Merge branch 'origin/content-changes' into gh-pages

Includes commits from #13 which weren't in 2445236
TIBHannover · Apr 10, 2019 · 7870a80 · 7870a80
2 parents c123158 + ebf951b
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diff --git a/_episodes/03-func-R.md b/_episodes/03-func-R.md
@@ -555,7 +555,6 @@ it is mathematically correct by using `min`, `max`, and `plot`.
 [start-ep]: {{ page.root }}/02-starting-with-data/
 
 
-
 ### Defining defaults
 
 We have passed arguments to functions in two ways: directly, as in `dim(dat)`, and by name, as in `read.csv(file = "inflammation.csv", header = FALSE)`.
@@ -699,60 +698,19 @@ This is handy: if we usually want a function to work one way, but occasionally n
 
 
 ~~~
-answer <- rescale2(v = dat[, 4], lower = 2, upper = 5)
-min(answer)
-~~~
-{: .language-r}
-
-
-
-~~~
-[1] 2
-~~~
-{: .output}
-
-
-
-~~~
-max(answer)
+rescale2(v = dat[, 4], lower = 2, upper = 5))
+rescale2(dat[, 4], -5, -2))
 ~~~
 {: .language-r}
 
 
 
 ~~~
-[1] 5
-~~~
-{: .output}
-
-
-
-~~~
-answer <- rescale2(dat[, 4], -5, -2)
-min(answer)
+Error: <text>:1:45: unexpected ')'
+1: rescale2(v = dat[, 4], lower = 2, upper = 5))
+                                                ^
 ~~~
-{: .language-r}
-
-
-
-~~~
-[1] -5
-~~~
-{: .output}
-
-
-
-~~~
-max(answer)
-~~~
-{: .language-r}
-
-
-
-~~~
-[1] -2
-~~~
-{: .output}
+{: .error}
 
 Compare both `rescale2` calls: Which is more understandable? Although passing
 arguments purely by position is very convenient, because you have to type less,

diff --git a/_episodes/04-making-packages-R.md b/_episodes/04-making-packages-R.md
@@ -70,7 +70,7 @@ search()
 
 This means you can now take
 advantage of the console's auto-complete just like for any other package. Type
-`cen` and/or `resc`, press <kbd>Tab</kbd> and test some examples like `center(c(1, 2, 3))` or 
+`cen` and/or `resc`, then <kbd>Tab</kbd> and test some examples like `center(c(1, 2, 3), 0)` or 
 `rescale(c(1, 2, 3))`.
 
 ## Folder and file structure of an R package
@@ -144,10 +144,9 @@ The package name can only contain letters and numbers and has to start with a le
 > > The date is added in [ISO 8601](https://en.wikipedia.org/wiki/ISO_8601) format:
 > > `date: 2018-07-12`.
 > > 
-> > Before inserting the ORCiD, we have to merge the `Author` and `Maintainer`
-> > fields into a machine-readable `Authors@R` field using the `person(…)`.
-> > function. Look up its help page and convert your information.
-> > Afterwards, add your ORCiD as a `comment = c(ORCID = "…")`.
+> > ORCiDs can only be added to machine-readable `Authors@R` fields
+> > that use the `person(…)` function. `use_description()` created this
+> > already, so that you can add your ORCiD as a `comment = c(ORCID = "…")`.
 > {: .solution}
 >
 > In summary: Enabling `citation()` to convert `DESCRIPTION` into a rich BibTeX

diff --git a/_episodes/06-tidy-data.md b/_episodes/06-tidy-data.md
@@ -28,6 +28,13 @@ dataset about inflammation in 60 patients measured over 40 days. Let's pretend
 we need to tidy it up for further publication alongside the package we have been
 constructing.
 
+> ## Staying in spreadsheets
+> It is not always possible to abandon Excel & Co. and switch to R or Python.
+> In order to learn more about how to organise data in spreadsheets, please read
+> [DataCarpentry.org/spreadsheets-socialsci](https://datacarpentry.org/spreadsheets-socialsci/)
+> and/or [DataCarpentry.org/spreadsheet-ecology-lesson](https://datacarpentry.org/spreadsheet-ecology-lesson/).
+{: .callout}
+
 First, run `library(usethis)` and `use_data_raw()` and note the `Next:` instructions
 in the console. In particular, copy the `inflammation.csv` file to `data-raw`
 and start a `tidy-inflammation.R` file there to which you add the necessary lines
@@ -277,6 +284,11 @@ in their own ways. Two useful tools to find structural problems in CSVs are
 > {: .solution}
 {: .challenge}
 
+## [Reusing and citing public datasets](https://tibhannover.github.io/2018-07-09-FAIR-Data-and-Software/FAIR-remix-PANGAEA/)
+
+This is a bonus episode that shows how FAIR datasets, together with the right mindset
+of finding and reusing existing R code can help you in scientific data analyses.
+
 [csvl]: https://csvlint.io/
 [gt]: https://try.goodtables.io/
 [RFC 4180]: https://tools.ietf.org/html/rfc4180
diff --git a/_episodes_rmd/03-func-R.Rmd b/_episodes_rmd/03-func-R.Rmd
@@ -401,14 +401,6 @@ it is mathematically correct by using `min`, `max`, and `plot`.
 
 [start-ep]: {{ page.root }}/02-starting-with-data/
 
-```{r rescale-test1, include=FALSE}
-answer <- rescale(dat[, 4])
-min(answer)
-max(answer)
-plot(answer)
-plot(dat[, 4], answer)  # This hasn't been introduced yet, but it may be
-                        # useful to show when explaining the answer.
-```
 
 ### Defining defaults
 
@@ -525,13 +517,9 @@ rescale2 <- function(v, lower = 0, upper = 1) {
 }
 ```
 
-```{r rescale-test2}
-answer <- rescale2(v = dat[, 4], lower = 2, upper = 5)
-min(answer)
-max(answer)
-answer <- rescale2(dat[, 4], -5, -2)
-min(answer)
-max(answer)
+```{r rescale-test}
+rescale2(v = dat[, 4], lower = 2, upper = 5))
+rescale2(dat[, 4], -5, -2))
 ```
 
 Compare both `rescale2` calls: Which is more understandable? Although passing

diff --git a/_episodes_rmd/04-making-packages-R.Rmd b/_episodes_rmd/04-making-packages-R.Rmd
@@ -69,7 +69,7 @@ search()
 
 This means you can now take
 advantage of the console's auto-complete just like for any other package. Type
-`cen` and/or `resc`, press <kbd>Tab</kbd> and test some examples like `center(c(1, 2, 3))` or 
+`cen` and/or `resc`, then <kbd>Tab</kbd> and test some examples like `center(c(1, 2, 3), 0)` or 
 `rescale(c(1, 2, 3))`.
 
 ## Folder and file structure of an R package
@@ -143,10 +143,9 @@ The package name can only contain letters and numbers and has to start with a le
 > > The date is added in [ISO 8601](https://en.wikipedia.org/wiki/ISO_8601) format:
 > > `date: 2018-07-12`.
 > > 
-> > Before inserting the ORCiD, we have to merge the `Author` and `Maintainer`
-> > fields into a machine-readable `Authors@R` field using the `person(…)`.
-> > function. Look up its help page and convert your information.
-> > Afterwards, add your ORCiD as a `comment = c(ORCID = "…")`.
+> > ORCiDs can only be added to machine-readable `Authors@R` fields
+> > that use the `person(…)` function. `use_description()` created this
+> > already, so that you can add your ORCiD as a `comment = c(ORCID = "…")`.
 > {: .solution}
 >
 > In summary: Enabling `citation()` to convert `DESCRIPTION` into a rich BibTeX

diff --git a/_episodes_rmd/06-tidy-data.Rmd b/_episodes_rmd/06-tidy-data.Rmd
@@ -29,6 +29,13 @@ dataset about inflammation in 60 patients measured over 40 days. Let's pretend
 we need to tidy it up for further publication alongside the package we have been
 constructing.
 
+> ## Staying in spreadsheets
+> It is not always possible to abandon Excel & Co. and switch to R or Python.
+> In order to learn more about how to organise data in spreadsheets, please read
+> [DataCarpentry.org/spreadsheets-socialsci](https://datacarpentry.org/spreadsheets-socialsci/)
+> and/or [DataCarpentry.org/spreadsheet-ecology-lesson](https://datacarpentry.org/spreadsheet-ecology-lesson/).
+{: .callout}
+
 First, run `library(usethis)` and `use_data_raw()` and note the `Next:` instructions
 in the console. In particular, copy the `inflammation.csv` file to `data-raw`
 and start a `tidy-inflammation.R` file there to which you add the necessary lines
@@ -191,6 +198,11 @@ in their own ways. Two useful tools to find structural problems in CSVs are
 > {: .solution}
 {: .challenge}
 
+## [Reusing and citing public datasets](https://tibhannover.github.io/2018-07-09-FAIR-Data-and-Software/FAIR-remix-PANGAEA/)
+
+This is a bonus episode that shows how FAIR datasets, together with the right mindset
+of finding and reusing existing R code can help you in scientific data analyses.
+
 [csvl]: https://csvlint.io/
 [gt]: https://try.goodtables.io/
 [RFC 4180]: https://tools.ietf.org/html/rfc4180