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92 行
1.6 KiB
92 行
1.6 KiB
---
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title: "Read data"
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author: "Pjotr"
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date: "25/02/2020"
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output: html_document
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---
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Set the data directory. Note you have to use one on
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your own system! When it is set correctly 'Run->all' in the
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menu will recompute everything.
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```{r setup, include=FALSE}
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data <- "/home/wrk/iwrk/closed/kemri/Francis_Final_TregData_Jan2020/Data/"
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setwd(data)
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knitr::opts_knit$set(echo = TRUE, root.dir=data)
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```
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```{r}
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getwd()
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```
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## Read individuals and attributes
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load a table
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```{r ind_attr}
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ind_attr=read.csv("Individual_attributes.csv")
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ind_attr[1:3,1:3]
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```
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Show data structure
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```{r}
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summary(ind_attr)
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```
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```{r}
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colnames(ind_attr)
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```
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Three elements of phenotype column
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```{r}
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ind_attr[["Phenotype"]][0:3]
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```
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or
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```{r}
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ind_attr$Phenotype[0:3]
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```
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Let's do a simple plot. Plot ELISA values against inds:
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```{r}
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plot(ind_attr$ELISA)
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```
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Let's plot ELISA vs Time to diagnosis
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```{r}
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plot(ind_attr$ELISA ~ ind_attr$Time_to_diagnosis)
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```
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So, it looks like late diagnosis has an effect. This is just a quick example, let's continue loading sets from
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```
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cytokines.csv
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final_outcome_jan2020.csv
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Individual_attributes.csv
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pcr.csv
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supernatant.csv
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transcriptomics.csv
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treg_phenotype_data.csv
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```
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```{r}
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cytokines = read.csv("cytokines.csv")
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final = read.csv("final_outcome_jan2020.csv")
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pcr = read.csv("pcr.csv")
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supernatant = read.csv("supernatant.csv")
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transcriptomics = read.csv("transcriptomics.csv")
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treg = read.csv("treg_phenotype_data.csv")
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```
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when they load you can explore the data in the top right enviroment or
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```{r}
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show(pcr$day[1:3])
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```
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It will show that not all rows are labeled. That means we will need a way to cross-reference by ID.
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