as.Date("2024-07-23")
## [1] "2024-07-23"
as.POSIXct("2024-07-23 18:43:00")
## [1] "2024-07-23 18:43:00 -05"
Sys.time()
## [1] "2024-08-27 17:56:57 -05"
Sys.Date()
## [1] "2024-08-27"
nombre <- c("Luz", "Antonio", "Jorge")
table(nombre)
## nombre
## Antonio   Jorge     Luz 
##       1       1       1
names(nombre)
## NULL
nombre[2]
## [1] "Antonio"
nombre[c(1,3)]
## [1] "Luz"   "Jorge"
nombre["Jorge"]
## [1] NA
class(nombre)
## [1] "character"
seq(1:10)
##  [1]  1  2  3  4  5  6  7  8  9 10
seq(1,10,by=2)
## [1] 1 3 5 7 9
seq(0,100,length.out=5)
## [1]   0  25  50  75 100
a<- c(1,"a",3.14)
mat<- matrix(1:9, nrow = 3)
mat
##      [,1] [,2] [,3]
## [1,]    1    4    7
## [2,]    2    5    8
## [3,]    3    6    9
mat<- matrix(1:9, nrow = 3, byrow = TRUE)
mat
##      [,1] [,2] [,3]
## [1,]    1    2    3
## [2,]    4    5    6
## [3,]    7    8    9
mat<- matrix(1:9, nrow = 3, ncol = 4, byrow = TRUE)
## Warning in matrix(1:9, nrow = 3, ncol = 4, byrow = TRUE): data length [9] is
## not a sub-multiple or multiple of the number of columns [4]
mat
##      [,1] [,2] [,3] [,4]
## [1,]    1    2    3    4
## [2,]    5    6    7    8
## [3,]    9    1    2    3
rownames(mat)<-c("F1","F2","F3")
colnames(mat)<-c("C1","C2","C3","C4")

#mat[x,y]
#mat[2,3]
#mat[3,]
#mat[,6]
mat[1:2,]
##    C1 C2 C3 C4
## F1  1  2  3  4
## F2  5  6  7  8
notes<- list(name="Atrid", student_id="987654321", grade=c(5,4.8), final="A")
notes$student_id
## [1] "987654321"
notes[["student_id"]]
## [1] "987654321"
notes[[2]]
## [1] "987654321"
df<-as.data.frame(mat)
df
##    C1 C2 C3 C4
## F1  1  2  3  4
## F2  5  6  7  8
## F3  9  1  2  3
df1<-data.frame(nombres=c("Luz","Antonio","Jorge"),edades=c(23,35,34))
df1
##   nombres edades
## 1     Luz     23
## 2 Antonio     35
## 3   Jorge     34
city<-c("Medellin","Cali","Pasto")
temp_c<-c(14,34,28)
DFT<-data.frame(city,temp_c)
DFT
##       city temp_c
## 1 Medellin     14
## 2     Cali     34
## 3    Pasto     28
CaF<-function(x){
  return(9*x/5+32)
}
temp_f1<-CaF(temp_c)
DFT$temp_f1 <- temp_f1

DFT[temp_c<30,]
##       city temp_c temp_f1
## 1 Medellin     14    57.2
## 3    Pasto     28    82.4
vector <- sample(1:1000, 1000, replace = TRUE) 

num_na <- 100
na_indices <- sample(1:1000, num_na) 
vector[na_indices] <- NA 
print(vector)
##    [1]   63  164  198  236  998   99  185  243  104  791  660  908  733   47
##   [15]  457  122  725  326  567  307  684  277  128  355  175   39  458  547
##   [29]  818  857  682  117  907  331  807  792  206   NA  590  385  703  412
##   [43]  838  481  439  648  161   NA  953  772  853   NA  712  687  168   72
##   [57]  962  455  122  354  926  867  110  334  163  818  827  226  450   NA
##   [71]  776  992  700   NA  530  265  769   NA  544   65  505   59  944  585
##   [85]   NA   31   NA  223  447  795  746  672  128  332  494  549  415  663
##   [99]  261  431  102  316   NA  518  845   67  623   77  558   11  498  960
##  [113]  776    5   69  717  735  942   55   NA   NA  703  276  626  450   65
##  [127]  273  180   NA  484  620  111  886  820   NA   95  518  367  530  329
##  [141]  284  523  910   42  592  498  983  379  221  517  511  710   NA  430
##  [155]  918  588  859  409   51  922  851   64   41  234  152  756  611  505
##  [169]  321  323  378  209   NA  711  940   NA  681  231  962  445  310  430
##  [183]   20  841   NA  779  369   95  826  711   NA  359   16   NA  356  316
##  [197]  976  273  354   NA   NA  420   NA  609  314  735  370  930  100  542
##  [211]  847  906  362   51  850  318  961  296  927  804  734  756  555   27
##  [225]   NA   NA  333  660  816   76  453  860  965  778  411  987  254  494
##  [239]   48  197  800  165  748  938  377  867  217  319  662  655  779  216
##  [253]  351  482   31  550  791  118  703   NA  346   NA  900   NA   NA  921
##  [267]  148  919  851  182   NA  574  525   NA   26  206  818  475  150   18
##  [281]  117  632   NA  245   NA   NA  319  736  879  504  458  722   81  766
##  [295]  504  385  838  930  386  567  269   NA  700  251  704  625  217   92
##  [309]  628  962   NA  834  743  401  710  984   45  450  573  242   NA  967
##  [323]  691   NA  651  231  735  889  959   41  706  319  162  405   31  752
##  [337]  801  384   NA  577  298   57  565  326  975  694  179  931  527  829
##  [351]   71  538   NA   NA  877   NA  424    5  637  476  201  742  477  280
##  [365]  588  720   83  969  872  952  662  661  774  308  657  468  277   NA
##  [379]  357   NA  266  667  360  207   NA  558   49  690   61  666   43  390
##  [393]  306  175   36  274  341  412  516  934  555  459  112  661  788  663
##  [407]   72  784  269  674  419   NA  777  431  499  973  762  376  993  402
##  [421]  282  892  432  764  865   NA  469  959  414  368   NA  749  422  707
##  [435]   14   NA  592  581  474  950  651  350  992  667  922  455  157  107
##  [449]  144  558  796  991  199  484  755  126  269  579  181  309  636  268
##  [463]  418  430  410  125   21  645  967  131  141  399   24  134  536  914
##  [477]  860   NA  783  171  230   NA  475  177  251  903  942  293  192   25
##  [491]   NA   NA   NA  423   73  412  688  950   27    8  830   NA  822  838
##  [505]  671   NA  170  331  320   96  687   79  614   NA  352  343 1000  505
##  [519]  566  915  205  936   NA  224  558  203   60  725  492  586  651   NA
##  [533]   NA  404  374  695  386  468  152  724  787  972  453  249   NA  533
##  [547]   70  873  518  941  268   NA  565  132  325   NA  396  260  656  704
##  [561]  447  345  458  548   92  801   NA  442  208  316  857  361  626  579
##  [575]   NA  690  474  821  653  432   77  469   33  525  723  332  653  749
##  [589]  108  527  404  994  708  566  901  911  838  800  977  980  596  774
##  [603]  107  848    9  956  132  147  990  628  720   12  330  987  175  183
##  [617]  784  317  898  873  253   49   36   66  127   48  224  988  480  508
##  [631]  294  799   19   52  407  400   NA  104   NA  340  936  516  652  608
##  [645]  780  531  119  955  355  847  821  104  641  887  655  184  849   NA
##  [659]  435  731   NA  602  195  493  901  616  460  149   NA  492  827   31
##  [673]  494  827  938  122  554  982  806  898  801  763  472  321  766  345
##  [687]  662  104  841  701  177  644  466   68  124  233    2  681  194  206
##  [701]  128  505    7  282  217  803   29   NA   16  762   95  332  349  664
##  [715]  920  855  436  206  272  559  324  434  445   NA  530   53  408  264
##  [729]  608  193  521  302  558  130  356  809  977  418  115  114  130  929
##  [743]  628  852   52  977  619  221   NA  770  562  601  987  256  914  303
##  [757]   NA  918  380  267  818  704  148   NA  136   NA  888  942  337  809
##  [771]  484  899  898  710  614  333  104  848  741   23  164  904  815  283
##  [785]  550  588  469  697  428  485   NA   NA  639  347  128  816   60  533
##  [799]   41  972  127  376  375  798   51  754   NA  976   23  240  938   68
##  [813]  176  431  636  911  168   NA  837  635  886   NA  655  486   NA   NA
##  [827]  282  312   NA  684  300   25  501  332  255  147  562  708  800  792
##  [841]  917  792  644  339  336  257  226  492  356  983  436   28  169  251
##  [855]  993  505   NA   NA  448   NA  822  789  657   NA   91  110  732  270
##  [869]  487  604  579  668   94  506  270  241   NA  804  144  459   NA   34
##  [883]  273  534  873  594  241  247  482  955  681   NA  489  133  211  865
##  [897]   NA  906  396  522  690   38  772  168  916   85  459  882  212  633
##  [911]  637  383  430  437  340  943  944  209  456  722  148  376  262  222
##  [925]  446  359  202  229  559  903  880  349   NA  187  281  288  273  943
##  [939]  475   52  478  918  241  843  289  787   NA   NA  359  677  941  740
##  [953]  120  203  284  541  339  244  206  780  472  143   NA  325  931   30
##  [967]  397    2  313  344  947  782  904  379   30  768  247  128   NA  792
##  [981]   NA  140  974   NA  282  495  984  270  810  474   NA   43  736  741
##  [995]  985  300  715  548  914   NA

#paquetes

library(dslabs)
data("murders")

nlevels(murders$region)
## [1] 4
murders
##                   state abb        region population total
## 1               Alabama  AL         South    4779736   135
## 2                Alaska  AK          West     710231    19
## 3               Arizona  AZ          West    6392017   232
## 4              Arkansas  AR         South    2915918    93
## 5            California  CA          West   37253956  1257
## 6              Colorado  CO          West    5029196    65
## 7           Connecticut  CT     Northeast    3574097    97
## 8              Delaware  DE         South     897934    38
## 9  District of Columbia  DC         South     601723    99
## 10              Florida  FL         South   19687653   669
## 11              Georgia  GA         South    9920000   376
## 12               Hawaii  HI          West    1360301     7
## 13                Idaho  ID          West    1567582    12
## 14             Illinois  IL North Central   12830632   364
## 15              Indiana  IN North Central    6483802   142
## 16                 Iowa  IA North Central    3046355    21
## 17               Kansas  KS North Central    2853118    63
## 18             Kentucky  KY         South    4339367   116
## 19            Louisiana  LA         South    4533372   351
## 20                Maine  ME     Northeast    1328361    11
## 21             Maryland  MD         South    5773552   293
## 22        Massachusetts  MA     Northeast    6547629   118
## 23             Michigan  MI North Central    9883640   413
## 24            Minnesota  MN North Central    5303925    53
## 25          Mississippi  MS         South    2967297   120
## 26             Missouri  MO North Central    5988927   321
## 27              Montana  MT          West     989415    12
## 28             Nebraska  NE North Central    1826341    32
## 29               Nevada  NV          West    2700551    84
## 30        New Hampshire  NH     Northeast    1316470     5
## 31           New Jersey  NJ     Northeast    8791894   246
## 32           New Mexico  NM          West    2059179    67
## 33             New York  NY     Northeast   19378102   517
## 34       North Carolina  NC         South    9535483   286
## 35         North Dakota  ND North Central     672591     4
## 36                 Ohio  OH North Central   11536504   310
## 37             Oklahoma  OK         South    3751351   111
## 38               Oregon  OR          West    3831074    36
## 39         Pennsylvania  PA     Northeast   12702379   457
## 40         Rhode Island  RI     Northeast    1052567    16
## 41       South Carolina  SC         South    4625364   207
## 42         South Dakota  SD North Central     814180     8
## 43            Tennessee  TN         South    6346105   219
## 44                Texas  TX         South   25145561   805
## 45                 Utah  UT          West    2763885    22
## 46              Vermont  VT     Northeast     625741     2
## 47             Virginia  VA         South    8001024   250
## 48           Washington  WA          West    6724540    93
## 49        West Virginia  WV         South    1852994    27
## 50            Wisconsin  WI North Central    5686986    97
## 51              Wyoming  WY          West     563626     5
head(murders,3)
##     state abb region population total
## 1 Alabama  AL  South    4779736   135
## 2  Alaska  AK   West     710231    19
## 3 Arizona  AZ   West    6392017   232
str(murders)
## 'data.frame':    51 obs. of  5 variables:
##  $ state     : chr  "Alabama" "Alaska" "Arizona" "Arkansas" ...
##  $ abb       : chr  "AL" "AK" "AZ" "AR" ...
##  $ region    : Factor w/ 4 levels "Northeast","South",..: 2 4 4 2 4 4 1 2 2 2 ...
##  $ population: num  4779736 710231 6392017 2915918 37253956 ...
##  $ total     : num  135 19 232 93 1257 ...
class(murders)
## [1] "data.frame"
names(murders)
## [1] "state"      "abb"        "region"     "population" "total"
sort(murders$total)
##  [1]    2    4    5    5    7    8   11   12   12   16   19   21   22   27   32
## [16]   36   38   53   63   65   67   84   93   93   97   97   99  111  116  118
## [31]  120  135  142  207  219  232  246  250  286  293  310  321  351  364  376
## [46]  413  457  517  669  805 1257
order(murders$total,decreasing = TRUE)
##  [1]  5 44 10 33 39 23 11 14 19 26 36 21 34 47 31  3 43 41 15  1 25 22 18 37  9
## [26]  7 50  4 48 29 32  6 17 24  8 38 28 49 45 16  2 40 13 27 20 42 12 30 51 35
## [51] 46
murders$state[5]
## [1] "California"
murders$state[46]
## [1] "Vermont"
attach(murders)

state[5]
## [1] "California"
pos_max<-which.max(total)
pos_max
## [1] 5
min(total)
## [1] 2
pos_min<-which.min(total)
pos_min
## [1] 46
state[pos_max]
## [1] "California"
state[pos_min]
## [1] "Vermont"