{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[]},"kernelspec":{"name":"ir","display_name":"R"},"language_info":{"name":"R"},"gpuClass":"standard"},"cells":[{"cell_type":"markdown","source":["# Taller de introducción a R\n"],"metadata":{"id":"P4_vewukfiBf"}},{"cell_type":"markdown","source":["## Matrices:\n"," - El comando en R para elaborar una matriz es 'matrix( )'.\n"," - Corresponde a una tabla de dos dimensiones de elementos de la misma naturaleza (números o carácteres pero no ambos).\n"],"metadata":{"id":"AInBgR_FaLKN"}},{"cell_type":"code","source":["# Generamos una matriz y la guardamos en un objeto llamado 'y'\n","y = matrix(\n"," 1:12, # Números con los que rellenaremos la matriz\n"," nrow = 4, # Cantidad de filas de la matriz\n"," ncol = 3, # Cantidad de columnas de la matriz\n"," byrow = TRUE # Indica si la matriz debe ser rellenada por filas (puede ser también por columnas)\n",")\n","y"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":177},"id":"3v3dchv6cr7d","outputId":"58d2fb1c-606e-4f18-dced-cc75deb6e2ca"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/html":["\n","\n","\n","\t\n","\t\n","\t\n","\t\n","\n","
A matrix: 4 × 3 of type int
1 2 3
4 5 6
7 8 9
101112
\n"],"text/markdown":"\nA matrix: 4 × 3 of type int\n\n| 1 | 2 | 3 |\n| 4 | 5 | 6 |\n| 7 | 8 | 9 |\n| 10 | 11 | 12 |\n\n","text/latex":"A matrix: 4 × 3 of type int\n\\begin{tabular}{lll}\n\t 1 & 2 & 3\\\\\n\t 4 & 5 & 6\\\\\n\t 7 & 8 & 9\\\\\n\t 10 & 11 & 12\\\\\n\\end{tabular}\n","text/plain":[" [,1] [,2] [,3]\n","[1,] 1 2 3 \n","[2,] 4 5 6 \n","[3,] 7 8 9 \n","[4,] 10 11 12 "]},"metadata":{}}]},{"cell_type":"code","source":["# Si cambiamos el argumento byrow a FALSE, veremos que la matriz resultante es diferente\n","z = matrix(\n"," 1:12, # Números con los que rellenaremos la matriz\n"," nrow = 4, # Cantidad de filas de la matriz\n"," ncol = 3, # Cantidad de columnas de la matriz\n"," byrow = TRUE # Indica si la matriz debe ser rellenada por filas (puede ser también por columnas, byrow = FALSE)\n",")\n","z"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":177},"id":"L3pX3fL2dKMz","outputId":"a229108e-1605-4de9-e230-67685e35350e"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/html":["\n","\n","\n","\t\n","\t\n","\t\n","\t\n","\n","
A matrix: 4 × 3 of type int
1 2 3
4 5 6
7 8 9
101112
\n"],"text/markdown":"\nA matrix: 4 × 3 of type int\n\n| 1 | 2 | 3 |\n| 4 | 5 | 6 |\n| 7 | 8 | 9 |\n| 10 | 11 | 12 |\n\n","text/latex":"A matrix: 4 × 3 of type int\n\\begin{tabular}{lll}\n\t 1 & 2 & 3\\\\\n\t 4 & 5 & 6\\\\\n\t 7 & 8 & 9\\\\\n\t 10 & 11 & 12\\\\\n\\end{tabular}\n","text/plain":[" [,1] [,2] [,3]\n","[1,] 1 2 3 \n","[2,] 4 5 6 \n","[3,] 7 8 9 \n","[4,] 10 11 12 "]},"metadata":{}}]},{"cell_type":"code","source":["# Podemos extraer un valor de la matriz indicando la fila y la columna.\n","# Usaremos la matriz 'z'.\n","# Consultaremos el valor que está en la segunda fila y tercera columna.\n","z[2,3]"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":34},"id":"dVMNhCy3dV2h","outputId":"0b4bee63-59a5-41a7-eba0-397df0698fd3"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/html":["6"],"text/markdown":"6","text/latex":"6","text/plain":["[1] 6"]},"metadata":{}}]},{"cell_type":"code","source":["# Podemos consultar filas completas\n","# Consultaremos la primer fila, para ello, en el número de columna no ingresamos nada\n","z[1,]"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":34},"id":"IYR2QUMFdpdh","outputId":"f176f3ab-8c45-4c07-9c0e-f6636b6746d6"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/html":["\n","
  1. 1
  2. 2
  3. 3
\n"],"text/markdown":"1. 1\n2. 2\n3. 3\n\n\n","text/latex":"\\begin{enumerate*}\n\\item 1\n\\item 2\n\\item 3\n\\end{enumerate*}\n","text/plain":["[1] 1 2 3"]},"metadata":{}}]},{"cell_type":"code","source":["# De igual manera, podemos consultar los valores de una columna completa\n","# Consultaremos los valores de la tercera columna\n","z[,3]"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":34},"id":"jNijRk4Nd1aF","outputId":"2d1f9b6a-bc19-4ad0-f8e1-344254832283"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/html":["\n","
  1. 3
  2. 6
  3. 9
  4. 12
\n"],"text/markdown":"1. 3\n2. 6\n3. 9\n4. 12\n\n\n","text/latex":"\\begin{enumerate*}\n\\item 3\n\\item 6\n\\item 9\n\\item 12\n\\end{enumerate*}\n","text/plain":["[1] 3 6 9 12"]},"metadata":{}}]},{"cell_type":"markdown","source":["## Factor\n"," - El comando en R para elaborar una factor es 'factor( )'.\n"," - Es un vector de caracteres que poseen niveles de orden."],"metadata":{"id":"dgPT14jzaLsk"}},{"cell_type":"code","source":["Toxicidad = factor(\n"," x = c(\"Alta\", \"Baja\", \"Media\"), # Vector de caracteres\n"," levels = c(c(\"Alta\", \"Media\", \"Baja\")) # Indicamos el orden de los caracteres según un criterio,\n"," # por ejmeplo de más alta a más baja toxicidad.\n"," # Para ello, deben ir en el orden deseado\n",")\n","Toxicidad\n","\n","# En la salida, se aprecia el vector de caracteres original, junto a una glosa que indaca los niveles con los\n","# que se debe considerar.\n","\n","# La utilidad recae en posteriores aplicaciónes gráficas que serán vistas durante el curso"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":53},"id":"qIFjR4rOnlZs","outputId":"144921e5-1177-4d13-e931-e3392b38e639"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/html":["\n","
  1. Alta
  2. Baja
  3. Media
\n","\n","
\n","\t\n","\t\tLevels:\n","\t\n","\t\n","\t
  1. 'Alta'
  2. 'Media'
  3. 'Baja'
\n","
"],"text/markdown":"1. Alta\n2. Baja\n3. Media\n\n\n\n**Levels**: 1. 'Alta'\n2. 'Media'\n3. 'Baja'\n\n\n","text/latex":"\\begin{enumerate*}\n\\item Alta\n\\item Baja\n\\item Media\n\\end{enumerate*}\n\n\\emph{Levels}: \\begin{enumerate*}\n\\item 'Alta'\n\\item 'Media'\n\\item 'Baja'\n\\end{enumerate*}\n","text/plain":["[1] Alta Baja Media\n","Levels: Alta Media Baja"]},"metadata":{}}]},{"cell_type":"markdown","source":["## Data frame:\n"," - El comando en R para elaborar un *data frame* es 'data.frame( )'.\n"," - Corresponde a una tabla de dos dimensiones. Las columnas pueden ser de diferente naturaleza, pero deben tener el mismo largo.\n"],"metadata":{"id":"EkMNx2AWaLVf"}},{"cell_type":"code","source":["# Tenemos un data.frame (base de datos) que\n","# contiene el sexo, altura y peso\n","# de 6 personas (cantidad de datos por cada vector)\n","\n","base = data.frame(\n"," Gender=c(\"M\",\"F\",\"M\",\"F\",\"M\",\"F\"),\n"," Height=c(1.83,1.76,1.82,1.60,1.90,1.66),\n"," Weight=c(67,58,66,48,75,55)\n",")\n","base"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":286},"id":"_qUQ6SI-t-ym","outputId":"a7d47420-8782-49d8-bac3-809c80112de0"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/html":["\n","\n","\n","\t\n","\t\n","\n","\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\n","
A data.frame: 6 × 3
GenderHeightWeight
<chr><dbl><dbl>
M1.8367
F1.7658
M1.8266
F1.6048
M1.9075
F1.6655
\n"],"text/markdown":"\nA data.frame: 6 × 3\n\n| Gender <chr> | Height <dbl> | Weight <dbl> |\n|---|---|---|\n| M | 1.83 | 67 |\n| F | 1.76 | 58 |\n| M | 1.82 | 66 |\n| F | 1.60 | 48 |\n| M | 1.90 | 75 |\n| F | 1.66 | 55 |\n\n","text/latex":"A data.frame: 6 × 3\n\\begin{tabular}{lll}\n Gender & Height & Weight\\\\\n & & \\\\\n\\hline\n\t M & 1.83 & 67\\\\\n\t F & 1.76 & 58\\\\\n\t M & 1.82 & 66\\\\\n\t F & 1.60 & 48\\\\\n\t M & 1.90 & 75\\\\\n\t F & 1.66 & 55\\\\\n\\end{tabular}\n","text/plain":[" Gender Height Weight\n","1 M 1.83 67 \n","2 F 1.76 58 \n","3 M 1.82 66 \n","4 F 1.60 48 \n","5 M 1.90 75 \n","6 F 1.66 55 "]},"metadata":{}}]},{"cell_type":"code","source":["# Podemos consultar las dimensiones del data.frame\n","dim(base)\n","# 6 filas y 3 columnas"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":34},"id":"H_m2DoUxt_B-","outputId":"b7c1535f-4b42-40f4-9f31-ada01af6115a"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/html":["\n","
  1. 6
  2. 3
\n"],"text/markdown":"1. 6\n2. 3\n\n\n","text/latex":"\\begin{enumerate*}\n\\item 6\n\\item 3\n\\end{enumerate*}\n","text/plain":["[1] 6 3"]},"metadata":{}}]},{"cell_type":"code","source":["# Podemos extraer una de las columas, de igual modo que con las matrices\n","# Aunque, es posible usar un sufijo luego del signo peso\n","# Por ejemplo, queremos extraer la columna del peso\n","\n","base$Weight"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":34},"id":"cdmkhOPYwRNR","outputId":"e5c7fd09-5c4e-45d4-f626-177561940e03"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/html":["\n","
  1. 67
  2. 58
  3. 66
  4. 48
  5. 75
  6. 55
\n"],"text/markdown":"1. 67\n2. 58\n3. 66\n4. 48\n5. 75\n6. 55\n\n\n","text/latex":"\\begin{enumerate*}\n\\item 67\n\\item 58\n\\item 66\n\\item 48\n\\item 75\n\\item 55\n\\end{enumerate*}\n","text/plain":["[1] 67 58 66 48 75 55"]},"metadata":{}}]},{"cell_type":"code","source":["# Es posible filtrar toda la tabla en base a un criterio de alguna de las columnas\n","# Por ejemplo, ver solo la información de las mujeres\n","# Para ello debemos escribir las codiciones antes de la coma, tal como se muestra\n","\n","base[base$Gender == \"F\",]\n","\n","# La condición expresa que el sexo debe ser femenino, el cual recordemos es un caracter,\n","# además, la igualdad se debe escribir con el signo '=='."],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":192},"id":"kJFUe0GOt_ET","outputId":"61e5e750-6ac2-49c6-f0e1-be0945260908"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/html":["\n","\n","\n","\t\n","\t\n","\n","\n","\t\n","\t\n","\t\n","\n","
A data.frame: 3 × 3
GenderHeightWeight
<chr><dbl><dbl>
2F1.7658
4F1.6048
6F1.6655
\n"],"text/markdown":"\nA data.frame: 3 × 3\n\n| | Gender <chr> | Height <dbl> | Weight <dbl> |\n|---|---|---|---|\n| 2 | F | 1.76 | 58 |\n| 4 | F | 1.60 | 48 |\n| 6 | F | 1.66 | 55 |\n\n","text/latex":"A data.frame: 3 × 3\n\\begin{tabular}{r|lll}\n & Gender & Height & Weight\\\\\n & & & \\\\\n\\hline\n\t2 & F & 1.76 & 58\\\\\n\t4 & F & 1.60 & 48\\\\\n\t6 & F & 1.66 & 55\\\\\n\\end{tabular}\n","text/plain":[" Gender Height Weight\n","2 F 1.76 58 \n","4 F 1.60 48 \n","6 F 1.66 55 "]},"metadata":{}}]},{"cell_type":"code","source":["# Podemos hacer un dopble filtro.\n","# Podemos consultar la información de las mujeres, pero solo la altura.\n","# Para ello ocupamos el segundo espacio, luego de la coman. Allí, se debe\n","# ingresar la columna que desea extraer luedo de aplicado el filtro\n","\n","base[base$Gender == \"F\", \"Height\"]"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":34},"id":"opsMk_Dlt_MG","outputId":"2e2a4cee-893e-47d6-f979-0129bdd3dad9"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/html":["\n","
  1. 1.76
  2. 1.6
  3. 1.66
\n"],"text/markdown":"1. 1.76\n2. 1.6\n3. 1.66\n\n\n","text/latex":"\\begin{enumerate*}\n\\item 1.76\n\\item 1.6\n\\item 1.66\n\\end{enumerate*}\n","text/plain":["[1] 1.76 1.60 1.66"]},"metadata":{}}]},{"cell_type":"code","source":["# Otro forma de hacerlo, es ingresando el número de la columna\n","base[base$Gender == \"F\", 2]"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":34},"id":"FevNyfjXt_PA","outputId":"0d6e5bb7-ea59-4b05-e10e-c8b05dc61bbf"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/html":["\n","
  1. 1.76
  2. 1.6
  3. 1.66
\n"],"text/markdown":"1. 1.76\n2. 1.6\n3. 1.66\n\n\n","text/latex":"\\begin{enumerate*}\n\\item 1.76\n\\item 1.6\n\\item 1.66\n\\end{enumerate*}\n","text/plain":["[1] 1.76 1.60 1.66"]},"metadata":{}}]},{"cell_type":"code","source":["# Aquí hay otros ejemplos de condiciones y consiciones múltiples\n","\n","# Información de solo mujeres y con altura mayor a 1.6\n","base[base$Gender == \"F\" & base$Height > 1.6,]"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":161},"id":"7UL3BFyOt_Rl","outputId":"186ddc30-a2d6-4b48-e89f-d69bb099d670"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/html":["\n","\n","\n","\t\n","\t\n","\n","\n","\t\n","\t\n","\n","
A data.frame: 2 × 3
GenderHeightWeight
<chr><dbl><dbl>
2F1.7658
6F1.6655
\n"],"text/markdown":"\nA data.frame: 2 × 3\n\n| | Gender <chr> | Height <dbl> | Weight <dbl> |\n|---|---|---|---|\n| 2 | F | 1.76 | 58 |\n| 6 | F | 1.66 | 55 |\n\n","text/latex":"A data.frame: 2 × 3\n\\begin{tabular}{r|lll}\n & Gender & Height & Weight\\\\\n & & & \\\\\n\\hline\n\t2 & F & 1.76 & 58\\\\\n\t6 & F & 1.66 & 55\\\\\n\\end{tabular}\n","text/plain":[" Gender Height Weight\n","2 F 1.76 58 \n","6 F 1.66 55 "]},"metadata":{}}]},{"cell_type":"code","source":["# Información de personas y con altura mayor a 1.82 o peso mayor a 55\n","base[base$Height > 1.82 | base$Weight > 55,]"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":224},"id":"jVAYaqDmt_Ub","outputId":"801eb479-e13b-44ec-8f1e-a840d60d5b26"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/html":["\n","\n","\n","\t\n","\t\n","\n","\n","\t\n","\t\n","\t\n","\t\n","\n","
A data.frame: 4 × 3
GenderHeightWeight
<chr><dbl><dbl>
1M1.8367
2F1.7658
3M1.8266
5M1.9075
\n"],"text/markdown":"\nA data.frame: 4 × 3\n\n| | Gender <chr> | Height <dbl> | Weight <dbl> |\n|---|---|---|---|\n| 1 | M | 1.83 | 67 |\n| 2 | F | 1.76 | 58 |\n| 3 | M | 1.82 | 66 |\n| 5 | M | 1.90 | 75 |\n\n","text/latex":"A data.frame: 4 × 3\n\\begin{tabular}{r|lll}\n & Gender & Height & Weight\\\\\n & & & \\\\\n\\hline\n\t1 & M & 1.83 & 67\\\\\n\t2 & F & 1.76 & 58\\\\\n\t3 & M & 1.82 & 66\\\\\n\t5 & M & 1.90 & 75\\\\\n\\end{tabular}\n","text/plain":[" Gender Height Weight\n","1 M 1.83 67 \n","2 F 1.76 58 \n","3 M 1.82 66 \n","5 M 1.90 75 "]},"metadata":{}}]},{"cell_type":"code","source":["# Información de personas y con altura mayor a 1.82 o peso mayor a 55\n","# Pero solo el sexo\n","base[base$Height > 1.82 | base$Weight > 55, \"Gender\"]"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":34},"id":"QFVtn8qAyIyO","outputId":"ceea6c9e-6fbf-40f4-fa96-c6579ca05f8a"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/html":["\n","
  1. 'M'
  2. 'F'
  3. 'M'
  4. 'M'
\n"],"text/markdown":"1. 'M'\n2. 'F'\n3. 'M'\n4. 'M'\n\n\n","text/latex":"\\begin{enumerate*}\n\\item 'M'\n\\item 'F'\n\\item 'M'\n\\item 'M'\n\\end{enumerate*}\n","text/plain":["[1] \"M\" \"F\" \"M\" \"M\""]},"metadata":{}}]},{"cell_type":"code","source":["# Información de personas y con altura mayor a 1.82 o peso mayor a 55\n","# Pero solo el sexo y la altura\n","base[base$Height > 1.82 | base$Weight > 55, c(\"Gender\", \"Height\")]"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":224},"id":"6twUb-LCyRAc","outputId":"3006d3e9-04be-4f69-cb4f-4b3ac283a741"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/html":["\n","\n","\n","\t\n","\t\n","\n","\n","\t\n","\t\n","\t\n","\t\n","\n","
A data.frame: 4 × 2
GenderHeight
<chr><dbl>
1M1.83
2F1.76
3M1.82
5M1.90
\n"],"text/markdown":"\nA data.frame: 4 × 2\n\n| | Gender <chr> | Height <dbl> |\n|---|---|---|\n| 1 | M | 1.83 |\n| 2 | F | 1.76 |\n| 3 | M | 1.82 |\n| 5 | M | 1.90 |\n\n","text/latex":"A data.frame: 4 × 2\n\\begin{tabular}{r|ll}\n & Gender & Height\\\\\n & & \\\\\n\\hline\n\t1 & M & 1.83\\\\\n\t2 & F & 1.76\\\\\n\t3 & M & 1.82\\\\\n\t5 & M & 1.90\\\\\n\\end{tabular}\n","text/plain":[" Gender Height\n","1 M 1.83 \n","2 F 1.76 \n","3 M 1.82 \n","5 M 1.90 "]},"metadata":{}}]},{"cell_type":"code","source":["# Igualmente sirve\n","base[base$Height > 1.82 | base$Weight > 55, c(1,2)]"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":224},"id":"ldM94iCOyWU_","outputId":"4c26207b-2f61-499a-de00-68b63269d1a0"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/html":["\n","\n","\n","\t\n","\t\n","\n","\n","\t\n","\t\n","\t\n","\t\n","\n","
A data.frame: 4 × 2
GenderHeight
<chr><dbl>
1M1.83
2F1.76
3M1.82
5M1.90
\n"],"text/markdown":"\nA data.frame: 4 × 2\n\n| | Gender <chr> | Height <dbl> |\n|---|---|---|\n| 1 | M | 1.83 |\n| 2 | F | 1.76 |\n| 3 | M | 1.82 |\n| 5 | M | 1.90 |\n\n","text/latex":"A data.frame: 4 × 2\n\\begin{tabular}{r|ll}\n & Gender & Height\\\\\n & & \\\\\n\\hline\n\t1 & M & 1.83\\\\\n\t2 & F & 1.76\\\\\n\t3 & M & 1.82\\\\\n\t5 & M & 1.90\\\\\n\\end{tabular}\n","text/plain":[" Gender Height\n","1 M 1.83 \n","2 F 1.76 \n","3 M 1.82 \n","5 M 1.90 "]},"metadata":{}}]},{"cell_type":"code","source":["# SI tengo muchas condiciones de un mismo valor, puedo incluirlas todas al mismo\n","# de la siguiente manera\n","\n","# Por ejemplo, filtrar solo aquellas personas con altura 66, 58 y 75\n","base[base$Weight %in% c(66,58,75),]\n","\n","# El comando %in% verifica si cada observación (fila) de la variable elegida\n","# Esta dentro de las opciones dadas."],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":192},"id":"oELumjnVymxF","outputId":"852f1bb7-6cfc-4b57-bced-3f5bbe4adf2a"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/html":["\n","\n","\n","\t\n","\t\n","\n","\n","\t\n","\t\n","\t\n","\n","
A data.frame: 3 × 3
GenderHeightWeight
<chr><dbl><dbl>
2F1.7658
3M1.8266
5M1.9075
\n"],"text/markdown":"\nA data.frame: 3 × 3\n\n| | Gender <chr> | Height <dbl> | Weight <dbl> |\n|---|---|---|---|\n| 2 | F | 1.76 | 58 |\n| 3 | M | 1.82 | 66 |\n| 5 | M | 1.90 | 75 |\n\n","text/latex":"A data.frame: 3 × 3\n\\begin{tabular}{r|lll}\n & Gender & Height & Weight\\\\\n & & & \\\\\n\\hline\n\t2 & F & 1.76 & 58\\\\\n\t3 & M & 1.82 & 66\\\\\n\t5 & M & 1.90 & 75\\\\\n\\end{tabular}\n","text/plain":[" Gender Height Weight\n","2 F 1.76 58 \n","3 M 1.82 66 \n","5 M 1.90 75 "]},"metadata":{}}]},{"cell_type":"code","source":["# Es posible agregar una nueva columan en un data.frame de manera directa\n","# Se debe tener cuidado en que tienen que tener la misma cantidad de observaciones\n","# que las columnas ya presentes.\n","\n","base$Edad = c(35,21,34,66,61,18)\n","base"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":286},"id":"NCLshc8czQ4O","outputId":"f1cd0105-db30-4ed5-da9c-e00909ab7888"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/html":["\n","\n","\n","\t\n","\t\n","\n","\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\n","
A data.frame: 6 × 4
GenderHeightWeightEdad
<chr><dbl><dbl><dbl>
M1.836735
F1.765821
M1.826634
F1.604866
M1.907561
F1.665518
\n"],"text/markdown":"\nA data.frame: 6 × 4\n\n| Gender <chr> | Height <dbl> | Weight <dbl> | Edad <dbl> |\n|---|---|---|---|\n| M | 1.83 | 67 | 35 |\n| F | 1.76 | 58 | 21 |\n| M | 1.82 | 66 | 34 |\n| F | 1.60 | 48 | 66 |\n| M | 1.90 | 75 | 61 |\n| F | 1.66 | 55 | 18 |\n\n","text/latex":"A data.frame: 6 × 4\n\\begin{tabular}{llll}\n Gender & Height & Weight & Edad\\\\\n & & & \\\\\n\\hline\n\t M & 1.83 & 67 & 35\\\\\n\t F & 1.76 & 58 & 21\\\\\n\t M & 1.82 & 66 & 34\\\\\n\t F & 1.60 & 48 & 66\\\\\n\t M & 1.90 & 75 & 61\\\\\n\t F & 1.66 & 55 & 18\\\\\n\\end{tabular}\n","text/plain":[" Gender Height Weight Edad\n","1 M 1.83 67 35 \n","2 F 1.76 58 21 \n","3 M 1.82 66 34 \n","4 F 1.60 48 66 \n","5 M 1.90 75 61 \n","6 F 1.66 55 18 "]},"metadata":{}}]},{"cell_type":"code","source":["# Se puede eliminar una columna al igual que en matrices\n","base[,-2] # Eliminamos la columna 2"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":286},"id":"7uycvx29zo5K","outputId":"8963c0ad-e2b2-4a10-d26d-8bc4f36121aa"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/html":["\n","\n","\n","\t\n","\t\n","\n","\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\n","
A data.frame: 6 × 3
GenderWeightEdad
<chr><dbl><dbl>
M6735
F5821
M6634
F4866
M7561
F5518
\n"],"text/markdown":"\nA data.frame: 6 × 3\n\n| Gender <chr> | Weight <dbl> | Edad <dbl> |\n|---|---|---|\n| M | 67 | 35 |\n| F | 58 | 21 |\n| M | 66 | 34 |\n| F | 48 | 66 |\n| M | 75 | 61 |\n| F | 55 | 18 |\n\n","text/latex":"A data.frame: 6 × 3\n\\begin{tabular}{lll}\n Gender & Weight & Edad\\\\\n & & \\\\\n\\hline\n\t M & 67 & 35\\\\\n\t F & 58 & 21\\\\\n\t M & 66 & 34\\\\\n\t F & 48 & 66\\\\\n\t M & 75 & 61\\\\\n\t F & 55 & 18\\\\\n\\end{tabular}\n","text/plain":[" Gender Weight Edad\n","1 M 67 35 \n","2 F 58 21 \n","3 M 66 34 \n","4 F 48 66 \n","5 M 75 61 \n","6 F 55 18 "]},"metadata":{}}]},{"cell_type":"markdown","source":["Para guardar los cambios, siempre se debe sobreescribir el objeto que se utiliza, en caso contrario, lo realizado solo se proyectará en consola (justo como el último ejemplo en el que eliminó la columna 2; si consultamos la base se verá que no ha sufrido cambios)."],"metadata":{"id":"Exv_NxXlzzjL"}},{"cell_type":"code","source":["base"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":286},"id":"NyirAF7mzx8s","outputId":"7673b108-3823-4f88-c6be-816d4a71aaa6"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/html":["\n","\n","\n","\t\n","\t\n","\n","\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\n","
A data.frame: 6 × 4
GenderHeightWeightEdad
<chr><dbl><dbl><dbl>
M1.836735
F1.765821
M1.826634
F1.604866
M1.907561
F1.665518
\n"],"text/markdown":"\nA data.frame: 6 × 4\n\n| Gender <chr> | Height <dbl> | Weight <dbl> | Edad <dbl> |\n|---|---|---|---|\n| M | 1.83 | 67 | 35 |\n| F | 1.76 | 58 | 21 |\n| M | 1.82 | 66 | 34 |\n| F | 1.60 | 48 | 66 |\n| M | 1.90 | 75 | 61 |\n| F | 1.66 | 55 | 18 |\n\n","text/latex":"A data.frame: 6 × 4\n\\begin{tabular}{llll}\n Gender & Height & Weight & Edad\\\\\n & & & \\\\\n\\hline\n\t M & 1.83 & 67 & 35\\\\\n\t F & 1.76 & 58 & 21\\\\\n\t M & 1.82 & 66 & 34\\\\\n\t F & 1.60 & 48 & 66\\\\\n\t M & 1.90 & 75 & 61\\\\\n\t F & 1.66 & 55 & 18\\\\\n\\end{tabular}\n","text/plain":[" Gender Height Weight Edad\n","1 M 1.83 67 35 \n","2 F 1.76 58 21 \n","3 M 1.82 66 34 \n","4 F 1.60 48 66 \n","5 M 1.90 75 61 \n","6 F 1.66 55 18 "]},"metadata":{}}]},{"cell_type":"code","source":["# Es posible cambiar los nombre de la columnas\n","# Podemos cambiar una en específico\n","# Por ejemplo la primera\n","\n","colnames(base)[1] = \"Sexo\"\n","base"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":286},"id":"paNOIzoi0nNJ","outputId":"67d7a3e7-ca4c-4310-aef0-caa3aa24ef57"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/html":["\n","\n","\n","\t\n","\t\n","\n","\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\n","
A data.frame: 6 × 4
SexoHeightWeightEdad
<chr><dbl><dbl><dbl>
M1.836735
F1.765821
M1.826634
F1.604866
M1.907561
F1.665518
\n"],"text/markdown":"\nA data.frame: 6 × 4\n\n| Sexo <chr> | Height <dbl> | Weight <dbl> | Edad <dbl> |\n|---|---|---|---|\n| M | 1.83 | 67 | 35 |\n| F | 1.76 | 58 | 21 |\n| M | 1.82 | 66 | 34 |\n| F | 1.60 | 48 | 66 |\n| M | 1.90 | 75 | 61 |\n| F | 1.66 | 55 | 18 |\n\n","text/latex":"A data.frame: 6 × 4\n\\begin{tabular}{llll}\n Sexo & Height & Weight & Edad\\\\\n & & & \\\\\n\\hline\n\t M & 1.83 & 67 & 35\\\\\n\t F & 1.76 & 58 & 21\\\\\n\t M & 1.82 & 66 & 34\\\\\n\t F & 1.60 & 48 & 66\\\\\n\t M & 1.90 & 75 & 61\\\\\n\t F & 1.66 & 55 & 18\\\\\n\\end{tabular}\n","text/plain":[" Sexo Height Weight Edad\n","1 M 1.83 67 35 \n","2 F 1.76 58 21 \n","3 M 1.82 66 34 \n","4 F 1.60 48 66 \n","5 M 1.90 75 61 \n","6 F 1.66 55 18 "]},"metadata":{}}]},{"cell_type":"code","source":["# O podemos todas, dándole a R un vector con todos los nombres que debe considerar\n","colnames(base) = c(\"Gender\", \"Altura\", \"Peso\", \"Age\")\n","base"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":286},"id":"lSH_KzJj02du","outputId":"562ca90b-3cc9-4f9b-8f55-4c82bd65342a"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/html":["\n","\n","\n","\t\n","\t\n","\n","\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\n","
A data.frame: 6 × 4
GenderAlturaPesoAge
<chr><dbl><dbl><dbl>
M1.836735
F1.765821
M1.826634
F1.604866
M1.907561
F1.665518
\n"],"text/markdown":"\nA data.frame: 6 × 4\n\n| Gender <chr> | Altura <dbl> | Peso <dbl> | Age <dbl> |\n|---|---|---|---|\n| M | 1.83 | 67 | 35 |\n| F | 1.76 | 58 | 21 |\n| M | 1.82 | 66 | 34 |\n| F | 1.60 | 48 | 66 |\n| M | 1.90 | 75 | 61 |\n| F | 1.66 | 55 | 18 |\n\n","text/latex":"A data.frame: 6 × 4\n\\begin{tabular}{llll}\n Gender & Altura & Peso & Age\\\\\n & & & \\\\\n\\hline\n\t M & 1.83 & 67 & 35\\\\\n\t F & 1.76 & 58 & 21\\\\\n\t M & 1.82 & 66 & 34\\\\\n\t F & 1.60 & 48 & 66\\\\\n\t M & 1.90 & 75 & 61\\\\\n\t F & 1.66 & 55 & 18\\\\\n\\end{tabular}\n","text/plain":[" Gender Altura Peso Age\n","1 M 1.83 67 35 \n","2 F 1.76 58 21 \n","3 M 1.82 66 34 \n","4 F 1.60 48 66 \n","5 M 1.90 75 61 \n","6 F 1.66 55 18 "]},"metadata":{}}]},{"cell_type":"code","source":["# Es posible revisar los nombres de las columnas con el comando 'colanmes( )'\n","colnames(base)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":34},"id":"7ClNIPKm1IdY","outputId":"0945bcaf-e991-465c-f27b-1c46d8b115fe"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/html":["\n","
  1. 'Gender'
  2. 'Altura'
  3. 'Peso'
  4. 'Age'
\n"],"text/markdown":"1. 'Gender'\n2. 'Altura'\n3. 'Peso'\n4. 'Age'\n\n\n","text/latex":"\\begin{enumerate*}\n\\item 'Gender'\n\\item 'Altura'\n\\item 'Peso'\n\\item 'Age'\n\\end{enumerate*}\n","text/plain":["[1] \"Gender\" \"Altura\" \"Peso\" \"Age\" "]},"metadata":{}}]},{"cell_type":"code","source":["# Para obtener un resumen general de los datos que hay un data frame\n","# es posible usar el comando 'str( )'"],"metadata":{"id":"ZYwj9Ymu8Ol9"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["## Ejercicios: trabajo personal\n","\n","Para este ejercicio se trabajará con el conjunto de datos conocido como Fisher's iris data. Este conjunto contiene la medida en centímetros de las variables longitud y ancho de sépalo y pétalo para un total de 150 flores de tres especies diferentes (setosa, versicolor y virginica). Para acceder a los datos en R usaremos: data(iris).\n","\n","1. ¿Cuántas filas y columnas posee la base de datos?\n","2. ¿Qué nombre tienen las variables (columnas) en la base de datos?\n","3. ¿Cuántas flores poseen una longitud de sépalo mayor a 5cm?\n","4. ¿Cuántas flores poseen una longitud de pétalo menor a 5cm?\n","5. ¿Cuántas flores poseen un pétalo más ancho que su sépalo?\n","6. ¿Cuál es la longitud y ancho de pétalo máximo y mínimo registrado en la base de datos para la especie de flor \"virginica\"?\n","7. Filtre una base de datos que contenga a las especies de \"virginica\" y \"setosa\".\n","7. En la base incial, convierta la columna de \"Especie\" en factor, de tal manera de que los niveles tengan el orden: setosa, versicolor y virginica."],"metadata":{"id":"fc5JAH-71_2F"}},{"cell_type":"code","source":["# Esta base de datos está incorporada en R: ejecute esta celda y luego desarrolle los ejercicios.\n","datos = iris\n","colnames(datos) = c(\"LargoSepalo\", \"AnchoSepalo\", \"LargoPetalo\", \"AnchoPetalo\", \"Especie\")\n","datos"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"id":"UPzhfjfL8KcC","outputId":"1e34b8d1-20a5-4735-a20d-bd30c09f8517"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/html":["\n","\n","\n","\t\n","\t\n","\n","\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\n","
A data.frame: 150 × 5
LargoSepaloAnchoSepaloLargoPetaloAnchoPetaloEspecie
<dbl><dbl><dbl><dbl><fct>
5.13.51.40.2setosa
4.93.01.40.2setosa
4.73.21.30.2setosa
4.63.11.50.2setosa
5.03.61.40.2setosa
5.43.91.70.4setosa
4.63.41.40.3setosa
5.03.41.50.2setosa
4.42.91.40.2setosa
4.93.11.50.1setosa
5.43.71.50.2setosa
4.83.41.60.2setosa
4.83.01.40.1setosa
4.33.01.10.1setosa
5.84.01.20.2setosa
5.74.41.50.4setosa
5.43.91.30.4setosa
5.13.51.40.3setosa
5.73.81.70.3setosa
5.13.81.50.3setosa
5.43.41.70.2setosa
5.13.71.50.4setosa
4.63.61.00.2setosa
5.13.31.70.5setosa
4.83.41.90.2setosa
5.03.01.60.2setosa
5.03.41.60.4setosa
5.23.51.50.2setosa
5.23.41.40.2setosa
4.73.21.60.2setosa
⋮⋮⋮⋮⋮
6.93.25.72.3virginica
5.62.84.92.0virginica
7.72.86.72.0virginica
6.32.74.91.8virginica
6.73.35.72.1virginica
7.23.26.01.8virginica
6.22.84.81.8virginica
6.13.04.91.8virginica
6.42.85.62.1virginica
7.23.05.81.6virginica
7.42.86.11.9virginica
7.93.86.42.0virginica
6.42.85.62.2virginica
6.32.85.11.5virginica
6.12.65.61.4virginica
7.73.06.12.3virginica
6.33.45.62.4virginica
6.43.15.51.8virginica
6.03.04.81.8virginica
6.93.15.42.1virginica
6.73.15.62.4virginica
6.93.15.12.3virginica
5.82.75.11.9virginica
6.83.25.92.3virginica
6.73.35.72.5virginica
6.73.05.22.3virginica
6.32.55.01.9virginica
6.53.05.22.0virginica
6.23.45.42.3virginica
5.93.05.11.8virginica
\n"],"text/markdown":"\nA data.frame: 150 × 5\n\n| LargoSepalo <dbl> | AnchoSepalo <dbl> | LargoPetalo <dbl> | AnchoPetalo <dbl> | Especie <fct> |\n|---|---|---|---|---|\n| 5.1 | 3.5 | 1.4 | 0.2 | setosa |\n| 4.9 | 3.0 | 1.4 | 0.2 | setosa |\n| 4.7 | 3.2 | 1.3 | 0.2 | setosa |\n| 4.6 | 3.1 | 1.5 | 0.2 | setosa |\n| 5.0 | 3.6 | 1.4 | 0.2 | setosa |\n| 5.4 | 3.9 | 1.7 | 0.4 | setosa |\n| 4.6 | 3.4 | 1.4 | 0.3 | setosa |\n| 5.0 | 3.4 | 1.5 | 0.2 | setosa |\n| 4.4 | 2.9 | 1.4 | 0.2 | setosa |\n| 4.9 | 3.1 | 1.5 | 0.1 | setosa |\n| 5.4 | 3.7 | 1.5 | 0.2 | setosa |\n| 4.8 | 3.4 | 1.6 | 0.2 | setosa |\n| 4.8 | 3.0 | 1.4 | 0.1 | setosa |\n| 4.3 | 3.0 | 1.1 | 0.1 | setosa |\n| 5.8 | 4.0 | 1.2 | 0.2 | setosa |\n| 5.7 | 4.4 | 1.5 | 0.4 | setosa |\n| 5.4 | 3.9 | 1.3 | 0.4 | setosa |\n| 5.1 | 3.5 | 1.4 | 0.3 | setosa |\n| 5.7 | 3.8 | 1.7 | 0.3 | setosa |\n| 5.1 | 3.8 | 1.5 | 0.3 | setosa |\n| 5.4 | 3.4 | 1.7 | 0.2 | setosa |\n| 5.1 | 3.7 | 1.5 | 0.4 | setosa |\n| 4.6 | 3.6 | 1.0 | 0.2 | setosa |\n| 5.1 | 3.3 | 1.7 | 0.5 | setosa |\n| 4.8 | 3.4 | 1.9 | 0.2 | setosa |\n| 5.0 | 3.0 | 1.6 | 0.2 | setosa |\n| 5.0 | 3.4 | 1.6 | 0.4 | setosa |\n| 5.2 | 3.5 | 1.5 | 0.2 | setosa |\n| 5.2 | 3.4 | 1.4 | 0.2 | setosa |\n| 4.7 | 3.2 | 1.6 | 0.2 | setosa |\n| ⋮ | ⋮ | ⋮ | ⋮ | ⋮ |\n| 6.9 | 3.2 | 5.7 | 2.3 | virginica |\n| 5.6 | 2.8 | 4.9 | 2.0 | virginica |\n| 7.7 | 2.8 | 6.7 | 2.0 | virginica |\n| 6.3 | 2.7 | 4.9 | 1.8 | virginica |\n| 6.7 | 3.3 | 5.7 | 2.1 | virginica |\n| 7.2 | 3.2 | 6.0 | 1.8 | virginica |\n| 6.2 | 2.8 | 4.8 | 1.8 | virginica |\n| 6.1 | 3.0 | 4.9 | 1.8 | virginica |\n| 6.4 | 2.8 | 5.6 | 2.1 | virginica |\n| 7.2 | 3.0 | 5.8 | 1.6 | virginica |\n| 7.4 | 2.8 | 6.1 | 1.9 | virginica |\n| 7.9 | 3.8 | 6.4 | 2.0 | virginica |\n| 6.4 | 2.8 | 5.6 | 2.2 | virginica |\n| 6.3 | 2.8 | 5.1 | 1.5 | virginica |\n| 6.1 | 2.6 | 5.6 | 1.4 | virginica |\n| 7.7 | 3.0 | 6.1 | 2.3 | virginica |\n| 6.3 | 3.4 | 5.6 | 2.4 | virginica |\n| 6.4 | 3.1 | 5.5 | 1.8 | virginica |\n| 6.0 | 3.0 | 4.8 | 1.8 | virginica |\n| 6.9 | 3.1 | 5.4 | 2.1 | virginica |\n| 6.7 | 3.1 | 5.6 | 2.4 | virginica |\n| 6.9 | 3.1 | 5.1 | 2.3 | virginica |\n| 5.8 | 2.7 | 5.1 | 1.9 | virginica |\n| 6.8 | 3.2 | 5.9 | 2.3 | virginica |\n| 6.7 | 3.3 | 5.7 | 2.5 | virginica |\n| 6.7 | 3.0 | 5.2 | 2.3 | virginica |\n| 6.3 | 2.5 | 5.0 | 1.9 | virginica |\n| 6.5 | 3.0 | 5.2 | 2.0 | virginica |\n| 6.2 | 3.4 | 5.4 | 2.3 | virginica |\n| 5.9 | 3.0 | 5.1 | 1.8 | virginica |\n\n","text/latex":"A data.frame: 150 × 5\n\\begin{tabular}{lllll}\n LargoSepalo & AnchoSepalo & LargoPetalo & AnchoPetalo & Especie\\\\\n & & & & \\\\\n\\hline\n\t 5.1 & 3.5 & 1.4 & 0.2 & setosa\\\\\n\t 4.9 & 3.0 & 1.4 & 0.2 & setosa\\\\\n\t 4.7 & 3.2 & 1.3 & 0.2 & setosa\\\\\n\t 4.6 & 3.1 & 1.5 & 0.2 & setosa\\\\\n\t 5.0 & 3.6 & 1.4 & 0.2 & setosa\\\\\n\t 5.4 & 3.9 & 1.7 & 0.4 & setosa\\\\\n\t 4.6 & 3.4 & 1.4 & 0.3 & setosa\\\\\n\t 5.0 & 3.4 & 1.5 & 0.2 & setosa\\\\\n\t 4.4 & 2.9 & 1.4 & 0.2 & setosa\\\\\n\t 4.9 & 3.1 & 1.5 & 0.1 & setosa\\\\\n\t 5.4 & 3.7 & 1.5 & 0.2 & setosa\\\\\n\t 4.8 & 3.4 & 1.6 & 0.2 & setosa\\\\\n\t 4.8 & 3.0 & 1.4 & 0.1 & setosa\\\\\n\t 4.3 & 3.0 & 1.1 & 0.1 & setosa\\\\\n\t 5.8 & 4.0 & 1.2 & 0.2 & setosa\\\\\n\t 5.7 & 4.4 & 1.5 & 0.4 & setosa\\\\\n\t 5.4 & 3.9 & 1.3 & 0.4 & setosa\\\\\n\t 5.1 & 3.5 & 1.4 & 0.3 & setosa\\\\\n\t 5.7 & 3.8 & 1.7 & 0.3 & setosa\\\\\n\t 5.1 & 3.8 & 1.5 & 0.3 & setosa\\\\\n\t 5.4 & 3.4 & 1.7 & 0.2 & setosa\\\\\n\t 5.1 & 3.7 & 1.5 & 0.4 & setosa\\\\\n\t 4.6 & 3.6 & 1.0 & 0.2 & setosa\\\\\n\t 5.1 & 3.3 & 1.7 & 0.5 & setosa\\\\\n\t 4.8 & 3.4 & 1.9 & 0.2 & setosa\\\\\n\t 5.0 & 3.0 & 1.6 & 0.2 & setosa\\\\\n\t 5.0 & 3.4 & 1.6 & 0.4 & setosa\\\\\n\t 5.2 & 3.5 & 1.5 & 0.2 & setosa\\\\\n\t 5.2 & 3.4 & 1.4 & 0.2 & setosa\\\\\n\t 4.7 & 3.2 & 1.6 & 0.2 & setosa\\\\\n\t ⋮ & ⋮ & ⋮ & ⋮ & ⋮\\\\\n\t 6.9 & 3.2 & 5.7 & 2.3 & virginica\\\\\n\t 5.6 & 2.8 & 4.9 & 2.0 & virginica\\\\\n\t 7.7 & 2.8 & 6.7 & 2.0 & virginica\\\\\n\t 6.3 & 2.7 & 4.9 & 1.8 & virginica\\\\\n\t 6.7 & 3.3 & 5.7 & 2.1 & virginica\\\\\n\t 7.2 & 3.2 & 6.0 & 1.8 & virginica\\\\\n\t 6.2 & 2.8 & 4.8 & 1.8 & virginica\\\\\n\t 6.1 & 3.0 & 4.9 & 1.8 & virginica\\\\\n\t 6.4 & 2.8 & 5.6 & 2.1 & virginica\\\\\n\t 7.2 & 3.0 & 5.8 & 1.6 & virginica\\\\\n\t 7.4 & 2.8 & 6.1 & 1.9 & virginica\\\\\n\t 7.9 & 3.8 & 6.4 & 2.0 & virginica\\\\\n\t 6.4 & 2.8 & 5.6 & 2.2 & virginica\\\\\n\t 6.3 & 2.8 & 5.1 & 1.5 & virginica\\\\\n\t 6.1 & 2.6 & 5.6 & 1.4 & virginica\\\\\n\t 7.7 & 3.0 & 6.1 & 2.3 & virginica\\\\\n\t 6.3 & 3.4 & 5.6 & 2.4 & virginica\\\\\n\t 6.4 & 3.1 & 5.5 & 1.8 & virginica\\\\\n\t 6.0 & 3.0 & 4.8 & 1.8 & virginica\\\\\n\t 6.9 & 3.1 & 5.4 & 2.1 & virginica\\\\\n\t 6.7 & 3.1 & 5.6 & 2.4 & virginica\\\\\n\t 6.9 & 3.1 & 5.1 & 2.3 & virginica\\\\\n\t 5.8 & 2.7 & 5.1 & 1.9 & virginica\\\\\n\t 6.8 & 3.2 & 5.9 & 2.3 & virginica\\\\\n\t 6.7 & 3.3 & 5.7 & 2.5 & virginica\\\\\n\t 6.7 & 3.0 & 5.2 & 2.3 & virginica\\\\\n\t 6.3 & 2.5 & 5.0 & 1.9 & virginica\\\\\n\t 6.5 & 3.0 & 5.2 & 2.0 & virginica\\\\\n\t 6.2 & 3.4 & 5.4 & 2.3 & virginica\\\\\n\t 5.9 & 3.0 & 5.1 & 1.8 & virginica\\\\\n\\end{tabular}\n","text/plain":[" LargoSepalo AnchoSepalo LargoPetalo AnchoPetalo Especie \n","1 5.1 3.5 1.4 0.2 setosa \n","2 4.9 3.0 1.4 0.2 setosa \n","3 4.7 3.2 1.3 0.2 setosa \n","4 4.6 3.1 1.5 0.2 setosa \n","5 5.0 3.6 1.4 0.2 setosa \n","6 5.4 3.9 1.7 0.4 setosa \n","7 4.6 3.4 1.4 0.3 setosa \n","8 5.0 3.4 1.5 0.2 setosa \n","9 4.4 2.9 1.4 0.2 setosa \n","10 4.9 3.1 1.5 0.1 setosa \n","11 5.4 3.7 1.5 0.2 setosa \n","12 4.8 3.4 1.6 0.2 setosa \n","13 4.8 3.0 1.4 0.1 setosa \n","14 4.3 3.0 1.1 0.1 setosa \n","15 5.8 4.0 1.2 0.2 setosa \n","16 5.7 4.4 1.5 0.4 setosa \n","17 5.4 3.9 1.3 0.4 setosa \n","18 5.1 3.5 1.4 0.3 setosa \n","19 5.7 3.8 1.7 0.3 setosa \n","20 5.1 3.8 1.5 0.3 setosa \n","21 5.4 3.4 1.7 0.2 setosa \n","22 5.1 3.7 1.5 0.4 setosa \n","23 4.6 3.6 1.0 0.2 setosa \n","24 5.1 3.3 1.7 0.5 setosa \n","25 4.8 3.4 1.9 0.2 setosa \n","26 5.0 3.0 1.6 0.2 setosa \n","27 5.0 3.4 1.6 0.4 setosa \n","28 5.2 3.5 1.5 0.2 setosa \n","29 5.2 3.4 1.4 0.2 setosa \n","30 4.7 3.2 1.6 0.2 setosa \n","⋮ ⋮ ⋮ ⋮ ⋮ ⋮ \n","121 6.9 3.2 5.7 2.3 virginica\n","122 5.6 2.8 4.9 2.0 virginica\n","123 7.7 2.8 6.7 2.0 virginica\n","124 6.3 2.7 4.9 1.8 virginica\n","125 6.7 3.3 5.7 2.1 virginica\n","126 7.2 3.2 6.0 1.8 virginica\n","127 6.2 2.8 4.8 1.8 virginica\n","128 6.1 3.0 4.9 1.8 virginica\n","129 6.4 2.8 5.6 2.1 virginica\n","130 7.2 3.0 5.8 1.6 virginica\n","131 7.4 2.8 6.1 1.9 virginica\n","132 7.9 3.8 6.4 2.0 virginica\n","133 6.4 2.8 5.6 2.2 virginica\n","134 6.3 2.8 5.1 1.5 virginica\n","135 6.1 2.6 5.6 1.4 virginica\n","136 7.7 3.0 6.1 2.3 virginica\n","137 6.3 3.4 5.6 2.4 virginica\n","138 6.4 3.1 5.5 1.8 virginica\n","139 6.0 3.0 4.8 1.8 virginica\n","140 6.9 3.1 5.4 2.1 virginica\n","141 6.7 3.1 5.6 2.4 virginica\n","142 6.9 3.1 5.1 2.3 virginica\n","143 5.8 2.7 5.1 1.9 virginica\n","144 6.8 3.2 5.9 2.3 virginica\n","145 6.7 3.3 5.7 2.5 virginica\n","146 6.7 3.0 5.2 2.3 virginica\n","147 6.3 2.5 5.0 1.9 virginica\n","148 6.5 3.0 5.2 2.0 virginica\n","149 6.2 3.4 5.4 2.3 virginica\n","150 5.9 3.0 5.1 1.8 virginica"]},"metadata":{}}]}]}