malaria-infect/doc/02-load-data.Rmd

---
title: "Read data"
author: "Pjotr"
date: "25/02/2020"
output: html_document
---

Set the data directory. Note you have to use one on
your own system! When it is set correctly 'Run->all' in the
menu will recompute everything.

```{r setup, include=FALSE}
data <- "/home/wrk/iwrk/closed/kemri/Francis_Final_TregData_Jan2020/Data/"
setwd(data)
knitr::opts_knit$set(echo = TRUE, root.dir=data)
```

```{r}
getwd()
```

## Read individuals and attributes

load a table

```{r ind_attr}
ind_attr=read.csv("Individual_attributes.csv")
ind_attr[1:3,1:3]
```

Show data structure

```{r}
summary(ind_attr)
```

```{r}
colnames(ind_attr)
```

Three elements of phenotype column

```{r}
ind_attr[["Phenotype"]][0:3]
```

or

```{r}
ind_attr$Phenotype[0:3]
```

Let's do a simple plot. Plot ELISA values against inds:

```{r}
plot(ind_attr$ELISA)
```

Let's plot ELISA vs Time to diagnosis

```{r}
plot(ind_attr$ELISA ~ ind_attr$Time_to_diagnosis)
```

So, it looks like late diagnosis has an effect. This is just a quick example, let's continue loading sets from 

```
cytokines.csv
final_outcome_jan2020.csv
Individual_attributes.csv
pcr.csv
supernatant.csv
transcriptomics.csv
treg_phenotype_data.csv
```

```{r}
cytokines = read.csv("cytokines.csv")
final = read.csv("final_outcome_jan2020.csv")
pcr = read.csv("pcr.csv")
supernatant = read.csv("supernatant.csv")
transcriptomics = read.csv("transcriptomics.csv")
treg = read.csv("treg_phenotype_data.csv")
```

when they load you can explore the data in the top right enviroment or

```{r}
show(pcr$day[1:3])
```

It will show that not all rows are labeled. That means we will need a way to cross-reference by ID.