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歡迎來到醫(yī)科研，這里是白介素2的讀書筆記，跟我一起聊臨床與科研的故事, 生物醫(yī)學(xué)數(shù)據(jù)挖掘，R語言，TCGA、GEO數(shù)據(jù)挖掘。 
 高效的整合多個(gè)數(shù)據(jù)框 
在base包中可以通過cbind, rbind按行或列連接數(shù)據(jù)框,這個(gè)我們不多解釋了 dplyr中有更高級(jí)的函數(shù)bind_rows, bind_cols,能用于高效的連接多個(gè)數(shù)據(jù)框 其實(shí)這套高級(jí)函數(shù)是** do.call(cbind,dfs),do.call(rbind,dfs)**模式的植入， 用于多個(gè)數(shù)據(jù)框的整合 list(數(shù)據(jù)框1，數(shù)據(jù)框2) . id參數(shù)可以生成一個(gè)關(guān)聯(lián)起原來數(shù)據(jù)框的id,標(biāo)記數(shù)據(jù)來源
舉例說明其應(yīng)用 數(shù)據(jù)框輸入作為參數(shù) 重要的特點(diǎn)是能夠接受list作為輸入?yún)?shù) 另一個(gè)特點(diǎn)是可以同時(shí)連接起多個(gè)數(shù)據(jù)框，不僅限于兩個(gè)
library(dplyr)
##
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
##
## filter, lag
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union
one <- mtcars[1:4, ]
two <- mtcars[11:14, ]
head(one)
## mpg cyl disp hp drat wt qsec vs am gear carb
## Mazda RX4 21.0 6 160 110 3.90 2.620 16.46 0 1 4 4
## Mazda RX4 Wag 21.0 6 160 110 3.90 2.875 17.02 0 1 4 4
## Datsun 710 22.8 4 108 93 3.85 2.320 18.61 1 1 4 1
## Hornet 4 Drive 21.4 6 258 110 3.08 3.215 19.44 1 0 3 1
head(two)
## mpg cyl disp hp drat wt qsec vs am gear carb
## Merc 280C 17.8 6 167.6 123 3.92 3.44 18.9 1 0 4 4
## Merc 450SE 16.4 8 275.8 180 3.07 4.07 17.4 0 0 3 3
## Merc 450SL 17.3 8 275.8 180 3.07 3.73 17.6 0 0 3 3
## Merc 450SLC 15.2 8 275.8 180 3.07 3.78 18.0 0 0 3 3
按行連接，但rowname丟失了
(bind_rows(one,two))
## mpg cyl disp hp drat wt qsec vs am gear carb
## 1 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4
## 2 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4
## 3 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1
## 4 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1
## 5 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4
## 6 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3
## 7 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3
## 8 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3
以list形式，自動(dòng)拼接
bind_rows(list(one, two))
## mpg cyl disp hp drat wt qsec vs am gear carb
## 1 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4
## 2 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4
## 3 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1
## 4 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1
## 5 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4
## 6 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3
## 7 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3
## 8 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3
按cyl裂解數(shù)據(jù)框,剛好獲得了list形式，能夠很好的與bind_rows銜接起來
且bind_rows不僅僅連接的是兩個(gè)，還是多個(gè)
class(mtcars)
## [1] "data.frame"
split(mtcars, mtcars$cyl)
## $`4`
## mpg cyl disp hp drat wt qsec vs am gear carb
## Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1
## Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2
## Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2
## Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1
## Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2
## Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1
## Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1
## Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1
## Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2
## Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2
## Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2
##
## $`6`
## mpg cyl disp hp drat wt qsec vs am gear carb
## Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4
## Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4
## Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1
## Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1
## Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4
## Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4
## Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6
##
## $`8`
## mpg cyl disp hp drat wt qsec vs am gear carb
## Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2
## Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4
## Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3
## Merc 450SL 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3
## Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3
## Cadillac Fleetwood 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4
## Lincoln Continental 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4
## Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4
## Dodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2
## AMC Javelin 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2
## Camaro Z28 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4
## Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2
## Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4
## Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8
bind_rows(split(mtcars, mtcars$cyl))
## mpg cyl disp hp drat wt qsec vs am gear carb
## 1 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1
## 2 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2
## 3 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2
## 4 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1
## 5 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2
## 6 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1
## 7 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1
## 8 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1
## 9 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2
## 10 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2
## 11 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2
## 12 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4
## 13 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4
## 14 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1
## 15 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1
## 16 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4
## 17 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4
## 18 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6
## 19 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2
## 20 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4
## 21 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3
## 22 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3
## 23 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3
## 24 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4
## 25 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4
## 26 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4
## 27 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2
## 28 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2
## 29 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4
## 30 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2
## 31 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4
## 32 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8
可以連接多個(gè)listbind_rows(list(one, two), list(two, one))
## mpg cyl disp hp drat wt qsec vs am gear carb
## 1 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4
## 2 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4
## 3 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1
## 4 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1
## 5 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4
## 6 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3
## 7 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3
## 8 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3
## 9 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4
## 10 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3
## 11 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3
## 12 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3
## 13 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4
## 14 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4
## 15 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1
## 16 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1
還可以連接向量，同時(shí)可以混合數(shù)據(jù)框bind_rows(
c(a = 1, b = 2),
c(a = 3, b = 4)
)
## # A tibble: 2 x 2
## a b
## <dbl> <dbl>
## 1 1 2
## 2 3 4
可以創(chuàng)建一個(gè)關(guān)聯(lián)連接兩個(gè)數(shù)據(jù)框的id,用于標(biāo)記數(shù)據(jù)框的來源bind_rows(list(one, two), .id = "id")
## id mpg cyl disp hp drat wt qsec vs am gear carb
## 1 1 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4
## 2 1 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4
## 3 1 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1
## 4 1 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1
## 5 2 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4
## 6 2 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3
## 7 2 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3
## 8 2 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3
bind_rows(list(a = one, b = two), .id = "id")
## id mpg cyl disp hp drat wt qsec vs am gear carb
## 1 a 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4
## 2 a 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4
## 3 a 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1
## 4 a 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1
## 5 b 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4
## 6 b 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3
## 7 b 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3
## 8 b 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3
bind_rows("group 1" = one, "group 2" = two, .id = "groups")
## groups mpg cyl disp hp drat wt qsec vs am gear carb
## 1 group 1 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4
## 2 group 1 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4
## 3 group 1 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1
## 4 group 1 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1
## 5 group 2 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4
## 6 group 2 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3
## 7 group 2 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3
## 8 group 2 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3
按行結(jié)合時(shí)不需要列名相同bind_rows(data.frame(x = 1:3), data.frame(y = 1:4))
## x y
## 1 1 NA
## 2 2 NA
## 3 3 NA
## 4 NA 1
## 5 NA 2
## 6 NA 3
## 7 NA 4
但按列結(jié)合時(shí)需要行名相同# bind_cols(data.frame(x = 1), data.frame(y = 1:2)) 報(bào)錯(cuò)結(jié)果
bind_cols(one, two)
## mpg cyl disp hp drat wt qsec vs am gear carb mpg1 cyl1 disp1 hp1
## 1 21.0 6 160 110 3.90 2.620 16.46 0 1 4 4 17.8 6 167.6 123
## 2 21.0 6 160 110 3.90 2.875 17.02 0 1 4 4 16.4 8 275.8 180
## 3 22.8 4 108 93 3.85 2.320 18.61 1 1 4 1 17.3 8 275.8 180
## 4 21.4 6 258 110 3.08 3.215 19.44 1 0 3 1 15.2 8 275.8 180
## drat1 wt1 qsec1 vs1 am1 gear1 carb1
## 1 3.92 3.44 18.9 1 0 4 4
## 2 3.07 4.07 17.4 0 0 3 3
## 3 3.07 3.73 17.6 0 0 3 3
## 4 3.07 3.78 18.0 0 0 3 3
bind_cols(list(one, two))
## mpg cyl disp hp drat wt qsec vs am gear carb mpg1 cyl1 disp1 hp1
## 1 21.0 6 160 110 3.90 2.620 16.46 0 1 4 4 17.8 6 167.6 123
## 2 21.0 6 160 110 3.90 2.875 17.02 0 1 4 4 16.4 8 275.8 180
## 3 22.8 4 108 93 3.85 2.320 18.61 1 1 4 1 17.3 8 275.8 180
## 4 21.4 6 258 110 3.08 3.215 19.44 1 0 3 1 15.2 8 275.8 180
## drat1 wt1 qsec1 vs1 am1 gear1 carb1
## 1 3.92 3.44 18.9 1 0 4 4
## 2 3.07 4.07 17.4 0 0 3 3
## 3 3.07 3.73 17.6 0 0 3 3
## 4 3.07 3.78 18.0 0 0 3 3
bind_cols用法與bind_rows相同，這里不去贅述了插一個(gè)split函數(shù)的應(yīng)用講解 split函數(shù)用于裂解數(shù)據(jù)框，可以根據(jù)因子來裂解，裂解后得到的是一個(gè)list list就非常適合與lapply,sapply,tapply等結(jié)合起來使用了
舉個(gè)例子說明一下head(mtcars)
## mpg cyl disp hp drat wt qsec vs am gear carb
## Mazda RX4 21.0 6 160 110 3.90 2.620 16.46 0 1 4 4
## Mazda RX4 Wag 21.0 6 160 110 3.90 2.875 17.02 0 1 4 4
## Datsun 710 22.8 4 108 93 3.85 2.320 18.61 1 1 4 1
## Hornet 4 Drive 21.4 6 258 110 3.08 3.215 19.44 1 0 3 1
## Hornet Sportabout 18.7 8 360 175 3.15 3.440 17.02 0 0 3 2
## Valiant 18.1 6 225 105 2.76 3.460 20.22 1 0 3 1
df<-as.data.frame(mtcars)
按cyl分組裂解數(shù)據(jù)框with(df,split(mpg,cyl))
## $`4`
## [1] 22.8 24.4 22.8 32.4 30.4 33.9 21.5 27.3 26.0 30.4 21.4
##
## $`6`
## [1] 21.0 21.0 21.4 18.1 19.2 17.8 19.7
##
## $`8`
## [1] 18.7 14.3 16.4 17.3 15.2 10.4 10.4 14.7 15.5 15.2 13.3 19.2 15.8 15.0
裂解整個(gè)數(shù)據(jù)框split(mtcars,mtcars$cyl)
## $`4`
## mpg cyl disp hp drat wt qsec vs am gear carb
## Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1
## Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2
## Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2
## Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1
## Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2
## Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1
## Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1
## Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1
## Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2
## Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2
## Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2
##
## $`6`
## mpg cyl disp hp drat wt qsec vs am gear carb
## Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4
## Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4
## Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1
## Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1
## Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4
## Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4
## Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6
##
## $`8`
## mpg cyl disp hp drat wt qsec vs am gear carb
## Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2
## Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4
## Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3
## Merc 450SL 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3
## Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3
## Cadillac Fleetwood 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4
## Lincoln Continental 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4
## Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4
## Dodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2
## AMC Javelin 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2
## Camaro Z28 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4
## Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2
## Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4
## Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8
按多個(gè)變量構(gòu)成的分組裂解with(df,split(mpg,list(cyl,am)))
## $`4.0`
## [1] 24.4 22.8 21.5
##
## $`6.0`
## [1] 21.4 18.1 19.2 17.8
##
## $`8.0`
## [1] 18.7 14.3 16.4 17.3 15.2 10.4 10.4 14.7 15.5 15.2 13.3 19.2
##
## $`4.1`
## [1] 22.8 32.4 30.4 33.9 27.3 26.0 30.4 21.4
##
## $`6.1`
## [1] 21.0 21.0 19.7
##
## $`8.1`
## [1] 15.8 15.0
按列裂解矩陣ma <- cbind(x = 1:10, y = (-4:5)^2)
head(ma)
## x y
## [1,] 1 16
## [2,] 2 9
## [3,] 3 4
## [4,] 4 1
## [5,] 5 0
## [6,] 6 1
split(ma, col(ma))
## $`1`
## [1] 1 2 3 4 5 6 7 8 9 10
##
## $`2`
## [1] 16 9 4 1 0 1 4 9 16 25
split(1:10, 1:2)
## $`1`
## [1] 1 3 5 7 9
##
## $`2`
## [1] 2 4 6 8 10
do.call函數(shù) 執(zhí)行函數(shù)及傳遞給函數(shù)的參數(shù) 舉例說明比較清楚
在給定的參數(shù)下執(zhí)行paste函數(shù)tmp <- expand.grid(letters[1:2], 1:3, c("+", "-"))
tmp
## Var1 Var2 Var3
## 1 a 1 +
## 2 b 1 +
## 3 a 2 +
## 4 b 2 +
## 5 a 3 +
## 6 b 3 +
## 7 a 1 -
## 8 b 1 -
## 9 a 2 -
## 10 b 2 -
## 11 a 3 -
## 12 b 3 -
do.call("paste", c(tmp, sep = ""))
## [1] "a1+" "b1+" "a2+" "b2+" "a3+" "b3+" "a1-" "b1-" "a2-" "b2-" "a3-"
## [12] "b3-"
quote參數(shù)決定是否quote起來list(as.name("A"))
## [[1]]
## A
do.call(paste, list(as.name("A"), as.name("B")), quote = TRUE)
## [1] "A B"
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