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id"    "surgery"    "sex"    "age"    "weight"    "length"    "BMIpre"    "BMIpost"    "complic"    "comorb"    "depressi"    "diabetes"    "SES" 1    0    "Female"    26    100    1,57    40,56959714    31,03574182    0    0    0    0    2 2    0    "Female"    26    71    1,48    32,41417093    29,67494522    0    0    0    0    2 3    0    "Female"    53    97    1,6    37,890625    NA    0    0    0    0    2 4    0    "Female"    18    109    1,68    38,61961451    31,17913832    0    1    0    1    3 5    0    "Female"    37    100    1,63    37,63784862    27,47562949    0    0    0    0    1 6    0    "Female"    43    94    1,56    38,62590401    34,92767916    0    0    0    0    2 7    0    "Female"    36    105    1,64    39,03926234    24,5389649    1    1    0    0    3 8    0    "Female"    43    107    1,57    43,40946894    27,99302203    1    0    0    0    2 9    0    "Female"    41    103    1,6    40,234375    37,5    1    0    0    0    2 10    0    "Female"    24    93    1,69    32,56188509    27,30996814    0    0    0    0    3 By using R language do the following for the following data : Q1) -Why do you need to add the argument na.rm=TRUE to these commands? What happens if you don’t? Check the help for the mean() function for the solution. -Calculate the mean age for females and males and correlation coefficient between weights and lengths

id"    "surgery"    "sex"    "age"    "weight"    "length"    "BMIpre"    "BMIpost"    "complic"    "comorb"    "depressi"    "diabetes"    "SES" 1    0    "Female"    26    100    1,57    40,56959714    31,03574182    0    0    0    0    2 2    0    "Female"    26    71    1,48    32,41417093    29,67494522    0    0    0    0    2 3    0    "Female"    53    97    1,6    37,890625    NA    0    0    0    0    2 4    0    "Female"    18    109    1,68    38,61961451    31,17913832    0    1    0    1    3 5    0    "Female"    37    100    1,63    37,63784862    27,47562949    0    0    0    0    1 6    0    "Female"    43    94    1,56    38,62590401    34,92767916    0    0    0    0    2 7    0    "Female"    36    105    1,64    39,03926234    24,5389649    1    1    0    0    3 8    0    "Female"    43    107    1,57    43,40946894    27,99302203    1    0    0    0    2 9    0    "Female"    41    103    1,6    40,234375    37,5    1    0    0    0    2 10    0    "Female"    24    93    1,69    32,56188509    27,30996814    0    0    0    0    3 By using R language do the following for the following data : Q1) -Why do you need to add the argument na.rm=TRUE to these commands? What happens if you don’t? Check the help for the mean() function for the solution. -Calculate the mean age for females and males and correlation coefficient between weights and lengths

Programming Logic & Design Comprehensive
Programming Logic & Design Comprehensive
9th Edition
ISBN:9781337669405
Author:FARRELL
Publisher:FARRELL
Chapter3: Understanding Structure
Section: Chapter Questions
Problem 4PE
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"id"    "surgery"    "sex"    "age"    "weight"    "length"    "BMIpre"    "BMIpost"    "complic"    "comorb"    "depressi"    "diabetes"    "SES"
1    0    "Female"    26    100    1,57    40,56959714    31,03574182    0    0    0    0    2
2    0    "Female"    26    71    1,48    32,41417093    29,67494522    0    0    0    0    2
3    0    "Female"    53    97    1,6    37,890625    NA    0    0    0    0    2
4    0    "Female"    18    109    1,68    38,61961451    31,17913832    0    1    0    1    3
5    0    "Female"    37    100    1,63    37,63784862    27,47562949    0    0    0    0    1
6    0    "Female"    43    94    1,56    38,62590401    34,92767916    0    0    0    0    2
7    0    "Female"    36    105    1,64    39,03926234    24,5389649    1    1    0    0    3
8    0    "Female"    43    107    1,57    43,40946894    27,99302203    1    0    0    0    2
9    0    "Female"    41    103    1,6    40,234375    37,5    1    0    0    0    2
10    0    "Female"    24    93    1,69    32,56188509    27,30996814    0    0    0    0    3

By using R language do the following for the following data :

Q1)

-Why do you need to add the argument na.rm=TRUE to these commands? What happens if you don’t? Check the help for the mean() function for the solution.

-Calculate the mean age for females and males and correlation coefficient between weights and lengths

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Follow-up Question

 -Convert SES variable to a factor and assign the value labels “low”, ”middle” and “high” to the 3 levels of the
variable.

Then

-Define the variables complic, comorb, depressi, diabetes as factors. For all these factors, a zero means “no” and a 1 means “yes”. Assign these value labels through the function factor() and check the change using str() 

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Follow-up Question

Try to perform the previous on this data in the two pictures and the rest of, written here:

 

丽的顶住刀及仙乃龙刀龙龙鲍肚驳驳解5部可能的笼吐让5 68 69 70 71 72 73 74 75 76 77 78 79 88 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 186 187 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 "Female" "Female" "Female" "Female" "Female" "Female" "Female" "Female" "Female" "Female" "Female" "Female" "Female" "Female" "Female" "Female" "Female" "Female" "Female" "Female" "Female" "Female" "Female" "Female" "Female" "Female" "Female" "Female" "Female" "Female" "Female" "Female" "Female" "Female" "Female" "Female" "Female" "Female" "Female" "Female" "Female" "Female" "Female" "Female" "Female" "Female" "Female" "Female" "Female" "Female" "Female" "Female" "Female" "Female" "Female" "Female" "Female" "Female" "Female" "Female" "Female" "Female" "Female" "Female" "Female" "Female" "Female" "Female" 20 24 26 34 44 46 NA 16 29 40 47 47 33 34 46 53 22 41 35 21 26 51 30 33 47 44 34 65 36 20 24 30 47 55 31 39 35 37 34 36 26 34 40 28 25 32 29 47 23 23 43 49 32 18 28 27 28 43 30 20 33 37 42 49 37 32 27 57 NA 102 98 105 120 100 184 109 82 133 111 91 128 94 145 92 115 92 128 93 153 98 90 1,6 105 1,67 1,48 1,7 122 117 182 103 102 1,57 100 1,57 1,65 1,59 150 1,5 120 1,73 127 121 104 1,7 115 100 108 113 96 93 80 122 NA 1,72 NA 34,47889627 38,28125 42,06056722 1,6 1,58 1,72 40,5624662 1,71 1,72 1,6 1,55 1,52 43,28254848 35,56649909 36,84424013 32,03125 55,35900104 37,52028123 35, 10667027 49,38081093 1,61 36, 26403302 1,72 49,01297999 1,53 39,3011235 42,2405877 33,79247016 1,72 1,61 1,61 1,65 1,65 1,59 50,63090859 1,76 1,67 1,5 118 98 98 154 103 103 131 101 NA 112 165 1,54 1,61 1,8 1,71 1,61 1,5 87 95 1,67 112 1,65 104 1,62 NA 1,63 1,76 1,68 30,0232438 54,8603392 43,55555556 NA 35,15625 37,64925239 29,296875 27,96801606 39,26223521 46,56683711 35,64 26,64359862 1 41,38098909 40,56959714 44,8117539 46,27981488 66,66666667 40,09489124 53,55034576 46,68029783 35,98615917 1,64 42,75728733 1,67 35,85643885 33,33333333 38,64436921 37,03560819 41,33333333 30,1102789 39,38533058 30,82482993 34,06360931 41,13865932 39,62810547 1,64 43,87269482 1,7 33,9100346 32,36887303 44,7513599 59,41128814 40,234375 42,32412886 48,11753903 34,94809689 26,02758248 1 36,45249159 30,42184965 NA 25 NA NA NA 29,74588855 26,953125 39,5421436 22,47836668 30,47721924 33,94930751 25,46198863 32,78799351 33,32051775 37,09825528 30,11937557 27,29322416 24, 21229339 50,91613181 NA 28,398718 30,02150189 32,32323232 33,22653376 61,77777778 33,41248937 51,44206443 37,80718336 29,75778547 37,7379536 24,74893729 1,74 116 1,61 1,61 1,6 1,56 1,65 1,7 NA 105 1,59 41,5331672 95 182 150 113 25,82644628 34,47285263 30,28605441 37,37024221 108 1,68 38,26530612 26,57312925 113 1,68 40,03684807 1,56 39,03681788 1,76 32,92871901 1,77 47,87896198 1,69 39,56444102 1,7 51,55709343 149 109 1,65 40,03673095 34,19075964 27,18889991 26,25958475 1,69 39,21431322 1,6 64,453125 r.co Co Ø 1 28,72678773 28,16249373 29,77777778 21,45357371 30, 34607438 NA 1 28,32658037 31,22130395 35,05563176 37,99821535 22,14532872 NA 1 1 37,42139578 50,92396127 37,109375 28,14760026 33,4251607 27,85467128 1 35,5998576 NA NA 1 STICKE HHHHOHO. ✪ H⠀⠀⠀⠀⠀⠀⠀⠀m. 3 1 HNHANNANIM لا لا ما HNMNNMANNEN HMHNN لیا - لا لا - ما لا ما لنا ما لا MHANHNNN
Transcribed Image Text:丽的顶住刀及仙乃龙刀龙龙鲍肚驳驳解5部可能的笼吐让5 68 69 70 71 72 73 74 75 76 77 78 79 88 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 186 187 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 "Female" "Female" "Female" "Female" "Female" "Female" "Female" "Female" "Female" "Female" "Female" "Female" "Female" "Female" "Female" "Female" "Female" "Female" "Female" "Female" "Female" "Female" "Female" "Female" "Female" "Female" "Female" "Female" "Female" "Female" "Female" "Female" "Female" "Female" "Female" "Female" "Female" "Female" "Female" "Female" "Female" "Female" "Female" "Female" "Female" "Female" "Female" "Female" "Female" "Female" "Female" "Female" "Female" "Female" "Female" "Female" "Female" "Female" "Female" "Female" "Female" "Female" "Female" "Female" "Female" "Female" "Female" "Female" 20 24 26 34 44 46 NA 16 29 40 47 47 33 34 46 53 22 41 35 21 26 51 30 33 47 44 34 65 36 20 24 30 47 55 31 39 35 37 34 36 26 34 40 28 25 32 29 47 23 23 43 49 32 18 28 27 28 43 30 20 33 37 42 49 37 32 27 57 NA 102 98 105 120 100 184 109 82 133 111 91 128 94 145 92 115 92 128 93 153 98 90 1,6 105 1,67 1,48 1,7 122 117 182 103 102 1,57 100 1,57 1,65 1,59 150 1,5 120 1,73 127 121 104 1,7 115 100 108 113 96 93 80 122 NA 1,72 NA 34,47889627 38,28125 42,06056722 1,6 1,58 1,72 40,5624662 1,71 1,72 1,6 1,55 1,52 43,28254848 35,56649909 36,84424013 32,03125 55,35900104 37,52028123 35, 10667027 49,38081093 1,61 36, 26403302 1,72 49,01297999 1,53 39,3011235 42,2405877 33,79247016 1,72 1,61 1,61 1,65 1,65 1,59 50,63090859 1,76 1,67 1,5 118 98 98 154 103 103 131 101 NA 112 165 1,54 1,61 1,8 1,71 1,61 1,5 87 95 1,67 112 1,65 104 1,62 NA 1,63 1,76 1,68 30,0232438 54,8603392 43,55555556 NA 35,15625 37,64925239 29,296875 27,96801606 39,26223521 46,56683711 35,64 26,64359862 1 41,38098909 40,56959714 44,8117539 46,27981488 66,66666667 40,09489124 53,55034576 46,68029783 35,98615917 1,64 42,75728733 1,67 35,85643885 33,33333333 38,64436921 37,03560819 41,33333333 30,1102789 39,38533058 30,82482993 34,06360931 41,13865932 39,62810547 1,64 43,87269482 1,7 33,9100346 32,36887303 44,7513599 59,41128814 40,234375 42,32412886 48,11753903 34,94809689 26,02758248 1 36,45249159 30,42184965 NA 25 NA NA NA 29,74588855 26,953125 39,5421436 22,47836668 30,47721924 33,94930751 25,46198863 32,78799351 33,32051775 37,09825528 30,11937557 27,29322416 24, 21229339 50,91613181 NA 28,398718 30,02150189 32,32323232 33,22653376 61,77777778 33,41248937 51,44206443 37,80718336 29,75778547 37,7379536 24,74893729 1,74 116 1,61 1,61 1,6 1,56 1,65 1,7 NA 105 1,59 41,5331672 95 182 150 113 25,82644628 34,47285263 30,28605441 37,37024221 108 1,68 38,26530612 26,57312925 113 1,68 40,03684807 1,56 39,03681788 1,76 32,92871901 1,77 47,87896198 1,69 39,56444102 1,7 51,55709343 149 109 1,65 40,03673095 34,19075964 27,18889991 26,25958475 1,69 39,21431322 1,6 64,453125 r.co Co Ø 1 28,72678773 28,16249373 29,77777778 21,45357371 30, 34607438 NA 1 28,32658037 31,22130395 35,05563176 37,99821535 22,14532872 NA 1 1 37,42139578 50,92396127 37,109375 28,14760026 33,4251607 27,85467128 1 35,5998576 NA NA 1 STICKE HHHHOHO. ✪ H⠀⠀⠀⠀⠀⠀⠀⠀m. 3 1 HNHANNANIM لا لا ما HNMNNMANNEN HMHNN لیا - لا لا - ما لا ما لنا ما لا MHANHNNN
"id" 9 10 11 2396998=2============#99999858003536 12 13 14 15 16 17 18 19 21 24 27 31 34 37 40 41 42 43 44 45 46 47 48 49 51 54 57 61 64 67 'surgery" B B B 0 0 B B B @ @ @ 0 8 1 1 1 1 1 "Female" "Female" "Female" "Female" "Female" "Female" "Female" "Female" "Female" "Female" "Female" "Female" "Female" "Female" "Female" "Female" "Female" "Female" "Female" "Female" "Female" "Female" "Female" "Female" "Female" "Female" "Female" "Female" "Female" "Female" "Female" "Female" "Female" "Female" "Female" "Female" "Female" "Female" "Female" "Female" "Female" "Female" "Female" "Female" "Female" "Female" "Female" "Female" "Female" "Female" "Female" "Female" "Female" "Female" "Female" "Female" "Female" "Female" "Female" "Female" "Female" "Female" "Female" "Female" "Female" "Female" "Female" "sex" age 26 26 53 18 37 43 36 43 41 24 39 27 33 NA 63 29 32 33 33 41 33 42 45 42 44 29 42 45 24 36 36 40 52 33 43 38 39 15 30 30 34 36 30 NA 26 35 44 43 48 25 24 51 34 43 33 38 52 46 33 40 44 15 42 NA 49 35 44 "weight" 100 71 97 93 91 97 111 NA 94 74 109 100 94 105 187 103 1,6 112 92 188 70 98 130 NA 87 86 146 103 91 90 131 100 NA 95 83 92 86 93 "length" 40,56959714 32,41417093 37,890625 38,61961451 1,68 1,63 37,63784862 1,56 38,62590401 115 100 1,57 1,48 1,6 89 86 121 1,78 145 1,64 92 92 1,69 1,66 1,66 1,67 113 188 100 100 102 102 150 122 1,64 1,57 85 98 1,69 1,61 1,64 1,78 NA 1,63 1,57 1,67 1,66 1,55 1,62 1,7 1,7 NA 33,38655828 44,95317378 26,6727633 33,9100346 44,98269896 NA 38,18962252 53,91136228 32,21175729 33,38655828 41,00740311 38,72494532 1,66 36,28973726 1,63 37,63784862 1,68 36,13945578 1,52 44, 14819945 1,6 1,8 NA 37,109375 42,43663439 110 187 1,67 38,36638101 98 129 102 125 104 NA 39,03926234 43,40946894 40,234375 32,56188509 35,10667027 36,06484236 35,03345537 NA 1,78 47,34250726 1,68 43,22562358 1,65 31,95592287 NA 35,3795777 30,02150189 40,15920255 1,62 33,59375 45,0617284 39,247066 33,83402736 1,64 1,64 33,46222487 1,68 46,41439909 1,67 35,85643885 NA 1,6 1,61 NA 1,7 1,62 1,6 1,65 NA 1,58 1,6 1,67 1,67 1,74 1,7 1,68 34,72222222 1,63 48,55282472 NA 30,84 28,08 NA 33,9100346 39,06 36,43664485 100 1,6 98 1,64 108 1,74 35,67181926 110 1,55 45,78563996 "BMIpre" 31,03574182 29,67494522 NA 31,17913832 27,47562949 34,92767916 24,5389649 27,99302203 37,5 27,30996814 31,634582 1 NA NA 32,19290494 8 26,34649403 1 NA 25,4580659 23,22543185 8 NA NA 290,41 0 26,64359862 23,67125363 @ 25,55932916 19,95935549 1 35,9375 1 NA 1 33,705045 28,69897959 46,2945538 32,69054178 1,65 37,46556474 1,68 44,28854875 36,49376417 1,69 36,41329085 33,26214068 NA NA NA 8 35,15625 27,60945176 1 35,00319922 25,15589569 31,59626039 41,34578967 38,26530612 28,65013774 32,03125 37,65432099 37,34186862 29, 00059488 31,41731112 36,84807256 25,0995016 NA NA NA NA 28,72 22,49134948 8 35,05563176 29,7218791 NA 33,59375 0 34,15977961 30,85399449 46,06633552 NA 39,0625 35,9375 € 31,91222346 NA 26,17519452 1 1 1 8 27,6816609 22,67995241 33,69005153 32,67429761 "BMIpost" SOTDTOO "complic" INDOMO HOOMING "comorb" NOMON NH W 2 NNOOOO 00 MMNNMH 2 3 NNN "depressi" "diabetes"
Transcribed Image Text:"id" 9 10 11 2396998=2============#99999858003536 12 13 14 15 16 17 18 19 21 24 27 31 34 37 40 41 42 43 44 45 46 47 48 49 51 54 57 61 64 67 'surgery" B B B 0 0 B B B @ @ @ 0 8 1 1 1 1 1 "Female" "Female" "Female" "Female" "Female" "Female" "Female" "Female" "Female" "Female" "Female" "Female" "Female" "Female" "Female" "Female" "Female" "Female" "Female" "Female" "Female" "Female" "Female" "Female" "Female" "Female" "Female" "Female" "Female" "Female" "Female" "Female" "Female" "Female" "Female" "Female" "Female" "Female" "Female" "Female" "Female" "Female" "Female" "Female" "Female" "Female" "Female" "Female" "Female" "Female" "Female" "Female" "Female" "Female" "Female" "Female" "Female" "Female" "Female" "Female" "Female" "Female" "Female" "Female" "Female" "Female" "Female" "sex" age 26 26 53 18 37 43 36 43 41 24 39 27 33 NA 63 29 32 33 33 41 33 42 45 42 44 29 42 45 24 36 36 40 52 33 43 38 39 15 30 30 34 36 30 NA 26 35 44 43 48 25 24 51 34 43 33 38 52 46 33 40 44 15 42 NA 49 35 44 "weight" 100 71 97 93 91 97 111 NA 94 74 109 100 94 105 187 103 1,6 112 92 188 70 98 130 NA 87 86 146 103 91 90 131 100 NA 95 83 92 86 93 "length" 40,56959714 32,41417093 37,890625 38,61961451 1,68 1,63 37,63784862 1,56 38,62590401 115 100 1,57 1,48 1,6 89 86 121 1,78 145 1,64 92 92 1,69 1,66 1,66 1,67 113 188 100 100 102 102 150 122 1,64 1,57 85 98 1,69 1,61 1,64 1,78 NA 1,63 1,57 1,67 1,66 1,55 1,62 1,7 1,7 NA 33,38655828 44,95317378 26,6727633 33,9100346 44,98269896 NA 38,18962252 53,91136228 32,21175729 33,38655828 41,00740311 38,72494532 1,66 36,28973726 1,63 37,63784862 1,68 36,13945578 1,52 44, 14819945 1,6 1,8 NA 37,109375 42,43663439 110 187 1,67 38,36638101 98 129 102 125 104 NA 39,03926234 43,40946894 40,234375 32,56188509 35,10667027 36,06484236 35,03345537 NA 1,78 47,34250726 1,68 43,22562358 1,65 31,95592287 NA 35,3795777 30,02150189 40,15920255 1,62 33,59375 45,0617284 39,247066 33,83402736 1,64 1,64 33,46222487 1,68 46,41439909 1,67 35,85643885 NA 1,6 1,61 NA 1,7 1,62 1,6 1,65 NA 1,58 1,6 1,67 1,67 1,74 1,7 1,68 34,72222222 1,63 48,55282472 NA 30,84 28,08 NA 33,9100346 39,06 36,43664485 100 1,6 98 1,64 108 1,74 35,67181926 110 1,55 45,78563996 "BMIpre" 31,03574182 29,67494522 NA 31,17913832 27,47562949 34,92767916 24,5389649 27,99302203 37,5 27,30996814 31,634582 1 NA NA 32,19290494 8 26,34649403 1 NA 25,4580659 23,22543185 8 NA NA 290,41 0 26,64359862 23,67125363 @ 25,55932916 19,95935549 1 35,9375 1 NA 1 33,705045 28,69897959 46,2945538 32,69054178 1,65 37,46556474 1,68 44,28854875 36,49376417 1,69 36,41329085 33,26214068 NA NA NA 8 35,15625 27,60945176 1 35,00319922 25,15589569 31,59626039 41,34578967 38,26530612 28,65013774 32,03125 37,65432099 37,34186862 29, 00059488 31,41731112 36,84807256 25,0995016 NA NA NA NA 28,72 22,49134948 8 35,05563176 29,7218791 NA 33,59375 0 34,15977961 30,85399449 46,06633552 NA 39,0625 35,9375 € 31,91222346 NA 26,17519452 1 1 1 8 27,6816609 22,67995241 33,69005153 32,67429761 "BMIpost" SOTDTOO "complic" INDOMO HOOMING "comorb" NOMON NH W 2 NNOOOO 00 MMNNMH 2 3 NNN "depressi" "diabetes"
Solution
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