data-visualisation

In reference to a previous topic on changing tile providers from the community support discourse, I noticed that the attribution in the bottom right hand corner is not removed when changing the TileRender which leads to the attributions stacking on top of each other.

Screensho

Hi all,

This is nitpicking given that so much awesome work has been done in this repository, but here are some notes on grammar, as well as some suggestions on style:

In Manipulating, analyzing and exporting data with tidyverse:

Grammar

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.

In Section 8, Creating Publication-Quality Graphics with ggplot2, the two charts after the alpha argument is introduced should also have the alpha value set on them.

I.e. after this example

ggplot(data = gapminder, mapping = aes(x = gdpPercap, y = lifeExp)) +
  geom_point(alpha = 0.5) + scale_x_log10()

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

I have helped in a few courses run based on the Gapminder material, and in common with Issue #528, getting to the correct directory and accessing the files is a common source of grief. My suggestion would be:

1. put in material in Episode 1 covering working directories in Python, and
2. remove the

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