skim(iris)
# Use tidyselect
skim(iris, Species)
skim(iris, starts_with("Sepal"))
skim(iris, where(is.numeric))
# Skim also works groupwise
iris %>%
dplyr::group_by(Species) %>%
skim()
# Which five numeric columns have the greatest mean value?
# Look in the `numeric.mean` column.
iris %>%
skim() %>%
dplyr::select(numeric.mean) %>%
dplyr::top_n(5)
# Which of my columns have missing values? Use the base skimmer n_missing.
iris %>%
skim() %>%
dplyr::filter(n_missing > 0)
# Use skim_tee to view the skim results and
# continue using the original data.
chickwts %>%
skim_tee() %>%
dplyr::filter(feed == "sunflower")
# Produce a summary without spark graphs
iris %>%
skim_without_charts()
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