list2df(list.object, col1 = "X1", col2 = "X2")matrix2df(matrix.object, col1 = "var1")vect2df(vector.object, col1 = "X1", col2 = "X2", order = TRUE, rev = FALSE)list_df2df(list.df.object, col1 = "X1")list_vect2df(list.vector.object, col1 = "X1", col2 = "X2", col3 = "X3", order = TRUE, ...)counts2list(mat, nm = rownames(mat))vect2list(vector.object, use.names = TRUE, numbered.names = FALSE)df2matrix(data.frame.object, i = 1)matrix2long(matrix.object, col1 = "cols", col2 = "rows", col3 = "vals")
list of vectors..TRUE the dataframe will be ordered.TRUE and order = TRUE the dataframe will
be ordered in descending order.list_vect2df).TRUE and the vector is named, these
names will be transferred to the list names.TRUE padded numbers will be used
as list names. If FALSE the vector elements themselves will become
the list names.data.frame object.matrix.vect2df.list2df - Returns a dataframe with two columns.
matrix2df - Returns a dataframe.
vect2df - Returns a dataframe.
list_df2df - Returns a dataframe.
list_vect2df - Returns a dataframe.
counts2list - Returns a list of elements.
vect2list - Returns a list of named elements.
df2matrix - Returns a matrix.
matrix2long - Returns a long format dataframe.
list2df - Convert a named list of vectors to a dataframe.
matrix2df - Convert a matrix to a dataframe and convert the rownames
to the first column.
vect2df - Convert a named vector to a dataframe.
list_df2df - Convert a list of equal numbered/named columns to a
dataframe using the list names as the level two variable.
list_vect2df - Convert a list of named vectors to a hierarchical
dataframe.
counts2list - Convert a count matrix to a named list of elements.
vect2list - Convert a vector to a named list.
df2matrix - Convert a dataframe to a matrix and simultaneously
move a column (default is the first column) to the rownames of a
matrix.
matrix2long - Convert a matrix to a long format dataframe where column
names become column 1, row names, column 2 and the values become column 3.
lst1 <- list(x=c("foo", "bar"), y=1:5) list2df(lst1)X1 X2 1 foo x 2 bar x 3 1 y 4 2 y 5 3 y 6 4 y 7 5 ylst2 <- list(a=c("hello", "everybody"), b = mtcars[1:6, 1]) list2df(lst2, "col 1", "col 2")col 1 col 2 1 hello a 2 everybody a 3 21 b 4 21 b 5 22.8 b 6 21.4 b 7 18.7 b 8 18.1 bmatrix2df(mtcars)var1 mpg cyl disp hp drat wt qsec vs am gear carb 1 Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4 2 Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4 3 Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1 4 Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1 5 Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2 6 Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1 7 Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4 8 Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2 9 Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2 10 Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4 11 Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4 12 Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3 13 Merc 450SL 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3 14 Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3 15 Cadillac Fleetwood 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4 16 Lincoln Continental 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4 17 Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4 18 Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1 19 Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2 20 Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1 21 Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1 22 Dodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2 23 AMC Javelin 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2 24 Camaro Z28 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4 25 Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2 26 Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1 27 Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2 28 Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2 29 Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4 30 Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6 31 Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8 32 Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2matrix2df(cor(mtcars))var1 mpg cyl disp hp drat wt qsec vs am gear 1 mpg 1.0000000 -0.8521620 -0.8475514 -0.7761684 0.68117191 -0.8676594 0.41868403 0.6640389 0.59983243 0.4802848 2 cyl -0.8521620 1.0000000 0.9020329 0.8324475 -0.69993811 0.7824958 -0.59124207 -0.8108118 -0.52260705 -0.4926866 3 disp -0.8475514 0.9020329 1.0000000 0.7909486 -0.71021393 0.8879799 -0.43369788 -0.7104159 -0.59122704 -0.5555692 4 hp -0.7761684 0.8324475 0.7909486 1.0000000 -0.44875912 0.6587479 -0.70822339 -0.7230967 -0.24320426 -0.1257043 5 drat 0.6811719 -0.6999381 -0.7102139 -0.4487591 1.00000000 -0.7124406 0.09120476 0.4402785 0.71271113 0.6996101 6 wt -0.8676594 0.7824958 0.8879799 0.6587479 -0.71244065 1.0000000 -0.17471588 -0.5549157 -0.69249526 -0.5832870 7 qsec 0.4186840 -0.5912421 -0.4336979 -0.7082234 0.09120476 -0.1747159 1.00000000 0.7445354 -0.22986086 -0.2126822 8 vs 0.6640389 -0.8108118 -0.7104159 -0.7230967 0.44027846 -0.5549157 0.74453544 1.0000000 0.16834512 0.2060233 9 am 0.5998324 -0.5226070 -0.5912270 -0.2432043 0.71271113 -0.6924953 -0.22986086 0.1683451 1.00000000 0.7940588 10 gear 0.4802848 -0.4926866 -0.5555692 -0.1257043 0.69961013 -0.5832870 -0.21268223 0.2060233 0.79405876 1.0000000 11 carb -0.5509251 0.5269883 0.3949769 0.7498125 -0.09078980 0.4276059 -0.65624923 -0.5696071 0.05753435 0.2740728 carb 1 -0.55092507 2 0.52698829 3 0.39497686 4 0.74981247 5 -0.09078980 6 0.42760594 7 -0.65624923 8 -0.56960714 9 0.05753435 10 0.27407284 11 1.00000000matrix2df(matrix(1:9, ncol=3))var1 1 2 3 1 1 1 4 7 2 2 2 5 8 3 3 3 6 9vect2df(1:10)X1 X2 1 x01 1 2 x02 2 3 x03 3 4 x04 4 5 x05 5 6 x06 6 7 x07 7 8 x08 8 9 x09 9 10 x10 10vect2df(c(table(mtcars[, "gear"])))X1 X2 1 5 5 2 4 12 3 3 15list_df2df(list(mtcars, mtcars))X1 mpg cyl disp hp drat wt qsec vs am gear carb 1 L1 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4 2 L1 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4 3 L1 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1 4 L1 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1 5 L1 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2 6 L1 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1 7 L1 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4 8 L1 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2 9 L1 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2 10 L1 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4 11 L1 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4 12 L1 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3 13 L1 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3 14 L1 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3 15 L1 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4 16 L1 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4 17 L1 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4 18 L1 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1 19 L1 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2 20 L1 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1 21 L1 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1 22 L1 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2 23 L1 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2 24 L1 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4 25 L1 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2 26 L1 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1 27 L1 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2 28 L1 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2 29 L1 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4 30 L1 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6 31 L1 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8 32 L1 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2 33 L2 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4 34 L2 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4 35 L2 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1 36 L2 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1 37 L2 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2 38 L2 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1 39 L2 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4 40 L2 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2 41 L2 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2 42 L2 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4 43 L2 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4 44 L2 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3 45 L2 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3 46 L2 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3 47 L2 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4 48 L2 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4 49 L2 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4 50 L2 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1 51 L2 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2 52 L2 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1 53 L2 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1 54 L2 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2 55 L2 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2 56 L2 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4 57 L2 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2 58 L2 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1 59 L2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2 60 L2 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2 61 L2 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4 62 L2 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6 63 L2 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8 64 L2 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2L1 <- list(a=1:10, b=1:6, c=5:8) list_vect2df(L1)X1 X2 X3 1 a x01 1 2 a x02 2 3 a x03 3 4 a x04 4 5 a x05 5 6 a x06 6 7 a x07 7 8 a x08 8 9 a x09 9 10 a x10 10 11 b x1 1 12 b x2 2 13 b x3 3 14 b x4 4 15 b x5 5 16 b x6 6 17 c x5 5 18 c x6 6 19 c x7 7 20 c x8 8L2 <- list( months=setNames(1:12, month.abb), numbers=1:6, states=setNames(factor(state.name[1:4]), state.abb[1:4]) ) list_vect2df(L2)X1 X2 X3 1 months x 1 1 2 months x10 10 3 months x11 11 4 months x12 12 5 months x 2 2 6 months x 3 3 7 months x 4 4 8 months x 5 5 9 months x 6 6 10 months x 7 7 11 months x 8 8 12 months x 9 9 13 numbers x1 1 14 numbers x2 2 15 numbers x3 3 16 numbers x4 4 17 numbers x5 5 18 numbers x6 6 19 states x Alaska Alabama 20 states x Alabama Alaska 21 states x Arizona Arizona 22 states xArkansas Arkansasset.seed(10) cnts <- data.frame(month=month.name, matrix(sample(0:2, 36, TRUE), ncol=3)) counts2list(cnts[, -1], cnts[, 1])$January [1] "X1" "X3" $February [1] "X2" "X3" "X3" $March [1] "X1" "X2" "X3" "X3" $April [1] "X1" "X1" "X2" $May [1] "X3" "X3" $June [1] "X3" $July [1] "X2" "X3" $August [1] "X2" "X2" $September [1] "X1" "X2" "X2" $October [1] "X1" "X2" "X3" "X3" $November [1] "X1" "X2" "X2" "X3" $December [1] "X1" "X2" "X3" "X3"df2matrix(cnts)X1 X2 X3 January 1 0 1 February 0 1 2 March 1 1 2 April 2 1 0 May 0 0 2 June 0 0 1 July 0 1 1 August 0 2 0 September 1 2 0 October 1 1 2 November 1 2 1 December 1 1 2counts2list(df2matrix(cnts))$January [1] "X1" "X3" $February [1] "X2" "X3" "X3" $March [1] "X1" "X2" "X3" "X3" $April [1] "X1" "X1" "X2" $May [1] "X3" "X3" $June [1] "X3" $July [1] "X2" "X3" $August [1] "X2" "X2" $September [1] "X1" "X2" "X2" $October [1] "X1" "X2" "X3" "X3" $November [1] "X1" "X2" "X2" "X3" $December [1] "X1" "X2" "X3" "X3"counts2list(t(df2matrix(cnts)))$X1 [1] "January" "March" "April" "April" "September" "October" "November" "December" $X2 [1] "February" "March" "April" "July" "August" "August" "September" "September" "October" "November" [11] "November" "December" $X3 [1] "January" "February" "February" "March" "March" "May" "May" "June" "July" "October" "October" [12] "November" "December" "December"mat <- matrix(1:9, ncol=3) matrix2long(mat)cols rows vals 1 1 1 1 2 1 2 2 3 1 3 3 4 2 1 4 5 2 2 5 6 2 3 6 7 3 1 7 8 3 2 8 9 3 3 9matrix2long(mtcars)cols rows vals 1 mpg Mazda RX4 21.000 2 mpg Mazda RX4 Wag 21.000 3 mpg Datsun 710 22.800 4 mpg Hornet 4 Drive 21.400 5 mpg Hornet Sportabout 18.700 6 mpg Valiant 18.100 7 mpg Duster 360 14.300 8 mpg Merc 240D 24.400 9 mpg Merc 230 22.800 10 mpg Merc 280 19.200 11 mpg Merc 280C 17.800 12 mpg Merc 450SE 16.400 13 mpg Merc 450SL 17.300 14 mpg Merc 450SLC 15.200 15 mpg Cadillac Fleetwood 10.400 16 mpg Lincoln Continental 10.400 17 mpg Chrysler Imperial 14.700 18 mpg Fiat 128 32.400 19 mpg Honda Civic 30.400 20 mpg Toyota Corolla 33.900 21 mpg Toyota Corona 21.500 22 mpg Dodge Challenger 15.500 23 mpg AMC Javelin 15.200 24 mpg Camaro Z28 13.300 25 mpg Pontiac Firebird 19.200 26 mpg Fiat X1-9 27.300 27 mpg Porsche 914-2 26.000 28 mpg Lotus Europa 30.400 29 mpg Ford Pantera L 15.800 30 mpg Ferrari Dino 19.700 31 mpg Maserati Bora 15.000 32 mpg Volvo 142E 21.400 33 cyl Mazda RX4 6.000 34 cyl Mazda RX4 Wag 6.000 35 cyl Datsun 710 4.000 36 cyl Hornet 4 Drive 6.000 37 cyl Hornet Sportabout 8.000 38 cyl Valiant 6.000 39 cyl Duster 360 8.000 40 cyl Merc 240D 4.000 41 cyl Merc 230 4.000 42 cyl Merc 280 6.000 43 cyl Merc 280C 6.000 44 cyl Merc 450SE 8.000 45 cyl Merc 450SL 8.000 46 cyl Merc 450SLC 8.000 47 cyl Cadillac Fleetwood 8.000 48 cyl Lincoln Continental 8.000 49 cyl Chrysler Imperial 8.000 50 cyl Fiat 128 4.000 51 cyl Honda Civic 4.000 52 cyl Toyota Corolla 4.000 53 cyl Toyota Corona 4.000 54 cyl Dodge Challenger 8.000 55 cyl AMC Javelin 8.000 56 cyl Camaro Z28 8.000 57 cyl Pontiac Firebird 8.000 58 cyl Fiat X1-9 4.000 59 cyl Porsche 914-2 4.000 60 cyl Lotus Europa 4.000 61 cyl Ford Pantera L 8.000 62 cyl Ferrari Dino 6.000 63 cyl Maserati Bora 8.000 64 cyl Volvo 142E 4.000 65 disp Mazda RX4 160.000 66 disp Mazda RX4 Wag 160.000 67 disp Datsun 710 108.000 68 disp Hornet 4 Drive 258.000 69 disp Hornet Sportabout 360.000 70 disp Valiant 225.000 71 disp Duster 360 360.000 72 disp Merc 240D 146.700 73 disp Merc 230 140.800 74 disp Merc 280 167.600 75 disp Merc 280C 167.600 76 disp Merc 450SE 275.800 77 disp Merc 450SL 275.800 78 disp Merc 450SLC 275.800 79 disp Cadillac Fleetwood 472.000 80 disp Lincoln Continental 460.000 81 disp Chrysler Imperial 440.000 82 disp Fiat 128 78.700 83 disp Honda Civic 75.700 84 disp Toyota Corolla 71.100 85 disp Toyota Corona 120.100 86 disp Dodge Challenger 318.000 87 disp AMC Javelin 304.000 88 disp Camaro Z28 350.000 89 disp Pontiac Firebird 400.000 90 disp Fiat X1-9 79.000 91 disp Porsche 914-2 120.300 92 disp Lotus Europa 95.100 93 disp Ford Pantera L 351.000 94 disp Ferrari Dino 145.000 95 disp Maserati Bora 301.000 96 disp Volvo 142E 121.000 97 hp Mazda RX4 110.000 98 hp Mazda RX4 Wag 110.000 99 hp Datsun 710 93.000 100 hp Hornet 4 Drive 110.000 101 hp Hornet Sportabout 175.000 102 hp Valiant 105.000 103 hp Duster 360 245.000 104 hp Merc 240D 62.000 105 hp Merc 230 95.000 106 hp Merc 280 123.000 107 hp Merc 280C 123.000 108 hp Merc 450SE 180.000 109 hp Merc 450SL 180.000 110 hp Merc 450SLC 180.000 111 hp Cadillac Fleetwood 205.000 112 hp Lincoln Continental 215.000 113 hp Chrysler Imperial 230.000 114 hp Fiat 128 66.000 115 hp Honda Civic 52.000 116 hp Toyota Corolla 65.000 117 hp Toyota Corona 97.000 118 hp Dodge Challenger 150.000 119 hp AMC Javelin 150.000 120 hp Camaro Z28 245.000 121 hp Pontiac Firebird 175.000 122 hp Fiat X1-9 66.000 123 hp Porsche 914-2 91.000 124 hp Lotus Europa 113.000 125 hp Ford Pantera L 264.000 126 hp Ferrari Dino 175.000 127 hp Maserati Bora 335.000 128 hp Volvo 142E 109.000 129 drat Mazda RX4 3.900 130 drat Mazda RX4 Wag 3.900 131 drat Datsun 710 3.850 132 drat Hornet 4 Drive 3.080 133 drat Hornet Sportabout 3.150 134 drat Valiant 2.760 135 drat Duster 360 3.210 136 drat Merc 240D 3.690 137 drat Merc 230 3.920 138 drat Merc 280 3.920 139 drat Merc 280C 3.920 140 drat Merc 450SE 3.070 141 drat Merc 450SL 3.070 142 drat Merc 450SLC 3.070 143 drat Cadillac Fleetwood 2.930 144 drat Lincoln Continental 3.000 145 drat Chrysler Imperial 3.230 146 drat Fiat 128 4.080 147 drat Honda Civic 4.930 148 drat Toyota Corolla 4.220 149 drat Toyota Corona 3.700 150 drat Dodge Challenger 2.760 151 drat AMC Javelin 3.150 152 drat Camaro Z28 3.730 153 drat Pontiac Firebird 3.080 154 drat Fiat X1-9 4.080 155 drat Porsche 914-2 4.430 156 drat Lotus Europa 3.770 157 drat Ford Pantera L 4.220 158 drat Ferrari Dino 3.620 159 drat Maserati Bora 3.540 160 drat Volvo 142E 4.110 161 wt Mazda RX4 2.620 162 wt Mazda RX4 Wag 2.875 163 wt Datsun 710 2.320 164 wt Hornet 4 Drive 3.215 165 wt Hornet Sportabout 3.440 166 wt Valiant 3.460 167 wt Duster 360 3.570 168 wt Merc 240D 3.190 169 wt Merc 230 3.150 170 wt Merc 280 3.440 171 wt Merc 280C 3.440 172 wt Merc 450SE 4.070 173 wt Merc 450SL 3.730 174 wt Merc 450SLC 3.780 175 wt Cadillac Fleetwood 5.250 176 wt Lincoln Continental 5.424 177 wt Chrysler Imperial 5.345 178 wt Fiat 128 2.200 179 wt Honda Civic 1.615 180 wt Toyota Corolla 1.835 181 wt Toyota Corona 2.465 182 wt Dodge Challenger 3.520 183 wt AMC Javelin 3.435 184 wt Camaro Z28 3.840 185 wt Pontiac Firebird 3.845 186 wt Fiat X1-9 1.935 187 wt Porsche 914-2 2.140 188 wt Lotus Europa 1.513 189 wt Ford Pantera L 3.170 190 wt Ferrari Dino 2.770 191 wt Maserati Bora 3.570 192 wt Volvo 142E 2.780 193 qsec Mazda RX4 16.460 194 qsec Mazda RX4 Wag 17.020 195 qsec Datsun 710 18.610 196 qsec Hornet 4 Drive 19.440 197 qsec Hornet Sportabout 17.020 198 qsec Valiant 20.220 199 qsec Duster 360 15.840 200 qsec Merc 240D 20.000 201 qsec Merc 230 22.900 202 qsec Merc 280 18.300 203 qsec Merc 280C 18.900 204 qsec Merc 450SE 17.400 205 qsec Merc 450SL 17.600 206 qsec Merc 450SLC 18.000 207 qsec Cadillac Fleetwood 17.980 208 qsec Lincoln Continental 17.820 209 qsec Chrysler Imperial 17.420 210 qsec Fiat 128 19.470 211 qsec Honda Civic 18.520 212 qsec Toyota Corolla 19.900 213 qsec Toyota Corona 20.010 214 qsec Dodge Challenger 16.870 215 qsec AMC Javelin 17.300 216 qsec Camaro Z28 15.410 217 qsec Pontiac Firebird 17.050 218 qsec Fiat X1-9 18.900 219 qsec Porsche 914-2 16.700 220 qsec Lotus Europa 16.900 221 qsec Ford Pantera L 14.500 222 qsec Ferrari Dino 15.500 223 qsec Maserati Bora 14.600 224 qsec Volvo 142E 18.600 225 vs Mazda RX4 0.000 226 vs Mazda RX4 Wag 0.000 227 vs Datsun 710 1.000 228 vs Hornet 4 Drive 1.000 229 vs Hornet Sportabout 0.000 230 vs Valiant 1.000 231 vs Duster 360 0.000 232 vs Merc 240D 1.000 233 vs Merc 230 1.000 234 vs Merc 280 1.000 235 vs Merc 280C 1.000 236 vs Merc 450SE 0.000 237 vs Merc 450SL 0.000 238 vs Merc 450SLC 0.000 239 vs Cadillac Fleetwood 0.000 240 vs Lincoln Continental 0.000 241 vs Chrysler Imperial 0.000 242 vs Fiat 128 1.000 243 vs Honda Civic 1.000 244 vs Toyota Corolla 1.000 245 vs Toyota Corona 1.000 246 vs Dodge Challenger 0.000 247 vs AMC Javelin 0.000 248 vs Camaro Z28 0.000 249 vs Pontiac Firebird 0.000 250 vs Fiat X1-9 1.000 251 vs Porsche 914-2 0.000 252 vs Lotus Europa 1.000 253 vs Ford Pantera L 0.000 254 vs Ferrari Dino 0.000 255 vs Maserati Bora 0.000 256 vs Volvo 142E 1.000 257 am Mazda RX4 1.000 258 am Mazda RX4 Wag 1.000 259 am Datsun 710 1.000 260 am Hornet 4 Drive 0.000 261 am Hornet Sportabout 0.000 262 am Valiant 0.000 263 am Duster 360 0.000 264 am Merc 240D 0.000 265 am Merc 230 0.000 266 am Merc 280 0.000 267 am Merc 280C 0.000 268 am Merc 450SE 0.000 269 am Merc 450SL 0.000 270 am Merc 450SLC 0.000 271 am Cadillac Fleetwood 0.000 272 am Lincoln Continental 0.000 273 am Chrysler Imperial 0.000 274 am Fiat 128 1.000 275 am Honda Civic 1.000 276 am Toyota Corolla 1.000 277 am Toyota Corona 0.000 278 am Dodge Challenger 0.000 279 am AMC Javelin 0.000 280 am Camaro Z28 0.000 281 am Pontiac Firebird 0.000 282 am Fiat X1-9 1.000 283 am Porsche 914-2 1.000 284 am Lotus Europa 1.000 285 am Ford Pantera L 1.000 286 am Ferrari Dino 1.000 287 am Maserati Bora 1.000 288 am Volvo 142E 1.000 289 gear Mazda RX4 4.000 290 gear Mazda RX4 Wag 4.000 291 gear Datsun 710 4.000 292 gear Hornet 4 Drive 3.000 293 gear Hornet Sportabout 3.000 294 gear Valiant 3.000 295 gear Duster 360 3.000 296 gear Merc 240D 4.000 297 gear Merc 230 4.000 298 gear Merc 280 4.000 299 gear Merc 280C 4.000 300 gear Merc 450SE 3.000 301 gear Merc 450SL 3.000 302 gear Merc 450SLC 3.000 303 gear Cadillac Fleetwood 3.000 304 gear Lincoln Continental 3.000 305 gear Chrysler Imperial 3.000 306 gear Fiat 128 4.000 307 gear Honda Civic 4.000 308 gear Toyota Corolla 4.000 309 gear Toyota Corona 3.000 310 gear Dodge Challenger 3.000 311 gear AMC Javelin 3.000 312 gear Camaro Z28 3.000 313 gear Pontiac Firebird 3.000 314 gear Fiat X1-9 4.000 315 gear Porsche 914-2 5.000 316 gear Lotus Europa 5.000 317 gear Ford Pantera L 5.000 318 gear Ferrari Dino 5.000 319 gear Maserati Bora 5.000 320 gear Volvo 142E 4.000 321 carb Mazda RX4 4.000 322 carb Mazda RX4 Wag 4.000 323 carb Datsun 710 1.000 324 carb Hornet 4 Drive 1.000 325 carb Hornet Sportabout 2.000 326 carb Valiant 1.000 327 carb Duster 360 4.000 328 carb Merc 240D 2.000 329 carb Merc 230 2.000 330 carb Merc 280 4.000 331 carb Merc 280C 4.000 332 carb Merc 450SE 3.000 333 carb Merc 450SL 3.000 334 carb Merc 450SLC 3.000 335 carb Cadillac Fleetwood 4.000 336 carb Lincoln Continental 4.000 337 carb Chrysler Imperial 4.000 338 carb Fiat 128 1.000 339 carb Honda Civic 2.000 340 carb Toyota Corolla 1.000 341 carb Toyota Corona 1.000 342 carb Dodge Challenger 2.000 343 carb AMC Javelin 2.000 344 carb Camaro Z28 4.000 345 carb Pontiac Firebird 2.000 346 carb Fiat X1-9 1.000 347 carb Porsche 914-2 2.000 348 carb Lotus Europa 2.000 349 carb Ford Pantera L 4.000 350 carb Ferrari Dino 6.000 351 carb Maserati Bora 8.000 352 carb Volvo 142E 2.000## <strong>Not run</strong>: # library(qdap) # term <- c("the ", "she", " wh") # (out <- with(raj.act.1, termco(dialogue, person, term))) # x <- counts(out) # # counts2list(x[, -c(1:2)], x[, 1]) # ## <strong>End(Not run)</strong> vect2list(LETTERS[1:10])$A [1] "A" $B [1] "B" $C [1] "C" $D [1] "D" $E [1] "E" $F [1] "F" $G [1] "G" $H [1] "H" $I [1] "I" $J [1] "J"vect2list(LETTERS[1:10], numbered.names = TRUE)$`01` [1] "A" $`02` [1] "B" $`03` [1] "C" $`04` [1] "D" $`05` [1] "E" $`06` [1] "F" $`07` [1] "G" $`08` [1] "H" $`09` [1] "I" $`10` [1] "J"x <- setNames(LETTERS[1:4], paste0("Element_", 1:4)) vect2list(x)$Element_1 [1] "A" $Element_2 [1] "B" $Element_3 [1] "C" $Element_4 [1] "D"vect2list(x, FALSE)$A [1] "A" $B [1] "B" $C [1] "C" $D [1] "D"vect2list(x, FALSE, TRUE)$`1` [1] "A" $`2` [1] "B" $`3` [1] "C" $`4` [1] "D"
mtabulate