Lat_HSP60_ArrangeData.Rmd

---
title: "Lat_HSP60_ArrangeData"
author: "Nathan"
date: "04/08/2021"
---

```{r Load Data}
library("tidyr")
library("tidyverse")
library("dplyr")

#0.1 Set working directory
setwd("~/Dropbox/UTS/PhD/Projects/Chapter1_Vibrio_latitude_study/Data/HSP60/Data")

#1.0 Load and prepare your asv_data file (this is called otu_data in the tutorial)

#1.1 load in the data
HSP60_data <- read_csv("SampleID_L7.csv")
sample_data <- read_csv("SMD.csv")
sample_data <- sample_data %>%  arrange(sample_data$SampleID)
```

```{r }
#1.2 Make the long version of your data
HSP60_data_long <- gather(data = HSP60_data, key = SampleID, value = Abundance, -c(1,2))

#1.3 Left join in your meta data
HSP60_data_long <- HSP60_data_long %>% left_join(sample_data)

HSP60 <- HSP60_data_long

setwd("~/Dropbox/UTS/PhD/Projects/Chapter1_Vibrio_latitude_study/Data/HSP60")
write_csv(HSP60,'HSP60.csv')
```

```{r }
HSP60_Normalised_long <- HSP60_data_long %>% unite('Species.SampleID', c(Species, SampleID), remove=F, sep='_')
HSP60_Normalised_long <- HSP60_Normalised_long %>% group_by(SampleID) %>% mutate(Total_Abundance = sum(Abundance))
HSP60_Normalised_long <- HSP60_Normalised_long %>% group_by(SampleID) %>% mutate(RA = (Abundance/Total_Abundance)*100)
check                 <- HSP60_Normalised_long %>% group_by(SampleID) %>% summarise(Check=sum(RA))
```

```{r Write CSV}
setwd("~/Dropbox/UTS/PhD/Projects/Chapter1_Vibrio_latitude_study/Data/HSP60/Data")
write_csv(HSP60_Normalised_long, "HSP60_Normalised_long.csv")
```