data-wrangling

It would be good to be able to have the field name escaped with the SQL importer, and to be able to choose by which character.
This was discussed in Issue #1940

Proposed solution

Add a text field for an escape character in the SQL Exporter.

Alternatives considered

Data manipulation at the text level after exportation.

Additional context

![image](https://user-images.githubu

Starting at line 135 the lesson uses view() to open the data in RStudio's Data Viewer.
I suggest adding to the note on line 141 that tibble::view() and utils::View() have similar functionality but are part of different packages which is why the function does not seem to be case sensitive.

The Introduction to R and RStudio of R for Reproducible Scientific Analysis introduces arithmetic calculations as the simplest way to use R. I think the section [Using R as a Calculator](https://swcarpentry.github.io/r-novice-gapminder/01-rstudio-intro/index.html#usi

• 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

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 the second episode I believe that it would be more usefull to have the sections "Python is case-sensitive" and "Use meaningful variable names" right after "Use variables to store values". It would keep all the information and instructions for using and allocating variables in one place before going into printing and slicing.

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 Setup section (https://datacarpentry.org/r-socialsci/setup.html) probably needs to be updated to make references to Rtools in the Windows instructions. As noted in the CRAN repository "Starting with R 4.0.0 (released April 2020), R for Windows uses a toolchain bundle called rtools4." As I discovered through a recent install in a new machine not having gone through this process gave me problems