01_lab_R_learning--1-.docx

Lab 01 R Learning Kelly 2020-03-26 Logical Operators: 1. Use logical operations to get R to agree that “two plus two equals 5” is FALSE. 2 + 2 == 5 ## [1] FALSE 2. Use logical operations to test whether 8 ^ 13 is less than 15 ^ 9. 8 ^ 13 < 15 ^ 9 ## [1] FALSE Variables: 3. Create a variable called potato whose value corresponds to the number of potatoes you’ve eaten in the last week. Or something equally ridiculous. Print out the value of potato. potato= 88 potato ## [1] 88 4. Calculate the square root of potato using the sqrt() function. Print out the value of potato again to verify that the value of potato hasn’t changed. sqrt (potato) ## [1] 9.380832 potato ## [1] 88 5. Reassign the value of potato to potato * 2. Print out the new value of potato to verify that it has changed. potato = potato * 2 potato ## [1] 176

6. Try making a character (string) variable and a logical variable . Try creating a variable with a “missing” value NA. You can call these variables whatever you would like. Use class(variablename) to make sure they are the right type of variable. city = "age" class (city) ## [1] "character" areyoufat = FALSE class (areyoufat) ## [1] "logical" email= NA class (email) ## [1] "logical" Vectors: 7. Create a numeric vector with three elements using c(). a= c ( 3 , 4 , 5 ) a ## [1] 3 4 5 8. Create a character vector with three elements using c(). myfavfood= c ( "icecream" , "candy" , "cake" ) myfavfood ## [1] "icecream" "candy" "cake" 9. Create a numeric vector called age whose elements contain the ages of three people you know, where the names of each element correspond to the names of those people. age= c ( 25 , 55 , 60 ) names (age)= c ( "kelly" , "stanley" , "cora" ) age ## kelly stanley cora ## 25 55 60 10. Use “indexing by number” to get R to print out the first element of one of the vectors you created in the last questions. age[ 1 ] ## kelly ## 25 11. Use logical indexing to return all the ages of all people in age greater than 20. age > 20

## kelly stanley cora ## TRUE TRUE TRUE 12. Use indexing by name to return the age of one of the people whose ages you’ve stored in age age[ "kelly" ] ## kelly ## 25 Matrices: Dataframes: 13. Load the airquality dataset. 14. Use the $ method to print out the Wind variable in airquality. 15. Print out the third element of the Wind variable. airquality ## Ozone Solar.R Wind Temp Month Day ## 1 41 190 7.4 67 5 1 ## 2 36 118 8.0 72 5 2 ## 3 12 149 12.6 74 5 3 ## 4 18 313 11.5 62 5 4 ## 5 NA NA 14.3 56 5 5 ## 6 28 NA 14.9 66 5 6 ## 7 23 299 8.6 65 5 7 ## 8 19 99 13.8 59 5 8 ## 9 8 19 20.1 61 5 9 ## 10 NA 194 8.6 69 5 10 ## 11 7 NA 6.9 74 5 11 ## 12 16 256 9.7 69 5 12 ## 13 11 290 9.2 66 5 13 ## 14 14 274 10.9 68 5 14 ## 15 18 65 13.2 58 5 15 ## 16 14 334 11.5 64 5 16 ## 17 34 307 12.0 66 5 17 ## 18 6 78 18.4 57 5 18 ## 19 30 322 11.5 68 5 19 ## 20 11 44 9.7 62 5 20 ## 21 1 8 9.7 59 5 21 ## 22 11 320 16.6 73 5 22 ## 23 4 25 9.7 61 5 23 ## 24 32 92 12.0 61 5 24 ## 25 NA 66 16.6 57 5 25 ## 26 NA 266 14.9 58 5 26 ## 27 NA NA 8.0 57 5 27 ## 28 23 13 12.0 67 5 28

## 29 45 252 14.9 81 5 29 ## 30 115 223 5.7 79 5 30 ## 31 37 279 7.4 76 5 31 ## 32 NA 286 8.6 78 6 1 ## 33 NA 287 9.7 74 6 2 ## 34 NA 242 16.1 67 6 3 ## 35 NA 186 9.2 84 6 4 ## 36 NA 220 8.6 85 6 5 ## 37 NA 264 14.3 79 6 6 ## 38 29 127 9.7 82 6 7 ## 39 NA 273 6.9 87 6 8 ## 40 71 291 13.8 90 6 9 ## 41 39 323 11.5 87 6 10 ## 42 NA 259 10.9 93 6 11 ## 43 NA 250 9.2 92 6 12 ## 44 23 148 8.0 82 6 13 ## 45 NA 332 13.8 80 6 14 ## 46 NA 322 11.5 79 6 15 ## 47 21 191 14.9 77 6 16 ## 48 37 284 20.7 72 6 17 ## 49 20 37 9.2 65 6 18 ## 50 12 120 11.5 73 6 19 ## 51 13 137 10.3 76 6 20 ## 52 NA 150 6.3 77 6 21 ## 53 NA 59 1.7 76 6 22 ## 54 NA 91 4.6 76 6 23 ## 55 NA 250 6.3 76 6 24 ## 56 NA 135 8.0 75 6 25 ## 57 NA 127 8.0 78 6 26 ## 58 NA 47 10.3 73 6 27 ## 59 NA 98 11.5 80 6 28 ## 60 NA 31 14.9 77 6 29 ## 61 NA 138 8.0 83 6 30 ## 62 135 269 4.1 84 7 1 ## 63 49 248 9.2 85 7 2 ## 64 32 236 9.2 81 7 3 ## 65 NA 101 10.9 84 7 4 ## 66 64 175 4.6 83 7 5 ## 67 40 314 10.9 83 7 6 ## 68 77 276 5.1 88 7 7 ## 69 97 267 6.3 92 7 8 ## 70 97 272 5.7 92 7 9 ## 71 85 175 7.4 89 7 10 ## 72 NA 139 8.6 82 7 11 ## 73 10 264 14.3 73 7 12 ## 74 27 175 14.9 81 7 13 ## 75 NA 291 14.9 91 7 14 ## 76 7 48 14.3 80 7 15 ## 77 48 260 6.9 81 7 16 ## 78 35 274 10.3 82 7 17

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