17.2 Manipulación de datos
Una vez cargada una (o varias) bases de datos hay una series de operaciones que serán de interés para el tratamiento de datos:
- Operaciones con variables:
- crear
- recodificar (e.g. categorizar)
- …
- Operaciones con casos:
- ordenar
- filtrar
- …
A continuación se tratan algunas operaciones básicas.
17.2.1 Operaciones con variables
17.2.1.1 Creación (y eliminación) de variables
Consideremos de nuevo la
base de datos cars incluida en el paquete datasets:
data(cars)
# str(cars)
head(cars)## Price Mileage Cylinder Doors Cruise Sound Leather Buick Cadillac Chevy
## 1 22661.05 20105 6 4 1 0 0 1 0 0
## 2 21725.01 13457 6 2 1 1 0 0 0 1
## 3 29142.71 31655 4 2 1 1 1 0 0 0
## 4 30731.94 22479 4 2 1 0 0 0 0 0
## 5 33358.77 17590 4 2 1 1 1 0 0 0
## 6 30315.17 23635 4 2 1 0 0 0 0 0
## Pontiac Saab Saturn convertible coupe hatchback sedan wagon
## 1 0 0 0 0 0 0 1 0
## 2 0 0 0 0 1 0 0 0
## 3 0 1 0 1 0 0 0 0
## 4 0 1 0 1 0 0 0 0
## 5 0 1 0 1 0 0 0 0
## 6 0 1 0 1 0 0 0 0Utilizando el comando help(cars)
se obtiene que cars es un data.frame con 50 observaciones y dos
variables:
speed: Velocidad (millas por hora)dist: tiempo hasta detenerse (pies)
Recordemos que, para acceder a la variable speed se puede
hacer directamente con su nombre o bien utilizando notación
“matricial.”
cars$speed## NULLcars[, 1] # Equivalente## [1] 22661.05 21725.01 29142.71 30731.94 33358.77 30315.17 33381.82 30251.02
## [9] 30166.85 27060.14 26841.08 25790.51 25148.38 24173.53 24852.50 27825.95
## [17] 26698.08 28185.78 27241.44 30800.66 28416.46 26653.24 27610.86 27788.81
## [25] 29986.79 29197.79 29908.18 29481.53 26792.30 29321.08 37383.50 34393.00
## [33] 38324.81 35304.49 28777.96 33248.34 35580.33 30274.71 30122.43 32197.34
## [41] 27109.41 30353.59 27256.49 28291.76 24912.08 33183.33 28502.96 35033.22
## [49] 29844.20 30075.99 28054.98 28432.82 32746.13 31002.73 28829.03 31849.31
## [57] 31554.41 28678.08 29961.25 30575.25 29914.38 17325.27 16391.93 15505.29
## [65] 15297.84 16078.67 16551.22 14546.88 37288.94 46732.61 47065.21 41371.38
## [73] 39547.59 42773.03 36970.90 11203.15 13041.87 12207.87 11726.00 13007.98
## [81] 11149.62 10788.97 12570.14 14023.94 12706.91 11961.62 12810.91 12845.17
## [89] 12897.93 39713.67 39875.85 31186.74 39365.88 34297.31 38990.61 35261.44
## [97] 27370.96 31024.87 28502.31 32219.59 29595.79 29664.70 27548.63 12464.07
## [105] 13160.12 12832.46 12258.86 12465.51 12828.03 11903.10 17750.88 18145.13
## [113] 17772.97 18348.90 17394.02 20099.26 20698.08 19924.16 17808.20 19774.25
## [121] 20538.09 19344.17 18543.43 20512.09 23785.92 22358.88 21895.76 22926.09
## [129] 21058.14 19556.90 21183.12 21341.26 20986.02 21683.03 21799.17 23447.69
## [137] 23016.01 23547.24 38600.24 36154.30 36077.80 40335.74 35338.65 39307.01
## [145] 39801.55 32537.19 37510.25 32954.14 36245.16 40856.39 41378.05 37215.17
## [153] 17202.83 17675.84 15253.87 14703.14 15059.13 16792.68 17141.94 19204.81
## [161] 18529.34 20627.66 18158.08 19540.24 21875.10 21903.32 15788.10 17162.87
## [169] 16569.14 16391.17 15457.17 16283.96 18254.92 15802.65 14396.27 14869.28
## [177] 15568.97 16353.10 15730.05 15977.91 19646.72 20173.91 22100.39 18701.22
## [185] 19448.23 21281.88 21230.98 23578.16 22231.56 22189.12 22525.27 23449.31
## [193] 21403.76 21200.69 68566.19 70755.47 52001.99 60567.55 63913.12 69133.73
## [201] 66374.31 17322.08 16507.07 16425.17 15118.89 17978.36 13585.64 15731.13
## [209] 21908.37 20952.22 19425.85 19191.99 21956.34 19981.13 21646.12 32075.98
## [217] 27666.23 34819.30 33540.54 31969.07 35622.14 32737.08 23249.84 29246.24
## [225] 24801.62 22244.88 26775.03 25996.81 25299.97 31156.60 29114.54 30443.88
## [233] 24903.48 31084.94 33005.78 31153.01 23329.21 23274.48 27280.98 25959.12
## [241] 12630.78 12425.39 12549.89 12274.96 13446.21 12319.70 12234.89 12684.99
## [249] 12553.07 14185.02 13019.07 15747.80 13699.04 13530.07 19177.41 16357.99
## [257] 15797.20 17515.40 18040.14 16345.94 18800.09 10386.04 11017.17 12163.82
## [265] 11096.86 10777.05 12146.19 11472.02 16825.19 14914.20 17801.23 16543.98
## [273] 16744.03 16143.96 17891.63 12409.95 12314.59 12649.11 10491.08 11318.01
## [281] 11700.11 11215.02 14894.98 13167.70 13230.92 12573.90 11328.96 12383.40
## [289] 14678.11 13545.03 14304.74 14847.04 14593.85 13744.85 13688.95 12741.19
## [297] 19075.68 17839.80 21757.05 17789.35 18527.21 17294.18 18912.98 16472.90
## [305] 16300.47 15138.40 17158.92 15623.20 19164.61 16993.78 12230.10 12507.49
## [313] 13141.05 15053.93 13594.09 13464.80 14642.32 14255.75 13308.83 14145.88
## [321] 12944.94 12846.06 14266.91 13688.00 20017.97 20839.15 17586.93 20676.17
## [329] 22384.12 21745.03 11179.95 11031.13 10921.95 11343.05 11013.87 11070.06
## [337] 11391.21 18273.01 19471.97 17663.22 18009.85 16988.30 17553.75 18311.76
## [345] 11918.46 9919.05 10805.13 11302.90 10770.11 10872.01 12408.81 14401.91
## [353] 15163.17 14508.75 16116.84 14897.04 15084.82 14418.17 16403.25 17316.10
## [361] 17119.46 16860.87 16536.74 19446.88 16295.21 16860.09 18324.83 18974.92
## [369] 16027.29 16267.09 19581.23 18169.38 14881.96 13719.24 14077.97 14411.86
## [377] 13991.04 15033.15 14771.00 13518.24 13825.15 16256.24 13869.15 16916.87
## [385] 13811.16 11539.05 18566.07 18856.02 19682.04 20318.89 17844.73 18678.41
## [393] 17768.06 13961.11 15077.18 13034.07 15604.15 15979.01 14841.92 16379.85
## [401] 15709.05 15048.04 15295.02 16339.17 17542.04 16218.85 17314.10 21831.82
## [409] 23420.71 25098.63 23077.57 22435.20 25589.98 46747.67 51154.05 42677.60
## [417] 43892.47 44300.64 40619.07 44084.91 30792.15 29275.21 30392.75 30957.08
## [425] 30781.52 33417.97 28040.13 31181.72 33220.03 32501.25 32509.48 31059.18
## [433] 31132.21 35715.77 36633.63 38297.46 42741.52 40966.61 32038.34 37192.90
## [441] 34974.38 44205.88 42377.96 48310.33 41516.43 41671.58 41053.48 38208.50
## [449] 13072.84 11539.05 11699.03 11574.17 12243.06 11671.86 14061.12 15553.21
## [457] 11581.91 13161.94 14077.97 15047.00 12981.95 14220.01 13159.82 12678.85
## [465] 13994.91 14194.82 14696.03 14582.77 12495.97 20021.20 15554.28 16644.09
## [473] 18835.19 17154.58 15951.81 16508.59 16646.77 17218.69 17089.92 17463.05
## [481] 15680.86 16752.51 15664.62 18620.87 20452.67 20677.59 21383.07 19294.79
## [489] 18042.22 16216.98 15724.25 19822.12 15756.15 15979.01 16256.24 16041.69
## [497] 19567.26 15110.19 12036.22 12099.01 12284.29 14203.00 14116.92 12791.75
## [505] 14739.07 12965.22 12412.52 10563.07 12333.60 12119.09 11521.53 12105.98
## [513] 12162.14 13122.91 13494.29 13174.07 13216.91 12594.18 11679.92 20830.99
## [521] 21607.77 22004.93 19116.13 23197.44 20109.90 23102.02 26781.81 21536.74
## [529] 26190.27 26060.34 23159.54 26302.07 23348.02 16706.67 16927.78 14909.05
## [537] 16379.10 15622.12 17418.07 15821.95 23573.82 21020.84 23527.73 20047.95
## [545] 22470.36 20619.11 21525.34 27714.05 25948.96 22064.29 23151.55 20294.58
## [553] 22894.44 24809.04 10354.04 10287.98 10897.08 9789.04 10106.02 9506.05
## [561] 9220.83 8769.00 8870.95 10315.02 9654.06 9041.91 10971.10 8638.93
## [569] 29612.15 33586.91 37088.56 24405.07 27703.20 25618.28 23733.40 28204.60
## [577] 27284.75 30959.93 28328.27 26012.37 33984.43 38167.17 36338.75 25267.37
## [585] 32053.10 26789.83 30271.92 26955.04 32649.76 16106.83 15174.35 16033.93
## [593] 38824.87 44749.69 39691.73 13135.91 12045.92 39092.19 34739.21 35575.42
## [601] 13106.90 12487.05 11539.85 12469.53 27425.84 25527.01 12830.10 12209.56
## [609] 32422.76 12878.05 19027.86 17944.86 17645.75 21335.85 17968.84 19105.13
## [617] 21460.01 21273.06 20406.10 22230.03 20902.10 22625.07 35866.58 38445.90
## [625] 34685.66 42820.33 36332.89 41419.04 15595.88 17360.81 15594.81 17095.04
## [633] 14963.05 16997.69 22104.97 22736.83 22311.05 15086.90 15589.78 17803.28
## [641] 21300.02 19423.17 19956.76 25452.47 21765.07 21982.65 55639.09 65281.48
## [649] 57154.44 18490.98 16175.96 18173.98 21562.05 19641.74 21575.46 34355.00
## [657] 33287.41 31970.54 24896.60 24063.01 26337.83 25845.21 30661.26 30322.15
## [665] 15635.80 17685.20 14584.45 15503.51 13681.70 18910.80 12327.64 14619.08
## [673] 13310.06 9928.19 11137.05 11045.11 18950.91 16723.99 18957.89 11555.27
## [681] 18083.40 11080.52 13471.01 14198.09 19409.75 19528.10 15000.99 9954.05
## [689] 18800.96 15128.99 14997.88 15233.16 17458.22 10144.95 14397.93 12733.86
## [697] 13678.86 14275.13 14222.31 13762.90 20537.14 21233.91 18876.87 11115.01
## [705] 11247.86 11394.89 17115.12 18004.87 16803.12 17312.91 11169.92 16428.58
## [713] 14191.88 10921.95 14175.88 11615.02 16341.80 16713.98 17173.94 17986.22
## [721] 16456.97 13600.03 15395.01 15277.07 13032.87 14702.80 15639.04 15327.10
## [729] 20127.04 15846.01 13162.85 18063.00 19751.04 23493.08 21878.12 21698.01
## [737] 16336.91 14862.09 15230.00 49248.16 35129.34 44130.62 31431.13 48365.98
## [745] 43374.05 36210.12 39072.39 35651.68 30646.44 38795.38 35165.76 35895.50
## [753] 45061.95 28817.08 14072.14 13436.00 17162.48 10546.78 13540.04 12379.13
## [761] 14429.79 13830.25 12257.16 18727.51 15623.92 16507.07 11464.63 16805.06
## [769] 15832.52 16853.11 16516.96 15792.83 18548.98 17023.94 15967.25 12293.06
## [777] 11504.82 11873.53 14568.00 13998.13 13258.37 11413.53 15194.98 19689.74
## [785] 22460.53 19338.38 26831.19 25508.21 23406.69 17214.33 14853.20 14398.92
## [793] 20221.81 20382.15 22113.63 25097.47 22120.76 23345.33 11167.86 10813.34
## [801] 9720.98 9482.22 9563.79 9665.85Supongamos ahora que queremos transformar la variable original speed
(millas por hora) en una nueva variable velocidad (kilómetros por
hora) y añadir esta nueva variable al data.frame cars.
La transformación que permite pasar millas a kilómetros es
kilómetros=millas/0.62137 que en R se hace directamente con:
cars$speed/0.62137Finalmente, incluimos la nueva variable que llamaremos
velocidad en cars:
cars[, c("velocidad")] <- cars[, 1]/0.62137
head(cars)## Price Mileage Cylinder Doors Cruise Sound Leather Buick Cadillac Chevy
## 1 22661.05 20105 6 4 1 0 0 1 0 0
## 2 21725.01 13457 6 2 1 1 0 0 0 1
## 3 29142.71 31655 4 2 1 1 1 0 0 0
## 4 30731.94 22479 4 2 1 0 0 0 0 0
## 5 33358.77 17590 4 2 1 1 1 0 0 0
## 6 30315.17 23635 4 2 1 0 0 0 0 0
## Pontiac Saab Saturn convertible coupe hatchback sedan wagon velocidad
## 1 0 0 0 0 0 0 1 0 36469.49
## 2 0 0 0 0 1 0 0 0 34963.08
## 3 0 1 0 1 0 0 0 0 46900.74
## 4 0 1 0 1 0 0 0 0 49458.36
## 5 0 1 0 1 0 0 0 0 53685.84
## 6 0 1 0 1 0 0 0 0 48787.63También transformaremos la variable dist (en pies) en una nueva
variable distancia (en metros). Ahora la transformación deseada es
metros=pies/3.2808:
cars[, c("distancia")] <- cars[, 2]/3.2808
head(cars)## Price Mileage Cylinder Doors Cruise Sound Leather Buick Cadillac Chevy
## 1 22661.05 20105 6 4 1 0 0 1 0 0
## 2 21725.01 13457 6 2 1 1 0 0 0 1
## 3 29142.71 31655 4 2 1 1 1 0 0 0
## 4 30731.94 22479 4 2 1 0 0 0 0 0
## 5 33358.77 17590 4 2 1 1 1 0 0 0
## 6 30315.17 23635 4 2 1 0 0 0 0 0
## Pontiac Saab Saturn convertible coupe hatchback sedan wagon velocidad
## 1 0 0 0 0 0 0 1 0 36469.49
## 2 0 0 0 0 1 0 0 0 34963.08
## 3 0 1 0 1 0 0 0 0 46900.74
## 4 0 1 0 1 0 0 0 0 49458.36
## 5 0 1 0 1 0 0 0 0 53685.84
## 6 0 1 0 1 0 0 0 0 48787.63
## distancia
## 1 6128.079
## 2 4101.743
## 3 9648.561
## 4 6851.683
## 5 5361.497
## 6 7204.036Ahora, eliminaremos las variables originales speed y
dist, y guardaremos el data.frame resultante con el nombre coches.
En primer lugar, veamos varias formas de acceder a las variables de
interés:
cars[, c(3, 4)]
cars[, c("velocidad", "distancia")]
cars[, -c(1, 2)]Utilizando alguna de las opciones anteriores se obtiene el data.frame
deseado:
coches <- cars[, c("velocidad", "distancia")]
# head(coches)
str(coches)## 'data.frame': 804 obs. of 2 variables:
## $ velocidad: num 36469 34963 46901 49458 53686 ...
## $ distancia: num 6128 4102 9649 6852 5361 ...Finalmente los datos anteriores podrían ser guardados en un fichero exportable a Excel con el siguiente comando:
write.csv2(coches, file = "coches.csv")17.2.2 Operaciones con casos
17.2.2.1 Ordenación
Continuemos con el data.frame cars.
Se puede comprobar que los datos disponibles están ordenados por
los valores de speed. A continuación haremos la ordenación utilizando
los valores de dist. Para ello utilizaremos el conocido como vector de
índices de ordenación.
Este vector establece el orden en que tienen que ser elegidos los
elementos para obtener la ordenación deseada.
Veamos un ejemplo sencillo:
x <- c(2.5, 4.3, 1.2, 3.1, 5.0) # valores originales
ii <- order(x)
ii # vector de ordenación## [1] 3 1 4 2 5x[ii] # valores ordenados## [1] 1.2 2.5 3.1 4.3 5.0En el caso de vectores, el procedimiento anterior se podría hacer directamente con:
sort(x)Sin embargo, para ordenar data.frames será necesario la utilización del
vector de índices de ordenación. A continuación, los datos de cars
ordenados por dist:
ii <- order(cars$dist) # Vector de índices de ordenación
cars2 <- cars[ii, ] # Datos ordenados por dist
head(cars2)## Price Mileage Cylinder Doors Cruise Sound Leather Buick Cadillac Chevy
## 800 10813.34 266 4 4 1 0 1 0 0 1
## 196 70755.47 583 8 2 1 1 1 0 1 0
## 549 25948.96 636 6 4 1 0 0 0 0 1
## 444 48310.33 788 8 4 1 0 1 0 1 0
## 355 16116.84 865 4 4 1 1 1 0 0 1
## 630 17360.81 881 6 2 0 1 1 0 0 0
## Pontiac Saab Saturn convertible coupe hatchback sedan wagon velocidad
## 800 0 0 0 0 0 1 0 0 17402.42
## 196 0 0 0 1 0 0 0 0 113870.11
## 549 0 0 0 0 0 0 1 0 41760.88
## 444 0 0 0 0 0 0 1 0 77748.09
## 355 0 0 0 0 0 0 1 0 25937.59
## 630 1 0 0 0 1 0 0 0 27939.57
## distancia
## 800 81.07779
## 196 177.70056
## 549 193.85516
## 444 240.18532
## 355 263.65521
## 630 268.5320717.2.2.2 Filtrado
El filtrado de datos consiste en
elegir una submuestra que cumpla determinadas condiciones. Para ello se
puede utilizar la función subset()
(que además permite seleccionar variables).
A continuación se muestran un par de ejemplos:
subset(cars, cars$dist > 85) # datos con dis>85## Price Mileage Cylinder Doors Cruise Sound Leather Buick Cadillac Chevy
## 1 22661.05 20105 6 4 1 0 0 1 0 0
## 2 21725.01 13457 6 2 1 1 0 0 0 1
## 3 29142.71 31655 4 2 1 1 1 0 0 0
## 4 30731.94 22479 4 2 1 0 0 0 0 0
## 5 33358.77 17590 4 2 1 1 1 0 0 0
## 6 30315.17 23635 4 2 1 0 0 0 0 0
## 7 33381.82 17381 4 2 1 1 1 0 0 0
## 8 30251.02 27558 4 2 1 0 1 0 0 0
## 9 30166.85 25049 4 2 1 0 0 0 0 0
## 10 27060.14 17319 4 4 1 0 1 0 0 0
## 11 26841.08 10003 4 4 1 1 0 0 0 0
## 12 25790.51 21160 4 4 1 1 1 0 0 0
## 13 25148.38 22272 4 4 1 1 1 0 0 0
## 14 24173.53 27015 4 4 1 0 0 0 0 0
## 15 24852.50 22814 4 4 1 1 1 0 0 0
## 16 27825.95 10014 4 4 1 0 1 0 0 0
## 17 26698.08 23055 4 4 1 1 1 0 0 0
## 18 28185.78 19854 4 4 1 1 1 0 0 0
## 19 27241.44 23204 4 4 1 1 1 0 0 0
## 20 30800.66 8017 4 4 1 0 1 0 0 0
## 21 28416.46 14613 4 4 1 0 1 0 0 0
## 22 26653.24 22590 4 4 1 1 1 0 0 0
## 23 27610.86 22881 4 4 1 1 1 0 0 0
## 24 27788.81 26786 4 4 1 0 1 0 0 0
## 25 29986.79 18464 4 4 1 0 1 0 0 0
## 26 29197.79 20907 4 4 1 0 1 0 0 0
## 27 29908.18 19830 4 4 1 1 1 0 0 0
## 28 29481.53 21822 4 4 1 1 1 0 0 0
## 29 26792.30 25357 4 4 1 0 1 0 0 0
## 30 29321.08 21545 4 4 1 0 1 0 0 0
## 31 37383.50 16088 4 2 1 0 1 0 0 0
## 32 34393.00 24031 4 2 1 0 1 0 0 0
## 33 38324.81 12090 4 2 1 1 1 0 0 0
## 34 35304.49 21293 4 2 1 0 1 0 0 0
## 35 28777.96 48991 4 2 1 1 1 0 0 0
## 36 33248.34 27051 4 2 1 0 1 0 0 0
## 37 35580.33 21167 4 2 1 1 0 0 0 0
## 38 30274.71 10800 4 4 1 1 0 0 0 0
## 39 30122.43 14568 4 4 1 1 1 0 0 0
## 40 32197.34 3867 4 4 1 1 0 0 0 0
## 41 27109.41 22598 4 4 1 0 1 0 0 0
## 42 30353.59 11273 4 4 1 1 0 0 0 0
## 43 27256.49 26400 4 4 1 1 0 0 0 0
## 44 28291.76 22328 4 4 1 1 0 0 0 0
## 45 24912.08 38717 4 4 1 0 1 0 0 0
## 46 33183.33 9795 4 4 1 1 1 0 0 0
## 47 28502.96 28598 4 4 1 1 1 0 0 0
## 48 35033.22 1676 4 4 1 1 1 0 0 0
## 49 29844.20 23143 4 4 1 1 1 0 0 0
## 50 30075.99 22052 4 4 1 1 1 0 0 0
## 51 28054.98 26276 4 4 1 1 1 0 0 0
## 52 28432.82 25247 4 4 1 1 1 0 0 0
## 53 32746.13 7924 4 4 1 0 1 0 0 0
## 54 31002.73 15087 4 4 1 1 1 0 0 0
## 55 28829.03 26503 4 4 1 0 1 0 0 0
## 56 31849.31 16956 4 4 1 0 1 0 0 0
## 57 31554.41 20103 4 4 1 1 1 0 0 0
## 58 28678.08 25380 4 4 1 0 1 0 0 0
## 59 29961.25 20015 4 4 1 1 1 0 0 0
## 60 30575.25 22298 4 4 1 1 1 0 0 0
## 61 29914.38 22105 4 4 1 0 1 0 0 0
## 62 17325.27 19894 4 4 1 0 0 0 0 0
## 63 16391.93 18096 4 4 1 1 0 0 0 0
## 64 15505.29 24239 4 4 1 0 0 0 0 0
## 65 15297.84 23062 4 4 0 0 0 0 0 0
## 66 16078.67 22779 4 4 0 0 1 0 0 0
## 67 16551.22 19531 4 4 1 1 1 0 0 0
## 68 14546.88 33374 4 4 0 1 1 0 0 0
## 69 37288.94 32039 8 2 1 1 1 0 0 1
## 70 46732.61 3625 8 2 1 1 1 0 0 1
## 71 47065.21 5239 8 2 1 1 1 0 0 1
## 72 41371.38 20000 8 2 1 0 1 0 0 1
## 73 39547.59 23826 8 2 1 1 1 0 0 1
## 74 42773.03 14546 8 2 1 1 1 0 0 1
## 75 36970.90 30502 8 2 1 1 1 0 0 1
## 76 11203.15 27364 4 2 1 1 1 0 0 1
## 77 13041.87 13607 4 2 0 1 1 0 0 1
## 78 12207.87 23512 4 2 1 1 1 0 0 1
## 79 11726.00 23103 4 2 0 1 1 0 0 1
## 80 13007.98 7372 4 2 1 1 1 0 0 1
## 81 11149.62 34447 4 2 1 1 1 0 0 1
## 82 10788.97 31436 4 2 1 1 1 0 0 1
## 83 12570.14 22479 4 2 0 1 1 0 0 1
## 84 14023.94 13776 4 2 0 1 1 0 0 1
## 85 12706.91 27521 4 2 1 1 1 0 0 1
## 86 11961.62 27394 4 2 1 1 1 0 0 1
## 87 12810.91 19461 4 2 1 1 1 0 0 1
## 88 12845.17 22382 4 2 1 1 1 0 0 1
## 89 12897.93 23200 4 2 1 1 1 0 0 1
## 90 39713.67 8967 8 2 1 1 1 0 0 1
## 91 39875.85 7054 8 2 1 0 1 0 0 1
## 92 31186.74 34191 8 2 1 0 1 0 0 1
## 93 39365.88 11619 8 2 1 1 1 0 0 1
## 94 34297.31 24259 8 2 1 0 1 0 0 1
## 95 38990.61 9410 8 2 1 1 1 0 0 1
## 96 35261.44 21350 8 2 1 1 1 0 0 1
## 97 27370.96 24960 8 2 1 1 1 0 0 0
## 98 31024.87 13678 8 2 1 1 1 0 0 0
## 99 28502.31 27199 8 2 1 1 1 0 0 0
## 100 32219.59 10915 8 2 1 1 1 0 0 0
## 101 29595.79 16193 8 2 1 0 1 0 0 0
## 102 29664.70 21418 8 2 1 0 1 0 0 0
## 103 27548.63 26126 8 2 1 1 1 0 0 0
## 104 12464.07 21891 4 2 1 1 1 0 0 0
## 105 13160.12 13145 4 2 0 1 1 0 0 0
## 106 12832.46 20618 4 2 1 1 1 0 0 0
## 107 12258.86 24318 4 2 1 1 1 0 0 0
## 108 12465.51 23931 4 2 0 1 1 0 0 0
## 109 12828.03 19081 4 2 1 1 1 0 0 0
## 110 11903.10 25285 4 2 0 1 1 0 0 0
## 111 17750.88 25040 6 4 1 1 0 1 0 0
## 112 18145.13 18339 6 4 1 1 0 1 0 0
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## distancia
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## 759 5286.2107
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## 761 1863.5699
## 762 5362.7164
## 763 6550.8413
## 764 4283.7113
## 765 6483.7844
## 766 5319.1295
## 767 8964.2770
## 768 5943.0627
## 769 9510.4852
## 770 5473.9698
## 771 6324.9817
## 772 12669.4709
## 773 6361.2534
## 774 9267.2519
## 775 7802.3653
## 776 5224.0307
## 777 10351.7435
## 778 8655.8157
## 779 5642.2214
## 780 5564.8013
## 781 4553.1578
## 782 9942.3921
## 783 3783.2236
## 784 8253.1700
## 785 2721.2875
## 786 8524.1405
## 787 1431.0534
## 788 5327.9688
## 789 7738.0517
## 790 3843.5747
## 791 7397.5860
## 792 6610.5828
## 793 7992.8676
## 794 7693.2455
## 795 6703.2431
## 796 4407.7664
## 797 8608.2663
## 798 6999.5123
## 799 1437.4543
## 801 6350.8900
## 802 7571.9337
## 803 5874.4818
## 804 5963.4845subset(cars, cars$speed > 10 & cars$speed < 15 & cars$dist > 45) # speed en (10,15) y dist>45## [1] Price Mileage Cylinder Doors Cruise Sound
## [7] Leather Buick Cadillac Chevy Pontiac Saab
## [13] Saturn convertible coupe hatchback sedan wagon
## [19] velocidad distancia
## <0 rows> (or 0-length row.names)También se pueden hacer el filtrado empleando directamente los correspondientes vectores de índices:
ii <- cars$dist > 85
cars[ii, ] # dis>85## Price Mileage Cylinder Doors Cruise Sound Leather Buick Cadillac Chevy
## 1 22661.05 20105 6 4 1 0 0 1 0 0
## 2 21725.01 13457 6 2 1 1 0 0 0 1
## 3 29142.71 31655 4 2 1 1 1 0 0 0
## 4 30731.94 22479 4 2 1 0 0 0 0 0
## 5 33358.77 17590 4 2 1 1 1 0 0 0
## 6 30315.17 23635 4 2 1 0 0 0 0 0
## 7 33381.82 17381 4 2 1 1 1 0 0 0
## 8 30251.02 27558 4 2 1 0 1 0 0 0
## 9 30166.85 25049 4 2 1 0 0 0 0 0
## 10 27060.14 17319 4 4 1 0 1 0 0 0
## 11 26841.08 10003 4 4 1 1 0 0 0 0
## 12 25790.51 21160 4 4 1 1 1 0 0 0
## 13 25148.38 22272 4 4 1 1 1 0 0 0
## 14 24173.53 27015 4 4 1 0 0 0 0 0
## 15 24852.50 22814 4 4 1 1 1 0 0 0
## 16 27825.95 10014 4 4 1 0 1 0 0 0
## 17 26698.08 23055 4 4 1 1 1 0 0 0
## 18 28185.78 19854 4 4 1 1 1 0 0 0
## 19 27241.44 23204 4 4 1 1 1 0 0 0
## 20 30800.66 8017 4 4 1 0 1 0 0 0
## 21 28416.46 14613 4 4 1 0 1 0 0 0
## 22 26653.24 22590 4 4 1 1 1 0 0 0
## 23 27610.86 22881 4 4 1 1 1 0 0 0
## 24 27788.81 26786 4 4 1 0 1 0 0 0
## 25 29986.79 18464 4 4 1 0 1 0 0 0
## 26 29197.79 20907 4 4 1 0 1 0 0 0
## 27 29908.18 19830 4 4 1 1 1 0 0 0
## 28 29481.53 21822 4 4 1 1 1 0 0 0
## 29 26792.30 25357 4 4 1 0 1 0 0 0
## 30 29321.08 21545 4 4 1 0 1 0 0 0
## 31 37383.50 16088 4 2 1 0 1 0 0 0
## 32 34393.00 24031 4 2 1 0 1 0 0 0
## 33 38324.81 12090 4 2 1 1 1 0 0 0
## 34 35304.49 21293 4 2 1 0 1 0 0 0
## 35 28777.96 48991 4 2 1 1 1 0 0 0
## 36 33248.34 27051 4 2 1 0 1 0 0 0
## 37 35580.33 21167 4 2 1 1 0 0 0 0
## 38 30274.71 10800 4 4 1 1 0 0 0 0
## 39 30122.43 14568 4 4 1 1 1 0 0 0
## 40 32197.34 3867 4 4 1 1 0 0 0 0
## 41 27109.41 22598 4 4 1 0 1 0 0 0
## 42 30353.59 11273 4 4 1 1 0 0 0 0
## 43 27256.49 26400 4 4 1 1 0 0 0 0
## 44 28291.76 22328 4 4 1 1 0 0 0 0
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## 46 33183.33 9795 4 4 1 1 1 0 0 0
## 47 28502.96 28598 4 4 1 1 1 0 0 0
## 48 35033.22 1676 4 4 1 1 1 0 0 0
## 49 29844.20 23143 4 4 1 1 1 0 0 0
## 50 30075.99 22052 4 4 1 1 1 0 0 0
## 51 28054.98 26276 4 4 1 1 1 0 0 0
## 52 28432.82 25247 4 4 1 1 1 0 0 0
## 53 32746.13 7924 4 4 1 0 1 0 0 0
## 54 31002.73 15087 4 4 1 1 1 0 0 0
## 55 28829.03 26503 4 4 1 0 1 0 0 0
## 56 31849.31 16956 4 4 1 0 1 0 0 0
## 57 31554.41 20103 4 4 1 1 1 0 0 0
## 58 28678.08 25380 4 4 1 0 1 0 0 0
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## 60 30575.25 22298 4 4 1 1 1 0 0 0
## 61 29914.38 22105 4 4 1 0 1 0 0 0
## 62 17325.27 19894 4 4 1 0 0 0 0 0
## 63 16391.93 18096 4 4 1 1 0 0 0 0
## 64 15505.29 24239 4 4 1 0 0 0 0 0
## 65 15297.84 23062 4 4 0 0 0 0 0 0
## 66 16078.67 22779 4 4 0 0 1 0 0 0
## 67 16551.22 19531 4 4 1 1 1 0 0 0
## 68 14546.88 33374 4 4 0 1 1 0 0 0
## 69 37288.94 32039 8 2 1 1 1 0 0 1
## 70 46732.61 3625 8 2 1 1 1 0 0 1
## 71 47065.21 5239 8 2 1 1 1 0 0 1
## 72 41371.38 20000 8 2 1 0 1 0 0 1
## 73 39547.59 23826 8 2 1 1 1 0 0 1
## 74 42773.03 14546 8 2 1 1 1 0 0 1
## 75 36970.90 30502 8 2 1 1 1 0 0 1
## 76 11203.15 27364 4 2 1 1 1 0 0 1
## 77 13041.87 13607 4 2 0 1 1 0 0 1
## 78 12207.87 23512 4 2 1 1 1 0 0 1
## 79 11726.00 23103 4 2 0 1 1 0 0 1
## 80 13007.98 7372 4 2 1 1 1 0 0 1
## 81 11149.62 34447 4 2 1 1 1 0 0 1
## 82 10788.97 31436 4 2 1 1 1 0 0 1
## 83 12570.14 22479 4 2 0 1 1 0 0 1
## 84 14023.94 13776 4 2 0 1 1 0 0 1
## 85 12706.91 27521 4 2 1 1 1 0 0 1
## 86 11961.62 27394 4 2 1 1 1 0 0 1
## 87 12810.91 19461 4 2 1 1 1 0 0 1
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## 89 12897.93 23200 4 2 1 1 1 0 0 1
## 90 39713.67 8967 8 2 1 1 1 0 0 1
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## 93 39365.88 11619 8 2 1 1 1 0 0 1
## 94 34297.31 24259 8 2 1 0 1 0 0 1
## 95 38990.61 9410 8 2 1 1 1 0 0 1
## 96 35261.44 21350 8 2 1 1 1 0 0 1
## 97 27370.96 24960 8 2 1 1 1 0 0 0
## 98 31024.87 13678 8 2 1 1 1 0 0 0
## 99 28502.31 27199 8 2 1 1 1 0 0 0
## 100 32219.59 10915 8 2 1 1 1 0 0 0
## 101 29595.79 16193 8 2 1 0 1 0 0 0
## 102 29664.70 21418 8 2 1 0 1 0 0 0
## 103 27548.63 26126 8 2 1 1 1 0 0 0
## 104 12464.07 21891 4 2 1 1 1 0 0 0
## 105 13160.12 13145 4 2 0 1 1 0 0 0
## 106 12832.46 20618 4 2 1 1 1 0 0 0
## 107 12258.86 24318 4 2 1 1 1 0 0 0
## 108 12465.51 23931 4 2 0 1 1 0 0 0
## 109 12828.03 19081 4 2 1 1 1 0 0 0
## 110 11903.10 25285 4 2 0 1 1 0 0 0
## 111 17750.88 25040 6 4 1 1 0 1 0 0
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## 117 20698.08 2992 6 4 1 0 1 1 0 0
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## 120 19774.25 23359 6 4 1 1 1 1 0 0
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## 123 18543.43 26034 6 4 1 1 1 1 0 0
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## 136 23447.69 15755 6 4 1 1 0 1 0 0
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## 138 23547.24 16235 6 4 1 1 0 1 0 0
## 139 38600.24 17138 8 4 1 0 1 0 1 0
## 140 36154.30 25339 8 4 1 1 1 0 1 0
## 141 36077.80 21966 8 4 1 0 1 0 1 0
## 142 40335.74 14743 8 4 1 0 1 0 1 0
## 143 35338.65 25163 8 4 1 0 1 0 1 0
## 144 39307.01 16041 8 4 1 0 1 0 1 0
## 145 39801.55 14095 8 4 1 0 1 0 1 0
## 146 32537.19 41829 8 4 1 1 1 0 1 0
## 147 37510.25 21593 8 4 1 0 1 0 1 0
## 148 32954.14 36074 8 4 1 0 1 0 1 0
## 149 36245.16 26250 8 4 1 1 1 0 1 0
## 150 40856.39 12791 8 4 1 1 1 0 1 0
## 151 41378.05 8125 8 4 1 0 1 0 1 0
## 152 37215.17 22211 8 4 1 0 1 0 1 0
## 153 17202.83 9380 6 2 0 1 1 0 0 0
## 154 17675.84 5131 6 2 0 1 1 0 0 0
## 155 15253.87 20917 6 2 1 1 1 0 0 0
## 156 14703.14 23335 6 2 0 1 1 0 0 0
## 157 15059.13 22641 6 2 1 1 1 0 0 0
## 158 16792.68 12071 6 2 1 1 1 0 0 0
## 159 17141.94 6761 6 2 1 1 1 0 0 0
## 160 19204.81 26477 6 4 1 0 1 0 0 0
## 161 18529.34 30063 6 4 1 0 1 0 0 0
## 162 20627.66 20770 6 4 1 1 1 0 0 0
## 163 18158.08 28354 6 4 1 0 0 0 0 0
## 164 19540.24 22628 6 4 1 1 0 0 0 0
## 165 21875.10 12313 6 4 1 0 1 0 0 0
## 166 21903.32 4537 6 4 1 1 0 0 0 0
## 167 15788.10 25295 6 4 1 1 0 0 0 0
## 168 17162.87 20829 6 4 1 0 0 0 0 0
## 169 16569.14 25777 6 4 1 0 0 0 0 0
## 170 16391.17 21304 6 4 1 1 0 0 0 0
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## 173 18254.92 16554 6 4 1 1 1 0 0 0
## 174 15802.65 21461 4 4 0 0 0 0 0 0
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## 176 14869.28 31791 4 4 1 1 1 0 0 0
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## 198 60567.55 23193 8 2 1 1 1 0 1 0
## 199 63913.12 18200 8 2 1 1 1 0 1 0
## 200 69133.73 7892 8 2 1 1 1 0 1 0
## 201 66374.31 12021 8 2 1 1 1 0 1 0
## 202 17322.08 10102 6 4 1 0 1 0 0 0
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## 804 5963.4845ii <- cars$speed > 10 & cars$speed < 15 & cars$dist > 45
cars[ii, ] # speed en (10,15) y dist>45## [1] Price Mileage Cylinder Doors Cruise Sound
## [7] Leather Buick Cadillac Chevy Pontiac Saab
## [13] Saturn convertible coupe hatchback sedan wagon
## [19] velocidad distancia
## <0 rows> (or 0-length row.names)En este caso puede ser de utilidad la función which():
it <- which(ii)
str(it)## int(0)cars[it, 1:2]## [1] Price Mileage
## <0 rows> (or 0-length row.names)# rownames(cars[it, 1:2])
id <- which(!ii)
str(cars[id, 1:2])## 'data.frame': 0 obs. of 2 variables:
## $ Price : num
## $ Mileage: int# Se podría p.e. emplear cars[id, ] para predecir cars[it, ]$speed
# ?which.min