apply, sapply, lapply ํจ์์ ์ฌ์ฉ๋ฒ์ ์ดํดํ๊ณ ์ ์ฉํ์ฌ ๋ณด์.
In:
library(dplyr)
df_iris = iris
df_iris_num = iris %>%
select(-Species)
str(df_iris_num)
Out:
'data.frame': 150 obs. of 4 variables:
$ Sepal.Length: num 5.1 4.9 4.7 4.6 5 5.4 4.6 5 4.4 4.9 ...
$ Sepal.Width : num 3.5 3 3.2 3.1 3.6 3.9 3.4 3.4 2.9 3.1 ...
$ Petal.Length: num 1.4 1.4 1.3 1.5 1.4 1.7 1.4 1.5 1.4 1.5 ...
$ Petal.Width : num 0.2 0.2 0.2 0.2 0.2 0.4 0.3 0.2 0.2 0.1 ...
โท Species ์ด์ ์ ์ธํ Iris ๋ฐ์ดํฐ๋ฅผ ์คํ ๋ฐ์ดํฐ๋ก ์ฌ์ฉํ ๊ฒ์ด๋ค.
โก apply ํจ์
In:
apply(X = df_iris_num, MARGIN = 1, FUN = mean)
apply(X = df_iris_num, MARGIN = 2, FUN = mean)
Out:
[1] 2.550 2.375 2.350 2.350 2.550 2.850 2.425 2.525 2.225 2.400 2.700 2.500 2.325 2.125 2.800 3.000 2.750 2.575 2.875 2.675 2.675 2.675 2.350
[24] 2.650 2.575 2.450 2.600 2.600 2.550 2.425 2.425 2.675 2.725 2.825 2.425 2.400 2.625 2.500 2.225 2.550 2.525 2.100 2.275 2.675 2.800 2.375
[47] 2.675 2.350 2.675 2.475 4.075 3.900 4.100 3.275 3.850 3.575 3.975 2.900 3.850 3.300 2.875 3.650 3.300 3.775 3.350 3.900 3.650 3.400 3.600
[70] 3.275 3.925 3.550 3.800 3.700 3.725 3.850 3.950 4.100 3.725 3.200 3.200 3.150 3.400 3.850 3.600 3.875 4.000 3.575 3.500 3.325 3.425 3.775
[93] 3.400 2.900 3.450 3.525 3.525 3.675 2.925 3.475 4.525 3.875 4.525 4.150 4.375 4.825 3.400 4.575 4.200 4.850 4.200 4.075 4.350 3.800 4.025
[116] 4.300 4.200 5.100 4.875 3.675 4.525 3.825 4.800 3.925 4.450 4.550 3.900 3.950 4.225 4.400 4.550 5.025 4.250 3.925 3.925 4.775 4.425 4.200
[139] 3.900 4.375 4.450 4.350 3.875 4.550 4.550 4.300 3.925 4.175 4.325 3.950
Sepal.Length Sepal.Width Petal.Length Petal.Width
5.843333 3.057333 3.758000 1.199333
โท ์ฒซ ๋ฒ์งธ ์ถ๋ ฅ ๊ฒฐ๊ณผ๋ ํ์ ๊ธฐ์ค์ผ๋ก FUN ์ธ์์ ํ ๋น๋ mean ํจ์๋ฅผ ์ ์ฉํ์ฌ ๊ณ์ฐํ ๊ฒฐ๊ณผ์ด๋ค. apply ํจ์์ MARGIN ์ธ์๋ ์ฐ์ฐ์ ๋ฐฉํฅ์ ๊ฒฐ์ ํ๋ค. 1์ธ ๊ฒฝ์ฐ, ํ์ ๊ธฐ์ค์ผ๋ก, 2์ธ ๊ฒฝ์ฐ, ์ด์ ๊ธฐ์ค์ผ๋ก ์ฐ์ฐ์ ์ํํ๊ฒ ๋๋ค.
In:
apply(X = df_iris_num, MARGIN = 1, FUN = function(x) {x*2+1})
apply(X = df_iris_num, MARGIN = 2, FUN = function(x) {x*2+1})
Out:
[,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [,13] [,14] [,15] [,16] [,17] [,18] [,19] [,20] [,21] [,22] [,23]
Sepal.Length 11.2 10.8 10.4 10.2 11.0 11.8 10.2 11.0 9.8 10.8 11.8 10.6 10.6 9.6 12.6 12.4 11.8 11.2 12.4 11.2 11.8 11.2 10.2
Sepal.Width 8.0 7.0 7.4 7.2 8.2 8.8 7.8 7.8 6.8 7.2 8.4 7.8 7.0 7.0 9.0 9.8 8.8 8.0 8.6 8.6 7.8 8.4 8.2
Petal.Length 3.8 3.8 3.6 4.0 3.8 4.4 3.8 4.0 3.8 4.0 4.0 4.2 3.8 3.2 3.4 4.0 3.6 3.8 4.4 4.0 4.4 4.0 3.0
Petal.Width 1.4 1.4 1.4 1.4 1.4 1.8 1.6 1.4 1.4 1.2 1.4 1.4 1.2 1.2 1.4 1.8 1.8 1.6 1.6 1.6 1.4 1.8 1.4
[,24] [,25] [,26] [,27] [,28] [,29] [,30] [,31] [,32] [,33] [,34] [,35] [,36] [,37] [,38] [,39] [,40] [,41] [,42] [,43] [,44] [,45]
Sepal.Length 11.2 10.6 11.0 11.0 11.4 11.4 10.4 10.6 11.8 11.4 12.0 10.8 11.0 12.0 10.8 9.8 11.2 11.0 10.0 9.8 11.0 11.2
Sepal.Width 7.6 7.8 7.0 7.8 8.0 7.8 7.4 7.2 7.8 9.2 9.4 7.2 7.4 8.0 8.2 7.0 7.8 8.0 5.6 7.4 8.0 8.6
Petal.Length 4.4 4.8 4.2 4.2 4.0 3.8 4.2 4.2 4.0 4.0 3.8 4.0 3.4 3.6 3.8 3.6 4.0 3.6 3.6 3.6 4.2 4.8
Petal.Width 2.0 1.4 1.4 1.8 1.4 1.4 1.4 1.4 1.8 1.2 1.4 1.4 1.4 1.4 1.2 1.4 1.4 1.6 1.6 1.4 2.2 1.8
[,46] [,47] [,48] [,49] [,50] [,51] [,52] [,53] [,54] [,55] [,56] [,57] [,58] [,59] [,60] [,61] [,62] [,63] [,64] [,65] [,66] [,67]
Sepal.Length 10.6 11.2 10.2 11.6 11.0 15.0 13.8 14.8 12.0 14.0 12.4 13.6 10.8 14.2 11.4 11 12.8 13.0 13.2 12.2 14.4 12.2
Sepal.Width 7.0 8.6 7.4 8.4 7.6 7.4 7.4 7.2 5.6 6.6 6.6 7.6 5.8 6.8 6.4 5 7.0 5.4 6.8 6.8 7.2 7.0
Petal.Length 3.8 4.2 3.8 4.0 3.8 10.4 10.0 10.8 9.0 10.2 10.0 10.4 7.6 10.2 8.8 8 9.4 9.0 10.4 8.2 9.8 10.0
Petal.Width 1.6 1.4 1.4 1.4 1.4 3.8 4.0 4.0 3.6 4.0 3.6 4.2 3.0 3.6 3.8 3 4.0 3.0 3.8 3.6 3.8 4.0
[,68] [,69] [,70] [,71] [,72] [,73] [,74] [,75] [,76] [,77] [,78] [,79] [,80] [,81] [,82] [,83] [,84] [,85] [,86] [,87] [,88] [,89]
Sepal.Length 12.6 13.4 12.2 12.8 13.2 13.6 13.2 13.8 14.2 14.6 14.4 13.0 12.4 12.0 12.0 12.6 13.0 11.8 13.0 14.4 13.6 12.2
Sepal.Width 6.4 5.4 6.0 7.4 6.6 6.0 6.6 6.8 7.0 6.6 7.0 6.8 6.2 5.8 5.8 6.4 6.4 7.0 7.8 7.2 5.6 7.0
Petal.Length 9.2 10.0 8.8 10.6 9.0 10.8 10.4 9.6 9.8 10.6 11.0 10.0 8.0 8.6 8.4 8.8 11.2 10.0 10.0 10.4 9.8 9.2
Petal.Width 3.0 4.0 3.2 4.6 3.6 4.0 3.4 3.6 3.8 3.8 4.4 4.0 3.0 3.2 3.0 3.4 4.2 4.0 4.2 4.0 3.6 3.6
[,90] [,91] [,92] [,93] [,94] [,95] [,96] [,97] [,98] [,99] [,100] [,101] [,102] [,103] [,104] [,105] [,106] [,107] [,108] [,109]
Sepal.Length 12.0 12.0 13.2 12.6 11.0 12.2 12.4 12.4 13.4 11.2 12.4 13.6 12.6 15.2 13.6 14.0 16.2 10.8 15.6 14.4
Sepal.Width 6.0 6.2 7.0 6.2 5.6 6.4 7.0 6.8 6.8 6.0 6.6 7.6 6.4 7.0 6.8 7.0 7.0 6.0 6.8 6.0
Petal.Length 9.0 9.8 10.2 9.0 7.6 9.4 9.4 9.4 9.6 7.0 9.2 13.0 11.2 12.8 12.2 12.6 14.2 10.0 13.6 12.6
Petal.Width 3.6 3.4 3.8 3.4 3.0 3.6 3.4 3.6 3.6 3.2 3.6 6.0 4.8 5.2 4.6 5.4 5.2 4.4 4.6 4.6
[,110] [,111] [,112] [,113] [,114] [,115] [,116] [,117] [,118] [,119] [,120] [,121] [,122] [,123] [,124] [,125] [,126] [,127]
Sepal.Length 15.4 14.0 13.8 14.6 12.4 12.6 13.8 14.0 16.4 16.4 13.0 14.8 12.2 16.4 13.6 14.4 15.4 13.4
Sepal.Width 8.2 7.4 6.4 7.0 6.0 6.6 7.4 7.0 8.6 6.2 5.4 7.4 6.6 6.6 6.4 7.6 7.4 6.6
Petal.Length 13.2 11.2 11.6 12.0 11.0 11.2 11.6 12.0 14.4 14.8 11.0 12.4 10.8 14.4 10.8 12.4 13.0 10.6
Petal.Width 6.0 5.0 4.8 5.2 5.0 5.8 5.6 4.6 5.4 5.6 4.0 5.6 5.0 5.0 4.6 5.2 4.6 4.6
[,128] [,129] [,130] [,131] [,132] [,133] [,134] [,135] [,136] [,137] [,138] [,139] [,140] [,141] [,142] [,143] [,144] [,145]
Sepal.Length 13.2 13.8 15.4 15.8 16.8 13.8 13.6 13.2 16.4 13.6 13.8 13.0 14.8 14.4 14.8 12.6 14.6 14.4
Sepal.Width 7.0 6.6 7.0 6.6 8.6 6.6 6.6 6.2 7.0 7.8 7.2 7.0 7.2 7.2 7.2 6.4 7.4 7.6
Petal.Length 10.8 12.2 12.6 13.2 13.8 12.2 11.2 12.2 13.2 12.2 12.0 10.6 11.8 12.2 11.2 11.2 12.8 12.4
Petal.Width 4.6 5.2 4.2 4.8 5.0 5.4 4.0 3.8 5.6 5.8 4.6 4.6 5.2 5.8 5.6 4.8 5.6 6.0
[,146] [,147] [,148] [,149] [,150]
Sepal.Length 14.4 13.6 14.0 13.4 12.8
Sepal.Width 7.0 6.0 7.0 7.8 7.0
Petal.Length 11.4 11.0 11.4 11.8 11.2
Petal.Width 5.6 4.8 5.0 5.6 4.6
Sepal.Length Sepal.Width Petal.Length Petal.Width
[1,] 11.2 8.0 3.8 1.4
[2,] 10.8 7.0 3.8 1.4
[3,] 10.4 7.4 3.6 1.4
[4,] 10.2 7.2 4.0 1.4
[5,] 11.0 8.2 3.8 1.4
[6,] 11.8 8.8 4.4 1.8
[7,] 10.2 7.8 3.8 1.6
[8,] 11.0 7.8 4.0 1.4
[9,] 9.8 6.8 3.8 1.4
[10,] 10.8 7.2 4.0 1.2
[11,] 11.8 8.4 4.0 1.4
[12,] 10.6 7.8 4.2 1.4
[13,] 10.6 7.0 3.8 1.2
[14,] 9.6 7.0 3.2 1.2
[15,] 12.6 9.0 3.4 1.4
[16,] 12.4 9.8 4.0 1.8
[17,] 11.8 8.8 3.6 1.8
[18,] 11.2 8.0 3.8 1.6
[19,] 12.4 8.6 4.4 1.6
[20,] 11.2 8.6 4.0 1.6
[21,] 11.8 7.8 4.4 1.4
[22,] 11.2 8.4 4.0 1.8
[23,] 10.2 8.2 3.0 1.4
[24,] 11.2 7.6 4.4 2.0
[25,] 10.6 7.8 4.8 1.4
[26,] 11.0 7.0 4.2 1.4
[27,] 11.0 7.8 4.2 1.8
[28,] 11.4 8.0 4.0 1.4
[29,] 11.4 7.8 3.8 1.4
[30,] 10.4 7.4 4.2 1.4
[31,] 10.6 7.2 4.2 1.4
[32,] 11.8 7.8 4.0 1.8
[33,] 11.4 9.2 4.0 1.2
[34,] 12.0 9.4 3.8 1.4
[35,] 10.8 7.2 4.0 1.4
[36,] 11.0 7.4 3.4 1.4
[37,] 12.0 8.0 3.6 1.4
[38,] 10.8 8.2 3.8 1.2
[39,] 9.8 7.0 3.6 1.4
[40,] 11.2 7.8 4.0 1.4
[41,] 11.0 8.0 3.6 1.6
[42,] 10.0 5.6 3.6 1.6
[43,] 9.8 7.4 3.6 1.4
[44,] 11.0 8.0 4.2 2.2
[45,] 11.2 8.6 4.8 1.8
[46,] 10.6 7.0 3.8 1.6
[47,] 11.2 8.6 4.2 1.4
[48,] 10.2 7.4 3.8 1.4
[49,] 11.6 8.4 4.0 1.4
[50,] 11.0 7.6 3.8 1.4
[51,] 15.0 7.4 10.4 3.8
[52,] 13.8 7.4 10.0 4.0
[53,] 14.8 7.2 10.8 4.0
[54,] 12.0 5.6 9.0 3.6
[55,] 14.0 6.6 10.2 4.0
[56,] 12.4 6.6 10.0 3.6
[57,] 13.6 7.6 10.4 4.2
[58,] 10.8 5.8 7.6 3.0
[59,] 14.2 6.8 10.2 3.6
[60,] 11.4 6.4 8.8 3.8
[61,] 11.0 5.0 8.0 3.0
[62,] 12.8 7.0 9.4 4.0
[63,] 13.0 5.4 9.0 3.0
[64,] 13.2 6.8 10.4 3.8
[65,] 12.2 6.8 8.2 3.6
[66,] 14.4 7.2 9.8 3.8
[67,] 12.2 7.0 10.0 4.0
[68,] 12.6 6.4 9.2 3.0
[69,] 13.4 5.4 10.0 4.0
[70,] 12.2 6.0 8.8 3.2
[71,] 12.8 7.4 10.6 4.6
[72,] 13.2 6.6 9.0 3.6
[73,] 13.6 6.0 10.8 4.0
[74,] 13.2 6.6 10.4 3.4
[75,] 13.8 6.8 9.6 3.6
[76,] 14.2 7.0 9.8 3.8
[77,] 14.6 6.6 10.6 3.8
[78,] 14.4 7.0 11.0 4.4
[79,] 13.0 6.8 10.0 4.0
[80,] 12.4 6.2 8.0 3.0
[81,] 12.0 5.8 8.6 3.2
[82,] 12.0 5.8 8.4 3.0
[83,] 12.6 6.4 8.8 3.4
[84,] 13.0 6.4 11.2 4.2
[85,] 11.8 7.0 10.0 4.0
[86,] 13.0 7.8 10.0 4.2
[87,] 14.4 7.2 10.4 4.0
[88,] 13.6 5.6 9.8 3.6
[89,] 12.2 7.0 9.2 3.6
[90,] 12.0 6.0 9.0 3.6
[91,] 12.0 6.2 9.8 3.4
[92,] 13.2 7.0 10.2 3.8
[93,] 12.6 6.2 9.0 3.4
[94,] 11.0 5.6 7.6 3.0
[95,] 12.2 6.4 9.4 3.6
[96,] 12.4 7.0 9.4 3.4
[97,] 12.4 6.8 9.4 3.6
[98,] 13.4 6.8 9.6 3.6
[99,] 11.2 6.0 7.0 3.2
[100,] 12.4 6.6 9.2 3.6
[101,] 13.6 7.6 13.0 6.0
[102,] 12.6 6.4 11.2 4.8
[103,] 15.2 7.0 12.8 5.2
[104,] 13.6 6.8 12.2 4.6
[105,] 14.0 7.0 12.6 5.4
[106,] 16.2 7.0 14.2 5.2
[107,] 10.8 6.0 10.0 4.4
[108,] 15.6 6.8 13.6 4.6
[109,] 14.4 6.0 12.6 4.6
[110,] 15.4 8.2 13.2 6.0
[111,] 14.0 7.4 11.2 5.0
[112,] 13.8 6.4 11.6 4.8
[113,] 14.6 7.0 12.0 5.2
[114,] 12.4 6.0 11.0 5.0
[115,] 12.6 6.6 11.2 5.8
[116,] 13.8 7.4 11.6 5.6
[117,] 14.0 7.0 12.0 4.6
[118,] 16.4 8.6 14.4 5.4
[119,] 16.4 6.2 14.8 5.6
[120,] 13.0 5.4 11.0 4.0
[121,] 14.8 7.4 12.4 5.6
[122,] 12.2 6.6 10.8 5.0
[123,] 16.4 6.6 14.4 5.0
[124,] 13.6 6.4 10.8 4.6
[125,] 14.4 7.6 12.4 5.2
[126,] 15.4 7.4 13.0 4.6
[127,] 13.4 6.6 10.6 4.6
[128,] 13.2 7.0 10.8 4.6
[129,] 13.8 6.6 12.2 5.2
[130,] 15.4 7.0 12.6 4.2
[131,] 15.8 6.6 13.2 4.8
[132,] 16.8 8.6 13.8 5.0
[133,] 13.8 6.6 12.2 5.4
[134,] 13.6 6.6 11.2 4.0
[135,] 13.2 6.2 12.2 3.8
[136,] 16.4 7.0 13.2 5.6
[137,] 13.6 7.8 12.2 5.8
[138,] 13.8 7.2 12.0 4.6
[139,] 13.0 7.0 10.6 4.6
[140,] 14.8 7.2 11.8 5.2
[141,] 14.4 7.2 12.2 5.8
[142,] 14.8 7.2 11.2 5.6
[143,] 12.6 6.4 11.2 4.8
[144,] 14.6 7.4 12.8 5.6
[145,] 14.4 7.6 12.4 6.0
[146,] 14.4 7.0 11.4 5.6
[147,] 13.6 6.0 11.0 4.8
[148,] 14.0 7.0 11.4 5.0
[149,] 13.4 7.8 11.8 5.6
[150,] 12.8 7.0 11.2 4.6
โท FUN ํจ์์ function ํจ์๋ฅผ ํตํด ์ฐ์ฐ์ ๊ตฌ์ฒดํํ์ฌ ์ํํ ์ ์๋ค. ์์ ์ฐ์ฐ์์๋ ๊ฐ ์์์ ๋ํ์ฌ 2๋ฅผ ๊ณฑํ ํ, 1์ ๋ํ๋๋ก ๋ง๋ค์๊ธฐ ๋๋ฌธ์ ์ฒซ ๋ฒ์งธ์ ๋ ๋ฒ์งธ ์ถ๋ ฅ ๊ฒฐ๊ณผ๋ ํ๊ณผ ์ด์ ์์๋ง ๋ค๋ฅผ ๋ฟ, ์์๋ ๊ฐ์ ๊ฒ์ ์ ์ ์๋ค.
In:
apply(X = df_iris_num, MARGIN = 1, FUN = function(x) {median(x*2+1)})
apply(X = df_iris_num, MARGIN = 2, FUN = function(x) {median(x*2+1)})
Out:
[1] 5.9 5.4 5.5 5.6 6.0 6.6 5.8 5.9 5.3 5.6 6.2 6.0 5.4 5.1 6.2 6.9 6.2 5.9 6.5 6.3 6.1 6.2 5.6 6.0 6.3 5.6 6.0 6.0
[29] 5.8 5.8 5.7 5.9 6.6 6.6 5.6 5.4 5.8 6.0 5.3 5.9 5.8 4.6 5.5 6.1 6.7 5.4 6.4 5.6 6.2 5.7 8.9 8.7 9.0 7.3 8.4 8.3
[57] 9.0 6.7 8.5 7.6 6.5 8.2 7.2 8.6 7.5 8.5 8.5 7.8 7.7 7.4 9.0 7.8 8.4 8.5 8.2 8.4 8.6 9.0 8.4 7.1 7.2 7.1 7.6 8.8
[85] 8.5 8.9 8.8 7.7 8.1 7.5 8.0 8.6 7.6 6.6 7.9 8.2 8.1 8.2 6.5 7.9 10.3 8.8 9.9 9.5 9.8 10.6 8.0 10.2 9.3 10.7 9.3 9.0
[113] 9.5 8.5 8.9 9.5 9.5 11.5 10.5 8.2 9.9 8.7 10.5 8.6 10.0 10.2 8.6 8.9 9.4 9.8 9.9 11.2 9.4 8.9 9.2 10.1 10.0 9.6 8.8 9.5
[141] 9.7 9.2 8.8 10.1 10.0 9.2 8.5 9.2 9.8 9.1
Sepal.Length Sepal.Width Petal.Length Petal.Width
12.6 7.0 9.7 3.6
โท function ํจ์๋ฅผ ์ด์ฉํ์ฌ ๊ฐ ์์์ 2๋ฅผ ๊ณฑํ๊ณ , 1์ ๋ํ ํ์ ์ค๊ฐ๊ฐ์ ๊ตฌํ๋๋ก ํ์๋ค.
โถ apply ํจ์๋ ๋ฒกํฐ ๊ธฐ๋ฐ์ ๋ณ๋ ฌ ์ฐ์ฐ์ ์ํํ๊ธฐ ๋๋ฌธ์, for ๋๋ while์ ์ด์ฉํ ์ฐ์ฐ๋ณด๋ค ๋น ๋ฅด๋ค. ๋ํ ์ฝ๋๊ฐ ๊ฐ๊ฒฐํด์ง๋ ์ฅ์ ์ด ์๋ค.
โก sapply & lapply ํจ์
In:
sapply(X = df_iris_num, FUN = mean)
sapply(X = df_iris_num, FUN = mean, simplify = F)
lapply(X = df_iris_num, FUN = mean)
Out:
Sepal.Length Sepal.Width Petal.Length Petal.Width
5.843333 3.057333 3.758000 1.199333
$Sepal.Length
[1] 5.843333
$Sepal.Width
[1] 3.057333
$Petal.Length
[1] 3.758
$Petal.Width
[1] 1.199333
$Sepal.Length
[1] 5.843333
$Sepal.Width
[1] 3.057333
$Petal.Length
[1] 3.758
$Petal.Width
[1] 1.199333
โท sapply์ lapply๋ ์์ apply ํจ์์๋ ๋ค๋ฅด๊ฒ, ์ถ๋ ฅ ๊ฒฐ๊ณผ์ ๋ฐ์ดํฐ ํ์ ์ ๋ฒกํฐ ๋๋ ๋ฆฌ์คํธ๋ก ๋ง๋ค ์ ์๋ค. ์ฒซ ๋ฒ์งธ ๊ฒฐ๊ณผ๋ sapply๋ฅผ ์ฌ์ฉํ์ฌ ๋ฒกํฐ์ ํํ๋ก ์ถ๋ ฅ๋ ๊ฒ์ ์ ์ ์๋ค. ๋ ๋ฒ์งธ๋ simplify ์ธ์๋ฅผ F๋ก ์ฃผ์ด, ์ธ ๋ฒ์งธ ์ถ๋ ฅ ๊ฒฐ๊ณผ์ ๊ฐ์ ํํ๋ก ๋ํ๋ ๊ฒ์ ์ ์ ์๋ค. lapply๋ ์ถ๋ ฅ ๊ฒฐ๊ณผ๋ก ๋ฆฌ์คํธ๋ฅผ ๋ฐํํ๊ณ , sapply ํจ์์ simplify ์ธ์์ F๋ฅผ ์ค ๊ฒฝ์ฐ, lapply์ ๊ฐ์ ํํ๋ก ์ถ๋ ฅํ๊ฒ ๋๋ค.
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