Start with R for data science (book by Hadley, available online for free)
Tidy verse assumes tidy data. If you are not working with tidy data, it is unlikely to be a big help. Most data can probably be thought of as tidy.
Remember that any and every operation on a data frame returns a data frame, so unlike chaining in Pandas, you never have to worry if a method you want to use belongs to a series or a data frame, or if your method is returning a series or a data frame.
Select() selects columns, filter() selects rows. This never changes unlike the [] which means different thing depending on if it is used on a data frame (which you are not guaranteed to be served after calling a method on data frame in pandas!) on a series or using the .loc or .iloc methods.
There is no index, instead you just filter on rows.
Pandas comes with a ton of build in utilities which the tidyverse doesn’t, mostly because R is already full of functions you can easily apply across columns.
But particularly pandas date handling functions are really cool
Tidy verse assumes tidy data. If you are not working with tidy data, it is unlikely to be a big help. Most data can probably be thought of as tidy.
Remember that any and every operation on a data frame returns a data frame, so unlike chaining in Pandas, you never have to worry if a method you want to use belongs to a series or a data frame, or if your method is returning a series or a data frame.
Select() selects columns, filter() selects rows. This never changes unlike the [] which means different thing depending on if it is used on a data frame (which you are not guaranteed to be served after calling a method on data frame in pandas!) on a series or using the .loc or .iloc methods.
There is no index, instead you just filter on rows.
Pandas comes with a ton of build in utilities which the tidyverse doesn’t, mostly because R is already full of functions you can easily apply across columns.
But particularly pandas date handling functions are really cool