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feb20_real_experiment.md

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real experiments

Real experiment 20Feb20

  1. 1500 start time.
  2. Cage sampling every other baseline, 120 sec duration.
  3. 14 ml water at start.
  4. Animals in all cages
  5. plan to run until Monday AM.

Cage 0 - 20.1g - 14F

Cage 1 - 22.6g - 75F

Cage 2 - 21.1g - 27F

Cage 3 - 23.1g - 51F

Cage 4 - 21.8g - 97F

Cage 5 - 17.6 - 1006F

Cage 6 - 15.4g - 2F

water refilled 24Feb20 at 1400.

Water

datafile <- "/Volumes/4TB_1/Dropbox/Cactus_Mouse_Physiology/data/20Feb20/feb20_4.csv"
feb20_4 <- read_csv(datafile, 
	col_types = cols(Animal = col_double(), 
        StartDate = col_date(format = "%m/%d/%Y"),
        deltaCO2 = col_double(), 
        deltaH2O = col_double(),
        H2Oml = col_double(),
        VCO2 = col_double(),
        StartTime = col_time(format = "%H:%M:%S")))

'%!in%' <- function(x,y)!('%in%'(x,y))

feb20_4 <- feb20_4 %>% 
	mutate(EE = 0.06*(3.941*VO2 + 1.106*VCO2)) %>% 
	mutate(RQ = VCO2/VO2) %>%
	mutate(H2Omg_edit8 = ifelse(hour(StartTime) == 8, H2Omg - 0.09536454, NA)) %>%
	mutate(H2Omg_edit7 = ifelse(hour(StartTime) == 7, H2Omg - 0.06536454, NA)) %>%
	mutate(H2Omg_edit9 = ifelse(hour(StartTime) == 9, H2Omg - 0.02219424, NA)) %>%
	mutate(H2Omg_edit10 = ifelse(hour(StartTime) == 10, H2Omg - 0.03273413, NA)) %>%
	mutate(H2Omg_edit19 = ifelse(hour(StartTime) == 19, H2Omg + 0.002130708, NA)) %>%	
	mutate(H2Omg_edit20 = ifelse(hour(StartTime) == 20, H2Omg + 0.02967473, NA)) %>%
	mutate(H2Omg_edit21 = ifelse(hour(StartTime) == 21, H2Omg + 0.02703453, NA)) %>%
	mutate(H2Omg_edit22 = ifelse(hour(StartTime) == 22, H2Omg + 0.01412046, NA)) %>%
	mutate(H2Omg_edits = ifelse(hour(StartTime) %!in% c(7,8,9,10,20,21,22,19), H2Omg, NA)) %>%
	replace_na(list(H2Omg_edit19 = "", H2Omg_edit20 = "", H2Omg_edit21 = "", H2Omg_edit22 = "", H2Omg_edit10 = "", H2Omg_edit7 = "", H2Omg_edit8 = "",H2Omg_edit9 = "",H2Omg_edits = "")) %>%
	mutate(animal = round(Animal, digits=0)) %>%
	unite("DateTime", StartDate:StartTime, remove = FALSE, sep =  " ") %>% 
	unite(H2Omg_edit, c(H2Omg_edit19, H2Omg_edit20, H2Omg_edit21, H2Omg_edit22, H2Omg_edits, H2Omg_edit10, H2Omg_edit7, H2Omg_edit8, H2Omg_edit9), sep = "", remove = TRUE) %>%
	mutate_at("H2Omg_edit", as.numeric) %>%
	mutate(Animal = NULL)

metric <- "H2Omg"

target <- c(0,1,2,3,4,5,6)
cages <- feb20_4 %>% filter(animal %in% target)
#cages <- with( cages ,cages[ hour( StartTime ) >= 0 & hour( StartTime ) < 4 , ] )

measurement <- cages %>%  select(metric)
df<-as.data.frame(measurement[[metric]])
legend_title <- "Cage Number"

p <- ggplot(data = cages,aes(x=as.POSIXct(StartTime),y=measurement[[metric]]))
p <- p + geom_point(aes(group=as.factor(animal), color=as.factor(animal)), size = 3)
p <- p + theme_grey(base_size = 15)
p <- p + geom_smooth(data=df$V1, method='loess', span=.9)
p <- p + labs(x = "", y = metric)
p <- p + scale_color_brewer(legend_title, palette="Paired")
p <- p + scale_x_datetime(date_breaks = "2 hours", date_labels = "%H:%M")
#p <- p + geom_hline(yintercept = 0.8907387)
p

This is the plot of the H2O data without any correction for 8PM/AM bias..

full water data

Maybe we should exclude these times??

We still see the basic pattern

Maybe we should correct for these biases mathmatically?

metric <- "H2Omg_edit"

target <- c(0,1,2,3,4,5,6)
cages <- feb20_4 %>% filter(animal %in% target)
#cages <- with( cages ,cages[ hour( StartTime ) >= 0 & hour( StartTime ) < 4 , ] )

measurement <- cages %>%  select(metric)
df<-as.data.frame(measurement[[metric]])
legend_title <- "Cage Number"

p <- ggplot(data = cages,aes(x=as.POSIXct(StartTime),y=measurement[[metric]]))
p <- p + geom_point(aes(group=as.factor(animal), color=as.factor(animal)), size = 3)
p <- p + theme_grey(base_size = 15)
p <- p + geom_smooth(data=df$V1, method='loess', span=.9)
p <- p + labs(x = "", y = metric)
p <- p + scale_color_brewer(legend_title, palette="Paired")
p <- p + scale_x_datetime(date_breaks = "2 hours", date_labels = "%H:%M")
#p <- p + geom_hline(yintercept = 0.8907387)
p
	


library(tidyverse)
library(lubridate)
library(readr)	

cageweight0 <- 20.1
cageweight1 <- 22.6
cageweight2 <- 21.1
cageweight3 <- 23.1
cageweight4 <- 21.8
cageweight5 <- 17.6
cageweight6 <- 15.4

animalID0 <- "14F"
animalID1 <- "75F"
animalID2 <- "27F"
animalID3 <- "51F"
animalID4 <- "97F"
animalID5 <- "1006F"
animalID6 <- "2F"

datafile <- "/Volumes/4TB_1/Dropbox/Cactus_Mouse_Physiology/data/20Feb20/feb20.csv"
feb20 <- read_csv(datafile, 
	col_types = cols(Animal = col_double(), 
        StartDate = col_date(format = "%m/%d/%Y"),
        deltaCO2 = col_double(), 
        deltaH2O = col_double(),
        H2Oml = col_double(),
        VCO2 = col_double(),
        StartTime = col_time(format = "%H:%M:%S")))

'%!in%' <- function(x,y)!('%in%'(x,y))

feb20 <- feb20 %>% 
	mutate(EE = 0.06*(3.941*VO2 + 1.106*VCO2)) %>% 
	mutate(RQ = VCO2/VO2) %>%
	mutate(animal = round(Animal, digits=0)) %>%
	mutate(Animal = NULL) %>%
	unite("DateTime", StartDate:StartTime, remove = FALSE, sep =  " ") %>%
	mutate(weight = 
		ifelse(animal == 0, cageweight0, 
		ifelse(animal == 1, cageweight1,
		ifelse(animal == 2, cageweight2,
		ifelse(animal == 3, cageweight3,
		ifelse(animal == 4, cageweight4,
		ifelse(animal == 5, cageweight5,
		ifelse(animal == 6, cageweight6, NA)))))))) %>% 
	mutate(Animal_ID =
        ifelse(animal == 7, 'baseline',
		ifelse(animal == 0, animalID0, 
		ifelse(animal == 1, animalID1,
		ifelse(animal == 2, animalID2,
		ifelse(animal == 3, animalID3,
		ifelse(animal == 4, animalID4,
		ifelse(animal == 5, animalID5,
		ifelse(animal == 6, animalID6, NA))))))))) %>% 
	mutate(H2Omg_edit = 
		ifelse(hour(StartTime) == 8, H2Omg - 0.001,
		ifelse(hour(StartTime) == 7, H2Omg - 0.001,
		ifelse(hour(StartTime) == 9, H2Omg - 0.001,
		ifelse(hour(StartTime) == 10, H2Omg - 0.001,
		ifelse(hour(StartTime) == 19, H2Omg + 0.001,
		ifelse(hour(StartTime) == 20, H2Omg + 0.001,
		ifelse(hour(StartTime) == 21, H2Omg + 0.001,
		ifelse(hour(StartTime) == 22, H2Omg + 0.001,
		ifelse(hour(StartTime) %!in% c(7,8,9,10,20,21,22,19), H2Omg, NA)))))))))) %>% 
	mutate_at("H2Omg_edit", as.numeric) %>%
	mutate(corEE = EE/weight)

metric <- "corEE"

target <- c(0,1,2,3,4,5,6)
cages20feb <- feb20 %>% filter(animal %in% target)
#cages <- with( cages20feb ,cages20feb[ hour( StartTime ) >= 0 & hour( StartTime ) < 4 , ] )

measurement <- cages20feb %>%  select(metric)
df<-as.data.frame(measurement[[metric]])
legend_title <- "Animal ID"

p <- ggplot(data = cages20feb,aes(x=as.POSIXct(StartTime),y=measurement[[metric]]))
p <- p + geom_point(aes(group=as.factor(Animal_ID), color=as.factor(Animal_ID)), size = 3)
p <- p + theme_grey(base_size = 15)
p <- p + geom_smooth(data=df$V1, method='loess', span=.9)
p <- p + labs(x = "", y = metric)
p <- p + scale_color_brewer(legend_title, palette="Paired")
p <- p + scale_x_datetime(date_breaks = "2 hours", date_labels = "%H:%M")
#p <- p + geom_hline(yintercept = 0.8907387)
p