{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "

1. Úvod do jazyka R

\n", "Preložené z knihy An Introduction to Statistical Learning with Applications in R, od autorov Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani.\n", "Kapitola 2 - časť 2.3 Lab: Introduction to R.\n", "\n", "

Základné príkazy

\n", "\n", "R používa funkcie na vykonanie operácií. Na to aby sme volali funkciu napr. funkcmeno , tak musíme napísať funkcmeno (vstup1, vstup2), kde vstupy alebo argumenty povedia R ako majú spustiť funkciu. \n", "Napríklad pre vytvorenie vektora čísiel musíme použiť funkciu c() (pre concatenate - zlúčenie, zreťazenie). \n", "Všetky čísla vo vnútry zátvoriek sú spojené spolu. \n", "Nasledovný príkaz zreťazí čísla 1, 3, 2, a 5 a uloží ich do vektora s názvom x. \n", "Potom napíšeme x, a vráti nám to naspäť vektor. " ] }, { "cell_type": "code", "execution_count": 380, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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\n" ], "text/latex": [ "\\begin{enumerate*}\n", "\\item 1\n", "\\item 3\n", "\\item 2\n", "\\item 5\n", "\\end{enumerate*}\n" ], "text/markdown": [ "1. 1\n", "2. 3\n", "3. 2\n", "4. 5\n", "\n", "\n" ], "text/plain": [ "[1] 1 3 2 5" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "x <- c(1, 3, 2, 5)\n", "x" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Uloženie do premenej sa vykonáva príkazom = namiesto <-. " ] }, { "cell_type": "code", "execution_count": 381, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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\n" ], "text/latex": [ "\\begin{enumerate*}\n", "\\item 1\n", "\\item 6\n", "\\item 2\n", "\\end{enumerate*}\n" ], "text/markdown": [ "1. 1\n", "2. 6\n", "3. 2\n", "\n", "\n" ], "text/plain": [ "[1] 1 6 2" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "x = c(1,6,2)\n", "x" ] }, { "cell_type": "code", "execution_count": 382, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
    \n", "\t
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\n" ], "text/latex": [ "\\begin{enumerate*}\n", "\\item 1\n", "\\item 4\n", "\\item 3\n", "\\end{enumerate*}\n" ], "text/markdown": [ "1. 1\n", "2. 4\n", "3. 3\n", "\n", "\n" ], "text/plain": [ "[1] 1 4 3" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "y = c(1,4,3)\n", "y" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Napísaným ?funkcmeno sa vždy spustí nové okno s dodatočnými informáciami o danej funkcii. \n", "V R môžeme pripočítať dve množiny čísiel spolu. Ono to pridá prvé číslo z x k prvému číslu z y a tak ďalej. Avšak, x a y by mali byť rovnakej veľkosti. Veľkosť môžeme zístiť pomocou funkcie length(). " ] }, { "cell_type": "code", "execution_count": 383, "metadata": {}, "outputs": [ { "data": { "text/html": [ "3" ], "text/latex": [ "3" ], "text/markdown": [ "3" ], "text/plain": [ "[1] 3" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "length(x)" ] }, { "cell_type": "code", "execution_count": 384, "metadata": {}, "outputs": [ { "data": { "text/html": [ "3" ], "text/latex": [ "3" ], "text/markdown": [ "3" ], "text/plain": [ "[1] 3" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "length(y)" ] }, { "cell_type": "code", "execution_count": 385, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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\n" ], "text/latex": [ "\\begin{enumerate*}\n", "\\item 2\n", "\\item 10\n", "\\item 5\n", "\\end{enumerate*}\n" ], "text/markdown": [ "1. 2\n", "2. 10\n", "3. 5\n", "\n", "\n" ], "text/plain": [ "[1] 2 10 5" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "x+y" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Funkcia ls() dovoľuje nám nazrieť na zoznam všetkých objektov, ako sú dáta a funkcie, ktoré sme doposial uložili. \n", "Funkcia rm() môže byť použitá na vymazanie toho čo už nepotrebujeme. " ] }, { "cell_type": "code", "execution_count": 386, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
    \n", "\t
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  2. 'Auto'
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\n" ], "text/latex": [ "\\begin{enumerate*}\n", "\\item 'A'\n", "\\item 'Auto'\n", "\\item 'cylinders'\n", "\\item 'f'\n", "\\item 'fa'\n", "\\item 'x'\n", "\\item 'y'\n", "\\end{enumerate*}\n" ], "text/markdown": [ "1. 'A'\n", "2. 'Auto'\n", "3. 'cylinders'\n", "4. 'f'\n", "5. 'fa'\n", "6. 'x'\n", "7. 'y'\n", "\n", "\n" ], "text/plain": [ "[1] \"A\" \"Auto\" \"cylinders\" \"f\" \"fa\" \"x\" \n", "[7] \"y\" " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ls()" ] }, { "cell_type": "code", "execution_count": 387, "metadata": {}, "outputs": [], "source": [ "rm(x, y)" ] }, { "cell_type": "code", "execution_count": 388, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
    \n", "\t
  1. 'A'
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\n" ], "text/latex": [ "\\begin{enumerate*}\n", "\\item 'A'\n", "\\item 'Auto'\n", "\\item 'cylinders'\n", "\\item 'f'\n", "\\item 'fa'\n", "\\end{enumerate*}\n" ], "text/markdown": [ "1. 'A'\n", "2. 'Auto'\n", "3. 'cylinders'\n", "4. 'f'\n", "5. 'fa'\n", "\n", "\n" ], "text/plain": [ "[1] \"A\" \"Auto\" \"cylinders\" \"f\" \"fa\" " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ls()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Tiež je možné vymazať všetky objekty naraz. " ] }, { "cell_type": "code", "execution_count": 389, "metadata": {}, "outputs": [], "source": [ "rm(list=ls())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Funkcia matrix() môže byť použitá na vytvorenie matice čísel. Pred tým než použijeme túto funkciu sa môžme o nej viac naučiť použitím príkazu ?matrix." ] }, { "cell_type": "code", "execution_count": 390, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "
matrix {base}R Documentation
\n", "\n", "

Matrices

\n", "\n", "

Description

\n", "\n", "

matrix creates a matrix from the given set of values.\n", "

\n", "

as.matrix attempts to turn its argument into a matrix.\n", "

\n", "

is.matrix tests if its argument is a (strict) matrix.\n", "

\n", "\n", "\n", "

Usage

\n", "\n", "
\n",
       "matrix(data = NA, nrow = 1, ncol = 1, byrow = FALSE,\n",
       "       dimnames = NULL)\n",
       "\n",
       "as.matrix(x, ...)\n",
       "## S3 method for class 'data.frame'\n",
       "as.matrix(x, rownames.force = NA, ...)\n",
       "\n",
       "is.matrix(x)\n",
       "
\n", "\n", "\n", "

Arguments

\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
data\n", "

an optional data vector (including a list or\n", "expression vector). Non-atomic classed R objects are\n", "coerced by as.vector and all attributes discarded.

\n", "
nrow\n", "

the desired number of rows.

\n", "
ncol\n", "

the desired number of columns.

\n", "
byrow\n", "

logical. If FALSE (the default) the matrix is\n", "filled by columns, otherwise the matrix is filled by rows.

\n", "
dimnames\n", "

A dimnames attribute for the matrix:\n", "NULL or a list of length 2 giving the row and column\n", "names respectively. An empty list is treated as NULL, and a\n", "list of length one as row names. The list can be named, and the\n", "list names will be used as names for the dimensions.

\n", "
x\n", "

an R object.

\n", "
...\n", "

additional arguments to be passed to or from methods.

\n", "
rownames.force\n", "

logical indicating if the resulting matrix\n", "should have character (rather than NULL)\n", "rownames. The default, NA, uses NULL\n", "rownames if the data frame has ‘automatic’ row.names or for a\n", "zero-row data frame.

\n", "
\n", "\n", "\n", "

Details

\n", "\n", "

If one of nrow or ncol is not given, an attempt is\n", "made to infer it from the length of data and the other\n", "parameter. If neither is given, a one-column matrix is returned.\n", "

\n", "

If there are too few elements in data to fill the matrix,\n", "then the elements in data are recycled. If data has\n", "length zero, NA of an appropriate type is used for atomic\n", "vectors (0 for raw vectors) and NULL for lists.\n", "

\n", "

is.matrix returns TRUE if x is a vector and has a\n", "\"dim\" attribute of length 2) and FALSE otherwise.\n", "Note that a data.frame is not a matrix by this\n", "test. The function is generic: you can write methods to handle\n", "specific classes of objects, see InternalMethods.\n", "

\n", "

as.matrix is a generic function. The method for data frames\n", "will return a character matrix if there is only atomic columns and any\n", "non-(numeric/logical/complex) column, applying as.vector\n", "to factors and format to other non-character columns.\n", "Otherwise, the usual coercion hierarchy (logical < integer < double <\n", "complex) will be used, e.g., all-logical data frames will be coerced\n", "to a logical matrix, mixed logical-integer will give a integer matrix,\n", "etc.\n", "

\n", "

The default method for as.matrix calls as.vector(x), and\n", "hence e.g. coerces factors to character vectors.\n", "

\n", "

When coercing a vector, it produces a one-column matrix, and\n", "promotes the names (if any) of the vector to the rownames of the matrix.\n", "

\n", "

is.matrix is a primitive function.\n", "

\n", "

The print method for a matrix gives a rectangular layout with\n", "dimnames or indices. For a list matrix, the entries of length not\n", "one are printed in the form integer,7 indicating the type\n", "and length.\n", "

\n", "\n", "\n", "

Note

\n", "\n", "

If you just want to convert a vector to a matrix, something like\n", "

\n", "
  dim(x) <- c(nx, ny)\n",
       "  dimnames(x) <- list(row_names, col_names)\n",
       "
\n", "

will avoid duplicating x.\n", "

\n", "\n", "\n", "

References

\n", "\n", "

Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988)\n", "The New S Language.\n", "Wadsworth & Brooks/Cole.\n", "

\n", "\n", "\n", "

See Also

\n", "\n", "

data.matrix, which attempts to convert to a numeric\n", "matrix.\n", "

\n", "

A matrix is the special case of a two-dimensional array.\n", "

\n", "\n", "\n", "

Examples

\n", "\n", "
\n",
       "is.matrix(as.matrix(1:10))\n",
       "!is.matrix(warpbreaks)  # data.frame, NOT matrix!\n",
       "warpbreaks[1:10,]\n",
       "as.matrix(warpbreaks[1:10,])  # using as.matrix.data.frame(.) method\n",
       "\n",
       "## Example of setting row and column names\n",
       "mdat <- matrix(c(1,2,3, 11,12,13), nrow = 2, ncol = 3, byrow = TRUE,\n",
       "               dimnames = list(c(\"row1\", \"row2\"),\n",
       "                               c(\"C.1\", \"C.2\", \"C.3\")))\n",
       "mdat\n",
       "
\n", "\n", "
[Package base version 3.4.2 ]
" ], "text/latex": [ "\\inputencoding{utf8}\n", "\\HeaderA{matrix}{Matrices}{matrix}\n", "\\aliasA{as.matrix}{matrix}{as.matrix}\n", "\\methaliasA{as.matrix.data.frame}{matrix}{as.matrix.data.frame}\n", "\\methaliasA{as.matrix.default}{matrix}{as.matrix.default}\n", "\\aliasA{is.matrix}{matrix}{is.matrix}\n", "\\keyword{array}{matrix}\n", "\\keyword{algebra}{matrix}\n", "%\n", "\\begin{Description}\\relax\n", "\\code{matrix} creates a matrix from the given set of values.\n", "\n", "\\code{as.matrix} attempts to turn its argument into a matrix.\n", "\n", "\\code{is.matrix} tests if its argument is a (strict) matrix.\n", "\\end{Description}\n", "%\n", "\\begin{Usage}\n", "\\begin{verbatim}\n", "matrix(data = NA, nrow = 1, ncol = 1, byrow = FALSE,\n", " dimnames = NULL)\n", "\n", "as.matrix(x, ...)\n", "## S3 method for class 'data.frame'\n", "as.matrix(x, rownames.force = NA, ...)\n", "\n", "is.matrix(x)\n", "\\end{verbatim}\n", "\\end{Usage}\n", "%\n", "\\begin{Arguments}\n", "\\begin{ldescription}\n", "\\item[\\code{data}] an optional data vector (including a list or\n", "\\code{\\LinkA{expression}{expression}} vector). Non-atomic classed \\R{} objects are\n", "coerced by \\code{\\LinkA{as.vector}{as.vector}} and all attributes discarded.\n", "\\item[\\code{nrow}] the desired number of rows.\n", "\\item[\\code{ncol}] the desired number of columns.\n", "\\item[\\code{byrow}] logical. If \\code{FALSE} (the default) the matrix is\n", "filled by columns, otherwise the matrix is filled by rows.\n", "\\item[\\code{dimnames}] A \\code{\\LinkA{dimnames}{dimnames}} attribute for the matrix:\n", "\\code{NULL} or a \\code{list} of length 2 giving the row and column\n", "names respectively. An empty list is treated as \\code{NULL}, and a\n", "list of length one as row names. The list can be named, and the\n", "list names will be used as names for the dimensions.\n", "\\item[\\code{x}] an \\R{} object.\n", "\\item[\\code{...}] additional arguments to be passed to or from methods.\n", "\\item[\\code{rownames.force}] logical indicating if the resulting matrix\n", "should have character (rather than \\code{NULL})\n", "\\code{\\LinkA{rownames}{rownames}}. The default, \\code{NA}, uses \\code{NULL}\n", "rownames if the data frame has `automatic' row.names or for a\n", "zero-row data frame.\n", "\\end{ldescription}\n", "\\end{Arguments}\n", "%\n", "\\begin{Details}\\relax\n", "If one of \\code{nrow} or \\code{ncol} is not given, an attempt is\n", "made to infer it from the length of \\code{data} and the other\n", "parameter. If neither is given, a one-column matrix is returned.\n", "\n", "If there are too few elements in \\code{data} to fill the matrix,\n", "then the elements in \\code{data} are recycled. If \\code{data} has\n", "length zero, \\code{NA} of an appropriate type is used for atomic\n", "vectors (\\code{0} for raw vectors) and \\code{NULL} for lists.\n", "\n", "\\code{is.matrix} returns \\code{TRUE} if \\code{x} is a vector and has a\n", "\\code{\"\\LinkA{dim}{dim}\"} attribute of length 2) and \\code{FALSE} otherwise.\n", "Note that a \\code{\\LinkA{data.frame}{data.frame}} is \\strong{not} a matrix by this\n", "test. The function is generic: you can write methods to handle\n", "specific classes of objects, see \\LinkA{InternalMethods}{InternalMethods}.\n", "\n", "\\code{as.matrix} is a generic function. The method for data frames\n", "will return a character matrix if there is only atomic columns and any\n", "non-(numeric/logical/complex) column, applying \\code{\\LinkA{as.vector}{as.vector}}\n", "to factors and \\code{\\LinkA{format}{format}} to other non-character columns.\n", "Otherwise, the usual coercion hierarchy (logical < integer < double <\n", "complex) will be used, e.g., all-logical data frames will be coerced\n", "to a logical matrix, mixed logical-integer will give a integer matrix,\n", "etc.\n", "\n", "The default method for \\code{as.matrix} calls \\code{as.vector(x)}, and\n", "hence e.g.~coerces factors to character vectors.\n", "\n", "When coercing a vector, it produces a one-column matrix, and\n", "promotes the names (if any) of the vector to the rownames of the matrix.\n", "\n", "\\code{is.matrix} is a \\LinkA{primitive}{primitive} function.\n", "\n", "The \\code{print} method for a matrix gives a rectangular layout with\n", "dimnames or indices. For a list matrix, the entries of length not\n", "one are printed in the form \\samp{integer,7} indicating the type\n", "and length.\n", "\\end{Details}\n", "%\n", "\\begin{Note}\\relax\n", "If you just want to convert a vector to a matrix, something like\n", "\\begin{alltt} dim(x) <- c(nx, ny)\n", " dimnames(x) <- list(row_names, col_names)\n", "\\end{alltt}\n", "\n", "will avoid duplicating \\code{x}.\n", "\\end{Note}\n", "%\n", "\\begin{References}\\relax\n", "Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988)\n", "\\emph{The New S Language}.\n", "Wadsworth \\& Brooks/Cole.\n", "\\end{References}\n", "%\n", "\\begin{SeeAlso}\\relax\n", "\\code{\\LinkA{data.matrix}{data.matrix}}, which attempts to convert to a numeric\n", "matrix.\n", "\n", "A matrix is the special case of a two-dimensional \\code{\\LinkA{array}{array}}.\n", "\\end{SeeAlso}\n", "%\n", "\\begin{Examples}\n", "\\begin{ExampleCode}\n", "is.matrix(as.matrix(1:10))\n", "!is.matrix(warpbreaks) # data.frame, NOT matrix!\n", "warpbreaks[1:10,]\n", "as.matrix(warpbreaks[1:10,]) # using as.matrix.data.frame(.) method\n", "\n", "## Example of setting row and column names\n", "mdat <- matrix(c(1,2,3, 11,12,13), nrow = 2, ncol = 3, byrow = TRUE,\n", " dimnames = list(c(\"row1\", \"row2\"),\n", " c(\"C.1\", \"C.2\", \"C.3\")))\n", "mdat\n", "\\end{ExampleCode}\n", "\\end{Examples}" ], "text/plain": [ "matrix package:base R Documentation\n", "\n", "_\bM_\ba_\bt_\br_\bi_\bc_\be_\bs\n", "\n", "_\bD_\be_\bs_\bc_\br_\bi_\bp_\bt_\bi_\bo_\bn:\n", "\n", " 'matrix' creates a matrix from the given set of values.\n", "\n", " 'as.matrix' attempts to turn its argument into a matrix.\n", "\n", " 'is.matrix' tests if its argument is a (strict) matrix.\n", "\n", "_\bU_\bs_\ba_\bg_\be:\n", "\n", " matrix(data = NA, nrow = 1, ncol = 1, byrow = FALSE,\n", " dimnames = NULL)\n", " \n", " as.matrix(x, ...)\n", " ## S3 method for class 'data.frame'\n", " as.matrix(x, rownames.force = NA, ...)\n", " \n", " is.matrix(x)\n", " \n", "_\bA_\br_\bg_\bu_\bm_\be_\bn_\bt_\bs:\n", "\n", " data: an optional data vector (including a list or 'expression'\n", " vector). Non-atomic classed R objects are coerced by\n", " 'as.vector' and all attributes discarded.\n", "\n", " nrow: the desired number of rows.\n", "\n", " ncol: the desired number of columns.\n", "\n", " byrow: logical. If 'FALSE' (the default) the matrix is filled by\n", " columns, otherwise the matrix is filled by rows.\n", "\n", "dimnames: A 'dimnames' attribute for the matrix: 'NULL' or a 'list' of\n", " length 2 giving the row and column names respectively. An\n", " empty list is treated as 'NULL', and a list of length one as\n", " row names. The list can be named, and the list names will be\n", " used as names for the dimensions.\n", "\n", " x: an R object.\n", "\n", " ...: additional arguments to be passed to or from methods.\n", "\n", "rownames.force: logical indicating if the resulting matrix should have\n", " character (rather than 'NULL') 'rownames'. The default,\n", " 'NA', uses 'NULL' rownames if the data frame has 'automatic'\n", " row.names or for a zero-row data frame.\n", "\n", "_\bD_\be_\bt_\ba_\bi_\bl_\bs:\n", "\n", " If one of 'nrow' or 'ncol' is not given, an attempt is made to\n", " infer it from the length of 'data' and the other parameter. If\n", " neither is given, a one-column matrix is returned.\n", "\n", " If there are too few elements in 'data' to fill the matrix, then\n", " the elements in 'data' are recycled. If 'data' has length zero,\n", " 'NA' of an appropriate type is used for atomic vectors ('0' for\n", " raw vectors) and 'NULL' for lists.\n", "\n", " 'is.matrix' returns 'TRUE' if 'x' is a vector and has a '\"dim\"'\n", " attribute of length 2) and 'FALSE' otherwise. Note that a\n", " 'data.frame' is *not* a matrix by this test. The function is\n", " generic: you can write methods to handle specific classes of\n", " objects, see InternalMethods.\n", "\n", " 'as.matrix' is a generic function. The method for data frames\n", " will return a character matrix if there is only atomic columns and\n", " any non-(numeric/logical/complex) column, applying 'as.vector' to\n", " factors and 'format' to other non-character columns. Otherwise,\n", " the usual coercion hierarchy (logical < integer < double <\n", " complex) will be used, e.g., all-logical data frames will be\n", " coerced to a logical matrix, mixed logical-integer will give a\n", " integer matrix, etc.\n", "\n", " The default method for 'as.matrix' calls 'as.vector(x)', and hence\n", " e.g. coerces factors to character vectors.\n", "\n", " When coercing a vector, it produces a one-column matrix, and\n", " promotes the names (if any) of the vector to the rownames of the\n", " matrix.\n", "\n", " 'is.matrix' is a primitive function.\n", "\n", " The 'print' method for a matrix gives a rectangular layout with\n", " dimnames or indices. For a list matrix, the entries of length not\n", " one are printed in the form 'integer,7' indicating the type and\n", " length.\n", "\n", "_\bN_\bo_\bt_\be:\n", "\n", " If you just want to convert a vector to a matrix, something like\n", " \n", " dim(x) <- c(nx, ny)\n", " dimnames(x) <- list(row_names, col_names)\n", "\n", " will avoid duplicating 'x'.\n", "\n", "_\bR_\be_\bf_\be_\br_\be_\bn_\bc_\be_\bs:\n", "\n", " Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988) _The New S\n", " Language_. Wadsworth & Brooks/Cole.\n", "\n", "_\bS_\be_\be _\bA_\bl_\bs_\bo:\n", "\n", " 'data.matrix', which attempts to convert to a numeric matrix.\n", "\n", " A matrix is the special case of a two-dimensional 'array'.\n", "\n", "_\bE_\bx_\ba_\bm_\bp_\bl_\be_\bs:\n", "\n", " is.matrix(as.matrix(1:10))\n", " !is.matrix(warpbreaks) # data.frame, NOT matrix!\n", " warpbreaks[1:10,]\n", " as.matrix(warpbreaks[1:10,]) # using as.matrix.data.frame(.) method\n", " \n", " ## Example of setting row and column names\n", " mdat <- matrix(c(1,2,3, 11,12,13), nrow = 2, ncol = 3, byrow = TRUE,\n", " dimnames = list(c(\"row1\", \"row2\"),\n", " c(\"C.1\", \"C.2\", \"C.3\")))\n", " mdat\n", " " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "?matrix" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Tento pomocný súbor nám prezradí to čo funkcia matrix() má za vstupy... ale pre začiatok sa sústredme na prvé tri: dáta (položky v matici), počet riadkov, a počet stĺpcov.\n", "Najprv vytvorme jednoduchú maticu." ] }, { "cell_type": "code", "execution_count": 391, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\t\n", "\t\n", "\n", "
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\n" ], "text/latex": [ "\\begin{tabular}{ll}\n", "\t 1 & 3\\\\\n", "\t 2 & 4\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "| 1 | 3 | \n", "| 2 | 4 | \n", "\n", "\n" ], "text/plain": [ " [,1] [,2]\n", "[1,] 1 3 \n", "[2,] 2 4 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "x = matrix(data = c(1,2,3,4), nrow = 2, ncol = 2)\n", "x" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Poznámka: môžeme vynechať písanie znakov data=, nrow=, ncol=, v funkcii matrix(). " ] }, { "cell_type": "code", "execution_count": 392, "metadata": {}, "outputs": [], "source": [ "x = matrix(c(1,2,3,4),2,2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Spustenie príkazu bude mať rovnaký efekt. Avšak, to môže byť niekedy užitočné, keď chceme špecifikovať mená argumentov, ktoré vstupujú do funkcie. R predpokladá, že argumenty sú písané v rovnakom poradí ako je to v pomocnom súbore. V predošlom príklade R vytvorilo maticu úspešne vyplnením stĺpcov. Alternatívne použitím možnosti byrow=TRUE môžeme vyplniť maticu v poradí začinajúc riadkami namiesto stĺpcov. " ] }, { "cell_type": "code", "execution_count": 393, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\t\n", "\t\n", "\n", "
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\n" ], "text/latex": [ "\\begin{tabular}{ll}\n", "\t 1 & 2\\\\\n", "\t 3 & 4\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "| 1 | 2 | \n", "| 3 | 4 | \n", "\n", "\n" ], "text/plain": [ " [,1] [,2]\n", "[1,] 1 2 \n", "[2,] 3 4 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "matrix(c(1,2,3,4), 2, 2, byrow=TRUE)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Všimnite si, že horné príkazy nepriradili hodnotu matice do premennej x. V tomto prípade je matica len vypísaná na obrazovku, ale nie je uložená pre budúce výpočty. \n", "Funkcia sqrt() vráti odmocninu každého prvku vo vektore alebo v matici. \n", "Príkaz x^2 zvýši hodnotu každého prvku na mocninu dvoch. Každá mocnina je možná, zahrňujúc zlomky alebo záporné čísla. " ] }, { "cell_type": "code", "execution_count": 394, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\t\n", "\t\n", "\n", "
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\n" ], "text/latex": [ "\\begin{tabular}{ll}\n", "\t 1 & 9\\\\\n", "\t 4 & 16\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "| 1 | 9 | \n", "| 4 | 16 | \n", "\n", "\n" ], "text/plain": [ " [,1] [,2]\n", "[1,] 1 9 \n", "[2,] 4 16 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "x^2" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Funkcia rnorm() vygeneruje vektor náhodných čísiel z normálového rozdelenia. Prvým argumentom funkcie je n - veľkosť generovanej vzorky dát. \n", "Zakaždým, keď sa volá táto funkcia, tak získame odlišné údaje. \n", "Teraz vytvoríme dve korelované množiny čísel, x a y, a použijeme funkciu cor() na vypočítanie korelácie medzi nimi. " ] }, { "cell_type": "code", "execution_count": 396, "metadata": {}, "outputs": [ { "data": { "text/html": [ "0.995528952267613" ], "text/latex": [ "0.995528952267613" ], "text/markdown": [ "0.995528952267613" ], "text/plain": [ "[1] 0.995529" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "x = rnorm(50)\n", "y = x + rnorm(50, mean=50, sd=.1)\n", "cor(x, y)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Defaultne funkcia rnorm() vytvorí štandardné normované náhodné premenné s priemerom 0 a štandardnou odchýlkou 1. Avšak, priemer a štandardná odchýlka môže byť zmenená použítím argumentov mean a sd, ako bolo ukazané vyššie. \n", "Niekedy chceme náš kód reprokukovať v presnej množine náhodných čísel. Na to, aby sme to spravili použijeme funkciu set.seed(). Táto funkcia berie integer resp. celé číslo ako argument. " ] }, { "cell_type": "code", "execution_count": 397, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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Grafika

\n", "\n", "Funkcia plot() je primárny spôsob na vykreslovanie údajov v R. \n", "Napríklad plot(x, y) produkuje scatterplot čísiel x verzus čísla z y. \n", "Je veľa dodatočných možností, ktoré môžu byť posunuté ako argument funkcii plot(). \n", "Napríklad agrument xlab označí výsledky na osi x-ovej. \n", "Príkazom ?plot zobrazíme ďalšie informácie o funkcii plot(). " ] }, { "cell_type": "code", "execution_count": 402, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "plot without title" ] }, "metadata": {}, "output_type": "display_data", "source": "R display func" } ], "source": [ "x = rnorm(100)\n", "y = rnorm(100)\n", "plot(x, y)" ] }, { "cell_type": "code", "execution_count": 403, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "Plot with title \"Graf X vs Y\"" ] }, "metadata": {}, "output_type": "display_data", "source": "R display func" } ], "source": [ "plot(x, y, xlab=\"Toto je x-ová os\", ylab=\"Toto je y-ová os\", main=\"Graf X vs Y\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Veľmi často budeme potrebovať uložiť výsledok grafu. Príkaz na to bude zaležať na type súboru, ktorý chceme vytvoriť. \n", "Napríklad na to, aby sme vytvorili pdf súbor, tak použijeme funkciu pdf(), a ďalej môžme vytvoriť aj jpg súbor použiťím funkcie jpeg(). " ] }, { "cell_type": "code", "execution_count": 404, "metadata": {}, "outputs": [ { "data": { "text/html": [ "png: 2" ], "text/latex": [ "\\textbf{png:} 2" ], "text/markdown": [ "**png:** 2" ], "text/plain": [ "png \n", " 2 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "pdf(\"ISLR - 1. Úvod do jazyka R - Graf.pdf\")\n", "plot(x, y, col = \"green\")\n", "dev.off()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Funkcia dev.off() indikuje R to, že sme skončili s vytváraním grafu. Alternatívne môžeme jednoducho skopírovať graf a prilepiť to do vhodného súborového typu, ako napríklad dokument Word. \n", "

\n", "Funkcia seq() môže byť použitá na vytvorenie sequencie/postupnosti čísel Napríklad, seq(a, b) vytvorí vektor čísiel v rozsahu od a do b. \n", "Existuje veľa ďalších možností: napríklad, seq(0, 1, length=10) vytvorí sekvenciu 10 čísliel, ktoré sú rovnako rozdelené medzi 0 a 1. Napísaním 3:11 je skratka pre príkaz seq(3, 11) pre celočíslené argumenty. " ] }, { "cell_type": "code", "execution_count": 405, "metadata": {}, "outputs": [ { "data": { "text/html": [ "

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    \n", "
  1. Vektor x hodnôt (prvá dimenzia).
    2. \n", "
  2. Vektor y hodnôt (druhá dimenzia).
    3. \n", "
  3. Matica, ktorej prvky sú zhodné / korešpondujú s z hodnotou (tretia dimenzia) pre každý pár z (x, y) súradníc.
    4. \n", "
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\n", "Ako s funkciou plot(), existuje veľa iných vstupov, ktoré môžu vyladiť výstupný graf funkcie contour(). \n", " Pre zistenie ďalších vlastností funkcie si môžete pozrieť pomocný text zadaním príkazu ?contour.\n", "

" ] }, { "cell_type": "code", "execution_count": 408, "metadata": {}, "outputs": [ { "data": { "image/png": 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kh5RxnoVPKPQkh5RxnoVPKPQkh5RxnoVPKPEigkIC9CAgQICRAgJECAkAABQgIE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\nQICQAAFCAgQICRAgJECAkACB/wE7xU1v6j0JwAAAAABJRU5ErkJggg==", "text/plain": [ "plot without title" ] }, "metadata": {}, "output_type": "display_data", "source": "R display func" } ], "source": [ "y = x\n", "f = outer(x, y, function(x, y)cos(y)/(1 + x^2)) \n", "contour(x, y, f) " ] }, { "cell_type": "code", "execution_count": 409, "metadata": {}, "outputs": [ { "data": { "text/html": [ "png: 2" ], "text/latex": [ "\\textbf{png:} 2" ], "text/markdown": [ "**png:** 2" ], "text/plain": [ "png \n", " 2 " ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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p1eGCQ+IyUNFKXyOihzgjQaSMUyglQulddBmRMkghQXQSJIcF+OIJUhI0jlUnkd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\nESSKAuj/aAAWX5QRvHgAAAAASUVORK5CYII=", "text/plain": [ "plot without title" ] }, "metadata": {}, "output_type": "display_data", "source": "R display func" } ], "source": [ "pdf(\"ISLR - 1. Úvod do jazyka R - Graf Contour.pdf\")\n", "contour(x, y, f, nlevels = 45)\n", "dev.off() \n", "contour(x, y, f, nlevels = 45)" ] }, { "cell_type": "code", "execution_count": 410, "metadata": {}, "outputs": [ { "data": { "image/png": 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nST+RqpUokguZhgRMoqRiScuQATBCmrCBLOXICJwNSOu3YqWVM77tqZlR4k4RQT\npHvpQdoeZoIUYiImIhVZGwTpToaItDnMBCnERFBqV0RtEKQ7WVK7rWEmSCEmoq6R+GSDVqZr\nJD7Z0KLsIMltE6Sb1CAJ2yFILiYIUlYRJJy5ABMEKasIEs5cgAmClFUECWcuwEQcSBIvIEg3\nGUFqnxiC5FmXIEWLIOHMBZggSFlFkHDmAkwQpKwiSChz//vPz/XV4Z8f//MyQZDyiiBhzH3+\nKDe9u5jQ1CVI0SJIGHMf5e2/f9a//v7zVj48TGjqEqRoESSMubfy5/r3n/LmYUJTlyBFiyBh\nzD08fLjxwOe9jCbWdozlCJK3CBLGHCPS7kevIYKEMffvNdI/f9e/eI30kiJIIHPvd7nbj08X\nE4q6BClaBAll7n8f632kt5//4X2kFxRBwpkLMEGQsoog4cwFmCBIWUWQcOYCTBCkrCJIOHMB\nJghSVhEknLkAEwQpqwgSzlyACYKUVQQJZy7ABEHKKoKEMxdggiBlFUHCmQswQZCyiiDhzAWY\nIEhZRZBw5gJMEKSsIkg4cwEmCFJWESScuQATBCmrCBLOXIAJgpRVBAlnLsAEQcoqgoQzF2CC\nIGUVQcKZCzBBkLKKIOHMBZggSFlFkHDmAkwQpKwiSDhzASYIUlYRJJy5ABMEKasIEs5cgAmC\nlFUECWcuwARByiqChDMXYIIgZRVBwpkLMEGQsoog4cwFmCBIWUWQcOYCTBCkrCJIOHP+JsS/\nrUSQopUfpBa/C6kSZULxE2XdOCJI2o/iSNI4kNVEUxVfE5ez1wyCB0cMSBXBhsyPpLMX6YGa\nACTLKfTjiCAhPtLPn86linqeBgepaApf6mz8fm3tAwlHBKkm4KDpJ0gTZAxuNTpIZVk0v968\nXTSSI4JU/cyJpL3Z37FWHv4lqqJUIpDKUi5JraT0zjAaMmwGJJOQw2Za7GSBqTxUl8E3Iki3\nsSjL7a9ak7vjEcoRQRJ85kqSgKXTdsN94Tp+A4JU7s5yPcNqRDoM6SCOCJJA2IEzk1QD486h\nyvVfFZTGBOlcoJwQOU59K4tJMEcESfKhP0nHflFuK/WVpYrGBql6jqIofviBHRAGpGeB16Da\nZNUmXxCYLpndnKmd7FJQfF2p+HcjRwRJ9mEQScuhk9xW6+XQz4SWEFWgJq67dJXALekniiOC\nJFPbKuRCkuCC6XHp3i2mVW+QlipI4vtK4RwRJOGnoSQJWKq2MyJIx6ft+eDqbscYkIRqDEmG\nKZIO+VEOM+c10mFF1wdXmzkiSK0hyZGkRek+ZisNVUJMKJ4U2jRi54ggidWaGktKNpBkZ0N9\ngsQAABzcSURBVGkWkNqfexdgw4DULnxIApNkZGkKkCxnDuSIICnUHJICSNJnNxOApD/lTQsh\nHBEkzRLTlaRFuzyPDZI5oUVyRJA0ak6PZWWfJth62S0uamhdX8XFhJEiUQgjR24ChCTV9FxL\nGFMXxStM2qb1VfAmzKMiWqpUE0WQVEKEJFnhjTehbfsIkkojgmQejs1Vb+NTJ44I0pcUq0zT\nqO890Wzynnql4UACQnQ21HR9RJC0AuzZSEqfJnwvj8QHpsFAQg9AWZ4pI0e+woSkysBfntne\nr24LTIru1FvTV+lmojZcG+/XKi9kCZJWoJB0XLzG0elwwyNB9d54VOliQvPlQoemQQGJIJ2k\nWWiUyVR5/Eu0SQCCaVKQlMNTNv6SdSYTSAUk724qjijH/vbRKWeX9ghwzhOCJPSF+68iehh/\nTV9ScQRryLmnqJB0VF79BHMrTLOBJB6P8vgNF7t2j5tLBBKyeV+UYCFJNl+N3x4qrRxSJcaE\nZiBuObT9+kg58b7jBm7dEyXVWtNMUlGNjZmlWUBSh/Lrf+15na6C7yKPb90PJd1iY5+Cw0zj\noEnbXaaQKr4mbLcEngz5cuQ6bD5tu6GkW23Mk3C9J6s/Eb1LDQ+SNRY/3flGcNQJJL/Y4USo\n7lDDcnZKOfyvFAYHqe3q8PECyZsjx2FzjXUejWuXG3tMqtc+lulWpLhxfRUHE+03PB7umag5\nyhOQvHcDHdqHhqQaSQA3kRQztKyvAjbRCtFzdXeOHNMvr4ZvFuAmsCHpYPaui2WbxwgcbjiQ\nABA914dk5n1ACpgNPErqgbJn2XfJuytMg4HUPBTiaQJz5DVqIRwtaJT0Hgm6Xm18BOp1n/6+\ntXgwgCCOegSkKI4WMErdSFoPeDxP+AIgVVchyIZdj4Dk/nypmznDmmPeutsuj4ZpcpAk4wXi\nKB6kWIxWiyiTlrHCkrQ053k6Y5gqPUxIhymEI49Bi+dogaFkWnTgJK1lQDDNCZJidDAb37Va\ns3C0oFDChyQrSQsmNE0HknJQYjjy2D2Btyg3DbDtEJIaSFqLNu6Oh1QJMgF51NDGUTRIHTla\nECjZhsuVpKUpNM0Ckm0IojiCj1lfjpZ2lIzrjjdJaw2+RgEwYwUiNiB152hpRsmFpK2jpm7q\n1+XBQWq6TNQPO3ClbFEGjpZGlKwrTxBJa0WNbw0MUvNTd2rD1unFDlnwbdgj+X3HQQqSFoWT\njQmS137lEBwhG2sWNh8QHY0laRF623ggge6gGQY8BUi5OFrsKEUONmK/Xv+cWbVJa18AJmDP\ndERyhByxdBwtVpTsi08fkpZj7xsMJFDzmwPiNrPA08nI0WJDqWHQLCR1+9ZLaJVTvUd5mBB2\nwmKTHB0KcxNPethUFfqMqtAmusqq331Bqlj1m1bc2eTlaDGg1DJs9izci6bI1O7P27u3iZ3m\n6qPnyBHsbFJztKhRalp/mqazvpLrFXqN9Kd8eJt4akk2ZC3zEgVSdo4WJUpt49a+UYqlKXaz\n4Xf5423irpG2lymkR4M4SnQb9kjI71RsmBTxoMNoyrNrJ76AkralKd9wOAakMTD6kvKHDMwF\n2kPSrSzfR3quaRkUV44g4zUOR4scpcYlCEjSWr6NpolAMo9E24Vra/PXYg0b7Nkkm4fWoQOT\ntNbxciJQlW8NwLMh+EPg4sM4jg5KjsbRIkSp1Q8cSDpVxGc1oCrfGkCC1Jzfek+WIs9Zi5Zn\nyVpIpva90noJL5LWytDLbFCVbw2gQEI4WQaOyvL044ETaHSSFpWDDQsSZqWutNF4G1dU4hyM\n5gMphqS2CRRI6GhDggRLdxo5wXE0JUghJHkHpVMjdYcbDiTkNUNj3gDk6O4aaSZhSOqb3t26\nwV+j0FoRmWr3gIcyBXxyOYRZbBrTu7oBqfg+kt5IYFp3/nNCjhbQetM2GQm8CVUlo4mmwcem\ndVMrQXoXsUS9KkjueTc5uggzWDkulKAWxgeplkW1Xx6RozvFkNQ5KL0cSO07mYD3m8Rl5hCI\npNZNVleYXgok/5f8wGVmUczSI7nUcoPpZUCSjiEgrSNHTwoiSdSGD0wvARLukSnU0yJzbnUf\nCEUSZlPBAabpQRJHIolhRUvt7cwl0A0D3PYcGKapQdJEIlg6Ro52FEPSqUx8ZJoWJN0YwWYH\ntaWn18Z7TGa5dBBGUnXXtWhSZ9D5TgmSemzKAkq/+2wzwJ3fiSbJtlp7O4YH6QGnOx1ItjEp\nmF8b6MCRYwDpckXePs5F3MxjtbbTnQokK0SXVWy/MurbPOD7KM6TgTYBI+lgquStbDRrPttp\nQLKPQXn8j70/gRwFMORjDHYJeTxX4SnNHCC1TfSt7l4jMI4wb8cHMoQ3606S5Hq32rb+XIcH\nqXl+y3nsd60K24/YruvDENY+jqTtYuV2ZH9ORc2n/xYhmAmIX10Gfq8paV8gRQ4q94boouaO\noG7N7hW7a795kZU3MCxIUL86aCkBR2kYuqmtS84k3R0HpdKArmCqgE1gHOu+jd2EuzdH+Ri6\nqXF/p7HEuQuHH7duPNzaS/ktQk0mYJ71kEDv59vStgBFvtfIDNFFxk7CSDosd+YIM4qVEx0M\nJPBXcdUin7ApOEdDMHSTobsRJF2nGLd5XzGmbK2hJ9lMHK4xuLROcUKDMXSTsuMCkhrXsfMW\nUtBovixI5/VqP/Aj07rIXciuwm5ztTV1yd0JkrON41EO9odhA9GzpKfiTlJ1jpF6XZAOl6vY\ndXUahm6SnBKSpIPdu5jBfUmQzrP8ldc12sdwJLU2liBXQY0zUa5Ryf3x3pAqiUw85B1TcVRg\nQnUIUaSVpLsnKV1heimQZEMJfT09jCOkk6DaAuW9Umuiue33+i+iSgIT4iFEhqN6GdjNQkQz\n8AZjL5SEBX1gegWQVOkKNK2rcyS11mamX6sZSVo8YJodJOWIjZfWOSb+kKYxJCGn5VYWmg6H\nVOljwuPBFUXBCI78XzVvbwJQwiMonUrDaJoVJNMAQde9AI4i7pBEvMknGk7x5AjL3Tcdsl4g\nqoSasK4y2Kly5yjsLm6zIdBQeKR3d5XaX7QOqBJmomE8sPPkzVHssxDuTiaL8X5B6dy+68uK\niCohJqzjUFQmUXvoLaPY4ZGiNpOonTnVJMVGpklAallMVBVBHLW4Za8H85z7DCVpOXquv17V\n4E0TgNSa3ipqC9M6x9tHHYIRxjiKJGEPTo+rysruNKA72bFBQuxeloHSuv5PiXsGftTwXks1\nu4bHjfymKh4mUHcACi7QXNprLrFjvztGXzJ3A0aS+IoLMV5SNxsTJPQTHpj7QsJixovZFBSd\n5HY9jh1j2IhJ3G08kHD3ou+age0OOHGUCaMvteyPtpWQFVuvkLBrbcXtBgMJGIkero1qzeKu\noiwDLk09UQJ26lslQImz9WozlyKKa+C61f2WBgMJbAK7y+DDkfTSDDhmfiYFA4QJSkW+SGL0\n2iBht1wdOJKu+/j3kYTF1CcEKXKyLTVJkPxtSG7b4SZM/TA62qcS2Ebdmq2Xu8vtAvTCIMmS\nu27hSB6MnEbLqwNAkoC7ra16XZBkV0ninoA56hmMvHsBHCqvmw16vSRI5zW07H4dl74j0BkV\nr/H+6b+8J5rzA5QQFrzmd+6vQIZUSWTiYXtXvvVTaxVQQms16EkHj4t6JEnHJe+Oou/i7xpy\nrJLEhHIoMzqGuhxADgESuvCIS3rC9DIgqYewxwKbKKez2ZOfAKDEzai46Km4yy5nSJW+JkzP\niHdI+ZPdGTHaxD3b6xOUzm3D77yFVOlnwjhi0HeUZLu0yS6NGgzLCgJvKNmWFtP6ut9aSJU+\nJuwDFR6OUgcjg3XQHR7oPGxXgz0CHVIl3kTTAEVzNAhGqh7I3xpqK2Ep+r0mAqYZQWocmPA7\nIuNg9CXgox7ou2/yshuVG0d3NpDaVxfsBjmOoxwYfQm3Mwd+MqF15oOymIYqQSbCQ3R9nwFl\nMA9GX4LtzOUiaWlwoXlAwlw1ZuVIVAgkWIdGJMm6Zs0CUo+9lzCOUNvJQgEfqh6SJNOaPAVI\nsPsBqilDeABu9YfOCg7c2F1NWXOiZtTvWRls6Kt4mgDeVQMvfZG3mMCTEgk4Lv5pi1Yacnow\ns6WKmwnovemcHAnKOGxF4LLJeJK6uMTIIGEflsFeHgEcyHUSQY2mJAk5HsCncBFVHEygHznL\nxpHilQSnCYEFpaFJknraoCDBnWdgjqQF9UIFpcFJErE0IkgOzz9n4+jruOyWjutskKRbg7BE\nvKUK0ET3t7JA3n10iiczRWDL/ZsIMOkdaEHoSlLF8wYDycdx4BPUtgKfv5pF0FD35EBaCBOS\netxQktofDKTu1gI4up8uaTlHYYISiKQuN5S8rM0GUjaOlnKNRuhkwibISc9OUiRIn79Kef/n\n3MjxWms1YVBCji7JXZYxgqRdk5MUCNLn2/ps8c9TI0M5iaYsxGHqFx0bJfye/oY8pjQ3SYEg\nfZTf/9L0++19bSQJSJk4uh8Tg1PCBs3c+CuTFAjS26ni37cff9OApJkTd47uj6nHB7nzsBmU\nrBVVbYxLUiBIl1H+fH9PAhJ+N7XFVYrUitnP5XJK73qQFOVLIVVW/Sifl7/eU4AET+sk1zaV\nQ8brJ/iA2dO7tomVjnO6oBQI0u/y6/zX3/KeAKRkHC2XnbrK0IRwtENSe1BCkZQuvQsEafm4\nTsQ/lQd9Ik49G0eykGT3b63sxLbd/RqUpEiQlj8/L3/9/fXUivr7N9qUj6OTA+ovMtwGy27s\nBUkKBSmRiYwcVfO6qLTuqOn29G5Okl4TJNW1ahxHlit116GyWzw6ky4kubtUSJVvDcBG0sv+\nfWFUuYpn1evHXR5BjCYjyXuoXg4k5QVYFEc2A72SgyFJ8nmV7dp4SJVvDXQDye/bymoFQzm6\nnSbAd7qQJB933fn5sfRKIOkHMQtH2z3f5eh6TJQzVo3LeySpWT0kOm4peS7v5FwhVb410AMk\n16+h9eZo88Pdmo/4tI+lDmNhmX4k9f+2AnuVbw3Eg2QauiQcif243P3/N5BaX6OQ90tQsXpI\ndNxS8lYF72EhVfqasK1AwC/E2i9g9sUjb78HyXLqUpLsvRdVdiWp7/cimqt0NGEdMOA0hnB0\neam2AFK7jSGbjyRwWJocJPNYJeFoJ607dnTANdIeqLJPK03Vj0gLGEo+VsOxNDNIDcOUhSPp\np+X7P8q3v7QCpncxJJlXTBBL84LUMkCKqvZr5rob6dK6+39fCLK7iTy9c1xKNJ23TzcEpUlB\naltnknAk/dhpQsSGhifJ7b41vEqwicZhycyROFAAJL1QmoCk9rA0H0itqwvyLeYGB7JdHmEl\nv1ByHIjKcWvZjdpRWUxDlTAT7TEaOXFw9xHHCJDk8W8GkoK2p1qqeJs4lwd4VWaOItO6fQPG\n9G4Mkq6n5/d4c1MVXxPnx1oQi7MmrXNznRRpndqq/SuLjWmuqS1JE2Vt5vVAAmY3yBmzcyT9\nOOYr22DpHX4Dc7MoYlAE357xVMVgRV/FycQlFKFcKgNHe6fSIa1TmzZ/0SqSJEhQ0rczMkiX\nZePyfFmMVVnRgwKHdeUuGsaRJhhazq1aT1TAWHa/het1t9wZDVacpeh7Ma0fW61l5kj2EUhW\n+wcfVw+mIunOocRr9NAg3WeykMFDlTV6jCKt89x52GRUas767ZCZSLqkOXcu1rC1hKyCNXFd\nNe/zOsDYSYu6cST+XObqZskwlXdXdKw+qsiUoVb9zsPWqFS9Dh8QpBs2t9MMfMCjXtTmLbvh\nSMZRpU9KyUgyBKUWkrCzJKpelnt/AxvMANJdBovoTgaO5J/7c9RqdnySloerb8FKPSJId0sE\n5m2SoTjailAOUyIzk5mk5qvm8nB1VGluQJAeLo38rSmLmvxkd9J7pHUqS7sdPzhZlUl1CVvZ\n3SaujjYpSAsMJPA1rMWBFN4Yx1HjhRJ0IHRFTGWPmpgVJNilkcDUQ9FojiQ98HxOaIskRHrX\nRlLohdK1nQmvkRbUpZHEkrKoM0eaZhHy2gdvIil2y+HcTrWhMUHqYegFOZLafAGSPAxNBFJe\njjpfHimt6h+9m46klwYpMUfPn8ReHmkNvzxJrwzSUBxpWgWrJb3LQFLMehNSJaEJzfiKik7L\nUVKSkgWllwUJPQ84h3l2EWFa5/gahaQD6i9zaCMpWXr3qiAl5khSTubsRpnfSHplkl4UpAk5\n2rVjEElSiyBBihKkg04QJFiVdCYUGw1thYbkaEKSEnlUS5VsJhiQKpoOpEwu1VAlmQn4+HcH\nCT5g0H64gZSIpFcESdO6G0g7ByS+6R+QzNiIz6p2AA9SIqeyV8lkQrVNPEhA6gTSWCHJ9xGH\nlwNJ1/QgIDmM13y5nbq0d9NDg+Qy8PNlduDczg+kNCS9GEjKhlsnU+0mTx/3Ckj9Q5ILSGk8\ny1gli4lgjgbO7PqDNBhJLwWSttmUIMVkdrPmdml8y1YlhwmvIXfM7Bp8t1nQzvjldklIeiGQ\n1I0mAKlfZtc/t/MCKYl3jQqS/iZC+0SOnNmNk9sZSPLwr5Aq/U04nidusU2V2YGN48K2vQ9N\nVUbohL8Jz9MMzeziAlIUSO25XQonztAHfxOW9uIzOxtIjtOBNJ8MpAwuNh5IpuZePrMzmYfy\nMhJJrwCSK0eemZ1xFw+mGJA6haT+TjYaSLbGMoDUOSBZOtAHpAQkTQ+Scauzx0X0FCCNk9uZ\nfQPVg6FAsraUISAZd/GQMvRg92ci84Wkzo42EkjuHL0aSLU+lfsfJq21VT3iDlJXTxsIJHM7\nniCNk9npu1B2SzmD1J2kqUHqyZEHSOEcqftQ9grtf3p0ICAkdfS1YUCyt8LMTmHyGaQeF0m9\nSZoYpAiOOmd2PabisFt738K/U7N6JAKkbt42CEgNbSQFqUNAwvYCN1itZVE1m9oYAqSWWwSu\nHA0PUkNczBqSEDeUJgWpqYEuIFkcdpfJO8co5w9wlxBZQWqa9B4eNwBIURzlzOzK3aETQFAX\nO+pHhdesuV1rZVv9/CC1VUfMXUhmd9TUA0hYD5PETu2+XX+QOvhcepD6c+QCUrVKufv/cvlz\n/cfFsU0/fakiupSdMgefHh4ZhaQJQQpcWiIzu3qdfZBk7VWaPejafW5nIykBSOFelxykgO7V\nTcFB2qi35fCrH9+4KY90CVUj6TC3K4eXZb4g9SRpOpAil5XAzK48X8jv+vcdSKUSIUR90eV2\ny3KwSZg6t4v2u8wgtd0QQHGEWHjL93+V8u3sdrl6yOT0qV09GtZAgkaeyJDU4D9zgdQe2jV3\nXGIzu+80HMSN+3cZyu0DsVpyu2sEROV2oSEp1PXyggTgaJGvSpip36nx5LtP+9h17zarKbc7\nhc4ub/dpCyNbmAmk5o4V1e3LUJCebgg5cmQISd8JL3sdsgxabEiKc76sICHGUAMSsiciLMp3\nd22z2dCdw9zu8t8uuV03kuYBCdKtspQe2UE9IFWrhIJU3W4oSJCiQ1KU+7V29fdb+fEbbgKR\n163TL2nouMzujoL8TZ3vew3lO9+uHLXldkevJPnndr1IinTZPz/L2+/lP+tAv4NNYNIx8T7D\nYcGdY+Xuf7UaT9R8987EIFU6M0JIinFAa0//rM7wUX59Ln9/lsOYpDWBe59E1lA5ROkYpK2j\nFT8tTwQ6c9RKkqZlSQvhIUnvUYEg/Sofy/JR3r7+/iw/gCaQi5CUpP2xPszfNi/B6pnd909n\nBClVSIrwQWtHz1tiP+/+gTGBGTnd3veB4cMl1wTS86cDgKSPPKlCUoATtoH031NOdwpMEBMw\nJzq+SpY3oz4qwqJEcqQnyRekHiHJ3Qvtqd2/V0cnfa5pHsYE0okQbVXasAekgwIJQJKfhXtu\n14WkQJA+366LajkOSBoTDj7UJn2H1CAFcCTZANF+YD/QLUdxtWnv5scFn7eNeHT0kqZHZ1ob\n0y+461FrRArO7IbO7XqQFAoS3ATodzku7MpuxlYK730sf47z8YONiilBAuR2+UCSe9jQICG3\nZ1TPfNtuIu3tIhx+cK5Y9o7vm2xVrtyuF0mOvtjcybrPykyArylVRrW+cFBNAtKSD6TXyO3E\nbQ0MEjB+axqsFN7N7PaOPn/0CNJzxVpoQEmZUFZORPD58ankJmlckJDRW3Mvdh+JwzZ2f4Gr\nupCHvol02JGK3faLpJwhycsdc4CEdR/VVqHtFxeuvNYqbHzS4xIJkNvNEpKc/DEFSHDv0TVo\n8wRZhUoTYRypQ9K8IPk4ZAaQ0N6DmqPjgCSqsRWQjo7nAck7t0tNUg+Qmk0gf9ZdZFBcWJ3j\nizK7w+P9QHqlkFT1uRFBwncANkP+IAVylC6360oS3il7g+RgPwQkYYU8l0gESdHgeCAFmDdr\nMo5IkrzB4UDKzBFBqjZ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"text/plain": [ "plot without title" ] }, "metadata": {}, "output_type": "display_data", "source": "R display func" } ], "source": [ "fa = (f - t(f)) / 2 \n", "contour(x, y, fa, nlevels = 15) " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Funkcia image() funguje podobným spôsobom ako funkcia contour(), až na to, že produkuje farebne-kódovaný graf, ktorého farby záležia od hodnoty z.\n", "Tento graf je známy pod pojmom heatmap, a môže byť niekedy použitý ako graf teplot v predpovedí počasia. Alternatívne, funkcia persp() môže byť použitá na produkovanie troj-dimenzionálneho grafu. Argumenty theta, phi nastavujú uhly pod ktorým je graf zobrazený. " ] }, { "cell_type": "code", "execution_count": 411, "metadata": {}, "outputs": [ { "data": { "image/png": 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V7angG5hySbnsehIu2Qusnw0vYMyD0k\n2fQ8DhVph9RNhpe2Z0DuIcmm53GoSDukbjK8tD0Dcg9JNj2PQ0XaIXWT4aXtGZB7SLLpeRwq\n0g6pmwwvbc+A3EOSTc/jUJF2SN1keGl7BuQekmx6HoeKtEPqJsNL2zMg95Bk0/M4VKQdUjcZ\nXtqeAbmHJJuex6Ei7ZC6yfDS9gzIPSTZ9DwOFWmH1E2Gl7ZnQO4hyabncahIO6RuMry0PQNy\nD0k2PY9DRdohdZPhpe0ZkHtIsul5HCrSDqmbDC9tz4DcQ5JNz+NQkXZI3WR4aXsG5B6SbHoe\nh2uK9F+C/y0gIZPNvDZIsqRjsraKtEPqTm/2kpBkScdkbRVph9Sd3uwlIcmSjsnaKtIOqTu9\n2UtCkiUdk7VVpB1Sd3qzl4QkSzoma6tIO6Tu9GYvCUmWdEzWVpF2SN3pzV4SkizpmKytIu2Q\nutObvSQkWdIxWVtF2iF1pzd7SUiypGOytoq0Q+pOb/aSkGRJx2RtFWmH1J3e7CUhyZKOydoq\n0g6pO73ZS0KSJR2TtVWkHVJ3erOXhCRLOiZrq0g7pO70Zi8JSZZ0TNZWkXZI3enNXhKSLOmY\nrK0i7ZC605u9JCRZ0jFZW0XaIXWnN3tJSLKkY7K2irRD6k5v9pKQZEnHZG0VaYfUnd7sJSHJ\nko7J2irSDqk7vdlLQpIlHZO1VaQdUnd6s5eEJEs6JmurSDuk7vRmLwlJlnRM1laRdkjd6c1e\nEpIs6ZisrSLtkLrTm70kJFnSMVlbRdohdac3e0lIsqRjsraKtEPqTm/2kpBkScdkbRVph9Sd\n3uwlIcmSjsnaKtIOqTu92UtCkiUdk7VVpB1Sd3qzl4QkSzoma6tIO6Tu9GYvCUmWdEzWVpF2\nSN3pzV4SkizpmKytIu2QutObvSQkWdIxWVtF2iF1pzd7SUiypGOytoq0Q+pOb/aSkGRJx2Rt\nDxTp9rdUpP+/IcmSjsnaHijSx12k+f+R00jIpO70Zi8JSZZ0TNb2yJd2n57e/s63JKeRkEnd\n6c1eEpIs6Zis7aGfI326ffh9b0hOIyGTutObvSQkWdIxWdtjv9jw8fbpd70dOY2ETOpOb/aS\nkGRJx2Rt/ardDqk7vdlLQpIlHZO1VaQdUnd6s5eEJEs6JmurSDuk7vRmLwlJlnRM1laRdkjd\n6c1eEpIs6ZisrSLtkLrTm70kJFnSMVlbRdohdac3e0lIsqRjsraKtEPqTm/2kpBkScdkbRVp\nh9Sd3uwlIcmSjsnaKtIOqTu92UtCkiUdk7VVpB1Sd3qzl4QkSzoma6tIO6Tu9GYvCUmWdEzW\nVpF2SN3pzV4SkizpmKytIu2QutObvSQkWdIxWVtF2iF1pzd7SUiypGOytoq0Q+pOb/aSkGRJ\nx2RtFWmH1J3e7CUhyZKOydoq0g6pO73ZS0KSJR2TtVWkHVJ3erOXhCRLOiZrq0g7pO70Zi8J\nSZZ0TNZWkXZI3enNXhKSLOmYrK0i7ZC605u9JCRZ0jFZW0XaIXWnN3tJSLKkY7K2irRD6k5v\n9pKQZEnHZG3XFOn/XAbS1H8I0sMfkHtIsul5HCrSDqmbDC9tz4DcQ5JNz+NQkXZI3WR4aXsG\n5B6SbHoeh4q0Q+omw0vbMyD3kGTT8zhUpB1SNxle2p4BuYckm57HoSLtkLrJ8NL2DMg9JNn0\nPA4VaYfUTYaXtmdA7iHJpudxqEg7pG4yvLQ9A3IPSTY9j0NF2iF1k+Gl7RmQe0iy6XkcKtIO\nqZsML23PgNxDkk3P41CRdkjdZHhpewbkHpJseh6HirRD6ibDS9szIPeQZNPzOFSkHVI3GV7a\nngG5hySbnsehIu2Qusnw0vYMyD0k2fQ8DhVph9RNhpe2Z0DuIcmm53GoSDukbjK8tD0Dcg9J\nNj2PQ0XaIXWT4aXtGZB7SLLpeRwq0g6pmwwvbc+A3EOSTc/jUJF2SN1keGl7BuQekmx6HoeK\ntEPqJsNL2zMg95Bk0/M4VKQdUjcZXtqeAbmHJJuex6Ei7ZC6yfDS9gzIPSTZ9DwOFWmH1E2G\nl7ZnQO4hyabncahIO6RuMry0PQNyD0k2PY9DRdohdZPhpe0ZkHtIsul5HCrSDqmbDC9tz4Dc\nQ5JNz+NQkXZI3WR4aXsG5B6SbHoeh4q0Q+omw0vbMyD3kGTT8zhUpB1SNxle2p4BuYckm57H\noSLtkLrJ8NL2DMg9JNn0PA4VaYfUTYaXtmdA7iHJpudxqEg7pG4yvLQ9A3IPSTY9j0NF2iF1\nk+Gl7RmQe0iy6XkcKtIOqZsML23PgNxDkk3P41CRdkjdZHhpewbkHpJseh6HirRD6ibDS9sz\nIPeQZNPzOFSkHVI3GV7angG5hySbnsehIu2Qusnw0vYMyD0k2fQ8DhVph9RNhpe2Z0DuIcmm\n53GoSDukbjK8tD0Dcg9JNj2PQ0XaIXWT4aXtGZB7SLLpeRwq0g6pmwwvbc+A3EOSTc/jUJF2\nSN1keGl7BuQekmx6HoeKtEPqJsNL2zMg95Bk0/M4VKQdUjcZXtqeAbmHJJuex6Ei7ZC6yfDS\n9gzIPSTZ9DwOFWmH1E2Gl7ZnQO4hyabncahIO6RuMry0PQNyD0k2PY9DRdohdZPhpe0ZkHtI\nsul5HCrSDqmbDC9tz4DcQ5JNz+NQkXZI3WR4aXsG5B6SbHoeh4q0Q+omw0vbMyD3kGTT8zhU\npB1SNxle2p4BuYckm57HoSLtkLrJ8NL2DMg9JNn0PA4VaYfUTYaXtmdA7iHJpudxqEg7pG4y\nvLQ9A3IPSTY9j8MfFOnNT5+ZPYN0KgdSNxle2p4BuYckm57H4Q+KdLvdXuDSLz+9u33j3Ydf\nKtJLSdszIPeQZNPzOPxBkZ7/8v53u/T85nZ4W5FeSNqeAbmHJJuexwF8jvTLT29+l0sfbk9/\n+fT9V59/frp9qEgvI23PgNxDkk3P42C+2PDp6esHmY//j9/3dPt0fsPtqSK9jLQ9A3IPSTY9\njwMR6ee3v+Pl2tfPp/7Rv/z6vxzSqRxI3WR4aXsG5B6SbHoehz8u0vNPXz8cvfn5+atN79bf\n149If4i0PQNyD0k2PY/DHxXpl29fbPjww5D/+VHmb/j6OdLPPz6R6udI/wRpewbkHpJseh6H\nP/p9pK8fjD4+//Z/rB9lvnx5O168vXne3jKdyoHUTYaXtmdA7iHJpudx+KPfR3r38wt+5y8f\nvn8f6endT/0+0otJ2zMg95Bk0/M4/NHvI738t/8e0qkcSN1keGl7BuQekmx6Hof+rN0OqZsM\nL23PgNxDkk3P41CRdkjdZHhpewbkHpJseh6HirRD6ibDS9szIPeQZNPzOFSkHVI3GV7angG5\nhySbnsehIu2Qusnw0vYMyD0k2fQ8DhVph9RNhpe2Z0DuIcmm53GoSDukbjK8tD0Dcg9JNj2P\nQ0XaIXWT4aXtGZB7SLLpeRwq0g6pmwwvbc+A3EOSTc/jUJF2SN1keGl7BuQekmx6HoeKtEPq\nJsNL2zMg95Bk0/M4VKQdUjcZXtqeAbmHJJuex6Ei7ZC6yfDS9gzIPSTZ9DwOFWmH1E2Gl7Zn\nQO4hyabncahIO6RuMry0PQNyD0k2PY9DRdohdZPhpe0ZkHtIsul5HCrSDqmbDC9tz4DcQ5JN\nz+NQkXZI3WR4aXsG5B6SbHoeh4q0Q+omw0vbMyD3kGTT8zhUpB1SNxle2p4BuYckm57HoSLt\nkLrJ8NL2DMg9JNn0PA4VaYfUTYaXtmdA7iHJpudxqEg7pG4yvLQ9A3IPSTY9j0NF2iF1k+Gl\n7RmQe0iy6XkcKtIOqZsML23PgNxDkk3P41CRdkjdZHhpewbkHpJseh6HirRD6ibDS9szIPeQ\nZNPzOFSkHVI3GV7angG5hySbnsehIu2Qusnw0vYMyD0k2fQ8DhVph9RNhpe2Z0DuIcmm53Go\nSDukbjK8tD0Dcg9JNj2PQ0XaIXWT4aXtGZB7SLLpeRwq0g6pmwwvbc+A3EOSTc/jUJF2SN1k\neGl7BuQekmx6HoeKtEPqJsNL2zMg95Bk0/M4VKQdUjcZXtqeAbmHJJuex6Ei7ZC6yfDS9gzI\nPSTZ9DwOFWmH1E2Gl7ZnQO4hyabncahIO6RuMry0PQNyD0k2PY9DRdohdZPhpe0ZkHtIsul5\nHCrSDqmbDC9tz4DcQ5JNz+NQkXZI3WR4aXsG5B6SbHoeh4q0Q+omw0vbMyD3kGTT8zhUpB1S\nNxle2p4BuYckm57HoSLtkLrJ8NL2DMg9JNn0PA4VaYfUTYaXtmdA7iHJpudxqEg7pG4yvLQ9\nA3IPSTY9j0NF2iF1k+Gl7RmQe0iy6XkcKtIOqZsML23PgNxDkk3P41CRdkjdZHhpewbkHpJs\neh6HirRD6ibDS9szIPeQZNPzOFSkHVI3GV7angG5hySbnsehIu2Qusnw0vYMyD0k2fQ8DhVp\nh9RNhpe2Z0DuIcmm53GoSDukbjK8tD0Dcg9JNj2PQ0XaIXWT4aXtGZB7SLLpeRyuKdJ/CUhT\n/ylID/+SkGRJx2RtFWmH1J3e7CUhyZKOydoq0g6pO73ZS0KSJR2TtVWkHVJ3erOXhCRLOiZr\nq0g7pO70Zi8JSZZ0TNZWkXZI3enNXhKSLOmYrK0i7ZC605u9JCRZ0jFZW0XaIXWnN3tJSLKk\nY7K2irRD6k5v9pKQZEnHZG0VaYfUnd7sJSHJko7J2irSDqk7vdlLQpIlHZO1VaQdUnd6s5eE\nJEs6JmurSDuk7vRmLwlJlnRM1laRdkjd6c1eEpIs6ZisrSLtkLrTm70kJFnSMVlbRdohdac3\ne0lIsqRjsraKtEPqTm/2kpBkScdkbRVph9Sd3uwlIcmSjsnaKtIOqTu92UtCkiUdk7VVpB1S\nd3qzl4QkSzoma6tIO6Tu9GYvCUmWdEzWVpF2SN3pzV4SkizpmKztgSLd/paK9P83JFnSMVnb\nA0X6WJHKgSRLOiZre+RLu09Pb3/nW5LTSMik7vRmLwlJlnRM1vbQz5E+3T78vjckp5GQSd3p\nzV4SkizpmKztsV9s+Hj79LvejpxGQiZ1pzd7SUiypGOytut81W5+/kROIyGTutObvSQkWdIx\nWdt1RJqQ00jIpO70Zi8JSZZ0TNZWkXZI3enNXhKSLOmYrC0h0v6l72+Q00jIpO70Zi8JSZZ0\nTNZWkXZI3enNXhKSLOmYrK0i7ZC605u9JCRZ0jFZW0XaIXWnN3tJSLKkY7K2irRD6k5v9pKQ\nZEnHZG0VaYfUnd7sJSHJko7J2vrl7x1Sd3qzl4QkSzoma6tIO6Tu9GYvCUmWdEzWVpF2SN3p\nzV4SkizpmKytIu2QutObvSQkWdIxWVtF2iF1pzd7SUiypGOytoq0Q+pOb/aSkGRJx2RtFWmH\n1J3e7CUhyZKOydoq0g6pO73ZS0KSJR2TtVWkHVJ3erOXhCRLOiZrq0g7pO70Zi8JSZZ0TNZW\nkXZI3enNXhKSLOmYrK0i7ZC605u9JCRZ0jFZW0XaIXWnN3tJSLKkY7K2irRD6k5v9pKQZEnH\nZG0VaYfUnd7sJSHJko7J2irSDqk7vdlLQpIlHZO1VaQdUnd6s5eEJEs6JmurSDuk7vRmLwlJ\nlnRM1laRdkjd6c1eEpIs6ZisrSLtkLrTm70kJFnSMVlbRdohdac3e0lIsqRjsraKtEPqTm/2\nkpBkScdkbRVph9Sd3uwlIcmSjsnaKtIOqTu92UtCkiUdk7VVpB1Sd3qzl4QkSzoma6tIO6Tu\n9GYvCUmWdEzWdk2RSMgEspl/F/zbdSD3kGTT8zhUpB1SNxle2p4BuYckm57HoSLtkLrJ8NL2\nDMg9JNn0PA4VaYfUTYaXtmdA7iHJpudxqEg7pG4yvLQ9A3IPSTY9j0NF2iF1k+Gl7RmQe0iy\n6XkcKtIOqZsML23PgNxDkk3P41CRdkjdZHhpewbkHpJseh6HirRD6ibDS9szIPeQZNPzOFSk\nHVI3GV7angG5hySbnsehIu2Qusnw0vYMyD0k2fQ8DhVph9RNhpe2Z0DuIcmm53GoSDukbjK8\ntD0Dcg9JNj2PQ0XaIXWT4aXtGZB7SLLpeRwq0g6pmwwvbc+A3EOSTc/jUJF2SN1keGl7BuQe\nkmx6HoeKtEPqJsNL2zMg95Bk0/M4VKQdUjcZXtqeAbmHJJuex6Ei7ZC6yfDS9gzIPSTZ9DwO\nFWmH1E2Gl7ZnQO4hyabncahIO6RuMry0PQNyD0k2PY9DRdohdZPhpe0ZkHtIsul5HCrSDqmb\nDC9tz4DcQ5JNz+NQkXZI3WR4aXsG5B6SbHoeh6Fj5j4AABAlSURBVIq0Q+omw0vbMyD3kGTT\n8zhUpB1SNxle2p4BuYckm57HoSLtkLrJ8NL2DMg9JNn0PA4VaYfUTYaXtmdA7iHJpudxqEg7\npG4yvLQ9A3IPSTY9j0NF2iF1k+Gl7RmQe0iy6XkcKtIOqZsML23PgNxDkk3P41CRdkjdZHhp\newbkHpJseh6HirRD6ibDS9szIPeQZNPzOFSkHVI3GV7angG5hySbnsehIu2Qusnw0vYMyD0k\n2fQ8DhVph9RNhpe2Z0DuIcmm53GoSDukbjK8tD0Dcg9JNj2PQ0XaIXWT4aXtGZB7SLLpeRwq\n0g6pmwwvbc+A3EOSTc/jUJF2SN1keGl7BuQekmx6HoeKtEPqJsNL2zMg95Bk0/M4VKQdUjcZ\nXtqeAbmHJJuex6Ei7ZC6yfDS9gzIPSTZ9DwOFWmH1E2Gl7ZnQO4hyabncahIO6RuMry0PQNy\nD0k2PY9DRdohdZPhpe0ZkHtIsul5HB4p0vP72+3tz7++k/W9pFM5kLrJ8NL2DMg9JNn0PA4P\nFOn56faNdz/eSUV6IWl7BuQekmx6HocHivTh9vGrTR+f3n5/JxXphaTtGZB7SLLpeRweKNLT\nj9/4+enN54r0ctL2DMg9JNn0PA4PFOk3d57fvq1ILydtz4DcQ5JNz+PwQJHe3J5/+9XbivRi\n0vYMyD0k2fQ8Dg8U6ePt/a+/+nx7W5FeStqeAbmHJJuex+GRX/7+8Fd7fr5VpJeStmdA7iHJ\npudxeOg3ZD+9++1Xn9//j/dyG6RTOZC6yfDS9gzIPSTZ9DwO/cmGHVI3GV7angG5hySbnseh\nIu2Qusnw0vYMyD0k2fQ8DgmR9s+PvpFO5UDqJsNL2zMg95Bk0/M4VKQdUjcZXtqeAbmHJJue\nx6Ei7ZC6yfDS9gzIPSTZ9DwOFWmH1E2Gl7ZnQO4hyabncahIO6RuMry0PQNyD0k2PY/DNUX6\nDwFpimyGrPdfrgO5hyRLOiZru+aXv8lpJGRSNxle2p4BuYckSzoma6tIO6RuMry0PQNyD0mW\ndEzWVpF2SN1keGl7BuQekizpmKytIu2Qusnw0vYMyD0kWdIxWVtF2iF1k+Gl7RmQe0iypGOy\ntoq0Q+omw0vbMyD3kGRJx2RtFWmH1E2Gl7ZnQO4hyZKOydoq0g6pmwwvbc+A3EOSJR2TtVWk\nHVI3GV7angG5hyRLOiZrq0g7pG4yvLQ9A3IPSZZ0TNZWkXZI3WR4aXsG5B6SLOmYrK0i7ZC6\nyfDS9gzIPSRZ0jFZW0XaIXWT4aXtGZB7SLKkY7K2irRD6ibDS9szIPeQZEnHZG0VaYfUTYaX\ntmdA7iHJko7J2irSDqmbDC9tz4DcQ5IlHZO1VaQdUjcZXtqeAbmHJEs6JmurSDukbjK8tD0D\ncg9JlnRM1laRdkjdZHhpewbkHpIs6ZisrSLtkLrJ8NL2DMg9JFnSMVlbRdohdZPhpe0ZkHtI\nsqRjsraKtEPqJsNL2zMg95BkScdkbRVph9RNhpe2Z0DuIcmSjsnaKtIOqZsML23PgNxDkiUd\nk7VVpB1SNxle2p4BuYckSzoma6tIO6RuMry0PQNyD0mWdEzWVpF2SN1keGl7BuQekizpmKyt\nIu2Qusnw0vYMyD0kWdIxWVtF2iF1k+Gl7RmQe0iypGOytoq0Q+omw0vbMyD3kGRJx2RtFWmH\n1E2Gl7ZnQO4hyZKOydoq0g6pmwwvbc+A3EOSJR2TtVWkHVI3GV7angG5hyRLOiZrq0g7pG4y\nvLQ9A3IPSZZ0TNZWkXZI3WR4aXsG5B6SLOmYrK0i7ZC6yfDS9gzIPSRZ0jFZW0XaIXWT4aXt\nGZB7SLKkY7K2irRD6ibDS9szIPeQZEnHZG0VaYfUTYaXtmdA7iHJko7J2irSDqmbDC9tz4Dc\nQ5IlHZO1VaQdUjcZXtqeAbmHJEs6JmurSDukbjK8tD0Dcg9JlnRM1laRdkjdZHhpewbkHpIs\n6ZisrSLtkLrJ8NL2DMg9JFnSMVlbRdohdZPhpe0ZkHtIsqRjsraKtEPqJsNL2zMg95BkScdk\nbRVph9RNhpe2Z0DuIcmSjsnaKtIOqZsML23PgNxDkiUdk7VVpB1SNxle2p4BuYckSzoma6tI\nO6RuMry0PQNyD0mWdEzWVpF2SN1keGl7BuQekizpmKytIu2Qusnw0vYMyD0kWdIxWVtF2iF1\nk+Gl7RmQe0iypGOytoq0Q+omw0vbMyD3kGRJx2RtFWmH1E2Gl7ZnQO4hyZKOydoq0g6pmwwv\nbc+A3EOSJR2TtVWkHVI3GV7angG5hyRLOiZrq0g7pG4yvLQ9A3IPSZZ0TNZWkXZI3WR4aXsG\n5B6SLOmYrK0i7ZC6yfDS9gzIPSRZ0jFZW0XaIXWT4aXtGZB7SLKkY7K2a4pE8iGQuv9VkLZn\nQO4hyabncahIO6RuMry0PQNyD0k2PY9DRdohdZPhpe0ZkHtIsul5HCrSDqmbDC9tz4DcQ5JN\nz+NQkXZI3WR4aXsG5B6SbHoeh4q0Q+omw0vbMyD3kGTT8zhUpB1SNxle2p4BuYckm57HoSLt\nkLrJ8NL2DMg9JNn0PA4VaYfUTYaXtmdA7iHJpudxqEg7pG4yvLQ9A3IPSTY9j0NF2iF1k+Gl\n7RmQe0iy6XkcAiJ9fLq9+bi/STqVA6mbDC9tz4DcQ5JNz+PwSJE+vbs9ffzy0+0bb9e3TKdy\nIHWT4aXtGZB7SLLpeRweKNKn7wZ9uL1//vL53W39mJRO5UDqJsNL2zMg95Bk0/M4PFCk97cP\nX758uD19+/Xz7c32pulUDqRuMry0PQNyD0k2PY/DA0W6ff+Nt3fjX/4R6VQOpG4yvLQ9A3IP\nSTY9j8PDRfrLj9d0Pz4w/SPSqRxI3WR4aXsG5B6SbHoeh4e+tPv62dEPnr+/zPvHpFM5kLrJ\n8NL2DMg9JNn0PA4PFOn56a+v5277B6SK9HdI2zMg95Bk0/M4PPT7SB9+0+fp73w8ug3SqRxI\n3WR4aXsG5B6SbHoeh/5kww6pmwwvbc+A3EOSTc/jUJF2SN1keGl7BuQekmx6HoeESPuXvr+R\nTuVA6ibDS9szIPeQZNPzOFSkHVI3GV7angG5hySbnsehIu2Qusnw0vYMyD0k2fQ8DhVph9RN\nhpe2Z0DuIcmm53GoSDukbjK8tD0Dcg9JNj2PQ0XaIXWT4aXtGZB7SLLpeRz65e8dUjcZXtqe\nAbmHJJuex6Ei7ZC6yfDS9gzIPSTZ9DwOFWmH1E2Gl7ZnQO4hyabncahIO6RuMry0PQNyD0k2\nPY9DRdohdZPhpe0ZkHtIsul5HCrSDqmbDC9tz4DcQ5JNz+NQkXZI3WR4aXsG5B6SbHoeh4q0\nQ+omw0vbMyD3kGTT8zhUpB1SNxle2p4BuYckm57HoSLtkLrJ8NL2DMg9JNn0PA4VaYfUTYaX\ntmdA7iHJpudxqEg7pG4yvLQ9A3IPSTY9j0NF2iF1k+Gl7RmQe0i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"text/plain": [ "plot without title" ] }, "metadata": {}, "output_type": "display_data", "source": "R display func" } ], "source": [ "image(x, y, fa)" ] }, { "cell_type": "code", "execution_count": 412, "metadata": {}, "outputs": [ { "data": { "image/png": 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JGklxnR8+PpA51x0jD1BwYDfz3uiAxVizVIFEn52/HU1xl9+VJF8g6qnU/W7lKv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\n+k9J63uESP20xz55VlWb+lIiZWyPsjsuRtlpVe2q8ztCq6KfF50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"text/plain": [ "plot without title" ] }, "metadata": {}, "output_type": "display_data", "source": "R display func" } ], "source": [ "persp(x, y, fa)" ] }, { "cell_type": "code", "execution_count": 413, "metadata": {}, "outputs": [ { "data": { "image/png": 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abTF1ZMC8QhIqX6y/rOGvsZfZk9kWf33wQhksc8xK2kjuKbFbB8BiBFitt/E+/+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", "text/plain": [ "plot without title" ] }, "metadata": {}, "output_type": "display_data", "source": "R display func" } ], "source": [ "persp(x, y, fa, theta = 30)" ] }, { "cell_type": "code", "execution_count": 414, "metadata": {}, "outputs": [ { "data": { "image/png": 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7qnh6xvI+szn4Rf2cnWyqoqvZmGF2kjTCBtlAMztRKoa3N7ADz0sJVrmpCE56Dt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\ntBbnWSNKHR/P+t0bZzbJPCHt2F8HFp2QVglTxbjX/keKdFOPVmn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"text/plain": [ "plot without title" ] }, "metadata": {}, "output_type": "display_data", "source": "R display func" } ], "source": [ "persp(x, y, fa, theta = 30, phi = 20)" ] }, { "cell_type": "code", "execution_count": 415, "metadata": {}, "outputs": [ { "data": { "image/png": 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cWobawWqXHMsKKjNOykXBMe8zC+4XU07CQ9Mm4b65w+8P1So5ZT6iBGKjHiEWLD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\nRU5ErkJggg==", "text/plain": [ "plot without title" ] }, "metadata": {}, "output_type": "display_data", "source": "R display func" } ], "source": [ "persp(x, y, fa, theta = 30, phi = 70)" ] }, { "cell_type": "code", "execution_count": 416, "metadata": {}, "outputs": [ { "data": { "image/png": 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4gN/y79xcWjukXpsVxAClWWF1QLF/xp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\nzNqPuQH2Q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"text/plain": [ "plot without title" ] }, "metadata": {}, "output_type": "display_data", "source": "R display func" } ], "source": [ "persp(x, y, fa, theta = 30, phi = 40)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "

Indexovanie dát

\n", "Pri spracovaní dát potrebujeme aj pristupovať k časti množiny dát. Predpokladajme, že naše dáta sú uložené v matici A. " ] }, { "cell_type": "code", "execution_count": 417, "metadata": {}, "outputs": [], "source": [ "A = matrix( 1:16, 4, 4)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Potom, napísaním" ] }, { "cell_type": "code", "execution_count": 418, "metadata": {}, "outputs": [ { "data": { "text/html": [ "10" ], "text/latex": [ "10" ], "text/markdown": [ "10" ], "text/plain": [ "[1] 10" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "A[2, 3]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "vyberieme prvok korešpondejúci druhý riadeok a tretí stĺpec. Prvé číslo potom je v po zátvorke [ vždy referuje riadku, a druhé číslo vždy sa spája so stĺpcom. \n", "Môžeme vybrať mnoho riadkov a stĺpcov v jednom čase, zabezpečujúc vektory ako indexy. " ] }, { "cell_type": "code", "execution_count": 419, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\t\n", "\t\n", "\n", "
5 13
7 15
\n" ], "text/latex": [ "\\begin{tabular}{ll}\n", "\t 5 & 13\\\\\n", "\t 7 & 15\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "| 5 | 13 | \n", "| 7 | 15 | \n", "\n", "\n" ], "text/plain": [ " [,1] [,2]\n", "[1,] 5 13 \n", "[2,] 7 15 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "A[c(1,3), c(2,4)]" ] }, { "cell_type": "code", "execution_count": 420, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\t\n", "\t\n", "\t\n", "\n", "
5 913
6 1014
7 1115
\n" ], "text/latex": [ "\\begin{tabular}{lll}\n", "\t 5 & 9 & 13\\\\\n", "\t 6 & 10 & 14\\\\\n", "\t 7 & 11 & 15\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "| 5 | 9 | 13 | \n", "| 6 | 10 | 14 | \n", "| 7 | 11 | 15 | \n", "\n", "\n" ], "text/plain": [ " [,1] [,2] [,3]\n", "[1,] 5 9 13 \n", "[2,] 6 10 14 \n", "[3,] 7 11 15 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "A[1:3, 2:4]" ] }, { "cell_type": "code", "execution_count": 421, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\t\n", "\t\n", "\n", "
1 5 913
2 6 1014
\n" ], "text/latex": [ "\\begin{tabular}{llll}\n", "\t 1 & 5 & 9 & 13\\\\\n", "\t 2 & 6 & 10 & 14\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "| 1 | 5 | 9 | 13 | \n", "| 2 | 6 | 10 | 14 | \n", "\n", "\n" ], "text/plain": [ " [,1] [,2] [,3] [,4]\n", "[1,] 1 5 9 13 \n", "[2,] 2 6 10 14 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "A[1:2, ]" ] }, { "cell_type": "code", "execution_count": 422, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\t\n", "\t\n", "\t\n", "\t\n", "\n", "
15
26
37
48
\n" ], "text/latex": [ "\\begin{tabular}{ll}\n", "\t 1 & 5\\\\\n", "\t 2 & 6\\\\\n", "\t 3 & 7\\\\\n", "\t 4 & 8\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "| 1 | 5 | \n", "| 2 | 6 | \n", "| 3 | 7 | \n", "| 4 | 8 | \n", "\n", "\n" ], "text/plain": [ " [,1] [,2]\n", "[1,] 1 5 \n", "[2,] 2 6 \n", "[3,] 3 7 \n", "[4,] 4 8 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "A[ , 1:2 ]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Posledné dva príklady zahrňajú buď žiadny index pre stĺpce, alebo žiadny index pre riadky. Tieto indikujú to, že R by mal zahnúť všetky stĺpce alebo všetky riadky respektívne. R spracováva jeden riadok alebo stĺpec matice ako vektor." ] }, { "cell_type": "code", "execution_count": 423, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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2 6 1014
4 8 1216
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    \n", "\t
  1. 4
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\n" ], "text/latex": [ "\\begin{enumerate*}\n", "\\item 4\n", "\\item 4\n", "\\end{enumerate*}\n" ], "text/markdown": [ "1. 4\n", "2. 4\n", "\n", "\n" ], "text/plain": [ "[1] 4 4" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "dim(A)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "

Načítanie Dát

\n", "

Pre väčšinu analýz, prvý krok je importovanie dátovej množiny do R. \n", "Funkcia read.table() je jedna z primárnych spôsobom načínia dát. \n", "Na exportovanie dát sa používa funkcia write.table(). \n", "

\n", "

\n", " Pred načítaním dátovej množiny sa musíme ujistiť, že R vie kde sa nachádzajú tie súbory. \n", " V tomto príklade začneme načítavaním dátovej množiny Auto. Tieto dáta sú súčasťou ISLR knižnice. \n", " Ale na ukážku načítavania dát, tak tieto dáta načítame zo súboru. \n", " Funkcia read.table() načíta dáta zo súboru Auto.data do R a uloží to ako objekt, ktorý pomenujeme Auto, a vo formáte referovanom ako data frame. Raz keď sú dáta načítané, tak voláme funkciu fix() na zobrazenie ukážky načtaných dát zobrazených v samostatnom okne v podobe tabuľky. \n", "

" ] }, { "cell_type": "code", "execution_count": 426, "metadata": {}, "outputs": [], "source": [ "Auto = read.table(\"Auto.data\")" ] }, { "cell_type": "code", "execution_count": 427, "metadata": {}, "outputs": [], "source": [ "fix(Auto)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Táto dátova množina nebola správne načítaný, pretože R predpokladal, že názov premenných je súčasťou dát a zahrnul ich v prvom riadku. \n", "Tento dáta tiež obsahujú niekoľko chýbajúcih údajov, ktoré sú označené znakom otáznika ? . \n", "Chýbajúce hodnoty sa často vyskytujú v reálnych dátových množinách. \n", "Použitím voľby header=TRUE vo funkcii read.table(), R urobí to, že prvý riadok v súbore, ktorý obsahuje názvy premenných, a použitím voľby na.string urobí R to, že za každým, keď uvidí chýbajúci údaj alebo znak ?, tak s týmto údajom bude zaobchádzať ako s chýbajúcim prvkom v dátovej množine. " ] }, { "cell_type": "code", "execution_count": 428, "metadata": {}, "outputs": [], "source": [ "Auto = read.table(\"Auto.data\", header = TRUE, na.string = \"?\")" ] }, { "cell_type": "code", "execution_count": 429, "metadata": {}, "outputs": [], "source": [ "fix(Auto)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Excel je najčastejší formát ukladania dát. Dá sa jednoducho načítať do R a uložiť ako ako csv (comma separated value) súbor a potom načítať ich pomocou funkcie read.csv()." ] }, { "cell_type": "code", "execution_count": 430, "metadata": {}, "outputs": [], "source": [ "Auto = read.csv(\"Auto.csv\", header = TRUE, na.string= \"?\" )" ] }, { "cell_type": "code", "execution_count": 431, "metadata": {}, "outputs": [], "source": [ "fix(Auto)" ] }, { "cell_type": "code", "execution_count": 432, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
    \n", "\t
  1. 397
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mpgcylindersdisplacementhorsepowerweightaccelerationyearoriginname
18 8 307 130 3504 12.0 70 1 chevrolet chevelle malibu
15 8 350 165 3693 11.5 70 1 buick skylark 320
18 8 318 150 3436 11.0 70 1 plymouth satellite
16 8 304 150 3433 12.0 70 1 amc rebel sst
\n" ], "text/latex": [ "\\begin{tabular}{r|lllllllll}\n", " mpg & cylinders & displacement & horsepower & weight & acceleration & year & origin & name\\\\\n", "\\hline\n", "\t 18 & 8 & 307 & 130 & 3504 & 12.0 & 70 & 1 & chevrolet chevelle malibu\\\\\n", "\t 15 & 8 & 350 & 165 & 3693 & 11.5 & 70 & 1 & buick skylark 320 \\\\\n", "\t 18 & 8 & 318 & 150 & 3436 & 11.0 & 70 & 1 & plymouth satellite \\\\\n", "\t 16 & 8 & 304 & 150 & 3433 & 12.0 & 70 & 1 & amc rebel sst \\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "mpg | cylinders | displacement | horsepower | weight | acceleration | year | origin | name | \n", "|---|---|---|---|\n", "| 18 | 8 | 307 | 130 | 3504 | 12.0 | 70 | 1 | chevrolet chevelle malibu | \n", "| 15 | 8 | 350 | 165 | 3693 | 11.5 | 70 | 1 | buick skylark 320 | \n", "| 18 | 8 | 318 | 150 | 3436 | 11.0 | 70 | 1 | plymouth satellite | \n", "| 16 | 8 | 304 | 150 | 3433 | 12.0 | 70 | 1 | amc rebel sst | \n", "\n", "\n" ], "text/plain": [ " mpg cylinders displacement horsepower weight acceleration year origin\n", "1 18 8 307 130 3504 12.0 70 1 \n", "2 15 8 350 165 3693 11.5 70 1 \n", "3 18 8 318 150 3436 11.0 70 1 \n", "4 16 8 304 150 3433 12.0 70 1 \n", " name \n", "1 chevrolet chevelle malibu\n", "2 buick skylark 320 \n", "3 plymouth satellite \n", "4 amc rebel sst " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "Auto[1:4, ]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Funkcia dim() nám povie, že dáta majú 397 záznamov, alebo riadkov, a deväť premenných/variables, alebo stĺpcov. \n", "Je niekoľko spôsobom ako si poradiť s chýbajúcimi dátmi. \n", "V tomto prípade máme len 5 riadkov, ktoré obsahujú chýbajúce záznamy a tak vyberieme funkciu na.omit(), ktorá jednoducho vymaže dané riadky. " ] }, { "cell_type": "code", "execution_count": 434, "metadata": {}, "outputs": [], "source": [ "Auto = na.omit(Auto)" ] }, { "cell_type": "code", "execution_count": 435, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
    \n", "\t
  1. 392
    2. \n", "\t
  2. 9
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\n" ], "text/latex": [ "\\begin{enumerate*}\n", "\\item 392\n", "\\item 9\n", "\\end{enumerate*}\n" ], "text/markdown": [ "1. 392\n", "2. 9\n", "\n", "\n" ], "text/plain": [ "[1] 392 9" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "dim(Auto)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Raz keď sú dáta načítané správne, môžeme zavolať funkciu na.omit(), ktorá skontroluje a vypíše názvy premenných. " ] }, { "cell_type": "code", "execution_count": 436, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
    \n", "\t
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    5. \n", "\t
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Dodatočné grafické a numerické zhrnutie

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\n", "Môžeme použiť funkciu plot() na vyprodukovanie scatterplots kvantitatívnych premenných. \n", " Avšak, jednoducho keď napíšeme názvy premenných, tak sa objavý chybná hláška, pretože R nevie sa pozrieť do Auto dátovej množiny pre tieto premenné. \n", " \n", "

" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "plot(cylinders, mpg)\n", "Error in plot(cylinders, mpg): object 'cylinders' not found\n", "\n", "Traceback:\n", "\n", "1. plot(cylinders, mpg)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Na to aby sme odkazovali na premennú, musíme napísať dátovú množinu a názov premennej s znakom $. \n", "Alternatívne môžeme použiť funkciu attach() na to, aby povedalo R, aby vyrobilo premenné v tejto dátovej množine, ak sú prístupne menom. " ] }, { "cell_type": "code", "execution_count": 437, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "plot without title" ] }, "metadata": {}, "output_type": "display_data", "source": "R display func" } ], "source": [ "plot(Auto$cylinders, Auto$mpg)" ] }, { "cell_type": "code", "execution_count": 438, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "The following objects are masked from Auto (pos = 3):\n", "\n", " acceleration, cylinders, displacement, horsepower, mpg, name,\n", " origin, weight, year\n", "\n", "The following objects are masked from Auto (pos = 4):\n", "\n", " acceleration, cylinders, displacement, horsepower, mpg, name,\n", " origin, weight, year\n", "\n", "The following objects are masked from Auto (pos = 5):\n", "\n", " acceleration, cylinders, displacement, horsepower, mpg, name,\n", " origin, weight, year\n", "\n" ] } ], "source": [ "attach(Auto)" ] }, { "cell_type": "code", "execution_count": 439, "metadata": {}, "outputs": [ { "data": { "image/png": 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NV6fyLZIk0RUjUh2UcqE1K13s9ssGo3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\n7wAAAABJRU5ErkJggg==", "text/plain": [ "plot without title" ] }, "metadata": {}, "output_type": "display_data", "source": "R display func" } ], "source": [ "plot(cylinders, mpg)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Premenná cylinders je uložená vektor čísel, tak R s ňou zaobchádza ako s kvantitatívnou (veľkostnou) hodnotou. \n", "Avšak, keďže je malý počet možných hodnôt cylinderov, tak je lepšie ak sa s nimi bude zaobchádzať ako kvalitatívnymi premennými. \n", "Funkcia as.factor() konvertuje kvantitatívnu premennú na kvalitatívnu. " ] }, { "cell_type": "code", "execution_count": 440, "metadata": {}, "outputs": [], "source": [ "cylinders = as.factor(cylinders)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Ak je premenná v grafe na x-ovej osy ako kategorická premenné tak boxplots budú automaticky produkované funkciou plot(). \n", "Ako zvyčajne, voľby čísiel môžu byť špecificky zmenené v grafe. " ] }, { "cell_type": "code", "execution_count": 441, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "plot without title" ] }, "metadata": {}, "output_type": "display_data", "source": "R display func" } ], "source": [ "plot(cylinders, mpg)" ] }, { "cell_type": "code", "execution_count": 442, "metadata": {}, "outputs": [ { "data": { "image/png": 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IREACEtFRJHEZKQEs27fiEJKdG86xdS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\nCAkCCAkCCAkCCAkCCAkC/D+Kz8FGKGlA2QAAAABJRU5ErkJggg==", "text/plain": [ "plot without title" ] }, "metadata": {}, "output_type": "display_data", "source": "R display func" } ], "source": [ "plot(cylinders, mpg, col = \"red\")" ] }, { "cell_type": "code", "execution_count": 443, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "plot without title" ] }, "metadata": {}, "output_type": "display_data", "source": "R display func" } ], "source": [ "plot(cylinders, mpg, col = \"red\", varwidth=TRUE)" ] }, { "cell_type": "code", "execution_count": 444, "metadata": {}, "outputs": [ { "data": { "image/png": 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BIEhAQBIUFASBAQEgSEBIFBQ4LJfOJW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\nQoKAkCAgJAgICQL/B9oNaDGLjY6rAAAAAElFTkSuQmCC", "text/plain": [ "plot without title" ] }, "metadata": {}, "output_type": "display_data", "source": "R display func" } ], "source": [ "plot(cylinders, mpg, col = \"red\", varwidth = TRUE, horizontal = TRUE)" ] }, { "cell_type": "code", "execution_count": 445, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "plot without title" ] }, "metadata": {}, "output_type": "display_data", "source": "R display func" } ], "source": [ "plot(cylinders, mpg, col = \"red\", varwidth=TRUE, xlab = \"cylinders\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Funkcia hist() môže byť použitá na vykreslenie histogramu. \n", "Berte do úvahy, že col=2 má rovnaké správanie ako col=\"red\"" ] }, { "cell_type": "code", "execution_count": 446, "metadata": {}, "outputs": [ { "data": { "image/png": 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czbMfe1ONfk49+32ny6yY2Y+1ooUmrmvhaKlJq5r8XXIh1fM226B6+RMjD3tfha\npPOhuo2jdhmY+1p8LdLp7NHq+P7pVqdIN2Tua/HNa6RVt3g6f3Ldd8sXRbohc1+rr7U5X+XA\nTShSrS6K1B0vb9ituvUNh3PvFKlWF0V6PL9C6m84mrunSLW6fGr3tOy6hcejW1IkCKBIEECR\nIIAiQQBFggCKBAEUCQIoEgRQJAigSBBAkSCAIkEARYIAigQBFAkCKBIEUCQIoEgQQJEggCJB\nAEWCAIoEARQJAigSBFAkCKBIEECRIMB/t5SI71Xc+2gAAAAASUVORK5CYII=", "text/plain": [ "Plot with title \"Histogram of mpg\"" ] }, "metadata": {}, "output_type": "display_data", "source": "R display func" } ], "source": [ "hist(mpg)" ] }, { "cell_type": "code", "execution_count": 447, "metadata": {}, "outputs": [ { "data": { "image/png": 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IcPEUSyzs0yRj18iCCSdW6WMerhQwSR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\nwIH/AVZHiFgwtR9vAAAAAElFTkSuQmCC", "text/plain": [ "Plot with title \"Histogram of mpg\"" ] }, "metadata": {}, "output_type": "display_data", "source": "R display func" } ], "source": [ "hist(mpg, col = 2)" ] }, { "cell_type": "code", "execution_count": 448, "metadata": {}, "outputs": [ { "data": { "image/png": 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bP/eJgzz5dwiwTw+ox4jLQ79KcGHyMBvD7ZN2/Lm5vIxVG5JYD6GPE60rp/Hald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\nSUVORK5CYII=", "text/plain": [ "Plot with title \"Histogram of mpg\"" ] }, "metadata": {}, "output_type": "display_data", "source": "R display func" } ], "source": [ "hist(mpg, col = 2, breaks = 15)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Funkcia pairs() vytvorí scatterplot maticu, to je pr každý pár premenných v danej dátovej množine. \n", "Môžeme vyprodukovať scatterplot aj len pre podmnožinu premenných. " ] }, { "cell_type": "code", "execution_count": 449, "metadata": {}, "outputs": [ { "data": { "image/png": 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29S64iOZEY7q9GSegynA08Z0sIpHnNA0ukr1YY/2Sisg9bKvhvB296R9LmiKmkz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"text/plain": [ "plot without title" ] }, "metadata": {}, "output_type": "display_data", "source": "R display func" } ], "source": [ "pairs(Auto)" ] }, { "cell_type": "code", "execution_count": 450, "metadata": {}, "outputs": [ { "data": { "image/png": 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lC+C9nTPFZvH0EFgxlLJ4q2lXmLBZ90jFq6u5hI/I35+F2gtM26lFnSJNbBrmZd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\nDgU4kRwOBTiRHA4FOJEcDgU4kRwOBfwfomzRz76nmRcAAAAASUVORK5CYII=", "text/plain": [ "plot without title" ] }, "metadata": {}, "output_type": "display_data", "source": "R display func" } ], "source": [ "pairs(~ horsepower + weight + acceleration, Auto)" ] }, { "cell_type": "code", "execution_count": 451, "metadata": {}, "outputs": [ { "data": { "image/png": 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jjbJiYSlKPLImgRKXZof3nTQTgNkxn/fr708dxckTwOP063RaSc+0v9+raItIg0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"text/plain": [ "plot without title" ] }, "metadata": {}, "output_type": "display_data", "source": "R display func" } ], "source": [ "pairs(~ mpg + displacement + horsepower + weight + acceleration, Auto)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Spojenie kombinácií alebo priesečník funkcií plot() , identify() dokáže vytvoriť užitočnú interaktívnu metódu na identifikovanie hodnoty pre určitú premennú pre dané body na grafe. \n", "Do funkcie indentify() vstupujú tri argumenty: premenná osi x, premenná osi y, a pemenná tých hodnôt, ktoré chceme vypísať pre každý bod. Potom klikneme na daný bod v grafe a R vypíše hodnotu premennej ktorá nás zaujíma. Pravým kliknutím na graf ukončíme funkciu identify(). Čísla sú vypísané pod funkciu identify() korešpondujúc s riadkami pre zvolené body. " ] }, { "cell_type": "code", "execution_count": 452, "metadata": {}, "outputs": [ { "data": { "text/html": [], "text/latex": [], "text/markdown": [], "text/plain": [ "integer(0)" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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"text/plain": [ "plot without title" ] }, "metadata": {}, "output_type": "display_data", "source": "R display func" } ], "source": [ "plot(horsepower, mpg)\n", "identify(horsepower,mpg,name)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Funkcia summary() produkuje numerické prehľadové zhrnutie každej premennej v danej dátovej množine. " ] }, { "cell_type": "code", "execution_count": 453, "metadata": {}, "outputs": [ { "data": { "text/plain": [ " mpg cylinders displacement horsepower weight \n", " Min. : 9.00 Min. :3.000 Min. : 68.0 Min. : 46.0 Min. :1613 \n", " 1st Qu.:17.00 1st Qu.:4.000 1st Qu.:105.0 1st Qu.: 75.0 1st Qu.:2225 \n", " Median :22.75 Median :4.000 Median :151.0 Median : 93.5 Median :2804 \n", " Mean :23.45 Mean :5.472 Mean :194.4 Mean :104.5 Mean :2978 \n", " 3rd Qu.:29.00 3rd Qu.:8.000 3rd Qu.:275.8 3rd Qu.:126.0 3rd Qu.:3615 \n", " Max. :46.60 Max. :8.000 Max. :455.0 Max. :230.0 Max. :5140 \n", " \n", " acceleration year origin name \n", " Min. : 8.00 Min. :70.00 Min. :1.000 amc matador : 5 \n", " 1st Qu.:13.78 1st Qu.:73.00 1st Qu.:1.000 ford pinto : 5 \n", " Median :15.50 Median :76.00 Median :1.000 toyota corolla: 5 \n", " Mean :15.54 Mean :75.98 Mean :1.577 amc gremlin : 4 \n", " 3rd Qu.:17.02 3rd Qu.:79.00 3rd Qu.:2.000 amc hornet : 4 \n", " Max. :24.80 Max. :82.00 Max. :3.000 ford maverick : 4 \n", " (Other) :365 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "summary(Auto)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Pre kvalitatívne premenné ako napríklad name, R vytorí zoznam počtu záznamov, ktoré patria do daných kategórií. \n", "Môžeme tiež vyprodukovať zhrnutie aj jednej premennej. " ] }, { "cell_type": "code", "execution_count": 454, "metadata": {}, "outputs": [ { "data": { "text/plain": [ " Min. 1st Qu. Median Mean 3rd Qu. Max. \n", " 9.00 17.00 22.75 23.45 29.00 46.60 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "summary(mpg)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "

Na záver

\n", "

Keď sme už dokončili prácu s R, tak zavoláme funkciu g() na to, aby R vypla a ukončila. \n", "Keď už existujúce R je spustené, tak máme možnosť uložiť súčastný workspace so všekými objektmi a dátovými množinami, ktoré sme vytvorili v danej R relácií, a toto bude prístupné pri ďalšej návšteve. \n", "Pred skončením R, možno chceme uložiť záznam všetkých príkazov, ktoré sme napísali, a toto môže byť spravené funkciou savehistory(). \n", "Pri návrate môžete načítať históriu volaním funkcie loadhistory().\n", "

" ] }, { "cell_type": "code", "execution_count": 455, "metadata": {}, "outputs": [], "source": [ "q()" ] } ], "metadata": { "kernelspec": { "display_name": "R", "language": "R", "name": "ir" }, "language_info": { "codemirror_mode": "r", "file_extension": ".r", "mimetype": "text/x-r-source", "name": "R", "pygments_lexer": "r", "version": "3.4.2" } }, "nbformat": 4, "nbformat_minor": 2 }