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data-wrangling

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logannc
logannc commented Aug 2, 2019

This is basically a shameless spin off of https://stackoverflow.com/questions/57330300/how-to-reproduce-hypertools-clusters-identified-from-hypertools-plot

I am trying to take the results of using hypertools.plot(...), but my attempts to replicate them by using other parts of hypertools are yielding surprisingly different results.

I would like some guidance on this, but I also feel like ha

shawnbrown
shawnbrown commented Apr 17, 2019

Add how-to documentation for checking counts and cardinality.

Should demonstrate:

  • using len(data) to validate count of data elements
  • using collections.Counter(data) to validate counts per value

Should also mention that cardinality is a descriptive statistic that can be calculated with many other tools that a developer might use (df[0].applymap(bool).sum(), ``select('A').fil

baeolophus
baeolophus commented Jan 22, 2019

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

MichaelBAnderson
MichaelBAnderson commented Oct 28, 2019

Dear Carpentries,

As I was preparing for my demonstration, I ran into an issue with episode 5: Command Line Programs.

Previous to this episode everything was straight-forward but, at-least for me, this lesson required some initial clarification for setup and use:

1. The lesson instructs the students to create an R script with a notepad editor (Notepad) and save the file as <session

lachlandeer
lachlandeer commented Jul 30, 2018

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
tidycells
maneesha
maneesha commented Jan 21, 2020

The lesson notes:

For instance, we can add transparency (alpha) to avoid overplotting:

I didn't know what overplotting meant when I first read this lesson. It may be useful to note that this means that overlapping points become invisible, and that adding transparency starts to address this problem as overlapping dots become darker.

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