README.Rmd 1.43 KB
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---
title: "32 drugs"
output: 
    md_document:
      preserve_yaml: FALSE
      fig_width: 7
      fig_height: 5
      toc: yes
      toc_depth: 2
---

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# Motivation
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Getting up to speed with R using dose-response for 32 drugs against 6
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bacterial strains.
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# Tasks

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In the following, we go through the most common steps in data analysis:
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exploration and transformation (i.e. deriving new variables). Integral to both
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steps is visualization i.e. making graphs.
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## Explore

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1. Plot growth curves following raw OD in time. Input
   [data](doc/tasks/01_dat.csv) and expected [output](doc/tasks/01_out.pdf)
   plot are provided. The data is for azithromycin against _S. flexneri_ M90T
   from day 2022-05-04 (first replicate). _A tip: Use `facet_wrap` with `ncol
   = 1` argument to have different concentrations on separate plots._
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2. Try again, now with [data](doc/tasks/02_dat.csv) from two days (let us plot
   days in different color). In addition, transform the y-axis to logarithmic
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   scale. Expected [output](doc/tasks/02_out.pdf). _A tip: you need to turn
   the `Date` variable into a factor._

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3. Once more, now with [data](doc/tasks/03_dat.csv) from three days. Expected
   [output](doc/tasks/03_out.pdf). You will encounter an issue because there
   were two biological replicates on that day. There are multiple ways to
   overcome this, but for now, I recommend to solve by using `group` parameter 
   of `aes` e.g. `ggplot(aes(..., group = Plt))`.