data-visualisation

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We would like to use this new feature to update our current templates for bug reports and feature requests (located in .github/ISSUE_TEMPLATE/). The forms should ask for

In the Manipulating data episode it says

You may also have noticed that the output from these calls doesn’t run off the screen anymore. It’s one of the advantages of tbl_df over data frame.

This is the first place the term tbl_df is used in this lesson, and it is not clear that this means tibble.

In many node editor, the connections between nodes are mostly curves rather than straight lines
Visually, this can achieve better results. Can you consider using Bezier curves, etc. instead of the current straight line connection?

There are some examples:

• Github workflow preview:
  
• [

Hello, I would like to know if there is already any method developed to match the flight_ids obtained from So6 to ADS-B files. I’m trying to use the assign_id method for both files but it seems to be assigned differnent new flight-ids for SO6 and ADS-B same flights.

Thank you.

The lesson on knitr using Rmarkdown currently does not mention or utilize the visual markdown editor. Consider adding a short note about this tool at the end of the lesson.

Consider also mentioning the gear icon in the code chunk menu as a way to edit chunk options.

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

• I felt like nunique was arbitrarily (re)introduced when it was necessary. It wouldn't be top-of-mind for students solving problems.
• The lesson answers need to be adjacent to the exercises.
• I like the pre-introduction of masks and then circling back around to explain them.
• I feel like Part 4 needs to be broken up and integrated across other lessons: it felt thin on its own.
• Horizo

Currently the episode 15 reflections text expects to happen after we go over functions but due to lesson episode reordering this is no longer the case. We either need to come up with other reflections or move the break after functions (which might be too long)

https://swcarpentry.github.io/python-novice-gapminder/15-coffee/index.html

https://swcarpentry.github.io/python-novice-gapminder/16-w

Currently all of the metrics computed are independent of a target variable or column, but if lens.summarise took the name of a column as the target variable, the output of some metrics could be more interpretable even if the target variable is not used in any kind of predictive modelling.

A good example of this could be PCA (see #14), which could plot the different categories of the target va

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

Currently SAFI.json is stored here
and here

The first also contains SAFI_clean.csv.

Need to make sure (1) references to data directory in lesson are consistent and (2) there is only one data directory