	1	942.7333	-0.002	i1
A	transfer	1	pbsPBS	gc	1	942.7333	-0.003	i1
A	transfer	1	pbsPBS	gc	1	942.7333	-0.004	i1
A	transfer	1	pbsPBS	gc	1	942.7333	-0.005	i1

ggplot(gc_data %>% filter(condition == "pbsPBS"), aes(x = E, 
    y = current, color = rep, group = rep)) + geom_path() + facet_wrap(biofilm ~ 
    electrode, scales = "free") + scale_x_reverse() + labs(title = "Transfer 1: PBS to PBS")

ggplot(gc_data %>% filter(condition == "pbsEtBr"), aes(x = E, 
    y = current, color = rep, group = rep)) + geom_path() + facet_wrap(biofilm ~ 
    electrode, scales = "free") + scale_x_reverse() + labs(title = "Transfer 2: PBS to EtBr")

ggplot(gc_data %>% filter(condition == "etbrEtBr"), aes(x = E, 
    y = current, color = rep, group = rep)) + geom_path() + facet_wrap(biofilm ~ 
    electrode, scales = "free") + scale_x_reverse() + labs(title = "Transfer 3: EtBr to EtBr")

There’s a little bit of noise, but it shouldn’t be an issue. When we process, we’ll just take the final datapoint from the collector:

gc_max <- gc_data %>% group_by(biofilm, condition) %>% mutate(min_time = min(minutes)) %>% 
    mutate(norm_time = minutes - min_time) %>% group_by(biofilm, 
    condition, rep, electrode) %>% filter(E == -0.399) %>% mutate(max_current = max(abs(current)), 
    ) %>% filter(abs(current) == max_current)

ggplot(gc_data %>% filter(electrode == "i2"), aes(x = E, y = abs(current), 
    color = rep, group = rep)) + geom_path() + geom_point(data = gc_max %>% 
    filter(electrode == "i2"), color = "red") + scale_x_reverse() + 
    facet_wrap(biofilm ~ condition)

# ggplot(gc_max %>% filter(electrode == 'i2'), aes(x =
# minutes, y = max_current, color = condition)) +
# geom_point() + facet_wrap(~biofilm, scales = 'free')

ggplot(gc_max %>% filter(electrode == "i2"), aes(x = norm_time, 
    y = max_current, color = condition)) + geom_point() + facet_wrap(~biofilm, 
    scales = "free")

Soak data

# soak paths
idaA_soak_1 = "../data/biofilm_A/soak_1/"
idaA_soak_2 = "../data/biofilm_A/soak_2/"
idaA_soak_3 = "../data/biofilm_A/soak_3/"

idaB_soak_1 = "../data/biofilm_B/soak_1/"
idaB_soak_2 = "../data/biofilm_B/soak_2/"
idaB_soak_3 = "../data/biofilm_B/soak_3/"

# Add 'reactor' to file name so it is parsed into column
soak_filename_cols = c("PHZadded", "PHZ", "biofilm", "reactor", 
    "reactor_num", "soak_condition", "echem", "rep")

swv_idaA_soak_1_names <- dir(path = idaA_soak_1, pattern = "SWV.*txt")
swv_idaA_soak_2_names <- dir(path = idaA_soak_2, pattern = "SWV.*txt")
swv_idaA_soak_3_names <- dir(path = idaA_soak_3, pattern = "SWV.*txt")

swv_idaB_soak_1_names <- dir(path = idaB_soak_1, pattern = "SWV.*txt")
swv_idaB_soak_2_names <- dir(path = idaB_soak_2, pattern = "SWV.*txt")
swv_idaB_soak_3_names <- dir(path = idaB_soak_3, pattern = "SWV.*txt")

# Add correct paths separate from filenames
swv_idaA_soak_1_paths <- paste(idaA_soak_1, swv_idaA_soak_1_names, 
    sep = "")
swv_idaA_soak_2_paths <- paste(idaA_soak_2, swv_idaA_soak_2_names, 
    sep = "")
swv_idaA_soak_3_paths <- paste(idaA_soak_3, swv_idaA_soak_3_names, 
    sep = "")

swv_idaB_soak_1_paths <- paste(idaB_soak_1, swv_idaB_soak_1_names, 
    sep = "")
swv_idaB_soak_2_paths <- paste(idaB_soak_2, swv_idaB_soak_2_names, 
    sep = "")
swv_idaB_soak_3_paths <- paste(idaB_soak_3, swv_idaB_soak_3_names, 
    sep = "")


# Combine all SWVs into single vector
swv_soak_names <- c(swv_idaA_soak_1_names, swv_idaA_soak_2_names, 
    swv_idaA_soak_3_names, swv_idaB_soak_1_names, swv_idaB_soak_2_names, 
    swv_idaB_soak_3_names)

swv_soak_paths <- c(swv_idaA_soak_1_paths, swv_idaA_soak_2_paths, 
    swv_idaA_soak_3_paths, swv_idaB_soak_1_paths, swv_idaB_soak_2_paths, 
    swv_idaB_soak_3_paths)



# Read in all SWVs with one function call
swv_soak_data <- echem_import_to_df(filenames = swv_soak_names, 
    file_paths = swv_soak_paths, data_cols = data_cols, skip_rows = swv_skip_rows, 
    filename_cols = soak_filename_cols, rep = T, PHZadded = F)

swv_soak_data %>% head() %>% kable() %>% kable_styling()

PHZadded	PHZ	biofilm	reactor	reactor_num	soak_condition	echem	rep	minutes	E	electrode	current
0uM	PYO	A	soak	1	PBS	SWV	1	910.7333	0.099	i1	1e-07
0uM	PYO	A	soak	1	PBS	SWV	1	910.7333	0.098	i1	1e-07
0uM	PYO	A	soak	1	PBS	SWV	1	910.7333	0.097	i1	1e-07
0uM	PYO	A	soak	1	PBS	SWV	1	910.7333	0.096	i1	1e-07
0uM	PYO	A	soak	1	PBS	SWV	1	910.7333	0.095	i1	1e-07
0uM	PYO	A	soak	1	PBS	SWV	1	910.7333	0.094	i1	1e-07

ggplot(swv_soak_data %>% filter(PHZadded == "75uM" & rep == 2), 
    aes(x = E, y = current, color = reactor_num)) + geom_path() + 
    facet_wrap(biofilm ~ electrode, scales = "free") + scale_x_reverse()

# Add 'reactor' to file name so it is parsed into column
soak_filename_cols = c("PHZadded", "PHZ", "biofilm", "reactor", 
    "reactor_num", "soak_condition", "echem", "rep")

gc_idaA_soak_1_names <- dir(path = idaA_soak_1, pattern = "GC.*txt")
gc_idaA_soak_2_names <- dir(path = idaA_soak_2, pattern = "GC.*txt")
gc_idaA_soak_3_names <- dir(path = idaA_soak_3, pattern = "GC.*txt")

gc_idaB_soak_1_names <- dir(path = idaB_soak_1, pattern = "GC.*txt")
gc_idaB_soak_2_names <- dir(path = idaB_soak_2, pattern = "GC.*txt")
gc_idaB_soak_3_names <- dir(path = idaB_soak_3, pattern = "GC.*txt")

# Add correct paths separate from filenames
gc_idaA_soak_1_paths <- paste(idaA_soak_1, gc_idaA_soak_1_names, 
    sep = "")
gc_idaA_soak_2_paths <- paste(idaA_soak_2, gc_idaA_soak_2_names, 
    sep = "")
gc_idaA_soak_3_paths <- paste(idaA_soak_3, gc_idaA_soak_3_names, 
    sep = "")

gc_idaB_soak_1_paths <- paste(idaB_soak_1, gc_idaB_soak_1_names, 
    sep = "")
gc_idaB_soak_2_paths <- paste(idaB_soak_2, gc_idaB_soak_2_names, 
    sep = "")
gc_idaB_soak_3_paths <- paste(idaB_soak_3, gc_idaB_soak_3_names, 
    sep = "")


# Combine all gcs into single vector
gc_soak_names <- c(gc_idaA_soak_1_names, gc_idaA_soak_2_names, 
    gc_idaA_soak_3_names, gc_idaB_soak_1_names, gc_idaB_soak_2_names, 
    gc_idaB_soak_3_names)

gc_soak_paths <- c(gc_idaA_soak_1_paths, gc_idaA_soak_2_paths, 
    gc_idaA_soak_3_paths, gc_idaB_soak_1_paths, gc_idaB_soak_2_paths, 
    gc_idaB_soak_3_paths)



# Read in all gcs with one function call
gc_soak_data <- echem_import_to_df(filenames = gc_soak_names, 
    file_paths = gc_soak_paths, data_cols = data_cols, skip_rows = gc_skip_rows, 
    filename_cols = soak_filename_cols, rep = T, PHZadded = F)

gc_soak_data %>% head() %>% kable() %>% kable_styling()

PHZadded	PHZ	biofilm	reactor	reactor_num	soak_condition	echem	rep	minutes	E	electrode
0uM	PYO	A	soak	1	PBS	GC	1	915.1167	0.000	i1
0uM	PYO	A	soak	1	PBS	GC	1	915.1167	-0.001	i1
0uM	PYO	A	soak	1	PBS	GC	1	915.1167	-0.002	i1
0uM	PYO	A	soak	1	PBS	GC	1	915.1167	-0.003	i1
0uM	PYO	A	soak	1	PBS	GC	1	915.1167	-0.004	i1
0uM	PYO	A	soak	1	PBS	GC	1	915.1167	-0.005	i1

ggplot(gc_soak_data %>% filter(PHZadded == "75uM"), aes(x = E, 
    y = current, color = reactor_num)) + geom_path() + facet_wrap(biofilm ~ 
    electrode, scales = "free") + scale_x_reverse()

SWV vs. GC

swv_gc_max <- left_join(gc_max, swv_smooth_max, by = c("biofilm", 
    "condition", "rep", "reactor", "reactor_num"), suffix = c("_from_gc", 
    "_from_swv"))

ggplot(swv_gc_max %>% filter(electrode_from_gc == "i2" & electrode_from_swv == 
    "i1"), aes(x = max_current_from_swv, y = max_current_from_gc, 
    color = condition)) + geom_point() + geom_smooth(method = "lm") + 
    facet_wrap(~biofilm, scales = "free")

Outputs

Paired SWV and GC maxes with time info etc:

# write_csv(swv_gc_max, '09_09_19_swv_gc_max_processed.csv')

The first SWV and GC from all the transfers:

first_scans <- bind_rows(swv_data_smooth %>% filter(rep <= 1), 
    gc_data %>% filter(rep <= 1))

# write_csv(first_scans, '09_09_19_first_transfer_reps.csv')

Let’s save a file with the soak data all together:

soak_data <- bind_rows(swv_soak_data, gc_soak_data)

# write_csv(soak_data, '09_09_19_soak_data.csv')

sessionInfo()

## R version 3.5.2 (2018-12-20)
## Platform: x86_64-apple-darwin15.6.0 (64-bit)
## Running under: macOS Mojave 10.14.6
## 
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/3.5/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/3.5/Resources/lib/libRlapack.dylib
## 
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
## 
## attached base packages:
## [1] stats     graphics  grDevices utils     datasets  methods   base     
## 
## other attached packages:
##  [1] kableExtra_1.0.1  knitr_1.23        hms_0.4.2        
##  [4] lubridate_1.7.4   viridis_0.5.1     viridisLite_0.3.0
##  [7] modelr_0.1.2      broom_0.5.1       cowplot_0.9.4    
## [10] forcats_0.3.0     stringr_1.3.1     dplyr_0.8.1      
## [13] purrr_0.2.5       readr_1.3.1       tidyr_0.8.2      
## [16] tibble_2.1.3      ggplot2_3.2.0     tidyverse_1.2.1  
## 
## loaded via a namespace (and not attached):
##  [1] tidyselect_0.2.5 xfun_0.7         haven_2.0.0      lattice_0.20-38 
##  [5] colorspace_1.4-0 generics_0.0.2   htmltools_0.3.6  yaml_2.2.0      
##  [9] rlang_0.3.4      pillar_1.3.1     glue_1.3.1       withr_2.1.2     
## [13] readxl_1.2.0     munsell_0.5.0    gtable_0.2.0     cellranger_1.1.0
## [17] rvest_0.3.2      evaluate_0.14    labeling_0.3     highr_0.7       
## [21] Rcpp_1.0.1       scales_1.0.0     backports_1.1.3  formatR_1.5     
## [25] webshot_0.5.1    jsonlite_1.6     gridExtra_2.3    digest_0.6.18   
## [29] stringi_1.2.4    grid_3.5.2       cli_1.0.1        tools_3.5.2     
## [33] magrittr_1.5     lazyeval_0.2.1   crayon_1.3.4     pkgconfig_2.0.2 
## [37] xml2_1.2.0       assertthat_0.2.1 rmarkdown_1.13   httr_1.4.0      
## [41] rstudioapi_0.9.0 R6_2.4.0         nlme_3.1-140     compiler_3.5.2

∆phz* +/- Ethidium

Processing

Scott Saunders

09_09_19

All SWVs

All GCs

Soak data

SWV vs. GC

Outputs