The Unix Shell: Lesson 4. Pipes and Filters
Word count without flags is used to initially introduce the command and outputs three points of data - character, word, and line count. Then -l is added to show how line count is attained. It would be helpful to also include wc -cwl to show that the output is the same as default without flags. This would help by teaching about default flags and also inc

The first episode is more a description of "a" version control system, rather than of Git. I think that's useful as this is the first exposure of many learners to the concept. Also, the model based on diffs in not completely accurate for Git. A compromise to be both accurate and keep it simple could be to change the second objective to reflect this. So, what about?

Understand the basics

I suggest either adding a short code piece to use the rename() function to change the column "genus" to "genera" (thus alerting the learners to their relationship here, while adding a new function) or changing the column name in the original dataset. Otherwise, I've found that using the correct plural for genus confuses learners who are not biologists. Although it's the R ecology lesson and one

NB: Good first issue label (cannot be added because not in contributor list)

Exercise Reading error Messages - Lesson "Error and Exceptions"
(http://swcarpentry.github.io/python-novice-inflammation/09-errors/index.html)

• As dictionaries are not introduced previously in lesson (maybe a consequence of reducing the lesson) I suggest the following:

1. Moving this exercise at the end of th

This means bridging by sending a state event https://matrix.org/docs/spec/client_server/r0.6.0#m-room-pinned-events when the https://api.slack.com/events/pin_added or https://api.slack.com/events/pin_removed events come over Slack. You'll need to add a handler to SlackEventHandler

This challenge asks student to print an informative message if there are any records in gapminder for the year 2002. Two solutions are provided, one using any(gapminder$year == 2002) (note any() isn't introduced until later in that episode) and one much more complicated one involving counting the number of rows for the year 2002. It seems to me the only reasonable way to do this is with %in%

Admittedly, I'm not a pythonista, but I wonder whether there would be value in using bash versions of the three python scripts. For whatever reason, I'm running into problems with getting python installed correctly on my Mac. Once I got it pointed in the right direct, I ran into problems with installing numpy. It's quickly becoming a tutorial on installing python rather than make :)

I suspect the

Dear Community,

There is a typo in the section titled "The StringsAsFactors argument" after the second block of code that demonstrates the use of the str() function. Right after the code boxes is written "We can see that the $Color and $State columns are factors and $Speed is a numeric column", but the box shows that the $Color column is a vector of strings.

Regards,

Rodolfo

In episode 3 (https://datacarpentry.org/python-ecology-lesson/03-index-slice-subset/index.html, actually listed as 4. in https://datacarpentry.org/python-ecology-lesson/ ), the distinction between .iloc method for accessing entries by position and .loc to access them by identifier is made, but a third possibility is shown with surveys_df[0:3], which accesses the indices by position.

That

The way the launching section is written now it sounds a bit like you can't launch a jupyter lab without using the terminal on a mac or jupyter prompt for windows. Might be good to offer alt directions for how you can launch it with anaconda navigator instead. Learners often seem to prefer that option during the workshops I've taught.

In episode _episodes_rmd/12-time-series-raster.Rmd

There is a big chunk of code that can probably be made to look nicer via dplyr:

# Plot RGB data for Julian day 133
 RGB_133 <- stack("data/NEON-DS-Landsat-NDVI/HARV/2011/RGB/133_HARV_landRGB.tif")
 RGB_133_df <- raster::as.data.frame(RGB_133, xy = TRUE)
 quantiles = c(0.02, 0.98)
 r <- quantile(RGB_133_df$X133_HARV_landRGB.1, q

The discussion of data types and data structures in "Vectors and data types" could be clarified. Perhaps even defining these terms before using them would help. Also note that the first sentence of the section reads "A vector is the most common and basic data type in R, and is pretty much the workhorse of R." perhaps this should be changed to "basic data structure"

The Survey table has a field called quant that holds what type of reading was taken. The values in this column are rad, sal, and temp. There is no legend that explains what these mean on the page where the data is introduced (the selecting data chapter). Much later in the course it's mentioned that these mean 'radiation', 'salinity' and 'temperature', but I think it would also be helpful