Add third task

README.Rmd

@@ -23,10 +23,6 @@ steps is visualization i.e. making graphs.
 
 ## Explore
 
-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._
 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
@@ -35,8 +31,13 @@ steps is visualization i.e. making graphs.
 
 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
-   scale. [Expected output](doc/tasks/02_out.pdf). _A tip: you need to turn
-   the `Date` variable into a factor.`
    scale. Expected [output](doc/tasks/02_out.pdf). _A tip: you need to turn
    the `Date` variable into a factor._
 
+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 issuing `group =
+   WellID` to `aes` of `ggplot`.
+   overcome this, but for now, I recommend to solve by using `group` parameter 
+   of `aes` e.g. `ggplot(aes(..., group = Plt))`.

README.md

@@ -23,3 +23,10 @@ both steps is visualization i.e. making graphs.
     to logarithmic scale. Expected [output](doc/tasks/02_out.pdf). *A
     tip: you need to turn the `Date` variable into a factor.*
 
+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
+    issuing `group =    WellID` to `aes` of `ggplot`.
+    using `group` parameter of `aes`
+    e.g. `ggplot(aes(..., group = Plt))`.