Jeux de données du package datasets =================================== il y a 206 jeu de données dans ce package. name class length nrow ncol name2 data_001 AirPassengers _______ ts ________________________________________________ 144 ___________ data_002 BJsales _____________ ts ________________________________________________ 150 ___________ data_003 BJsales.lead ________ ts ________________________________________________ 150 BJsales ____ data_004 BOD _________________ data.frame ________________________________________ 6 2 ___________ data_005 CO2 _________________ nfnGroupedData nfGroupedData groupedData data.frame 5 84 5 ___________ data_006 ChickWeight _________ nfnGroupedData nfGroupedData groupedData data.frame 4 578 4 ___________ data_007 DNase _______________ nfnGroupedData nfGroupedData groupedData data.frame 3 176 3 ___________ data_008 EuStockMarkets ______ mts ts matrix _____________________________________ 7440 1860 4 ___________ data_009 Formaldehyde ________ data.frame ________________________________________ 6 2 ___________ data_010 HairEyeColor ________ table _____________________________________________ 32 4 4 ___________ data_011 Harman23.cor ________ list ______________________________________________ 3 ___________ data_012 Harman74.cor ________ list ______________________________________________ 3 ___________ data_013 Indometh ____________ nfnGroupedData nfGroupedData groupedData data.frame 3 66 3 ___________ data_014 InsectSprays ________ data.frame ________________________________________ 72 2 ___________ data_015 JohnsonJohnson ______ ts ________________________________________________ 84 ___________ data_016 LakeHuron ___________ ts ________________________________________________ 98 ___________ data_017 LifeCycleSavings ____ data.frame ________________________________________ 50 5 ___________ data_018 Loblolly ____________ nfnGroupedData nfGroupedData groupedData data.frame 3 84 3 ___________ data_019 Nile ________________ ts ________________________________________________ 100 ___________ data_020 Orange ______________ nfnGroupedData nfGroupedData groupedData data.frame 3 35 3 ___________ data_021 OrchardSprays _______ data.frame ________________________________________ 64 4 ___________ data_022 PlantGrowth _________ data.frame ________________________________________ 30 2 ___________ data_023 Puromycin ___________ data.frame ________________________________________ 23 3 ___________ data_024 Seatbelts ___________ mts ts ____________________________________________ 1536 192 8 ___________ data_025 Theoph ______________ nfnGroupedData nfGroupedData groupedData data.frame 5 132 5 ___________ data_026 Titanic _____________ table _____________________________________________ 32 4 2 ___________ data_027 ToothGrowth _________ data.frame ________________________________________ 60 3 ___________ data_028 UCBAdmissions _______ table _____________________________________________ 24 2 2 ___________ data_029 UKDriverDeaths ______ ts ________________________________________________ 192 ___________ data_030 UKgas _______________ ts ________________________________________________ 108 ___________ data_031 USAccDeaths _________ ts ________________________________________________ 72 ___________ data_032 USArrests ___________ data.frame ________________________________________ 50 4 ___________ data_033 USJudgeRatings ______ data.frame ________________________________________ 43 12 ___________ data_034 USPersonalExpenditure matrix ____________________________________________ 5 5 ___________ data_035 VADeaths ____________ matrix ____________________________________________ 5 4 ___________ data_036 WWWusage ____________ ts ________________________________________________ 100 ___________ data_037 WorldPhones _________ matrix ____________________________________________ 7 7 ___________ data_038 ability.cov _________ list ______________________________________________ 3 ___________ data_039 airmiles ____________ ts ________________________________________________ 24 ___________ data_040 airquality __________ data.frame ________________________________________ 153 6 ___________ data_041 anscombe ____________ data.frame ________________________________________ 11 8 ___________ data_042 attenu ______________ data.frame ________________________________________ 182 5 ___________ data_043 attitude ____________ data.frame ________________________________________ 30 7 ___________ data_044 austres _____________ ts ________________________________________________ 89 ___________ data_045 beaver1 _____________ data.frame ________________________________________ 114 4 beavers ____ data_046 beaver2 _____________ data.frame ________________________________________ 100 4 beavers ____ data_047 cars ________________ data.frame ________________________________________ 50 2 ___________ data_048 chickwts ____________ data.frame ________________________________________ 71 2 ___________ data_049 co2 _________________ ts ________________________________________________ 468 ___________ data_050 crimtab _____________ table _____________________________________________ 924 42 22 ___________ data_051 discoveries _________ ts ________________________________________________ 100 ___________ data_052 esoph _______________ data.frame ________________________________________ 88 5 ___________ data_053 euro ________________ numeric ___________________________________________ 11 ___________ data_054 euro.cross __________ matrix ____________________________________________ 11 11 euro _______ data_055 eurodist ____________ dist ______________________________________________ 210 ___________ data_056 faithful ____________ data.frame ________________________________________ 272 2 ___________ data_057 fdeaths _____________ ts ________________________________________________ 72 UKLungDeaths data_058 freeny ______________ data.frame ________________________________________ 39 5 ___________ data_059 freeny.x ____________ matrix ____________________________________________ 39 4 freeny _____ data_060 freeny.y ____________ ts ________________________________________________ 39 freeny _____ data_061 infert ______________ data.frame ________________________________________ 248 8 ___________ data_062 iris ________________ data.frame ________________________________________ 150 5 ___________ data_063 iris3 _______________ array _____________________________________________ 600 50 4 ___________ data_064 islands _____________ numeric ___________________________________________ 48 ___________ data_065 ldeaths _____________ ts ________________________________________________ 72 UKLungDeaths data_066 lh __________________ ts ________________________________________________ 48 ___________ data_067 longley _____________ data.frame ________________________________________ 16 7 ___________ data_068 lynx ________________ ts ________________________________________________ 114 ___________ data_069 mdeaths _____________ ts ________________________________________________ 72 UKLungDeaths data_070 morley ______________ data.frame ________________________________________ 100 3 ___________ data_071 mtcars ______________ data.frame ________________________________________ 32 11 ___________ data_072 nhtemp ______________ ts ________________________________________________ 60 ___________ data_073 nottem ______________ ts ________________________________________________ 240 ___________ data_074 npk _________________ data.frame ________________________________________ 24 5 ___________ data_075 occupationalStatus __ table _____________________________________________ 64 8 8 ___________ data_076 precip ______________ numeric ___________________________________________ 70 ___________ data_077 presidents __________ ts ________________________________________________ 120 ___________ data_078 pressure ____________ data.frame ________________________________________ 19 2 ___________ data_079 quakes ______________ data.frame ________________________________________ 1000 5 ___________ data_080 randu _______________ data.frame ________________________________________ 400 3 ___________ data_081 rivers ______________ numeric ___________________________________________ 141 ___________ data_082 rock ________________ data.frame ________________________________________ 48 4 ___________ data_083 sleep _______________ data.frame ________________________________________ 20 3 ___________ data_084 stack.loss __________ numeric ___________________________________________ 21 stackloss __ data_085 stack.x _____________ matrix ____________________________________________ 21 3 stackloss __ data_086 stackloss ___________ data.frame ________________________________________ 21 4 ___________ data_087 state.abb ___________ character _________________________________________ 50 state ______ data_088 state.area __________ numeric ___________________________________________ 50 state ______ data_089 state.center ________ list ______________________________________________ 2 state ______ data_090 state.division ______ factor ____________________________________________ 50 state ______ data_091 state.name __________ character _________________________________________ 50 state ______ data_092 state.region ________ factor ____________________________________________ 50 state ______ data_093 state.x77 ___________ matrix ____________________________________________ 50 8 state ______ data_094 sunspot.month _______ ts ________________________________________________ 2988 ___________ data_095 sunspot.year ________ ts ________________________________________________ 289 ___________ data_096 sunspots ____________ ts ________________________________________________ 2820 ___________ data_097 swiss _______________ data.frame ________________________________________ 47 6 ___________ data_098 treering ____________ ts ________________________________________________ 7980 ___________ data_099 trees _______________ data.frame ________________________________________ 31 3 ___________ data_100 uspop _______________ ts ________________________________________________ 19 ___________ data_101 volcano _____________ matrix ____________________________________________ 87 61 ___________ data_102 warpbreaks __________ data.frame ________________________________________ 54 3 ___________ data_103 women _______________ data.frame ________________________________________ 15 2 ___________ data_104 AirPassengers _______ ts ________________________________________________ 144 ___________ data_105 BJsales _____________ ts ________________________________________________ 150 ___________ data_106 BJsales.lead ________ ts ________________________________________________ 150 BJsales ____ data_107 BOD _________________ data.frame ________________________________________ 6 2 ___________ data_108 CO2 _________________ nfnGroupedData nfGroupedData groupedData data.frame 5 84 5 ___________ data_109 ChickWeight _________ nfnGroupedData nfGroupedData groupedData data.frame 4 578 4 ___________ data_110 DNase _______________ nfnGroupedData nfGroupedData groupedData data.frame 3 176 3 ___________ data_111 EuStockMarkets ______ mts ts matrix _____________________________________ 7440 1860 4 ___________ data_112 Formaldehyde ________ data.frame ________________________________________ 6 2 ___________ data_113 HairEyeColor ________ table _____________________________________________ 32 4 4 ___________ data_114 Harman23.cor ________ list ______________________________________________ 3 ___________ data_115 Harman74.cor ________ list ______________________________________________ 3 ___________ data_116 Indometh ____________ nfnGroupedData nfGroupedData groupedData data.frame 3 66 3 ___________ data_117 InsectSprays ________ data.frame ________________________________________ 72 2 ___________ data_118 JohnsonJohnson ______ ts ________________________________________________ 84 ___________ data_119 LakeHuron ___________ ts ________________________________________________ 98 ___________ data_120 LifeCycleSavings ____ data.frame ________________________________________ 50 5 ___________ data_121 Loblolly ____________ nfnGroupedData nfGroupedData groupedData data.frame 3 84 3 ___________ data_122 Nile ________________ ts ________________________________________________ 100 ___________ data_123 Orange ______________ nfnGroupedData nfGroupedData groupedData data.frame 3 35 3 ___________ data_124 OrchardSprays _______ data.frame ________________________________________ 64 4 ___________ data_125 PlantGrowth _________ data.frame ________________________________________ 30 2 ___________ data_126 Puromycin ___________ data.frame ________________________________________ 23 3 ___________ data_127 Seatbelts ___________ mts ts ____________________________________________ 1536 192 8 ___________ data_128 Theoph ______________ nfnGroupedData nfGroupedData groupedData data.frame 5 132 5 ___________ data_129 Titanic _____________ table _____________________________________________ 32 4 2 ___________ data_130 ToothGrowth _________ data.frame ________________________________________ 60 3 ___________ data_131 UCBAdmissions _______ table _____________________________________________ 24 2 2 ___________ data_132 UKDriverDeaths ______ ts ________________________________________________ 192 ___________ data_133 UKgas _______________ ts ________________________________________________ 108 ___________ data_134 USAccDeaths _________ ts ________________________________________________ 72 ___________ data_135 USArrests ___________ data.frame ________________________________________ 50 4 ___________ data_136 USJudgeRatings ______ data.frame ________________________________________ 43 12 ___________ data_137 USPersonalExpenditure matrix ____________________________________________ 5 5 ___________ data_138 VADeaths ____________ matrix ____________________________________________ 5 4 ___________ data_139 WWWusage ____________ ts ________________________________________________ 100 ___________ data_140 WorldPhones _________ matrix ____________________________________________ 7 7 ___________ data_141 ability.cov _________ list ______________________________________________ 3 ___________ data_142 airmiles ____________ ts ________________________________________________ 24 ___________ data_143 airquality __________ data.frame ________________________________________ 153 6 ___________ data_144 anscombe ____________ data.frame ________________________________________ 11 8 ___________ data_145 attenu ______________ data.frame ________________________________________ 182 5 ___________ data_146 attitude ____________ data.frame ________________________________________ 30 7 ___________ data_147 austres _____________ ts ________________________________________________ 89 ___________ data_148 beaver1 _____________ data.frame ________________________________________ 114 4 beavers ____ data_149 beaver2 _____________ data.frame ________________________________________ 100 4 beavers ____ data_150 cars ________________ data.frame ________________________________________ 50 2 ___________ data_151 chickwts ____________ data.frame ________________________________________ 71 2 ___________ data_152 co2 _________________ ts ________________________________________________ 468 ___________ data_153 crimtab _____________ table _____________________________________________ 924 42 22 ___________ data_154 discoveries _________ ts ________________________________________________ 100 ___________ data_155 esoph _______________ data.frame ________________________________________ 88 5 ___________ data_156 euro ________________ numeric ___________________________________________ 11 ___________ data_157 euro.cross __________ matrix ____________________________________________ 11 11 euro _______ data_158 eurodist ____________ dist ______________________________________________ 210 ___________ data_159 faithful ____________ data.frame ________________________________________ 272 2 ___________ data_160 fdeaths _____________ ts ________________________________________________ 72 UKLungDeaths data_161 freeny ______________ data.frame ________________________________________ 39 5 ___________ data_162 freeny.x ____________ matrix ____________________________________________ 39 4 freeny _____ data_163 freeny.y ____________ ts ________________________________________________ 39 freeny _____ data_164 infert ______________ data.frame ________________________________________ 248 8 ___________ data_165 iris ________________ data.frame ________________________________________ 150 5 ___________ data_166 iris3 _______________ array _____________________________________________ 600 50 4 ___________ data_167 islands _____________ numeric ___________________________________________ 48 ___________ data_168 ldeaths _____________ ts ________________________________________________ 72 UKLungDeaths data_169 lh __________________ ts ________________________________________________ 48 ___________ data_170 longley _____________ data.frame ________________________________________ 16 7 ___________ data_171 lynx ________________ ts ________________________________________________ 114 ___________ data_172 mdeaths _____________ ts ________________________________________________ 72 UKLungDeaths data_173 morley ______________ data.frame ________________________________________ 100 3 ___________ data_174 mtcars ______________ data.frame ________________________________________ 32 11 ___________ data_175 nhtemp ______________ ts ________________________________________________ 60 ___________ data_176 nottem ______________ ts ________________________________________________ 240 ___________ data_177 npk _________________ data.frame ________________________________________ 24 5 ___________ data_178 occupationalStatus __ table _____________________________________________ 64 8 8 ___________ data_179 precip ______________ numeric ___________________________________________ 70 ___________ data_180 presidents __________ ts ________________________________________________ 120 ___________ data_181 pressure ____________ data.frame ________________________________________ 19 2 ___________ data_182 quakes ______________ data.frame ________________________________________ 1000 5 ___________ data_183 randu _______________ data.frame ________________________________________ 400 3 ___________ data_184 rivers ______________ numeric ___________________________________________ 141 ___________ data_185 rock ________________ data.frame ________________________________________ 48 4 ___________ data_186 sleep _______________ data.frame ________________________________________ 20 3 ___________ data_187 stack.loss __________ numeric ___________________________________________ 21 stackloss __ data_188 stack.x _____________ matrix ____________________________________________ 21 3 stackloss __ data_189 stackloss ___________ data.frame ________________________________________ 21 4 ___________ data_190 state.abb ___________ character _________________________________________ 50 state ______ data_191 state.area __________ numeric ___________________________________________ 50 state ______ data_192 state.center ________ list ______________________________________________ 2 state ______ data_193 state.division ______ factor ____________________________________________ 50 state ______ data_194 state.name __________ character _________________________________________ 50 state ______ data_195 state.region ________ factor ____________________________________________ 50 state ______ data_196 state.x77 ___________ matrix ____________________________________________ 50 8 state ______ data_197 sunspot.month _______ ts ________________________________________________ 2988 ___________ data_198 sunspot.year ________ ts ________________________________________________ 289 ___________ data_199 sunspots ____________ ts ________________________________________________ 2820 ___________ data_200 swiss _______________ data.frame ________________________________________ 47 6 ___________ data_201 treering ____________ ts ________________________________________________ 7980 ___________ data_202 trees _______________ data.frame ________________________________________ 31 3 ___________ data_203 uspop _______________ ts ________________________________________________ 19 ___________ data_204 volcano _____________ matrix ____________________________________________ 87 61 ___________ data_205 warpbreaks __________ data.frame ________________________________________ 54 3 ___________ data_206 women _______________ data.frame ________________________________________ 15 2 ___________ Jeux de données du package ade4 =============================== il y a 105 jeu de données dans ce package. name class length nrow ncol name2 data_001 abouheif.eg _ list _____ 6 data_002 acacia ______ data.frame 32 15 data_003 aminoacyl ___ list _____ 5 data_004 apis108 _____ data.frame 180 10 data_005 ardeche _____ list _____ 6 data_006 arrival _____ list _____ 2 data_007 atlas _______ list _____ 7 data_008 atya ________ list _____ 3 data_009 avijons _____ list _____ 4 data_010 avimedi _____ list _____ 3 data_011 aviurba _____ list _____ 6 data_012 bacteria ____ list _____ 4 data_013 banque ______ data.frame 810 21 data_014 baran95 _____ list _____ 3 data_015 bf88 ________ list _____ 6 data_016 bordeaux ____ data.frame 5 4 data_017 bsetal97 ____ list _____ 8 data_018 buech _______ list _____ 5 data_019 butterfly ___ list _____ 4 data_020 capitales ___ list _____ 4 data_021 carni19 _____ list _____ 2 data_022 carni70 _____ list _____ 2 data_023 carniherbi49 list _____ 5 data_024 casitas _____ data.frame 74 15 data_025 chatcat _____ list _____ 2 data_026 chats _______ data.frame 8 8 data_027 chazeb ______ list _____ 2 data_028 chevaine ____ list _____ 3 data_029 clementines _ data.frame 15 20 data_030 cnc2003 _____ data.frame 94 12 data_031 coleo _______ list _____ 5 data_032 corvus ______ data.frame 28 4 data_033 deug ________ list _____ 3 data_034 doubs _______ list _____ 4 data_035 dunedata ____ list _____ 2 data_036 ecg _________ ts _______ 2048 data_037 ecomor ______ list _____ 7 data_038 elec88 ______ list _____ 7 data_039 escopage ____ list _____ 3 data_040 euro123 _____ list _____ 4 data_041 fission _____ list _____ 2 data_042 friday87 ____ list _____ 4 data_043 fruits ______ list _____ 3 data_044 ggtortoises _ list _____ 6 data_045 granulo _____ list _____ 2 data_046 hdpg ________ list _____ 3 data_047 housetasks __ data.frame 13 4 data_048 humDNAm _____ list _____ 3 data_049 ichtyo ______ list _____ 3 data_050 irishdata ___ list _____ 11 data_051 julliot _____ list _____ 3 data_052 jv73 ________ list _____ 6 data_053 kcponds _____ list _____ 4 data_054 lascaux _____ list _____ 9 data_055 lizards _____ list _____ 3 data_056 macaca ______ list _____ 2 data_057 macon _______ data.frame 8 25 data_058 macroloire __ list _____ 5 data_059 mafragh _____ list _____ 7 data_060 maples ______ list _____ 2 data_061 mariages ____ data.frame 9 9 data_062 meau ________ list _____ 3 data_063 meaudret ____ list _____ 4 data_064 microsatt ___ list _____ 4 data_065 mjrochet ____ list _____ 2 data_066 mollusc _____ list _____ 2 data_067 monde84 _____ data.frame 48 5 data_068 morphosport _ list _____ 2 data_069 newick.eg ___ list _____ 14 data_070 njplot ______ list _____ 2 data_071 olympic _____ list _____ 2 data_072 oribatid ____ list _____ 3 data_073 ours ________ data.frame 38 10 data_074 palm ________ list _____ 2 data_075 pap _________ list _____ 2 data_076 perthi02 ____ list _____ 2 data_077 piosphere ___ list _____ 4 data_078 presid2002 __ list _____ 2 data_079 procella ____ list _____ 2 data_080 rankrock ____ data.frame 10 51 data_081 rhizobium ___ list _____ 2 data_082 rhone _______ list _____ 3 data_083 rpjdl _______ list _____ 5 data_084 santacatalina data.frame 11 10 data_085 sarcelles ___ list _____ 4 data_086 seconde _____ data.frame 22 8 data_087 skulls ______ data.frame 150 4 data_088 steppe ______ list _____ 2 data_089 syndicats ___ data.frame 5 4 data_090 t3012 _______ list _____ 3 data_091 tarentaise __ list _____ 6 data_092 taxo.eg _____ list _____ 2 data_093 tintoodiel __ list _____ 3 data_094 tithonia ____ list _____ 2 data_095 tortues _____ data.frame 48 4 data_096 toxicity ____ list _____ 3 data_097 trichometeo _ list _____ 3 data_098 ungulates ___ list _____ 2 data_099 vegtf _______ list _____ 5 data_100 veuvage _____ list _____ 2 data_101 westafrica __ list _____ 8 data_102 woangers ____ list _____ 2 data_103 worksurv ____ data.frame 319 4 data_104 yanomama ____ list _____ 3 data_105 zealand _____ list _____ 3