Last version Task-1

Task1.R

+library(ggplot2)
+library(dplyr)
+library(tidyverse)
+
+# EXPLORE
+## task1_1
+data <- read.csv("/Users/carlota/Desktop/EMBL/Vallo_R/01_dat.csv", header = TRUE) 
+data %>% 
+        select(Time_h, RawOD, uM) %>%
+        ggplot(aes(y= RawOD, x = Time_h)) +
+        geom_line() +
+        labs(x = "Time (h)", y = "Raw OD", title = "Growth curves of S.flexneri M90T with Azithromycin") +
+        facet_wrap(~ uM, labeller = label_both)
+ggsave(filename = "/Users/carlota/Desktop/EMBL/Vallo_R/plot1_1.pdf")
+
+## task1_2
+data2 <- read.csv("/Users/carlota/Desktop/EMBL/Vallo_R/02_dat.csv", header = TRUE)
+data2 %>%
+        select(Date, Time_h, RawOD, uM) %>%
+        mutate(Date = as.factor(Date)) %>%
+        ggplot(aes(y= RawOD, x = Time_h, color = Date)) +
+        geom_line() + 
+        labs(x = "Time (h)", y = "RawOD") +
+        # transform to logarithmic scale using trans = "log2"
+        scale_y_continuous(trans = "log2") + 
+        facet_wrap(~ uM, labeller = label_both)
+ggsave(filename = "/Users/carlota/Desktop/EMBL/Vallo_R/plot1_2.pdf")
+
+## task1_3
+data3 <- read.csv("/Users/carlota/Desktop/EMBL/Vallo_R/03_dat.csv", header = TRUE)
+data3 %>%
+        select(Date, Plt, Time_h, RawOD, uM) %>%
+        mutate(Date = as.factor(Date)) %>%
+        # day 3 has to biological replicates so we need to group the data by Plate (Plt) using group = Plt
+        ggplot(aes(y= RawOD, x = Time_h, color = Date, group = Plt)) + 
+        geom_line() + 
+        labs(x = "Time (h)", y = "RawOD") +
+        scale_y_continuous(trans = "log2") +
+        facet_wrap(~ uM, labeller = label_both)
+ggsave(filename = "/Users/carlota/Desktop/EMBL/Vallo_R/plot1_3.pdf")
+
+
+# TRANSFORM
+
+## task1_4: substract background OD
+data3 <- read.csv("/Users/carlota/Desktop/EMBL/Vallo_R/03_dat.csv", header = TRUE) %>% 
+        group_by(Date, Time_h, Plt) %>% 
+        mutate(  
+                background = RawOD[uM ==-1],
+                OD = RawOD - background)  
+        # this creates two new variables: (1) background OD (OD for uM = -1 for each Date, Time and plate) 
+        # (2 )the final OD, calculated by substracting the background from the RawOD readout
+data3 %>%
+        select(Date, Plt, Time_h, OD, uM) %>%
+        mutate(Date = as.factor(Date)) %>% 
+        filter(!str_detect(uM, "-1")) %>% # drops background plot
+        ggplot(aes(y= OD, x = Time_h, color = Date, group = Plt)) + 
+        geom_line() + 
+        labs(x = "Time (h)", y = "OD") +
+        scale_y_continuous(trans = "log2") +
+        facet_wrap(~ uM, labeller = label_both)
+ggsave(filename = "/Users/carlota/Desktop/EMBL/Vallo_R/plot1_4.pdf")
+
+# Another way of grouping the background OD for each date, plate and time
+#data3 <- filter(data3, uM == -1) %>%
+        #rename(bg= RawOD) %>% 
+        #select(Date, Plt, Time_h, bg) %>% 
+        #right_join(data3) %>% # it matches back based on date, plt and time_h
+        #mutate(OD = RawOD - bg)
+
+#task1_5: constrain OD to limit of detection (LOD)
+data3 <- read.csv("/Users/carlota/Desktop/EMBL/Vallo_R/03_dat.csv", header = TRUE) %>% 
+        group_by(Date, Time_h, Plt) %>% 
+        mutate(  
+                background = RawOD[uM ==-1],
+                OD = RawOD - background)  
+
+# constrain OD variable to LOD of 0.03
+data3$OD <- ifelse(data3$OD < 0.03, 0.03, data3$OD)
+data3 %>% 
+        select(Date, Plt, Time_h, OD, uM) %>%
+        mutate(Date = as.factor(Date)) %>% 
+        filter(!str_detect(uM, "-1")) %>% # drops background plot
+        ggplot(aes(y= OD, x = Time_h, color = Date, group = Plt)) + 
+        geom_line() + 
+        labs(x = "Time (h)", y = "OD") +
+        scale_y_continuous(trans = "log2") +
+        facet_wrap(~ uM, labeller = label_both)
+ggsave(filename = "/Users/carlota/Desktop/EMBL/Vallo_R/plot1_5.pdf")
+
+#task1_6: fitness
+
+data3 <- read.csv("/Users/carlota/Desktop/EMBL/Vallo_R/03_dat.csv", header = TRUE) %>% 
+        group_by(Date, Time_h, Plt) %>% 
+        mutate(  
+                background = RawOD[uM ==-1],
+                OD = RawOD - background)  
+
+# constrain OD variable to LOD of 0.03
+data3$OD <- ifelse(data3$OD < 0.03, 0.03, data3$OD)
+
+data3 <- group_by(data3, Date, Time_h, Plt) %>% 
+        mutate(fit_ref = OD[uM == 0],
+               fit = OD/OD[uM == 0])
+
+# constrain fitness to value of 1.1
+data3$fit <- ifelse(data3$fit > 1.1, 1.1, data3$fit)
+
+data3 %>% 
+        select(Date, Time_h, Plt, OD, fit, uM) %>%
+        mutate(Date = as.factor(Date)) %>% 
+        filter(!str_detect(uM, "-1")) %>% # drops background plot
+        ggplot(aes(y= fit, x = Time_h, color = Date, group = Plt)) + 
+        geom_line() + 
+        labs(x = "Time (h)", y = "Fitness") +
+        scale_y_continuous(breaks = c(0.3, 0.6, 0.9, 1.2), limits = c(0.0, 1.1)) +
+        facet_wrap(~ uM, labeller = label_both)
+ggsave(filename = "/Users/carlota/Desktop/EMBL/Vallo_R/plot1_6.pdf")
+
+
+
+        
+
+