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

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

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

Problem

Currently, both "Data frame" and "Dataframe" coexist in the text. This is particularly noticeable in the schedule, where lesson 5 uses "Data frame" and sections 13, 14 use "Dataframe".

Due to the importance of Data frames in R, I suggest being consistent with its naming.

Solution

According to the [R documentation](ht

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

I'm a member of The Carpentries staff and I'm submitting this issue on behalf of another member of the community. In most cases I won't be able to follow up or provide more details other that what I'm providing below.

Hi,

As part of the checkout process for carpentries, it is encouraged that we provide feedback to one of the modules. Going through the Python Novice Mindgap section on li

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

Have you taught this lesson? One way you can help us improve it is by filing issues here about typos you've discovered, but you can also help us fill out the instructor notes!

• What did you find difficult?
• Where was it too fast?
• What exercises could be improved?
• What did learners struggle with?
• What did you emphasize? W

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