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1 inner join #> data1 id mid 1 23 43 2 4 56 3 78 29 4 54 99 > data2 id final 1 4 77 2 23 2 3 54 19 4 70 31 > dplyr::inner_join(data1, data2, by="id") id mid final 1 23 43 2 2 4 56 77 3 54 99 19 [edit]
3 蠍磯蓋 ##install.packages("dplyr") library("dplyr") df <- tbl_df(iris) df class(df) # filter(df, Species == "setosa", Sepal.Length >= 4) filter(df, Species == "setosa" | Species == "versicolor") # arrange(df, Sepal.Length, desc(Sepal.Width)) #轟 貉朱 譟壱 select(df, Sepal.Length, Species) select(df, -Species) select(df, Sepal.Width:Petal.Width) select(df, -(Sepal.Width:Petal.Width)) #伎螳 mutate(df, compute = Sepal.Length * Sepal.Width, total.Sepal.Length = sum(Sepal.Length)) transform(df, compute = Sepal.Length * Sepal.Width, total.Sepal.Length = sum(Sepal.Length)) #讌螻 summarise(df, total=sum(Sepal.Length)) summarise(group_by(df, Species), total=sum(Sepal.Length)) #chain 蠍磯 group_by(df, Species) %>% filter(Sepal.Length >= 5) %>% summarise(total=sum(Sepal.Length))
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伎 襷 螳語. 螳伎 願 覲企 企 螳 蟇磯Μ螳 螳讌蟾. 螳 蟇磯Μ螳 螳 谿語 蟆 讀螳 螳伎 . 螳 蠏手碓襴螻 手 誤 磯Μ 覲旧 螻煙朱 蟷伎. (豈覺) |