次の表が1つあります（csvファイル）。
p1 p10 p16 p19 p25 p3 p5 p6 p8 p9
con1 567 0 3 0 18 17 8 4 6 7
con3 490 7 6 2 23 26 20 14 12 29
con4 737 1 4 1 6 4 1 4 8 5
con5 145 6 4 0 11 17 5 9 22 11
con10 68 0 0 34 4 0 0 0 0 0
con30 46 0 0 8 0 0 0 0 0 0
con2 72 0 0 8 0 1 0 0 0 0
2番目のテーブル（csvファイル）：
name superkingdom phylum class order family genus species
con1 Viruses Pox Alphaen Ano
con30 Viruses Her Allo Bat Ran
con4 Viruses Hud
con5 Viruses Mimi Cafe Caf
con10 Viruses Hud
con2 Viruses Pico Picorn Entero En
con3 Viruses Phyco Chloro
2番目のテーブルから最初のテーブル列（2：8）にコピーしたいです。すべての項目は、最初の列の同じ値に基づいています。
出力例
p1 p10 p16 p19 p25 p3 p5 p6 p8 p9 superkingdom phylum class order family genus species
con1 567 0 3 0 18 17 8 4 6 7 Viruses Pox Alphaen Ano
con3 490 7 6 2 23 26 20 14 12 29 Viruses Phyco Chloro
con4 737 1 4 1 6 4 1 4 8 5 Viruses Hud
con5 145 6 4 0 11 17 5 9 22 11 Viruses Mimi Cafe Caf
con10 68 0 0 34 4 0 0 0 0 0 Viruses Hud
con30 46 0 0 8 0 0 0 0 0 0 Viruses Her Allo Bat Ran
con2 72 0 0 8 0 1 0 0 0 0 Viruses Pico Picorn Entero En
ベストアンサー1
基本Rではmerge（パッケージbase）を使用します。
df1 <- read.csv(text="p1,p10,p16,p19,p25,p3,p5,p6,p8,p9
con1,567,0,3,0,18,17,8,4,6,7
con3,490,7,6,2,23,26,20,14,12,29
con4,737,1,4,1,6,4,1,4,8,5
con5,145,6,4,0,11,17,5,9,22,11
con10,68,0,0,34,4,0,0,0,0,0
con30,46,0,0,8,0,0,0,0,0,0
con2,72,0,0,8,0,1,0,0,0,0")
df2 <- read.csv(text="name,superkingdom,phylum,class,order,family,genus,species
con1,Viruses,,,,Pox,Alphaen,Ano
con30,Viruses,,,Her,Allo,Bat,Ran
con4,Viruses,,,,,,Hud
con5,Viruses,,,,Mimi,Cafe,Caf
con10,Viruses,,,,,,Hud
con2,Viruses,,,Pico,Picorn,Entero,En
con3,Viruses,,,,,Phyco,Chloro")
# by.x=0 joins df1 by rownames
merge(df1, df2, by.x=0, by.y="name")
# Row.names p1 p10 p16 p19 p25 p3 p5 p6 p8 p9 superkingdom phylum class order family genus species
# 1 con1 567 0 3 0 18 17 8 4 6 7 Viruses NA NA Pox Alphaen Ano
# 2 con10 68 0 0 34 4 0 0 0 0 0 Viruses NA NA Hud
# 3 con2 72 0 0 8 0 1 0 0 0 0 Viruses NA NA Pico Picorn Entero En
# 4 con3 490 7 6 2 23 26 20 14 12 29 Viruses NA NA Phyco Chloro
# 5 con30 46 0 0 8 0 0 0 0 0 0 Viruses NA NA Her Allo Bat Ran
# 6 con4 737 1 4 1 6 4 1 4 8 5 Viruses NA NA Hud
# 7 con5 145 6 4 0 11 17 5 9 22 11 Viruses NA NA Mimi Cafe Caf