add chapter one, figures and references

_bookdown.yml

 book_filename: "thesis-dissertaion" # Change this to the actual title
 delete_merged_file: true
-rmd_files: ["index.Rmd","00-preface.Rmd", "01-intro.Rmd","02-methods.Rmd", "03-results-01.Rmd", "04-results-02.Rmd","05-discussion.Rmd", "20-appendix.Rmd","21-appendix-a.Rmd","22-appendix-b.Rmd", "99-references.Rmd"]
+rmd_files: ["index.Rmd","00-preface.Rmd", "01-general_intro_new_3.Rmd","02-methods.Rmd", "03-results-01.Rmd", "04-results-02.Rmd","05-discussion.Rmd", "20-appendix.Rmd","21-appendix-a.Rmd","22-appendix-b.Rmd", "99-references.Rmd"]
+#rmd_files: ["index.Rmd","00-preface.Rmd", "01-intro.Rmd","02-methods.Rmd", "03-results-01.Rmd", "04-results-02.Rmd","05-discussion.Rmd", "20-appendix.Rmd","21-appendix-a.Rmd","22-appendix-b.Rmd", "99-references.Rmd"]
 language:
   ui:
     chapter_name: "Chapter "
\ No newline at end of file

bib/packages.bib

@@ -24,13 +24,6 @@
   title = {rmarkdown: Dynamic Documents for R},
   author = {JJ Allaire and Yihui Xie and Jonathan McPherson and Javier Luraschi and Kevin Ushey and Aron Atkins and Hadley Wickham and Joe Cheng and Winston Chang},
   year = {2018},
-  note = {R package version 1.10},
+  note = {R package version 1.9},
   url = {https://CRAN.R-project.org/package=rmarkdown},
 }
-@Manual{R-PepTools,
-  title = {PepTools - An R-package for making immunoinformatics accessible},
-  author = {Leon Eyrich Jessen},
-  year = {2018},
-  note = {R package version 0.1.0},
-  url = {https://github.com/leonjessen/PepTools},
-}

fig/Learning/1.Rmd

+---
+title: "Untitled"
+output: html_document
+---
+
+```{r setup, include=FALSE}
+knitr::opts_chunk$set(echo = TRUE)
+```
+
+
+```{r}
+# Dose response curve
+# A.Bailey 12th June 2018
+
+
+
+library(tidyverse)
+library(drc)
+library(janitor)
+
+# Read data from thrashing analysis - at 60 min time point 
+      dat <- read_csv("Learning/N2_DR_thrashing.csv") %>% clean_names()
+      dat1 <- read_csv("Learning/bus17_DR_thrashing.csv") %>% 
+        clean_names()
+
+# Drop dose and convert to matrix
+dat_m <- dat %>% dplyr::select(-dose) %>% as.matrix()
+dat_m1 <- dat1 %>%  dplyr::select(-dose) %>%  as.matrix()
+# Find NAs
+k <- which(is.na(dat_m), arr.ind=TRUE)
+k1 <- which(is.na(dat_m1), arr.ind=TRUE)
+# Copied the matrix
+m <- dat_m
+m1 <- dat_m1
+# Fill missing values with row mean, mean for that dose
+m[k] <- rowMeans(dat_m, na.rm=TRUE)[k[,1]]
+m1[k1] <- rowMeans(dat_m, na.rm=TRUE)[k1[,1]]
+# Convert back to tibble
+d_m <- as.tibble(m)
+d_m1 <- as.tibble(m1)
+# Re-bind the dose column to complete data
+dat_clean <- bind_cols(dose = dat$dose,d_m)
+
+dat_clean1 <- bind_cols(dose = dat$dose,d_m1)
+#combine data - look into this here 
+dat_comb <- bind_rows(dat_clean, dat_clean1)
+# Gather according to dose
+dat_melt <- dat_clean %>% gather(key = vars,value = response, -dose) #%>% dplyr::select(-vars)
+
+dat_melt2 <- dat %>% gather(key = vars,value = response,-dose) %>% dplyr::select(-vars)
+# Mutate log transformation
+dat_melt <- dat_melt %>% mutate(log_dose = log(dose))
+# Fit model with imputed data
+model <- drc::drm(data = dat_melt, response ~ dose, fct = L.3())
+# Fit original data
+model2 <- drc::drm(data = dat_melt2, response ~ dose, fct = L.3(), na.action = na.omit)
+
+summary(model2)
+plot(model2)
+plot(model)
+
+preds <- as.tibble(model$predres) %>% clean_names()
+
+dat_all <- dat_melt %>% mutate(predict = preds$predicted_values)
+
+dat_spread <- dat_all %>% 
+  dplyr::select(-log_dose) %>% 
+  group_by(dose) %>% 
+  mutate(mean_response = mean(response),
+         std_dev = sd(response),
+         se = std_dev/sqrt(length(response))) %>% 
+  dplyr::select(dose,mean_response,vars,se,predict) %>% 
+  spread(vars,mean_response) %>% 
+  dplyr::select(dose, se, mean_response = x10, predict) %>% 
+  ungroup() %>% 
+  mutate(norm_rep = (mean_response - min(mean_response)) / 
+           (max(mean_response) - min(mean_response)),
+         norm_pred = (predict - min(predict)) / (max(predict) - min(predict)),
+         norm_se = se/(mean_response))
+
+
+dat_spread %>% 
+  ggplot(aes(dose,norm_rep)) + 
+  geom_point() +
+  geom_errorbar(aes(ymin = norm_rep - norm_se,ymax = norm_rep + norm_se)) +
+  geom_line(aes(dose,norm_pred)) +
+  #geom_smooth(aes(dose,norm_pred)) +
+  scale_x_log10()
+  
+
+```
+

fig/Learning/Getting_started_with_R/Scripts/Coffee_consumtion_and_burger_eating.R

+# Monika Kudelska
+#01/06/2018
+#This script is the analysis of coffee consumtion and burger eating

fig/Learning/Getting_started_with_R/Scripts/Import_isolate_find_grap_parts_of_data.R

+# Monika Kudelska
+#01/06/2018
+#This script is the analysis of coffee consumtion and burger eating
+
+#Reset R's brain 
+rm(list=ls())
+
+#getwd tells you where R is currently looking
+getwd
+
+#setwd tells R where to look 
+setwd("//soton.ac.uk/ude/PersonalFiles/Users/mk11g11/mydocuments/PhD/R_analysis/Learning/Getting_started_with_R/datasets.zip")
+
+#use getwd to confirm that R is now looking here 
+getwd()
+
+#read csv file 
+comp.dat <- read.csv("compensation.csv")
+
+#list objects 
+ls()
+
+#check data 
+
+names(comp.dat) #Returns the names of columns 
+
+head(comp.dat) #Returns the names of headings 
+
+dim(comp.dat) #Returns the number of rows and columns 
+
+str(comp.dat) # A powerful compilations of the above 
+
+summary(comp.dat) # Summary of data 
+
+# Isolating, finding and grabbing data
+
+comp.dat[,1] #Retun all rows of column 1
+
+comp.dat[1,] # Retur all first rows 
+
+comp.dat[, 1:3] # Retun all observations from columns 1-3
+
+comp.dat [1:3,] # Return three rows from all columns 
+
+comp.dat [1:3, 1:3] # Retun three rows from three columns
+
+comp.dat [c(1:30), c(1:3)] # Use concatamate function if you want to select specific columns and rows. Select rows 1-30 and columns 1-3
+
+comp.dat [c(1, 20, 21:30), c(1)] # Select column 1, rows 20 21-30 
+
+comp.dat [c(1, 20, 21:30), -c(2,3)] # Select all columns except column 1 and return rows 1, 20, 21-30 
+
+
+#Using names to grab data 
+
+comp.dat[,"Fruit"] # all observations from Fruit header (variable)
+
+comp.dat[c("Fruit", "Grazing")] # all observation for Fruit and Grazing variables. Use concatamate to select mutliple variables 
+
+comp.dat$Grazing # Use dolar sign to select one variable. 
+
+subset(comp.dat, Grazing == "Grazed") #Return subset of the data (all 3 columns) with rows representing only "Grazed". 
+
+subset(comp.dat, Fruit == 35.53)
+subset(comp.dat, Fruit > 35.53 & Root > 2) 
+
+
+
+subset(comp.dat, Fruit > 38, select = c(Fruit, Root)) #select only Fruit and Root vaiables and rows containing fruit size larger than 38
+
+subset(comp.dat, c(Grazing == "Grazed", Fruit >30), select = c(-Root)) #Select all variables except Root, and show observations with fruit size over 30 and Grazed 
+
+# Aggregate - returns 
+mean.fruit <- aggregate(comp.dat$Fruit, list(comp.dat$Grazing), mean) #take the Fruit column from the object, divide it by the Grazing level system and calculate the mean in the grazing system 
+
+#tapply produces what aggregate does, but returns it as matrix and not data frame 
+
+tapply (comp.dat$Fruit, list(comp.dat$Grazing), mean)
+

fig/Learning/Getting_started_with_R/compensation.csv

+Root,Fruit,Grazing
+6.225,59.77,Ungrazed
+6.487,60.98,Ungrazed
+4.919,14.73,Ungrazed
+5.13,19.28,Ungrazed
+5.417,34.25,Ungrazed
+5.359,35.53,Ungrazed
+7.614,87.73,Ungrazed
+6.352,63.21,Ungrazed
+4.975,24.25,Ungrazed
+6.93,64.34,Ungrazed
+6.248,52.92,Ungrazed
+5.451,32.35,Ungrazed
+6.013,53.61,Ungrazed
+5.928,54.86,Ungrazed
+6.264,64.81,Ungrazed
+7.181,73.24,Ungrazed
+7.001,80.64,Ungrazed
+4.426,18.89,Ungrazed
+7.302,75.49,Ungrazed
+5.836,46.73,Ungrazed
+10.253,116.05,Grazed
+6.958,38.94,Grazed
+8.001,60.77,Grazed
+9.039,84.37,Grazed
+8.91,70.11,Grazed
+6.106,14.95,Grazed
+7.691,70.7,Grazed
+8.988,80.31,Grazed
+8.975,82.35,Grazed
+9.844,105.07,Grazed
+8.508,73.79,Grazed
+7.354,50.08,Grazed
+8.643,78.28,Grazed
+7.916,41.48,Grazed
+9.351,98.47,Grazed
+7.066,40.15,Grazed
+8.158,52.26,Grazed
+7.382,46.64,Grazed
+8.515,71.01,Grazed
+8.53,83.03,Grazed

fig/Learning/N2_DR_thrashing.csv

+Dose,,,,,,,,,,,,,,,,,,
+0.000001,49,48,36,48,45,52,41,37,48,51,54,44,45,44,42,52,40,
+0.001,47,42,46,22,40,51,43,49,23,46,40,13,37,42,42,52,48,42
+0.01,57,56,48,51,4,0,46,28,43,47,21,44,49,31,,,,
+0.025,35,52,38,43,44,44,0,29,47,43,0,0,38,35,33,43,0,0
+0.05,12,44,0,0,0,37,38,0,0,0,43,42,0,0,0,0,,
+0.1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0

fig/Learning/bus17_DR_thrashing.csv

+Dose,,,,,,,,,,,,,,,,,,,,,,,,
+0.000001,44,40,34,43,38,41,44,45,50,39,47,43,39,37,46,,,,,,,,,
+0.0001,39,35,32,36,40,48,45,41,34,24,35,47,50,35,43,45,38,,,,,,,
+0.001,39,0,37,32,37,35,36,45,40,43,0,0,41,0,51,44,54,49,51,51,40,48,46,40
+0.01,18,5,0,0,0,22,32,35,0,0,40,2,13,34,0,0,,,,,,,,
+0.025,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,,
+0.05,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,

fig/Learning/dose_response_curve.R

+# Dose response training
+# Monika Kudelska
+# 12/06/2018
+
+# Clear R
+
+#rm(list=ls())
+
+# Load packages 
+
+library(drc)
+#library(drm)
+library(reshape2)
+library(tidyverse)
+
+
+
+
+#setwd("//soton.ac.uk/ude/PersonalFiles/Users/mk11g11/mydocuments/PhD/R_analysis")
+
+data1 <- read_csv ("Learning/N2_DR_thrashing.csv")
+
+data1t <- data1 %>% 
+  gather (key = thrashing, value = response, -Dose) %>% 
+  drop_na() %>% 
+  select(Dose, response)
+
+data2t <- data1t %>% 
+  group_by(Dose) %>% 
+  summarise(mean_resp = mean(response),
+              sd=sd(response),
+              se = sd/sqrt(length(response)))
+
+#use drm function 
+model <- drc::drm(data = data1t, response ~ Dose,
+                   fct = L.4(),
+                   na.action = na.omit)
+
+
+  
+-------------------------------------------------------------------------------
+  
+
+model <- glm(mean_resp ~ log2(Dose), data = data2t)
+
+data2t %>% 
+  mutate(predict = model$fitted.values,
+         log_dose = log2(Dose)) %>% 
+  ggplot(aes(x = log_dose, y = mean_resp)) +
+  geom_point() +
+  geom_line(aes(log_dose,predict))
+

fig/Learning/drc_test.R

+# Dose response curve
+# A.Bailey 12th June 2018
+
+
+
+library(tidyverse)
+library(drc)
+library(janitor)
+
+# Read data from thrashing analysis - at 60 min time point 
+      dat <- read_csv("Learning/N2_DR_thrashing.csv") %>% clean_names()
+      dat1 <- read_csv("Learning/bus17_DR_thrashing.csv") %>% 
+        clean_names()
+
+# Drop dose and convert to matrix
+dat_m <- dat %>% dplyr::select(-dose) %>% as.matrix()
+dat_m1 <- dat1 %>%  dplyr::select(-dose) %>%  as.matrix()
+# Find NAs
+k <- which(is.na(dat_m), arr.ind=TRUE)
+k1 <- which(is.na(dat_m1), arr.ind=TRUE)
+# Copied the matrix
+m <- dat_m
+m1 <- dat_m1
+# Fill missing values with row mean, mean for that dose
+m[k] <- rowMeans(dat_m, na.rm=TRUE)[k[,1]]
+m1[k1] <- rowMeans(dat_m, na.rm=TRUE)[k1[,1]]
+# Convert back to tibble
+d_m <- as.tibble(m)
+d_m1 <- as.tibble(m1)
+# Re-bind the dose column to complete data
+dat_clean <- bind_cols(dose = dat$dose,d_m)
+
+dat_clean1 <- bind_cols(dose = dat$dose,d_m1)
+#combine data - look into this here 
+dat_comb <- bind_rows(dat_clean, dat_clean1)
+# Gather according to dose
+dat_melt <- dat_clean %>% gather(key = vars,value = response, -dose) #%>% dplyr::select(-vars)
+
+dat_melt2 <- dat %>% gather(key = vars,value = response,-dose) %>% dplyr::select(-vars)
+# Mutate log transformation
+dat_melt <- dat_melt %>% mutate(log_dose = log(dose))
+# Fit model with imputed data
+model <- drc::drm(data = dat_melt, response ~ dose, fct = L.3())
+# Fit original data
+model2 <- drc::drm(data = dat_melt2, response ~ dose, fct = L.3(), na.action = na.omit)
+
+summary(model2)
+plot(model2)
+plot(model)
+
+preds <- as.tibble(model$predres) %>% clean_names()
+
+dat_all <- dat_melt %>% mutate(predict = preds$predicted_values)
+
+dat_spread <- dat_all %>% 
+  dplyr::select(-log_dose) %>% 
+  group_by(dose) %>% 
+  mutate(mean_response = mean(response),
+         std_dev = sd(response),
+         se = std_dev/sqrt(length(response))) %>% 
+  dplyr::select(dose,mean_response,vars,se,predict) %>% 
+  spread(vars,mean_response) %>% 
+  dplyr::select(dose, se, mean_response = x10, predict) %>% 
+  ungroup() %>% 
+  mutate(norm_rep = (mean_response - min(mean_response)) / 
+           (max(mean_response) - min(mean_response)),
+         norm_pred = (predict - min(predict)) / (max(predict) - min(predict)),
+         norm_se = se/(mean_response))
+
+
+dat_spread %>% 
+  ggplot(aes(dose,norm_rep)) + 
+  geom_point() +
+  geom_errorbar(aes(ymin = norm_rep - norm_se,ymax = norm_rep + norm_se)) +
+  geom_line(aes(dose,norm_pred)) +
+  #geom_smooth(aes(dose,norm_pred)) +
+  scale_x_log10()
+dat_sprea
+  

fig/Learning/drc_test1.R

+# dat <- read_csv("Analysis/Data/Exported/EPG/5-HT DR_peak.csv")
+# dat1 <- dat[, 1:7]
+
+# Read data from thrashing analysis - at 60 min time point 
+dat <- read_csv("Analysis/Data/Exported/EPG/5-HT DR_peak.csv") %>% clean_names()
+
+library(tidyverse)
+library(drc)
+library(janitor)
+library(drc)
+library(reshape2)
+library(ggplot2)
+
+# Read data from thrashing analysis - at 60 min time point 
+dat <- read_csv("Analysis/Data/Exported/EPG/5-HT DR_peak.csv") %>% clean_names()
+dat1 <- read_csv("Learning/bus17_DR_thrashing.csv") %>% 
+  clean_names()
+
+# Drop dose and convert to matrix
+dat_m <- dat %>% dplyr::select(-dose) %>% as.matrix()
+dat_m1 <- dat1 %>%  dplyr::select(-dose) %>%  as.matrix()
+# Find NAs
+k <- which(is.na(dat_m), arr.ind=TRUE)
+k1 <- which(is.na(dat_m1), arr.ind=TRUE)
+# Copied the matrix
+m <- dat_m
+m1 <- dat_m1
+# Fill missing values with row mean, mean for that dose
+m[k] <- rowMeans(dat_m, na.rm=TRUE)[k[,1]]
+m1[k1] <- rowMeans(dat_m, na.rm=TRUE)[k1[,1]]
+# Convert back to tibble
+d_m <- as.tibble(m)
+d_m1 <- as.tibble(m1)
+# Re-bind the dose column to complete data
+dat_clean <- bind_cols(dose = dat$dose,d_m)
+
+dat_clean1 <- bind_cols(dose = dat$dose,d_m1)
+#combine data - look into this here 
+dat_comb <- bind_rows(dat_clean, dat_clean1)
+# Gather according to dose
+dat_melt <- dat_clean %>% gather(key = vars,value = response, -dose) #%>% dplyr::select(-vars)
+
+dat_melt2 <- dat %>% gather(key = vars,value = response,-dose) %>% dplyr::select(-vars)
+# Mutate log transformation
+dat_melt <- dat_melt %>% mutate(log_dose = log(dose))
+# Fit model with imputed data
+model <- drc::drm(data = dat_melt, response ~ dose, fct = LL.3())
+# Fit original data
+model2 <- drc::drm(data = dat_melt2, response ~ dose, fct = L.L4(), na.action = na.omit)
+
+summary(model)
+summary(model2)
+plot(model2)
+plot(model)
+
+preds <- as.tibble(model$predres) %>% clean_names()
+
+dat_all <- dat_melt %>% mutate(predict = preds$predicted_values)
+
+dat_spread <- dat_all %>% 
+  dplyr::select(-log_dose) %>% 
+  group_by(dose) %>% 
+  mutate(mean_response = mean(response),
+         std_dev = sd(response),
+         se = std_dev/sqrt(length(response))) %>% 
+  dplyr::select(dose,mean_response,vars,se,predict) 
+# %>% 
+  # spread(vars,mean_response) %>% 
+  # dplyr::select(dose, se, mean_response = x10, predict) %>% 
+  # ungroup() %>% 
+  # mutate(norm_rep = (mean_response - min(mean_response)) / 
+  #          (max(mean_response) - min(mean_response)),
+  #        norm_pred = (predict - min(predict)) / (max(predict) - min(predict)),
+  #        norm_se = se/(mean_response))
+
+
+dat_spread %>% 
+  ggplot(aes(dose,mean_response)) + 
+  geom_point() +
+  geom_line(aes(dose,predict)) +
+  geom_smooth(method = drm, fct = L.4(), se = FALSE) +
+  scale_x_log10() 
+  
+dat_spread
+# +geom_errorbar(aes(ymin = mean_response - norm_se,ymax = norm_rep + norm_se)) +
+# 
+#   #geom_smooth(aes(dose,norm_pred)) +
+#   scale_x_log10()
+# dat_sprea
+

fig/Learning/test.Rmd

+---
+title: "test"
+output: html_document
+---
+
+```{r echo=FALSE}
+
+library(tidyverse)
+library(drc)
+library(janitor)
+
+# Read data from thrashing analysis - at 60 min time point 
+          dat <- read_csv("N2_DR_thrashing.csv") %>% clean_names()
+      dat1 <- read_csv("bus17_DR_thrashing.csv") %>% 
+        clean_names()
+
+# Drop dose and convert to matrix
+dat_m <- dat %>% dplyr::select(-dose) %>% as.matrix()
+dat_m1 <- dat1 %>%  dplyr::select(-dose) %>%  as.matrix()
+# Find NAs
+k <- which(is.na(dat_m), arr.ind=TRUE)
+k1 <- which(is.na(dat_m1), arr.ind=TRUE)
+# Copied the matrix
+m <- dat_m
+m1 <- dat_m1
+# Fill missing values with row mean, mean for that dose
+m[k] <- rowMeans(dat_m, na.rm=TRUE)[k[,1]]
+m1[k1] <- rowMeans(dat_m, na.rm=TRUE)[k1[,1]]
+# Convert back to tibble
+d_m <- as.tibble(m)
+d_m1 <- as.tibble(m1)
+# Re-bind the dose column to complete data
+dat_clean <- bind_cols(dose = dat$dose,d_m)
+
+dat_clean1 <- bind_cols(dose = dat$dose,d_m1)
+#combine data - look into this here 
+dat_comb <- bind_rows(dat_clean, dat_clean1)
+# Gather according to dose
+dat_melt <- dat_clean %>% gather(key = vars,value = response, -dose) #%>% dplyr::select(-vars)
+
+dat_melt2 <- dat %>% gather(key = vars,value = response,-dose) %>% dplyr::select(-vars)
+# Mutate log transformation
+dat_melt <- dat_melt %>% mutate(log_dose = log(dose))
+# Fit model with imputed data
+model <- drc::drm(data = dat_melt, response ~ dose, fct = L.3())
+# Fit original data
+model2 <- drc::drm(data = dat_melt2, response ~ dose, fct = L.3(), na.action = na.omit)
+
+summary(model2)
+plot(model2)
+plot(model)
+
+preds <- as.tibble(model$predres) %>% clean_names()
+
+dat_all <- dat_melt %>% mutate(predict = preds$predicted_values)
+
+dat_spread <- dat_all %>% dplyr::select(-log_dose) %>% 
+  group_by(dose) %>% 
+  mutate(mean_response = mean(response),
+         std_dev = sd(response),
+         se = std_dev/sqrt(length(response))) %>% 
+  dplyr::select(dose,mean_response,vars,se,predict) %>% 
+  spread(vars,mean_response) %>% 
+  dplyr::select(dose, se, mean_response = x10, predict) %>% 
+  ungroup() %>% 
+  mutate(norm_rep = (mean_response - min(mean_response)) / 
+           (max(mean_response) - min(mean_response)),
+         norm_pred = (predict - min(predict)) / (max(predict) - min(predict)),
+         norm_se = se/(mean_response))
+
+
+dat_spread %>% 
+  ggplot(aes(dose,norm_rep)) + 
+  geom_point() +
+  geom_errorbar(aes(ymin = norm_rep - norm_se,ymax = norm_rep + norm_se)) +
+  geom_line(aes(dose,norm_pred)) +
+  #geom_smooth(aes(dose,norm_pred)) +
+  scale_x_log10()
+
+dat_spread
+```
+
+