df %>% group_by(cat) %>% mutate(id = row_number())

dplyr::coalesce() to replaces NAs with values from other vectors.

dplyr in R also lets you use a different syntax for querying SQL databases like Postgres, MySQL and SQLite, which is also in a more logical order

Now this can be simplified using the new {{}} syntax: summarise_groups <- function(dataframe, grouping_var, column_name){ dataframe %>% group_by({{grouping_var}}) %>% summarise({{column_name}} := mean({{column_name}}, na.rm = TRUE)) } Much easier and cleaner! You still have to use the := operator instead of = for the column name however. Also, from my understanding, if you want to modify the column names, for instance in this case return "mean_height" instead of height you have to keep using the enquo()–!! syntax.