knitr::opts_chunk$set(echo = TRUE) library(dovetail) library(reticulate) source(dvt_opts(), local = TRUE)
```{python cars} import pandas as pd slp = pd.read_csv('data/sleep.csv') slp
```{callout}
#' ## DID YOU KNOW
#'
#' Tip number `r format(sample(1e5, 1), big.mark = ",")`: Sleep is required for
#' a well-functioning body!
Here's a challenge!
#' ## Find good and bad sleep!
#'
#' Use logical indexing to find good and bad sleep patterns.
#'
#' Our current R environment looks like this:
#'
#' ```{python}
print("our files:")
dir()
#' ```
#'
#' We want to find these good and bad sleep patterns
#' ```{python chalng1, echo = FALSE}
good = slp[slp['extra'] > 0]
bad = slp[slp['extra'] <= 0]
print("good patterns")
good
print("bad patterns")
bad
#' ```
#'
#' @solution The Solution!
#'
#' Note that it uses a reference
#'
#' ```{python soln, ref.label = "chalng1"}
#' ```
There were r nrow(py$good) cases of good sleep and r nrow(py$bad) cases of
bad sleep.
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