├── R Basic Programming Part 1.ipynb ├── R Basic Programming part 2.ipynb ├── R Conditional Statement .ipynb ├── R Programming Basic3.ipynb ├── R Programming: Vector Exercise-4.ipynb ├── README.md ├── Vector Exercise 2.ipynb ├── Vector Exercise.ipynb └── iris.r /R Basic Programming Part 1.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "## R Programming: Basic Exercise" 8 | ] 9 | }, 10 | { 11 | "cell_type": "markdown", 12 | "metadata": {}, 13 | "source": [ 14 | "### Write a program to get the details of the objects in memory" 15 | ] 16 | }, 17 | { 18 | "cell_type": "code", 19 | "execution_count": 1, 20 | "metadata": {}, 21 | "outputs": [ 22 | { 23 | "name": "stdout", 24 | "output_type": "stream", 25 | "text": [ 26 | "[1] \"n1\" \"n2\" \"name\" \"nums\"\n", 27 | "[1] \"Details of the objects in memory:\"\n", 28 | "n1 : num 10\n", 29 | "n2 : num 0.5\n", 30 | "name : chr \"R\"\n", 31 | "nums : num [1:6] 10 20 30 40 50 60\n" 32 | ] 33 | } 34 | ], 35 | "source": [ 36 | "name = \"R\"; \n", 37 | "n1 = 10; \n", 38 | "n2 = 0.5\n", 39 | "nums = c(10, 20, 30, 40, 50, 60)\n", 40 | "print(ls())\n", 41 | "print(\"Details of the objects in memory:\")\n", 42 | "print(ls.str())" 43 | ] 44 | }, 45 | { 46 | "cell_type": "markdown", 47 | "metadata": {}, 48 | "source": [ 49 | "### Write a program to create a sequence of numbers from 20 to 50 and find the mean of numbers from 20 to 60 and sum of numbers from 51 to 91." 50 | ] 51 | }, 52 | { 53 | "cell_type": "code", 54 | "execution_count": 2, 55 | "metadata": {}, 56 | "outputs": [ 57 | { 58 | "name": "stdout", 59 | "output_type": "stream", 60 | "text": [ 61 | "[1] \"Sequence of numbers from 20 to 50:\"\n", 62 | " [1] 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44\n", 63 | "[26] 45 46 47 48 49 50\n", 64 | "[1] \"Mean of numbers from 20 to 60:\"\n", 65 | "[1] 40\n", 66 | "[1] \"Sum of numbers from 51 to 91:\"\n", 67 | "[1] 2911\n" 68 | ] 69 | } 70 | ], 71 | "source": [ 72 | "print(\"Sequence of numbers from 20 to 50:\")\n", 73 | "print(seq(20,50))\n", 74 | "print(\"Mean of numbers from 20 to 60:\")\n", 75 | "print(mean(20:60))\n", 76 | "print(\"Sum of numbers from 51 to 91:\")\n", 77 | "print(sum(51:91))" 78 | ] 79 | }, 80 | { 81 | "cell_type": "markdown", 82 | "metadata": {}, 83 | "source": [ 84 | "### Write a program to create a vector which contains 10 random integer values between -50 and +50." 85 | ] 86 | }, 87 | { 88 | "cell_type": "code", 89 | "execution_count": 3, 90 | "metadata": {}, 91 | "outputs": [ 92 | { 93 | "name": "stdout", 94 | "output_type": "stream", 95 | "text": [ 96 | "[1] \"Content of the vector:\"\n", 97 | "[1] \"10 random integer values between -50 and +50:\"\n", 98 | " [1] 17 -48 -11 -30 27 13 50 -47 24 -28\n" 99 | ] 100 | } 101 | ], 102 | "source": [ 103 | "v = sample(-50:50, 10, replace=TRUE)\n", 104 | "print(\"Content of the vector:\")\n", 105 | "print(\"10 random integer values between -50 and +50:\")\n", 106 | "print(v)" 107 | ] 108 | }, 109 | { 110 | "cell_type": "markdown", 111 | "metadata": {}, 112 | "source": [ 113 | "### Write a program to get the first 10 Fibonacci numbers" 114 | ] 115 | }, 116 | { 117 | "cell_type": "code", 118 | "execution_count": 4, 119 | "metadata": {}, 120 | "outputs": [ 121 | { 122 | "name": "stdout", 123 | "output_type": "stream", 124 | "text": [ 125 | "[1] \"First 10 Fibonacci numbers:\"\n", 126 | " [1] 1 1 2 3 5 8 13 21 34 55\n" 127 | ] 128 | } 129 | ], 130 | "source": [ 131 | "Fibonacci <- numeric(10)\n", 132 | "Fibonacci[1] <- Fibonacci[2] <- 1\n", 133 | "for (i in 3:10) Fibonacci[i] <- Fibonacci[i - 2] + Fibonacci[i - 1]\n", 134 | "print(\"First 10 Fibonacci numbers:\")\n", 135 | "print(Fibonacci)" 136 | ] 137 | }, 138 | { 139 | "cell_type": "markdown", 140 | "metadata": {}, 141 | "source": [ 142 | "### Write a program to get all prime numbers up to a given number (based on the sieve of Eratosthenes)" 143 | ] 144 | }, 145 | { 146 | "cell_type": "code", 147 | "execution_count": 5, 148 | "metadata": {}, 149 | "outputs": [ 150 | { 151 | "data": { 152 | "text/html": [ 153 | "
    \n", 154 | "\t
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    3. \n", 156 | "\t
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    4. \n", 157 | "\t
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    5. \n", 158 | "\t
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\n" 160 | ], 161 | "text/latex": [ 162 | "\\begin{enumerate*}\n", 163 | "\\item 2\n", 164 | "\\item 3\n", 165 | "\\item 5\n", 166 | "\\item 7\n", 167 | "\\item 11\n", 168 | "\\end{enumerate*}\n" 169 | ], 170 | "text/markdown": [ 171 | "1. 2\n", 172 | "2. 3\n", 173 | "3. 5\n", 174 | "4. 7\n", 175 | "5. 11\n", 176 | "\n", 177 | "\n" 178 | ], 179 | "text/plain": [ 180 | "[1] 2 3 5 7 11" 181 | ] 182 | }, 183 | "metadata": {}, 184 | "output_type": "display_data" 185 | } 186 | ], 187 | "source": [ 188 | "prime_numbers <- function(n) {\n", 189 | "if (n >= 2) {\n", 190 | " x = seq(2, n)\n", 191 | " prime_nums = c()\n", 192 | " for (i in seq(2, n)) {\n", 193 | " if (any(x == i)) {\n", 194 | " prime_nums = c(prime_nums, i)\n", 195 | " x = c(x[(x %% i) != 0], i)\n", 196 | " }\n", 197 | " }\n", 198 | " return(prime_nums)\n", 199 | " }\n", 200 | " else \n", 201 | " {\n", 202 | " stop(\"Input number should be at least 2.\")\n", 203 | " }\n", 204 | " } \n", 205 | "prime_numbers(12)" 206 | ] 207 | }, 208 | { 209 | "cell_type": "markdown", 210 | "metadata": {}, 211 | "source": [ 212 | "### Write a program to print the numbers from 1 to 100 and print \"Fizz\" for multiples of 3, print \"Buzz\" for multiples of 5, and print \"FizzBuzz\" for multiples of both" 213 | ] 214 | }, 215 | { 216 | "cell_type": "code", 217 | "execution_count": 6, 218 | "metadata": {}, 219 | "outputs": [ 220 | { 221 | "name": "stdout", 222 | "output_type": "stream", 223 | "text": [ 224 | "[1] 1\n", 225 | "[1] 2\n", 226 | "[1] \"Fizz\"\n", 227 | "[1] 4\n", 228 | "[1] \"Buzz\"\n", 229 | "[1] \"Fizz\"\n", 230 | "[1] 7\n", 231 | "[1] 8\n", 232 | "[1] \"Fizz\"\n", 233 | "[1] \"Buzz\"\n", 234 | "[1] 11\n", 235 | "[1] \"Fizz\"\n", 236 | "[1] 13\n", 237 | "[1] 14\n", 238 | "[1] \"FizzBuzz\"\n", 239 | "[1] 16\n", 240 | "[1] 17\n", 241 | "[1] \"Fizz\"\n", 242 | "[1] 19\n", 243 | "[1] \"Buzz\"\n", 244 | "[1] \"Fizz\"\n", 245 | "[1] 22\n", 246 | "[1] 23\n", 247 | "[1] \"Fizz\"\n", 248 | "[1] \"Buzz\"\n", 249 | "[1] 26\n", 250 | "[1] \"Fizz\"\n", 251 | "[1] 28\n", 252 | "[1] 29\n", 253 | "[1] \"FizzBuzz\"\n", 254 | "[1] 31\n", 255 | "[1] 32\n", 256 | "[1] \"Fizz\"\n", 257 | "[1] 34\n", 258 | "[1] \"Buzz\"\n", 259 | "[1] \"Fizz\"\n", 260 | "[1] 37\n", 261 | "[1] 38\n", 262 | "[1] \"Fizz\"\n", 263 | "[1] \"Buzz\"\n", 264 | "[1] 41\n", 265 | "[1] \"Fizz\"\n", 266 | "[1] 43\n", 267 | "[1] 44\n", 268 | "[1] \"FizzBuzz\"\n", 269 | "[1] 46\n", 270 | "[1] 47\n", 271 | "[1] \"Fizz\"\n", 272 | "[1] 49\n", 273 | "[1] \"Buzz\"\n", 274 | "[1] \"Fizz\"\n", 275 | "[1] 52\n", 276 | "[1] 53\n", 277 | "[1] \"Fizz\"\n", 278 | "[1] \"Buzz\"\n", 279 | "[1] 56\n", 280 | "[1] \"Fizz\"\n", 281 | "[1] 58\n", 282 | "[1] 59\n", 283 | "[1] \"FizzBuzz\"\n", 284 | "[1] 61\n", 285 | "[1] 62\n", 286 | "[1] \"Fizz\"\n", 287 | "[1] 64\n", 288 | "[1] \"Buzz\"\n", 289 | "[1] \"Fizz\"\n", 290 | "[1] 67\n", 291 | "[1] 68\n", 292 | "[1] \"Fizz\"\n", 293 | "[1] \"Buzz\"\n", 294 | "[1] 71\n", 295 | "[1] \"Fizz\"\n", 296 | "[1] 73\n", 297 | "[1] 74\n", 298 | "[1] \"FizzBuzz\"\n", 299 | "[1] 76\n", 300 | "[1] 77\n", 301 | "[1] \"Fizz\"\n", 302 | "[1] 79\n", 303 | "[1] \"Buzz\"\n", 304 | "[1] \"Fizz\"\n", 305 | "[1] 82\n", 306 | "[1] 83\n", 307 | "[1] \"Fizz\"\n", 308 | "[1] \"Buzz\"\n", 309 | "[1] 86\n", 310 | "[1] \"Fizz\"\n", 311 | "[1] 88\n", 312 | "[1] 89\n", 313 | "[1] \"FizzBuzz\"\n", 314 | "[1] 91\n", 315 | "[1] 92\n", 316 | "[1] \"Fizz\"\n", 317 | "[1] 94\n", 318 | "[1] \"Buzz\"\n", 319 | "[1] \"Fizz\"\n", 320 | "[1] 97\n", 321 | "[1] 98\n", 322 | "[1] \"Fizz\"\n", 323 | "[1] \"Buzz\"\n" 324 | ] 325 | } 326 | ], 327 | "source": [ 328 | "for (n in 1:100) {\n", 329 | " if (n %% 3 == 0 & n %% 5 == 0) {print(\"FizzBuzz\")}\n", 330 | " else if (n %% 3 == 0) {print(\"Fizz\")}\n", 331 | " else if (n %% 5 == 0) {print(\"Buzz\")}\n", 332 | " else print(n)\n", 333 | "}" 334 | ] 335 | }, 336 | { 337 | "cell_type": "markdown", 338 | "metadata": {}, 339 | "source": [ 340 | "### Write a program to extract first 10 english letter in lower case and last 10 letters in upper case and extract letters between 22nd to 24th letters in upper case\n", 341 | "\n", 342 | "Note: Use built-in datasets letters and LETTERS." 343 | ] 344 | }, 345 | { 346 | "cell_type": "code", 347 | "execution_count": 7, 348 | "metadata": {}, 349 | "outputs": [ 350 | { 351 | "name": "stdout", 352 | "output_type": "stream", 353 | "text": [ 354 | "[1] \"First 10 letters in lower case:\"\n", 355 | " [1] \"a\" \"b\" \"c\" \"d\" \"e\" \"f\" \"g\" \"h\" \"i\" \"j\"\n", 356 | "[1] \"Last 10 letters in upper case:\"\n", 357 | " [1] \"Q\" \"R\" \"S\" \"T\" \"U\" \"V\" \"W\" \"X\" \"Y\" \"Z\"\n", 358 | "[1] \"Letters between 22nd to 24th letters in upper case:\"\n", 359 | "[1] \"V\" \"W\" \"X\"\n" 360 | ] 361 | } 362 | ], 363 | "source": [ 364 | "print(\"First 10 letters in lower case:\")\n", 365 | "t = head(letters, 10)\n", 366 | "print(t)\n", 367 | "print(\"Last 10 letters in upper case:\")\n", 368 | "t = tail(LETTERS, 10)\n", 369 | "print(t)\n", 370 | "print(\"Letters between 22nd to 24th letters in upper case:\")\n", 371 | "e = tail(LETTERS[22:24])\n", 372 | "print(e)" 373 | ] 374 | }, 375 | { 376 | "cell_type": "markdown", 377 | "metadata": {}, 378 | "source": [ 379 | "### Write a program to find the factors of a given number" 380 | ] 381 | }, 382 | { 383 | "cell_type": "code", 384 | "execution_count": 8, 385 | "metadata": {}, 386 | "outputs": [ 387 | { 388 | "name": "stdout", 389 | "output_type": "stream", 390 | "text": [ 391 | "[1] \"The factors of 4 are:\"\n", 392 | "[1] 1\n", 393 | "[1] 2\n", 394 | "[1] 4\n", 395 | "[1] \"The factors of 7 are:\"\n", 396 | "[1] 1\n", 397 | "[1] 7\n", 398 | "[1] \"The factors of 12 are:\"\n", 399 | "[1] 1\n", 400 | "[1] 2\n", 401 | "[1] 3\n", 402 | "[1] 4\n", 403 | "[1] 6\n", 404 | "[1] 12\n" 405 | ] 406 | } 407 | ], 408 | "source": [ 409 | "print_factors = function(n) {\n", 410 | "print(paste(\"The factors of\",n,\"are:\"))\n", 411 | "for(i in 1:n) {\n", 412 | "if((n %% i) == 0) {\n", 413 | "print(i)\n", 414 | "}\n", 415 | "}\n", 416 | "}\n", 417 | "print_factors(4)\n", 418 | "print_factors(7)\n", 419 | "print_factors(12)" 420 | ] 421 | }, 422 | { 423 | "cell_type": "markdown", 424 | "metadata": {}, 425 | "source": [ 426 | "### Write a program to find the maximum and the minimum value of a given vector" 427 | ] 428 | }, 429 | { 430 | "cell_type": "code", 431 | "execution_count": 9, 432 | "metadata": {}, 433 | "outputs": [ 434 | { 435 | "name": "stdout", 436 | "output_type": "stream", 437 | "text": [ 438 | "[1] \"Original vector:\"\n", 439 | "[1] 10 20 30 40 50 60\n", 440 | "[1] \"Maximum value of the said vector: 60\"\n", 441 | "[1] \"Minimum value of the said vector: 10\"\n" 442 | ] 443 | } 444 | ], 445 | "source": [ 446 | "nums = c(10, 20, 30, 40, 50, 60)\n", 447 | "print('Original vector:')\n", 448 | "print(nums) \n", 449 | "print(paste(\"Maximum value of the said vector:\",max(nums)))\n", 450 | "print(paste(\"Minimum value of the said vector:\",min(nums)))" 451 | ] 452 | }, 453 | { 454 | "cell_type": "markdown", 455 | "metadata": {}, 456 | "source": [ 457 | "### Write a R program to get the unique elements of a given string and unique numbers of vector" 458 | ] 459 | }, 460 | { 461 | "cell_type": "code", 462 | "execution_count": 10, 463 | "metadata": {}, 464 | "outputs": [ 465 | { 466 | "name": "stdout", 467 | "output_type": "stream", 468 | "text": [ 469 | "[1] \"Original vector(string)\"\n", 470 | "[1] \"The quick brown fox jumps over the lazy dog.\"\n", 471 | "[1] \"Unique elements of the said vector:\"\n", 472 | "[1] \"the quick brown fox jumps over the lazy dog.\"\n", 473 | "[1] \"Original vector(number)\"\n", 474 | "[1] 1 2 2 3 4 4 5 6\n", 475 | "[1] \"Unique elements of the said vector:\"\n", 476 | "[1] 1 2 3 4 5 6\n" 477 | ] 478 | } 479 | ], 480 | "source": [ 481 | "str1 = \"The quick brown fox jumps over the lazy dog.\"\n", 482 | "print(\"Original vector(string)\")\n", 483 | "print(str1)\n", 484 | "print(\"Unique elements of the said vector:\")\n", 485 | "print(unique(tolower(str1)))\n", 486 | "nums = c(1, 2, 2, 3, 4, 4, 5, 6)\n", 487 | "print(\"Original vector(number)\")\n", 488 | "print(nums)\n", 489 | "print(\"Unique elements of the said vector:\")\n", 490 | "print(unique(nums))" 491 | ] 492 | }, 493 | { 494 | "cell_type": "markdown", 495 | "metadata": {}, 496 | "source": [ 497 | "### Write a program to create three vectors a,b,c with 3 integers. Combine the three vectors to become a 3×3 matrix where each column represents a vector. Print the content of the matrix" 498 | ] 499 | }, 500 | { 501 | "cell_type": "code", 502 | "execution_count": 11, 503 | "metadata": {}, 504 | "outputs": [ 505 | { 506 | "name": "stdout", 507 | "output_type": "stream", 508 | "text": [ 509 | "[1] \"Content of the said matrix:\"\n", 510 | " a b c\n", 511 | "[1,] 1 4 7\n", 512 | "[2,] 2 5 8\n", 513 | "[3,] 3 6 9\n" 514 | ] 515 | } 516 | ], 517 | "source": [ 518 | "a<-c(1,2,3)\n", 519 | "b<-c(4,5,6)\n", 520 | "c<-c(7,8,9)\n", 521 | "m<-cbind(a,b,c)\n", 522 | "print(\"Content of the said matrix:\")\n", 523 | "print(m)" 524 | ] 525 | }, 526 | { 527 | "cell_type": "markdown", 528 | "metadata": {}, 529 | "source": [ 530 | "### Write a program to create a list of random numbers in normal distribution and count occurrences of each value.\n", 531 | "\n", 532 | "Note: Sample random numbers in normal distribution" 533 | ] 534 | }, 535 | { 536 | "cell_type": "code", 537 | "execution_count": 12, 538 | "metadata": {}, 539 | "outputs": [ 540 | { 541 | "name": "stdout", 542 | "output_type": "stream", 543 | "text": [ 544 | "[1] \"List of random numbers in normal distribution:\"\n", 545 | " [1] 10 -44 61 50 282 60 83 -136 -132 200 29 150 -73 -85\n", 546 | " [15] 16 18 32 -43 138 108 -97 197 146 12 146 -60 28 91\n", 547 | " [29] -41 23 150 254 13 35 100 -28 49 43 168 25 101 -71\n", 548 | " [43] 9 250 -13 -18 -17 -117 104 131 98 54 -32 46 114 135\n", 549 | " [57] -19 12 -4 -25 -5 -32 -30 -16 -45 145 57 -6 93 139\n", 550 | " [71] -17 24 53 130 -39 53 19 23 184 3 -12 -234 67 -71\n", 551 | " [85] 106 141 -15 23 24 20 36 138 6 37 107 151 186 87\n", 552 | " [99] 147 97 19 176 130 37 172 68 7 52 179 92 -125 176\n", 553 | " [113] 124 235 59 188 159 60 69 42 69 -28 -18 -58 -16 99\n", 554 | " [127] -17 99 119 114 -80 104 80 5 22 29 33 85 108 165\n", 555 | " [141] 60 93 225 128 8 82 77 0 196 67 57 58 -53 126\n", 556 | " [155] -71 78 -9 53 82 5 48 11 156 7 259 110 50 111\n", 557 | " [169] 260 121 111 -207 -92 156 -75 17 110 50 39 30 71 -114\n", 558 | " [183] 18 32 -79 85 -31 306 41 -89 -40 -121 134 187 -134 -159\n", 559 | " [197] 193 21 131 118 27 35 41 -68 11 -37 74 -95 168 16\n", 560 | " [211] 25 74 -80 36 -65 28 96 -70 122 -20 196 -36 9 -30\n", 561 | " [225] 130 68 -80 -125 160 -87 74 195 46 255 0 -8 -115 -17\n", 562 | " [239] -55 42 95 64 -119 80 -31 108 81 -58 -7 98 -55 -54\n", 563 | " [253] -62 136 -109 -15 -45 -33 136 7 -10 102 -108 -68 -54 175\n", 564 | " [267] 196 -15 158 113 65 6 67 108 59 173 133 262 -107 53\n", 565 | " [281] 125 61 35 71 -86 104 58 -107 25 37 111 77 -136 -91\n", 566 | " [295] -78 16 -87 29 42 389 185 138 -15 -38 114 126 157 -55\n", 567 | " [309] -68 42 93 194 47 99 65 40 -77 111 -77 -90 7 43\n", 568 | " [323] 191 -45 51 132 185 119 90 -213 -51 123 60 89 166 3\n", 569 | " [337] 67 223 -275 -50 64 36 -21 95 -113 -160 31 5 71 77\n", 570 | " [351] 159 -62 -134 184 4 2 -9 16 -72 70 33 173 33 -52\n", 571 | " [365] -19 127 183 32 106 73 -135 58 133 56 5 -19 102 241\n", 572 | " [379] -30 -4 75 -147 23 89 197 114 51 65 112 247 62 81\n", 573 | " [393] 25 37 178 13 -51 87 -120 143 14 38 109 111 94 -144\n", 574 | " [407] -3 61 11 -116 39 -35 205 106 82 90 82 45 -14 124\n", 575 | " [421] -14 -148 174 129 24 71 56 363 10 37 68 131 74 97\n", 576 | " [435] -42 104 143 121 121 114 61 -9 88 60 -142 214 115 92\n", 577 | " [449] -60 90 140 148 49 -52 -173 35 -19 -122 -168 206 -110 -1\n", 578 | " [463] -80 47 71 -33 84 -60 -66 53 34 214 109 195 275 124\n", 579 | " [477] 146 46 40 135 68 74 37 6 185 22 -47 -209 229 140\n", 580 | " [491] 146 101 198 145 20 221 -24 54 41 -19 249 0 -32 -19\n", 581 | " [505] 230 57 -91 129 -17 1 108 56 175 52 -70 -44 33 -8\n", 582 | " [519] -13 130 -64 25 87 -11 190 83 117 61 -41 -49 -17 235\n", 583 | " [533] 233 67 228 45 -43 -12 53 -45 120 115 6 103 39 35\n", 584 | " [547] 100 -8 -154 93 -27 2 22 132 197 -9 23 133 56 84\n", 585 | " [561] 150 61 -20 171 0 -36 113 288 8 -76 31 -27 71 -86\n", 586 | " [575] 78 59 63 -81 8 31 199 -36 116 -47 91 100 93 9\n", 587 | " [589] 39 124 -10 22 -9 3 68 -88 -36 55 -40 42 44 -46\n", 588 | " [603] -147 137 132 151 -19 207 167 6 102 -79 159 199 80 -92\n", 589 | " [617] 193 183 -32 66 135 3 81 39 52 88 152 -47 128 -66\n", 590 | " [631] 88 213 -52 -32 38 -21 119 292 33 -21 79 140 112 -48\n", 591 | " [645] 2 23 -26 -33 -60 193 50 156 -151 44 -90 92 -16 181\n", 592 | " [659] 134 56 115 -19 12 132 117 158 -86 25 28 7 172 47\n", 593 | " [673] 136 183 1 105 -6 3 80 -4 124 223 125 61 129 39\n", 594 | " [687] 3 -78 58 10 11 69 74 132 -4 297 109 117 143 185\n", 595 | " [701] 109 -40 165 137 46 166 98 131 105 154 245 -159 117 46\n", 596 | " [715] 67 -44 -4 -164 76 -31 -26 79 71 49 123 18 -29 -36\n", 597 | " [729] 68 -28 -103 -55 -9 -39 -16 93 166 41 23 -94 84 4\n", 598 | " [743] -57 85 172 19 39 -50 -52 268 -215 -15 159 -63 167 161\n", 599 | " [757] 96 -12 292 83 169 15 47 11 -12 -33 71 -52 8 124\n", 600 | " [771] 189 245 -44 -88 161 27 119 129 -101 302 60 227 -22 37\n", 601 | " [785] 223 169 96 24 -22 148 99 134 -6 44 -92 35 46 -61\n", 602 | " [799] 103 1 147 -30 -133 -15 141 -49 43 164 202 71 -28 147\n", 603 | " [813] 41 126 188 200 66 -133 60 -34 63 20 103 -71 -31 208\n", 604 | " [827] 67 177 -42 -45 -11 -53 46 143 55 -3 6 15 -16 -107\n", 605 | " [841] 143 68 150 178 33 -56 -42 111 195 39 43 -105 3 163\n", 606 | " [855] -55 -51 -79 -123 87 57 113 24 47 121 -197 10 21 208\n", 607 | " [869] -1 -74 202 84 -14 -28 -33 234 261 199 -144 103 13 134\n", 608 | " [883] 34 100 -16 29 88 51 -38 168 -69 28 -131 72 121 34\n", 609 | " [897] -55 -68 55 24 227 -12 104 119 97 -26 42 -34 49 54\n", 610 | " [911] 130 87 121 -47 14 95 40 -115 48 -37 106 -55 134 49\n", 611 | " [925] 131 92 70 81 46 139 79 2 28 -57 -140 -46 35 146\n", 612 | " [939] 135 -52 45 47 51 217 83 186 -38 169 45 76 197 -150\n", 613 | " [953] -75 142 20 80 115 -70 -55 -235 24 13 -106 116 90 24\n", 614 | " [967] 99 -78 139 -33 -24 131 185 114 130 73 -12 118 160 89\n", 615 | " [981] -5 -31 -14 126 95 117 86 -54 179 -2 6 237 78 -82\n", 616 | " [995] -78 64 49 142 -153 86\n", 617 | "[1] \"Count occurrences of each value:\"\n", 618 | "n\n", 619 | "-275 -235 -234 -215 -213 -209 -207 -197 -173 -168 -164 -160 -159 -154 -153 -151 \n", 620 | " 1 1 1 1 1 1 1 1 1 1 1 1 2 1 1 1 \n", 621 | "-150 -148 -147 -144 -142 -140 -136 -135 -134 -133 -132 -131 -125 -123 -122 -121 \n", 622 | " 1 1 2 2 1 1 2 1 2 2 1 1 2 1 1 1 \n", 623 | "-120 -119 -117 -116 -115 -114 -113 -110 -109 -108 -107 -106 -105 -103 -101 -97 \n", 624 | " 1 1 1 1 2 1 1 1 1 1 3 1 1 1 1 1 \n", 625 | " -95 -94 -92 -91 -90 -89 -88 -87 -86 -85 -82 -81 -80 -79 -78 -77 \n", 626 | " 1 1 3 2 2 1 2 2 3 1 1 1 4 3 4 2 \n", 627 | " -76 -75 -74 -73 -72 -71 -70 -69 -68 -66 -65 -64 -63 -62 -61 -60 \n", 628 | " 1 2 1 1 1 4 3 1 4 2 1 1 1 2 1 4 \n", 629 | " -58 -57 -56 -55 -54 -53 -52 -51 -50 -49 -48 -47 -46 -45 -44 -43 \n", 630 | " 2 2 1 8 3 2 6 3 2 2 1 4 2 5 4 2 \n", 631 | " -42 -41 -40 -39 -38 -37 -36 -35 -34 -33 -32 -31 -30 -29 -28 -27 \n", 632 | " 3 2 3 2 3 2 5 1 2 6 5 5 4 1 5 2 \n", 633 | " -26 -25 -24 -22 -21 -20 -19 -18 -17 -16 -15 -14 -13 -12 -11 -10 \n", 634 | " 3 1 2 2 3 2 8 2 6 6 6 4 2 6 2 2 \n", 635 | " -9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 \n", 636 | " 6 3 1 3 2 5 2 1 2 4 3 4 7 2 4 7 \n", 637 | " 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 \n", 638 | " 5 4 3 4 5 3 4 2 2 4 1 3 3 4 2 4 \n", 639 | " 23 24 25 27 28 29 30 31 32 33 34 35 36 37 38 39 \n", 640 | " 7 8 6 2 5 4 1 3 3 6 3 7 3 7 2 8 \n", 641 | " 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 \n", 642 | " 3 5 6 4 3 4 8 6 2 6 4 4 3 6 3 3 \n", 643 | " 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 \n", 644 | " 5 4 4 3 7 7 1 2 3 3 2 7 7 3 2 9 \n", 645 | " 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 \n", 646 | " 1 2 6 1 2 3 3 3 5 4 4 4 4 3 2 5 \n", 647 | " 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 \n", 648 | " 4 3 4 2 4 6 1 4 3 3 3 5 4 2 3 4 \n", 649 | " 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 \n", 650 | " 5 2 4 1 5 4 2 6 2 3 6 4 2 5 2 5 \n", 651 | " 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 \n", 652 | " 1 6 1 2 6 2 4 1 2 4 6 6 5 3 5 4 \n", 653 | " 136 137 138 139 140 141 142 143 145 146 147 148 150 151 152 154 \n", 654 | " 3 2 3 3 3 2 2 5 2 5 3 2 4 2 1 1 \n", 655 | " 156 157 158 159 160 161 163 164 165 166 167 168 169 171 172 173 \n", 656 | " 3 1 2 4 2 2 1 1 2 3 2 3 3 1 3 2 \n", 657 | " 174 175 176 177 178 179 181 183 184 185 186 187 188 189 190 191 \n", 658 | " 1 2 2 1 2 2 1 3 2 5 2 1 2 1 1 1 \n", 659 | " 193 194 195 196 197 198 199 200 202 205 206 207 208 213 214 217 \n", 660 | " 3 1 3 3 4 1 3 2 2 1 1 1 2 1 2 1 \n", 661 | " 221 223 225 227 228 229 230 233 234 235 237 241 245 247 249 250 \n", 662 | " 1 3 1 2 1 1 1 1 1 2 1 1 2 1 1 1 \n", 663 | " 254 255 259 260 261 262 268 275 282 288 292 297 302 306 363 389 \n", 664 | " 1 1 1 1 1 1 1 1 1 1 2 1 1 1 1 1 \n" 665 | ] 666 | } 667 | ], 668 | "source": [ 669 | "n = floor(rnorm(1000, 50, 100))\n", 670 | "print('List of random numbers in normal distribution:')\n", 671 | "print(n)\n", 672 | "t = table(n)\n", 673 | "print(\"Count occurrences of each value:\")\n", 674 | "print(t)" 675 | ] 676 | }, 677 | { 678 | "cell_type": "markdown", 679 | "metadata": {}, 680 | "source": [ 681 | "### Write a R program to read the .csv file and display the content" 682 | ] 683 | }, 684 | { 685 | "cell_type": "code", 686 | "execution_count": 13, 687 | "metadata": {}, 688 | "outputs": [ 689 | { 690 | "name": "stderr", 691 | "output_type": "stream", 692 | "text": [ 693 | "Warning message in file(file, \"rt\"):\n", 694 | "“cannot open file 'movies.csv': No such file or directory”" 695 | ] 696 | }, 697 | { 698 | "ename": "ERROR", 699 | "evalue": "Error in file(file, \"rt\"): cannot open the connection\n", 700 | "output_type": "error", 701 | "traceback": [ 702 | "Error in file(file, \"rt\"): cannot open the connection\nTraceback:\n", 703 | "1. read.csv(file = \"movies.csv\", header = TRUE, sep = \",\")", 704 | "2. read.table(file = file, header = header, sep = sep, quote = quote, \n . dec = dec, fill = fill, comment.char = comment.char, ...)", 705 | "3. file(file, \"rt\")" 706 | ] 707 | } 708 | ], 709 | "source": [ 710 | "movie_data = read.csv(file=\"movies.csv\", header=TRUE, sep=\",\")\n", 711 | "print(\"Content of the .csv file:\")\n", 712 | "print(movie_data)" 713 | ] 714 | }, 715 | { 716 | "cell_type": "markdown", 717 | "metadata": {}, 718 | "source": [ 719 | "### Write a program to create three vectors numeric data, character data and logical data. Display the content of the vectors and their type" 720 | ] 721 | }, 722 | { 723 | "cell_type": "code", 724 | "execution_count": 14, 725 | "metadata": {}, 726 | "outputs": [ 727 | { 728 | "name": "stdout", 729 | "output_type": "stream", 730 | "text": [ 731 | "[1] 1 2 5 3 4 0 -1 -3\n", 732 | "[1] \"double\"\n", 733 | "[1] \"Red\" \"Green\" \"White\"\n", 734 | "[1] \"character\"\n", 735 | "[1] TRUE TRUE TRUE FALSE TRUE FALSE\n", 736 | "[1] \"logical\"\n" 737 | ] 738 | } 739 | ], 740 | "source": [ 741 | "a = c(1, 2, 5, 3, 4, 0, -1, -3)\n", 742 | "b = c(\"Red\", \"Green\", \"White\")\n", 743 | "c = c(TRUE, TRUE, TRUE, FALSE, TRUE, FALSE)\n", 744 | "print(a)\n", 745 | "print(typeof(a))\n", 746 | "print(b)\n", 747 | "print(typeof(b))\n", 748 | "print(c)\n", 749 | "print(typeof(c))" 750 | ] 751 | }, 752 | { 753 | "cell_type": "markdown", 754 | "metadata": {}, 755 | "source": [ 756 | "### Write a R program to create a 5 × 4 matrix , 3 × 3 matrix with labels and fill the matrix by rows and 2 × 2 matrix with labels and fill the matrix by columns" 757 | ] 758 | }, 759 | { 760 | "cell_type": "code", 761 | "execution_count": 15, 762 | "metadata": {}, 763 | "outputs": [ 764 | { 765 | "name": "stdout", 766 | "output_type": "stream", 767 | "text": [ 768 | "[1] \"5 × 4 matrix:\"\n", 769 | " [,1] [,2] [,3] [,4]\n", 770 | "[1,] 1 6 11 16\n", 771 | "[2,] 2 7 12 17\n", 772 | "[3,] 3 8 13 18\n", 773 | "[4,] 4 9 14 19\n", 774 | "[5,] 5 10 15 20\n", 775 | "[1] \"3 × 3 matrix with labels, filled by rows: \"\n", 776 | " Col1 Col2 Col3\n", 777 | "Row1 1 3 5\n", 778 | "Row2 7 8 9\n", 779 | "Row3 11 12 14\n", 780 | "[1] \"3 × 3 matrix with labels, filled by columns: \"\n", 781 | " Col1 Col2 Col3\n", 782 | "Row1 1 7 11\n", 783 | "Row2 3 8 12\n", 784 | "Row3 5 9 14\n" 785 | ] 786 | } 787 | ], 788 | "source": [ 789 | "m1 = matrix(1:20, nrow=5, ncol=4)\n", 790 | "print(\"5 × 4 matrix:\")\n", 791 | "print(m1)\n", 792 | "cells = c(1,3,5,7,8,9,11,12,14)\n", 793 | "rnames = c(\"Row1\", \"Row2\", \"Row3\")\n", 794 | "cnames = c(\"Col1\", \"Col2\", \"Col3\")\n", 795 | "m2 = matrix(cells, nrow=3, ncol=3, byrow=TRUE, dimnames=list(rnames, cnames))\n", 796 | "print(\"3 × 3 matrix with labels, filled by rows: \")\n", 797 | "print(m2)\n", 798 | "print(\"3 × 3 matrix with labels, filled by columns: \")\n", 799 | "m3 = matrix(cells, nrow=3, ncol=3, byrow=FALSE, dimnames=list(rnames, cnames))\n", 800 | "print(m3)" 801 | ] 802 | }, 803 | { 804 | "cell_type": "code", 805 | "execution_count": null, 806 | "metadata": {}, 807 | "outputs": [], 808 | "source": [] 809 | } 810 | ], 811 | "metadata": { 812 | "kernelspec": { 813 | "display_name": "R", 814 | "language": "R", 815 | "name": "ir" 816 | }, 817 | "language_info": { 818 | "codemirror_mode": "r", 819 | "file_extension": ".r", 820 | "mimetype": "text/x-r-source", 821 | "name": "R", 822 | "pygments_lexer": "r", 823 | "version": "3.5.1" 824 | } 825 | }, 826 | "nbformat": 4, 827 | "nbformat_minor": 2 828 | } 829 | -------------------------------------------------------------------------------- /R Basic Programming part 2.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "## R Programming: Basic Exercise part 2" 8 | ] 9 | }, 10 | { 11 | "cell_type": "markdown", 12 | "metadata": {}, 13 | "source": [ 14 | "### Write a R program to create an array, passing in a vector of values and a vector of dimensions. Also provide names for each dimension." 15 | ] 16 | }, 17 | { 18 | "cell_type": "code", 19 | "execution_count": 1, 20 | "metadata": {}, 21 | "outputs": [ 22 | { 23 | "name": "stdout", 24 | "output_type": "stream", 25 | "text": [ 26 | ", , Part1\n", 27 | "\n", 28 | " Row1 Row2 Row3\n", 29 | "Col1 6 10 14\n", 30 | "Col2 7 11 15\n", 31 | "Col3 8 12 16\n", 32 | "Col4 9 13 17\n", 33 | "\n", 34 | ", , Part2\n", 35 | "\n", 36 | " Row1 Row2 Row3\n", 37 | "Col1 18 22 26\n", 38 | "Col2 19 23 27\n", 39 | "Col3 20 24 28\n", 40 | "Col4 21 25 29\n", 41 | "\n" 42 | ] 43 | } 44 | ], 45 | "source": [ 46 | "a = array(\n", 47 | " 6:30,\n", 48 | " dim = c(4, 3, 2),\n", 49 | " dimnames = list(\n", 50 | " c(\"Col1\", \"Col2\", \"Col3\", \"Col4\"),\n", 51 | " c(\"Row1\", \"Row2\", \"Row3\"),\n", 52 | " c(\"Part1\", \"Part2\")\n", 53 | " )\n", 54 | ")\n", 55 | "print(a)" 56 | ] 57 | }, 58 | { 59 | "cell_type": "markdown", 60 | "metadata": {}, 61 | "source": [ 62 | "### Write a R program to create an array with three columns, three rows, and two \"tables\", taking two vectors as input to the array. Print the array." 63 | ] 64 | }, 65 | { 66 | "cell_type": "code", 67 | "execution_count": 2, 68 | "metadata": {}, 69 | "outputs": [ 70 | { 71 | "name": "stdout", 72 | "output_type": "stream", 73 | "text": [ 74 | ", , 1\n", 75 | "\n", 76 | " [,1] [,2] [,3]\n", 77 | "[1,] 1 7 6\n", 78 | "[2,] 3 2 8\n", 79 | "[3,] 5 4 10\n", 80 | "\n", 81 | ", , 2\n", 82 | "\n", 83 | " [,1] [,2] [,3]\n", 84 | "[1,] 1 7 6\n", 85 | "[2,] 3 2 8\n", 86 | "[3,] 5 4 10\n", 87 | "\n" 88 | ] 89 | } 90 | ], 91 | "source": [ 92 | "v1 = c(1, 3, 5, 7)\n", 93 | "v2 = c(2, 4, 6, 8, 10)\n", 94 | "arra1 = array(c(v1, v2),dim = c(3,3,2))\n", 95 | "print(arra1)" 96 | ] 97 | }, 98 | { 99 | "cell_type": "markdown", 100 | "metadata": {}, 101 | "source": [ 102 | "### Create a list of elements using vectors, matrices and a functions. Print the content of the list." 103 | ] 104 | }, 105 | { 106 | "cell_type": "code", 107 | "execution_count": 1, 108 | "metadata": {}, 109 | "outputs": [ 110 | { 111 | "name": "stdout", 112 | "output_type": "stream", 113 | "text": [ 114 | "[1] \"Content of the list:\"\n", 115 | "[[1]]\n", 116 | "[1] 1 2 2 5 7 12\n", 117 | "\n", 118 | "[[2]]\n", 119 | " [1] \"Jan\" \"Feb\" \"Mar\" \"Apr\" \"May\" \"Jun\" \"Jul\" \"Aug\" \"Sep\" \"Oct\" \"Nov\" \"Dec\"\n", 120 | "\n", 121 | "[[3]]\n", 122 | " [,1] [,2]\n", 123 | "[1,] 3 1\n", 124 | "[2,] -8 -3\n", 125 | "\n", 126 | "[[4]]\n", 127 | "function (x) .Primitive(\"asin\")\n", 128 | "\n" 129 | ] 130 | } 131 | ], 132 | "source": [ 133 | "l = list(\n", 134 | " c(1, 2, 2, 5, 7, 12), \n", 135 | " month.abb,\n", 136 | " matrix(c(3, -8, 1, -3), nrow = 2),\n", 137 | " asin\n", 138 | ")\n", 139 | "print(\"Content of the list:\")\n", 140 | "print(l)" 141 | ] 142 | }, 143 | { 144 | "cell_type": "markdown", 145 | "metadata": {}, 146 | "source": [ 147 | "### Write a R program to draw an empty plot and an empty plot specify the axes limits of the graphic." 148 | ] 149 | }, 150 | { 151 | "cell_type": "code", 152 | "execution_count": 2, 153 | "metadata": {}, 154 | "outputs": [ 155 | { 156 | "data": { 157 | "image/png": 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P8LLD0g/YPo9Jyo90R9b1T5yO8XR/3P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\nc55jmIp2KOUAAAAASUVORK5CYII=", 158 | "text/plain": [ 159 | "plot without title" 160 | ] 161 | }, 162 | "metadata": {}, 163 | "output_type": "display_data" 164 | } 165 | ], 166 | "source": [ 167 | "#print(\"Empty plot:\")\n", 168 | "plot.new()\n", 169 | "#print(\"Empty plot specify the axes limits of the graphic:\")\n", 170 | "plot(1, type=\"n\", xlab=\"\", ylab=\"\", xlim=c(0, 20), ylim=c(0, 20))" 171 | ] 172 | }, 173 | { 174 | "cell_type": "markdown", 175 | "metadata": {}, 176 | "source": [ 177 | "### Write a R program to create a simple bar plot of five subjects marks." 178 | ] 179 | }, 180 | { 181 | "cell_type": "code", 182 | "execution_count": 3, 183 | "metadata": {}, 184 | "outputs": [ 185 | { 186 | "data": { 187 | "image/png": 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Znh+Wv6tuQqo431G+B2W11o3n+07ZaZ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AAgoooMCoBMp3kNJJ+SjJrXZf7LClY2hL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\nUEABBRToM4H/B98WDIpktj0gAAAAAElFTkSuQmCC", 188 | "text/plain": [ 189 | "Plot with title “Comparing marks of 5 subjects”" 190 | ] 191 | }, 192 | "metadata": {}, 193 | "output_type": "display_data" 194 | } 195 | ], 196 | "source": [ 197 | "marks = c(70, 95, 80, 74)\n", 198 | "barplot(marks,\n", 199 | "main = \"Comparing marks of 5 subjects\",\n", 200 | "xlab = \"Marks\",\n", 201 | "ylab = \"Subject\",\n", 202 | "names.arg = c(\"English\", \"Science\", \"Math.\", \"Hist.\"),\n", 203 | "col = \"darkred\",\n", 204 | "horiz = FALSE)" 205 | ] 206 | }, 207 | { 208 | "cell_type": "markdown", 209 | "metadata": {}, 210 | "source": [ 211 | "### Write a R program to create bell curve of a random normal distribution." 212 | ] 213 | }, 214 | { 215 | "cell_type": "code", 216 | "execution_count": 4, 217 | "metadata": {}, 218 | "outputs": [ 219 | { 220 | "data": { 221 | "image/png": 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iZQ9QSpfPjCSxKvTbw4UT6pbucrSZ9J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X5khPS/2ysnGMVufrSSt3kDmn96QBlW0l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", 222 | "text/plain": [ 223 | "plot without title" 224 | ] 225 | }, 226 | "metadata": {}, 227 | "output_type": "display_data" 228 | } 229 | ], 230 | "source": [ 231 | "n = floor(rnorm(10000, 500, 100))\n", 232 | "t = table(n)\n", 233 | "barplot(t)" 234 | ] 235 | }, 236 | { 237 | "cell_type": "markdown", 238 | "metadata": {}, 239 | "source": [ 240 | "### Write a R program to compute sum, mean and product of a given vector elements." 241 | ] 242 | }, 243 | { 244 | "cell_type": "code", 245 | "execution_count": 5, 246 | "metadata": {}, 247 | "outputs": [ 248 | { 249 | "name": "stdout", 250 | "output_type": "stream", 251 | "text": [ 252 | "[1] \"Original vector:\"\n", 253 | "[1] 10 20 30\n", 254 | "[1] \"Sum of vector elements: 60\"\n", 255 | "[1] \"Mean of vector elements: 20\"\n", 256 | "[1] \"Product of vector elements: 6000\"\n" 257 | ] 258 | } 259 | ], 260 | "source": [ 261 | "nums = c(10, 20, 30)\n", 262 | "print('Original vector:')\n", 263 | "print(nums) \n", 264 | "print(paste(\"Sum of vector elements:\",sum(nums)))\n", 265 | "print(paste(\"Mean of vector elements:\",mean(nums)))\n", 266 | "print(paste(\"Product of vector elements:\",prod(nums)))" 267 | ] 268 | }, 269 | { 270 | "cell_type": "markdown", 271 | "metadata": {}, 272 | "source": [ 273 | "### Write a R program to create a list of heterogeneous data, which include character, numeric and logical vectors. Print the lists." 274 | ] 275 | }, 276 | { 277 | "cell_type": "code", 278 | "execution_count": 6, 279 | "metadata": {}, 280 | "outputs": [ 281 | { 282 | "name": "stdout", 283 | "output_type": "stream", 284 | "text": [ 285 | "$Chr\n", 286 | "[1] \"Python\"\n", 287 | "\n", 288 | "$nums\n", 289 | " [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15\n", 290 | "\n", 291 | "$flag\n", 292 | "[1] TRUE\n", 293 | "\n" 294 | ] 295 | } 296 | ], 297 | "source": [ 298 | "my_list = list(Chr=\"Python\", nums = 1:15, flag=TRUE)\n", 299 | "print(my_list)" 300 | ] 301 | }, 302 | { 303 | "cell_type": "markdown", 304 | "metadata": {}, 305 | "source": [ 306 | "### Write a R program to create a Dataframes which contain details of 5 employees and display the details." 307 | ] 308 | }, 309 | { 310 | "cell_type": "code", 311 | "execution_count": 7, 312 | "metadata": {}, 313 | "outputs": [ 314 | { 315 | "name": "stdout", 316 | "output_type": "stream", 317 | "text": [ 318 | "[1] \"Details of the employees:\"\n", 319 | " Name Gender Age Designation SSN\n", 320 | "1 Anastasia S M 23 Clerk 123-34-2346\n", 321 | "2 Dima R M 22 Manager 123-44-779\n", 322 | "3 Katherine S F 25 Exective 556-24-433\n", 323 | "4 JAMES A F 26 CEO 123-98-987\n", 324 | "5 LAURA MARTIN M 32 ASSISTANT 679-77-576\n" 325 | ] 326 | } 327 | ], 328 | "source": [ 329 | "Employees = data.frame(Name=c(\"Anastasia S\",\"Dima R\",\"Katherine S\", \"JAMES A\",\"LAURA MARTIN\"),\n", 330 | " Gender=c(\"M\",\"M\",\"F\",\"F\",\"M\"),\n", 331 | " Age=c(23,22,25,26,32),\n", 332 | " Designation=c(\"Clerk\",\"Manager\",\"Exective\",\"CEO\",\"ASSISTANT\"),\n", 333 | " SSN=c(\"123-34-2346\",\"123-44-779\",\"556-24-433\",\"123-98-987\",\"679-77-576\")\n", 334 | " )\n", 335 | "print(\"Details of the employees:\") \n", 336 | "print(Employees)" 337 | ] 338 | }, 339 | { 340 | "cell_type": "markdown", 341 | "metadata": {}, 342 | "source": [ 343 | "### Write a R program to create a Dataframes which contain details of 5 employees and display summary of the data" 344 | ] 345 | }, 346 | { 347 | "cell_type": "code", 348 | "execution_count": 8, 349 | "metadata": {}, 350 | "outputs": [ 351 | { 352 | "name": "stdout", 353 | "output_type": "stream", 354 | "text": [ 355 | "[1] \"Summary of the data:\"\n", 356 | " Name Gender Age Designation SSN \n", 357 | " Anastasia S :1 F:2 Min. :22.0 ASSISTANT:1 123-34-2346:1 \n", 358 | " Dima R :1 M:3 1st Qu.:23.0 CEO :1 123-44-779 :1 \n", 359 | " JAMES A :1 Median :25.0 Clerk :1 123-98-987 :1 \n", 360 | " Katherine S :1 Mean :25.6 Exective :1 556-24-433 :1 \n", 361 | " LAURA MARTIN:1 3rd Qu.:26.0 Manager :1 679-77-576 :1 \n", 362 | " Max. :32.0 \n" 363 | ] 364 | } 365 | ], 366 | "source": [ 367 | "Employees = data.frame(Name=c(\"Anastasia S\",\"Dima R\",\"Katherine S\", \"JAMES A\",\"LAURA MARTIN\"),\n", 368 | " Gender=c(\"M\",\"M\",\"F\",\"F\",\"M\"),\n", 369 | " Age=c(23,22,25,26,32),\n", 370 | " Designation=c(\"Clerk\",\"Manager\",\"Exective\",\"CEO\",\"ASSISTANT\"),\n", 371 | " SSN=c(\"123-34-2346\",\"123-44-779\",\"556-24-433\",\"123-98-987\",\"679-77-576\")\n", 372 | " )\n", 373 | "print(\"Summary of the data:\") \n", 374 | "print(summary(Employees))" 375 | ] 376 | }, 377 | { 378 | "cell_type": "markdown", 379 | "metadata": {}, 380 | "source": [ 381 | "### Write a R program to create the system's idea of the current date with and without time." 382 | ] 383 | }, 384 | { 385 | "cell_type": "code", 386 | "execution_count": 9, 387 | "metadata": {}, 388 | "outputs": [ 389 | { 390 | "name": "stdout", 391 | "output_type": "stream", 392 | "text": [ 393 | "[1] \"System's idea of the current date with and without time:\"\n", 394 | "[1] \"2019-11-25\"\n", 395 | "[1] \"2019-11-25 17:04:44 CET\"\n" 396 | ] 397 | } 398 | ], 399 | "source": [ 400 | "print(\"System's idea of the current date with and without time:\")\n", 401 | "print(Sys.Date())\n", 402 | "print(Sys.time())" 403 | ] 404 | }, 405 | { 406 | "cell_type": "code", 407 | "execution_count": null, 408 | "metadata": {}, 409 | "outputs": [], 410 | "source": [] 411 | } 412 | ], 413 | "metadata": { 414 | "kernelspec": { 415 | "display_name": "R", 416 | "language": "R", 417 | "name": "ir" 418 | }, 419 | "language_info": { 420 | "codemirror_mode": "r", 421 | "file_extension": ".r", 422 | "mimetype": "text/x-r-source", 423 | "name": "R", 424 | "pygments_lexer": "r", 425 | "version": "3.5.1" 426 | } 427 | }, 428 | "nbformat": 4, 429 | "nbformat_minor": 2 430 | } 431 | -------------------------------------------------------------------------------- /R Conditional Statement .ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "## Conditional Statements Exercises - Solutions" 8 | ] 9 | }, 10 | { 11 | "cell_type": "markdown", 12 | "metadata": {}, 13 | "source": [ 14 | "##### For these exercises, use if,else if, and else statements to answer the questions!" 15 | ] 16 | }, 17 | { 18 | "cell_type": "markdown", 19 | "metadata": {}, 20 | "source": [ 21 | "##### Example: Write a script that prints \"Hello\" if the variable x is equal to 1:" 22 | ] 23 | }, 24 | { 25 | "cell_type": "code", 26 | "execution_count": 1, 27 | "metadata": {}, 28 | "outputs": [ 29 | { 30 | "name": "stdout", 31 | "output_type": "stream", 32 | "text": [ 33 | "[1] \"Hello\"\n" 34 | ] 35 | } 36 | ], 37 | "source": [ 38 | "x <- 1\n", 39 | "\n", 40 | "if (x ==1){\n", 41 | " print(\"Hello\")\n", 42 | "}" 43 | ] 44 | }, 45 | { 46 | "cell_type": "markdown", 47 | "metadata": {}, 48 | "source": [ 49 | "##### Write a script that will print \"Even Number\" if the variable x is an even number, otherwise print \"Not Even\":" 50 | ] 51 | }, 52 | { 53 | "cell_type": "code", 54 | "execution_count": 2, 55 | "metadata": {}, 56 | "outputs": [ 57 | { 58 | "name": "stdout", 59 | "output_type": "stream", 60 | "text": [ 61 | "[1] \"Not Even\"\n" 62 | ] 63 | } 64 | ], 65 | "source": [ 66 | "x <- 3 # Change x to test\n", 67 | "\n", 68 | "if (x%%2 == 0){\n", 69 | " print('Even Number')\n", 70 | "}else{\n", 71 | " print('Not Even')\n", 72 | "}" 73 | ] 74 | }, 75 | { 76 | "cell_type": "markdown", 77 | "metadata": {}, 78 | "source": [ 79 | "##### Write a script that will print 'Is a Matrix' if the variable x is a matrix, otherwise print \"Not a Matrix\". Hint: You may want to check out help(is.matrix)" 80 | ] 81 | }, 82 | { 83 | "cell_type": "code", 84 | "execution_count": 3, 85 | "metadata": {}, 86 | "outputs": [ 87 | { 88 | "name": "stdout", 89 | "output_type": "stream", 90 | "text": [ 91 | "[1] \"Is a Matrix\"\n" 92 | ] 93 | } 94 | ], 95 | "source": [ 96 | "x <- matrix()\n", 97 | "\n", 98 | "if (is.matrix(x)){\n", 99 | " print('Is a Matrix')\n", 100 | "}else{\n", 101 | " print(\"Not a Matrix\")\n", 102 | "}" 103 | ] 104 | }, 105 | { 106 | "cell_type": "markdown", 107 | "metadata": {}, 108 | "source": [ 109 | "##### Create a script that given a numeric vector x with a length 3, will print out the elements in order from high to low. You must use if,else if, and else statements for your logic." 110 | ] 111 | }, 112 | { 113 | "cell_type": "code", 114 | "execution_count": 4, 115 | "metadata": {}, 116 | "outputs": [ 117 | { 118 | "name": "stdout", 119 | "output_type": "stream", 120 | "text": [ 121 | "[1] \"7 3 1\"\n" 122 | ] 123 | } 124 | ], 125 | "source": [ 126 | "x <- c(3,7,1)\n", 127 | "\n", 128 | "if (x[1] > x[2]){\n", 129 | " fir <- x[1]\n", 130 | " sec <- x[2]\n", 131 | "} else {\n", 132 | " fir <- x[2]\n", 133 | " sec <- x[1]\n", 134 | "}\n", 135 | "if ( x[3] > fir & x[3] > sec ) {\n", 136 | " thi <- sec\n", 137 | " sec <- fir\n", 138 | " fir <- x[3]\n", 139 | "} else if ( x[3] < fir & x[3] < sec ) {\n", 140 | " thi <- x[3]\n", 141 | "} else {\n", 142 | " thi <- sec\n", 143 | " sec <- x[3]\n", 144 | "}\n", 145 | "\n", 146 | "print(paste(fir, sec, thi))" 147 | ] 148 | }, 149 | { 150 | "cell_type": "markdown", 151 | "metadata": {}, 152 | "source": [ 153 | "##### Write a script that uses if,else if, and else statements to print the max element in a numeric vector with 3 elements." 154 | ] 155 | }, 156 | { 157 | "cell_type": "code", 158 | "execution_count": 5, 159 | "metadata": {}, 160 | "outputs": [ 161 | { 162 | "name": "stdout", 163 | "output_type": "stream", 164 | "text": [ 165 | "[1] 20\n" 166 | ] 167 | } 168 | ], 169 | "source": [ 170 | "x <- c(20, 10, 1)\n", 171 | "\n", 172 | "if (x[1] > x[2] & x[1] > x[3] ) {\n", 173 | " print(x[1] )\n", 174 | "} else if (x[2] > x[3] ) {\n", 175 | " print(x[2])\n", 176 | "} else {\n", 177 | " print(x[3])\n", 178 | "}" 179 | ] 180 | }, 181 | { 182 | "cell_type": "code", 183 | "execution_count": null, 184 | "metadata": {}, 185 | "outputs": [], 186 | "source": [] 187 | } 188 | ], 189 | "metadata": { 190 | "kernelspec": { 191 | "display_name": "R", 192 | "language": "R", 193 | "name": "ir" 194 | }, 195 | "language_info": { 196 | "codemirror_mode": "r", 197 | "file_extension": ".r", 198 | "mimetype": "text/x-r-source", 199 | "name": "R", 200 | "pygments_lexer": "r", 201 | "version": "3.5.1" 202 | } 203 | }, 204 | "nbformat": 4, 205 | "nbformat_minor": 2 206 | } 207 | -------------------------------------------------------------------------------- /R Programming Basic3.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "# R Programming Basic: Exercises, Practice, Solution" 8 | ] 9 | }, 10 | { 11 | "cell_type": "markdown", 12 | "metadata": {}, 13 | "source": [ 14 | "#### Write a R program to print the numbers from 1 to 100 and print \"Fizz\" for multiples of 3, print \"Buzz\" for multiples of 5, and print \"FizzBuzz\" for multiples of both" 15 | ] 16 | }, 17 | { 18 | "cell_type": "code", 19 | "execution_count": 1, 20 | "metadata": {}, 21 | "outputs": [ 22 | { 23 | "name": "stdout", 24 | "output_type": "stream", 25 | "text": [ 26 | "[1] 1\n", 27 | "[1] 2\n", 28 | "[1] \"Fizz\"\n", 29 | "[1] 4\n", 30 | "[1] \"Buzz\"\n", 31 | "[1] \"Fizz\"\n", 32 | "[1] 7\n", 33 | "[1] 8\n", 34 | "[1] \"Fizz\"\n", 35 | "[1] \"Buzz\"\n", 36 | "[1] 11\n", 37 | "[1] \"Fizz\"\n", 38 | "[1] 13\n", 39 | "[1] 14\n", 40 | "[1] \"FizzBuzz\"\n", 41 | "[1] 16\n", 42 | "[1] 17\n", 43 | "[1] \"Fizz\"\n", 44 | "[1] 19\n", 45 | "[1] \"Buzz\"\n", 46 | "[1] \"Fizz\"\n", 47 | "[1] 22\n", 48 | "[1] 23\n", 49 | "[1] \"Fizz\"\n", 50 | "[1] \"Buzz\"\n", 51 | "[1] 26\n", 52 | "[1] \"Fizz\"\n", 53 | "[1] 28\n", 54 | "[1] 29\n", 55 | "[1] \"FizzBuzz\"\n", 56 | "[1] 31\n", 57 | "[1] 32\n", 58 | "[1] \"Fizz\"\n", 59 | "[1] 34\n", 60 | "[1] \"Buzz\"\n", 61 | "[1] \"Fizz\"\n", 62 | "[1] 37\n", 63 | "[1] 38\n", 64 | "[1] \"Fizz\"\n", 65 | "[1] \"Buzz\"\n", 66 | "[1] 41\n", 67 | "[1] \"Fizz\"\n", 68 | "[1] 43\n", 69 | "[1] 44\n", 70 | "[1] \"FizzBuzz\"\n", 71 | "[1] 46\n", 72 | "[1] 47\n", 73 | "[1] \"Fizz\"\n", 74 | "[1] 49\n", 75 | "[1] \"Buzz\"\n", 76 | "[1] \"Fizz\"\n", 77 | "[1] 52\n", 78 | "[1] 53\n", 79 | "[1] \"Fizz\"\n", 80 | "[1] \"Buzz\"\n", 81 | "[1] 56\n", 82 | "[1] \"Fizz\"\n", 83 | "[1] 58\n", 84 | "[1] 59\n", 85 | "[1] \"FizzBuzz\"\n", 86 | "[1] 61\n", 87 | "[1] 62\n", 88 | "[1] \"Fizz\"\n", 89 | "[1] 64\n", 90 | "[1] \"Buzz\"\n", 91 | "[1] \"Fizz\"\n", 92 | "[1] 67\n", 93 | "[1] 68\n", 94 | "[1] \"Fizz\"\n", 95 | "[1] \"Buzz\"\n", 96 | "[1] 71\n", 97 | "[1] \"Fizz\"\n", 98 | "[1] 73\n", 99 | "[1] 74\n", 100 | "[1] \"FizzBuzz\"\n", 101 | "[1] 76\n", 102 | "[1] 77\n", 103 | "[1] \"Fizz\"\n", 104 | "[1] 79\n", 105 | "[1] \"Buzz\"\n", 106 | "[1] \"Fizz\"\n", 107 | "[1] 82\n", 108 | "[1] 83\n", 109 | "[1] \"Fizz\"\n", 110 | "[1] \"Buzz\"\n", 111 | "[1] 86\n", 112 | "[1] \"Fizz\"\n", 113 | "[1] 88\n", 114 | "[1] 89\n", 115 | "[1] \"FizzBuzz\"\n", 116 | "[1] 91\n", 117 | "[1] 92\n", 118 | "[1] \"Fizz\"\n", 119 | "[1] 94\n", 120 | "[1] \"Buzz\"\n", 121 | "[1] \"Fizz\"\n", 122 | "[1] 97\n", 123 | "[1] 98\n", 124 | "[1] \"Fizz\"\n", 125 | "[1] \"Buzz\"\n" 126 | ] 127 | } 128 | ], 129 | "source": [ 130 | "for (n in 1:100) {\n", 131 | " if (n %% 3 == 0 & n %% 5 == 0) {print(\"FizzBuzz\")}\n", 132 | " else if (n %% 3 == 0) {print(\"Fizz\")}\n", 133 | " else if (n %% 5 == 0) {print(\"Buzz\")}\n", 134 | " else print(n)\n", 135 | "}" 136 | ] 137 | }, 138 | { 139 | "cell_type": "markdown", 140 | "metadata": {}, 141 | "source": [ 142 | "### Write a R program to extract first 10 english letter in lower case and last 10 letters in upper case and extract letters between 22nd to 24th letters in upper case" 143 | ] 144 | }, 145 | { 146 | "cell_type": "code", 147 | "execution_count": 2, 148 | "metadata": {}, 149 | "outputs": [ 150 | { 151 | "name": "stdout", 152 | "output_type": "stream", 153 | "text": [ 154 | "[1] \"First 10 letters in lower case:\"\n", 155 | " [1] \"a\" \"b\" \"c\" \"d\" \"e\" \"f\" \"g\" \"h\" \"i\" \"j\"\n", 156 | "[1] \"Last 10 letters in upper case:\"\n", 157 | " [1] \"Q\" \"R\" \"S\" \"T\" \"U\" \"V\" \"W\" \"X\" \"Y\" \"Z\"\n", 158 | "[1] \"Letters between 22nd to 24th letters in upper case:\"\n", 159 | "[1] \"V\" \"W\" \"X\"\n" 160 | ] 161 | } 162 | ], 163 | "source": [ 164 | "print(\"First 10 letters in lower case:\")\n", 165 | "t = head(letters, 10)\n", 166 | "print(t)\n", 167 | "print(\"Last 10 letters in upper case:\")\n", 168 | "t = tail(LETTERS, 10)\n", 169 | "print(t)\n", 170 | "print(\"Letters between 22nd to 24th letters in upper case:\")\n", 171 | "e = tail(LETTERS[22:24])\n", 172 | "print(e)" 173 | ] 174 | }, 175 | { 176 | "cell_type": "markdown", 177 | "metadata": {}, 178 | "source": [ 179 | "\n", 180 | "### Write a R program to find the factors of a given number" 181 | ] 182 | }, 183 | { 184 | "cell_type": "code", 185 | "execution_count": 3, 186 | "metadata": {}, 187 | "outputs": [ 188 | { 189 | "name": "stdout", 190 | "output_type": "stream", 191 | "text": [ 192 | "[1] \"The factors of 4 are:\"\n", 193 | "[1] 1\n", 194 | "[1] 2\n", 195 | "[1] 4\n", 196 | "[1] \"The factors of 7 are:\"\n", 197 | "[1] 1\n", 198 | "[1] 7\n", 199 | "[1] \"The factors of 12 are:\"\n", 200 | "[1] 1\n", 201 | "[1] 2\n", 202 | "[1] 3\n", 203 | "[1] 4\n", 204 | "[1] 6\n", 205 | "[1] 12\n" 206 | ] 207 | } 208 | ], 209 | "source": [ 210 | "print_factors = function(n) {\n", 211 | "print(paste(\"The factors of\",n,\"are:\"))\n", 212 | "for(i in 1:n) {\n", 213 | "if((n %% i) == 0) {\n", 214 | "print(i)\n", 215 | "}\n", 216 | "}\n", 217 | "}\n", 218 | "print_factors(4)\n", 219 | "print_factors(7)\n", 220 | "print_factors(12)" 221 | ] 222 | }, 223 | { 224 | "cell_type": "markdown", 225 | "metadata": {}, 226 | "source": [ 227 | "### Write a R program to find common elements from multiple vectors." 228 | ] 229 | }, 230 | { 231 | "cell_type": "code", 232 | "execution_count": 1, 233 | "metadata": {}, 234 | "outputs": [ 235 | { 236 | "name": "stdout", 237 | "output_type": "stream", 238 | "text": [ 239 | "[1] \"Original Vectors:\"\n", 240 | "[1] \"x: \"\n", 241 | "[1] 10 20 30 20 20 25 29 26\n", 242 | "[1] \"y: \"\n", 243 | "[1] 10 50 30 20 20 35 19 56\n", 244 | "[1] \"z: \"\n", 245 | "[1] 10 40 30 20 20 25 49 26\n", 246 | "[1] \"Common elements from above vectors:\"\n", 247 | "[1] 10 20 30\n" 248 | ] 249 | } 250 | ], 251 | "source": [ 252 | "x = c(10, 20, 30, 20, 20, 25, 29, 26)\n", 253 | "y = c(10, 50, 30, 20, 20, 35, 19, 56)\n", 254 | "z = c(10, 40, 30, 20, 20, 25, 49, 26)\n", 255 | "print(\"Original Vectors:\")\n", 256 | "print(\"x: \")\n", 257 | "print(x)\n", 258 | "print(\"y: \")\n", 259 | "print(y)\n", 260 | "print(\"z: \")\n", 261 | "print(z)\n", 262 | "print(\"Common elements from above vectors:\")\n", 263 | "result = intersect(intersect(x,y),z)\n", 264 | "print(result)" 265 | ] 266 | }, 267 | { 268 | "cell_type": "code", 269 | "execution_count": null, 270 | "metadata": {}, 271 | "outputs": [], 272 | "source": [] 273 | } 274 | ], 275 | "metadata": { 276 | "kernelspec": { 277 | "display_name": "R", 278 | "language": "R", 279 | "name": "ir" 280 | }, 281 | "language_info": { 282 | "codemirror_mode": "r", 283 | "file_extension": ".r", 284 | "mimetype": "text/x-r-source", 285 | "name": "R", 286 | "pygments_lexer": "r", 287 | "version": "3.5.1" 288 | } 289 | }, 290 | "nbformat": 4, 291 | "nbformat_minor": 2 292 | } 293 | -------------------------------------------------------------------------------- /R Programming: Vector Exercise-4.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "# R Programming: Vector Exercise 4" 8 | ] 9 | }, 10 | { 11 | "cell_type": "markdown", 12 | "metadata": {}, 13 | "source": [ 14 | "#### Write a R program to convert given dataframe column(s) to a vector." 15 | ] 16 | }, 17 | { 18 | "cell_type": "code", 19 | "execution_count": 1, 20 | "metadata": {}, 21 | "outputs": [ 22 | { 23 | "name": "stdout", 24 | "output_type": "stream", 25 | "text": [ 26 | " dfc1 dfc2 dfc3 dfc4\n", 27 | "1 1 6 11 16\n", 28 | "2 2 7 12 17\n", 29 | "3 3 8 13 18\n", 30 | "4 4 9 14 19\n", 31 | "5 5 10 15 20\n" 32 | ] 33 | } 34 | ], 35 | "source": [ 36 | "dfc1 = c(1, 2, 3, 4, 5)\n", 37 | "dfc2 = c(6, 7, 8, 9, 10)\n", 38 | "dfc3 = c(11, 12, 13, 14, 15)\n", 39 | "dfc4 = c(16, 17, 18, 19, 20)\n", 40 | "v <- data.frame(dfc1=1:5, dfc2=6:10, dfc3=11:15, dfc4=16:20)\n", 41 | "print(v)" 42 | ] 43 | }, 44 | { 45 | "cell_type": "markdown", 46 | "metadata": {}, 47 | "source": [ 48 | "#### Write a R program to extract every nth element of a given vector." 49 | ] 50 | }, 51 | { 52 | "cell_type": "code", 53 | "execution_count": 2, 54 | "metadata": {}, 55 | "outputs": [ 56 | { 57 | "name": "stdout", 58 | "output_type": "stream", 59 | "text": [ 60 | "[1] \"Original vector:\"\n", 61 | " [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18\n", 62 | " [19] 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36\n", 63 | " [37] 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54\n", 64 | " [55] 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72\n", 65 | " [73] 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90\n", 66 | " [91] 91 92 93 94 95 96 97 98 99 100\n", 67 | "[1] \"After extracting every 5th element of the said vector:\"\n", 68 | " [1] 1 6 11 16 21 26 31 36 41 46 51 56 61 66 71 76 81 86 91 96\n" 69 | ] 70 | } 71 | ], 72 | "source": [ 73 | "v <- 1:100\n", 74 | "print(\"Original vector:\")\n", 75 | "print(v)\n", 76 | "print(\"After extracting every 5th element of the said vector:\")\n", 77 | "n <- v[seq(1, length(v), 5)]\n", 78 | "print(n)" 79 | ] 80 | }, 81 | { 82 | "cell_type": "markdown", 83 | "metadata": {}, 84 | "source": [ 85 | "#### Write a program to list the distinct values in a vector from a given vector." 86 | ] 87 | }, 88 | { 89 | "cell_type": "code", 90 | "execution_count": 1, 91 | "metadata": {}, 92 | "outputs": [ 93 | { 94 | "name": "stdout", 95 | "output_type": "stream", 96 | "text": [ 97 | "[1] \"Original vector:\"\n", 98 | "[1] 10 10 10 20 30 40 40 40 50\n", 99 | "[1] \"Distinct values of the said vector:\"\n", 100 | "[1] 10 20 30 40 50\n" 101 | ] 102 | } 103 | ], 104 | "source": [ 105 | "v = c(10, 10, 10, 20, 30, 40, 40, 40, 50)\n", 106 | "print(\"Original vector:\")\n", 107 | "print(v)\n", 108 | "print(\"Distinct values of the said vector:\")\n", 109 | "print(unique(v))" 110 | ] 111 | }, 112 | { 113 | "cell_type": "markdown", 114 | "metadata": {}, 115 | "source": [ 116 | "#### Write a program to find the elements of a given vector that are not in another given vector.\n", 117 | "\n" 118 | ] 119 | }, 120 | { 121 | "cell_type": "code", 122 | "execution_count": 2, 123 | "metadata": {}, 124 | "outputs": [ 125 | { 126 | "name": "stdout", 127 | "output_type": "stream", 128 | "text": [ 129 | "[1] \"Original vector-1:\"\n", 130 | " [1] 0 10 10 10 20 30 40 40 40 50 60\n", 131 | "[1] \"Original vector-2:\"\n", 132 | "[1] 10 10 20 30 40 40 50\n", 133 | "[1] \"Elements of a that are not in b:\"\n", 134 | "[1] 0 60\n" 135 | ] 136 | } 137 | ], 138 | "source": [ 139 | "a = c(0, 10, 10, 10, 20, 30, 40, 40, 40, 50, 60)\n", 140 | "b = c(10, 10, 20, 30, 40, 40, 50)\n", 141 | "print(\"Original vector-1:\")\n", 142 | "print(a)\n", 143 | "print(\"Original vector-2:\")\n", 144 | "print(b)\n", 145 | "print(\"Elements of a that are not in b:\")\n", 146 | "result = setdiff(a, b)\n", 147 | "print(result)" 148 | ] 149 | }, 150 | { 151 | "cell_type": "code", 152 | "execution_count": null, 153 | "metadata": {}, 154 | "outputs": [], 155 | "source": [] 156 | } 157 | ], 158 | "metadata": { 159 | "kernelspec": { 160 | "display_name": "R", 161 | "language": "R", 162 | "name": "ir" 163 | }, 164 | "language_info": { 165 | "codemirror_mode": "r", 166 | "file_extension": ".r", 167 | "mimetype": "text/x-r-source", 168 | "name": "R", 169 | "pygments_lexer": "r", 170 | "version": "3.5.1" 171 | } 172 | }, 173 | "nbformat": 4, 174 | "nbformat_minor": 2 175 | } 176 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # R-programming-Exercises-Practice-Solution 2 | 3 | The best way we learn anything is by practice and exercise questions. 4 | 5 | Here you have the opportunity to practice the R programming language concepts by solving the exercises starting from basic to more complex exercises. 6 | 7 | 8 | Happy Coding! 9 | 10 | -------------------------------------------------------------------------------- /Vector Exercise 2.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "### Write a R program to access the last value in a given vector." 8 | ] 9 | }, 10 | { 11 | "cell_type": "code", 12 | "execution_count": 1, 13 | "metadata": {}, 14 | "outputs": [ 15 | { 16 | "name": "stdout", 17 | "output_type": "stream", 18 | "text": [ 19 | "[1] \"Original Vectors:\"\n", 20 | "[1] 10 20 30 20 20 25 9 26\n", 21 | "[1] \"Access the last value of the said vector:\"\n", 22 | "[1] 26\n" 23 | ] 24 | } 25 | ], 26 | "source": [ 27 | "x = c(10, 20, 30, 20, 20, 25, 9, 26)\n", 28 | "print(\"Original Vectors:\")\n", 29 | "print(x)\n", 30 | "print(\"Access the last value of the said vector:\")\n", 31 | "print(tail(x, n=1))" 32 | ] 33 | }, 34 | { 35 | "cell_type": "markdown", 36 | "metadata": {}, 37 | "source": [ 38 | "### Write a R program to find nth highest value in a given vector." 39 | ] 40 | }, 41 | { 42 | "cell_type": "code", 43 | "execution_count": 2, 44 | "metadata": {}, 45 | "outputs": [ 46 | { 47 | "name": "stdout", 48 | "output_type": "stream", 49 | "text": [ 50 | "[1] \"Original Vectors:\"\n", 51 | "[1] 10 20 30 20 20 25 9 26\n", 52 | "[1] \"nth highest value in a given vector:\"\n", 53 | "[1] \"n = 1\"\n", 54 | "[1] 30\n", 55 | "[1] \"n = 2\"\n", 56 | "[1] 26\n", 57 | "[1] \"n = 3\"\n", 58 | "[1] 25\n", 59 | "[1] \"n = 4\"\n", 60 | "[1] 20\n" 61 | ] 62 | } 63 | ], 64 | "source": [ 65 | "x = c(10, 20, 30, 20, 20, 25, 9, 26)\n", 66 | "print(\"Original Vectors:\")\n", 67 | "print(x)\n", 68 | "print(\"nth highest value in a given vector:\")\n", 69 | "print(\"n = 1\")\n", 70 | "n = 1\n", 71 | "print(sort(x, TRUE)[n])\n", 72 | "print(\"n = 2\")\n", 73 | "n = 2\n", 74 | "print(sort(x, TRUE)[n])\n", 75 | "print(\"n = 3\")\n", 76 | "n = 3\n", 77 | "print(sort(x, TRUE)[n])\n", 78 | "print(\"n = 4\")\n", 79 | "n = 4\n", 80 | "print(sort(x, TRUE)[n])" 81 | ] 82 | }, 83 | { 84 | "cell_type": "code", 85 | "execution_count": null, 86 | "metadata": {}, 87 | "outputs": [], 88 | "source": [] 89 | } 90 | ], 91 | "metadata": { 92 | "kernelspec": { 93 | "display_name": "R", 94 | "language": "R", 95 | "name": "ir" 96 | }, 97 | "language_info": { 98 | "codemirror_mode": "r", 99 | "file_extension": ".r", 100 | "mimetype": "text/x-r-source", 101 | "name": "R", 102 | "pygments_lexer": "r", 103 | "version": "3.5.1" 104 | } 105 | }, 106 | "nbformat": 4, 107 | "nbformat_minor": 2 108 | } 109 | -------------------------------------------------------------------------------- /Vector Exercise.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "# Vector Exercise" 8 | ] 9 | }, 10 | { 11 | "cell_type": "markdown", 12 | "metadata": {}, 13 | "source": [ 14 | "### Write a R program to create a vector of a specified type and length. Create vector of numeric, complex, logical and character types of length 6." 15 | ] 16 | }, 17 | { 18 | "cell_type": "code", 19 | "execution_count": 1, 20 | "metadata": {}, 21 | "outputs": [ 22 | { 23 | "name": "stdout", 24 | "output_type": "stream", 25 | "text": [ 26 | "[1] \"Numeric Type:\"\n", 27 | "[1] 0 0 0 0 0\n", 28 | "[1] \"Complex Type:\"\n", 29 | "[1] 0+0i 0+0i 0+0i 0+0i 0+0i\n", 30 | "[1] \"Logical Type:\"\n", 31 | "[1] FALSE FALSE FALSE FALSE FALSE\n", 32 | "[1] \"Character Type:\"\n", 33 | "[1] \"\" \"\" \"\" \"\" \"\"\n" 34 | ] 35 | } 36 | ], 37 | "source": [ 38 | "x = vector(\"numeric\", 5)\n", 39 | "print(\"Numeric Type:\")\n", 40 | "print(x)\n", 41 | "c = vector(\"complex\", 5)\n", 42 | "print(\"Complex Type:\")\n", 43 | "print(c)\n", 44 | "l = vector(\"logical\", 5)\n", 45 | "print(\"Logical Type:\")\n", 46 | "print(l)\n", 47 | "chr = vector(\"character\", 5)\n", 48 | "print(\"Character Type:\")\n", 49 | "print(chr)" 50 | ] 51 | }, 52 | { 53 | "cell_type": "code", 54 | "execution_count": null, 55 | "metadata": {}, 56 | "outputs": [], 57 | "source": [] 58 | } 59 | ], 60 | "metadata": { 61 | "kernelspec": { 62 | "display_name": "R", 63 | "language": "R", 64 | "name": "ir" 65 | }, 66 | "language_info": { 67 | "codemirror_mode": "r", 68 | "file_extension": ".r", 69 | "mimetype": "text/x-r-source", 70 | "name": "R", 71 | "pygments_lexer": "r", 72 | "version": "3.5.1" 73 | } 74 | }, 75 | "nbformat": 4, 76 | "nbformat_minor": 2 77 | } 78 | -------------------------------------------------------------------------------- /iris.r: -------------------------------------------------------------------------------- 1 | library(datasets) 2 | library(readr) 3 | library(ggplot2) 4 | library(dplyr) 5 | library(caret) 6 | library(corrplot) 7 | library(corrgram) 8 | library(e1071) 9 | library(C50) 10 | library(GGally) 11 | library(shiny) 12 | library(leaflet) 13 | data(iris) 14 | View(iris) 15 | unique(iris$Species) 16 | ggpairs(iris, mapping=ggplot2::aes(colour = Species)) --------------------------------------------------------------------------------