One table:
arrange(df, x) -> df[order(x), , drop = FALSE]distinct(df, x) -> df[!duplicated(x), , drop = FALSE]; unique()filter(df, x) -> df[x & !is.na(x), , drop = FALSE]; subset()mutate(df, z = x + y) -> df$z <- df$x + df$y; transform()pull(df, x) -> df$xrename(df, y = x) -> ?select(df, x, y) -> df[c("x", "y")], subset()select(df, starts_with("x") -> df[grepl(names(df), "^x")], etcsummarise(df, mean(x)) -> mean(df$x)slice(df, c(1, 2, 5)) -> df[c(1, 2, 5), , drop = FALSE]Two table:
inner_join(df1, df2) -> merge(df1, df2) (but mention row order)left_join(df1, df2) -> merge(df1, df2, all.x = TRUE)right_join(df1, df2) -> merge(df1, df2, all.y = TRUE)full_join(df1, df2) -> merge(df1, df2, all = TRUE)semi_join(df1, df2) -> df1[df1$x %in% df2$x, , drop = FALSE]anti_join(df1, df2) -> df1[!df1$x %in% df2$x, , drop = FALSE]Probably worth a systematic comparison only ungrouped data frames. But might be worth spelling showing a couple of special cases (e.g. ave() for grouped mutate) and then illustrating the general split(), lapply(), do.call(rbind) pattern.
EmilHvitfeldt and XiangyunHuang
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