ImportData_RPrin

matrix( c(4,3,6,3,6,5,9,7,9,2,2,0), nrow = 4, ncol = 3 )

##      [,1] [,2] [,3]
## [1,]    4    6    9
## [2,]    3    5    2
## [3,]    6    9    2
## [4,]    3    7    0

data.frame(cbind( mm=c(10.3,15.8,14.6,18.5,12.4,13.6),trat=c(rep(c('a','b'),c(3,3))),grupo=c(rep(c('1','2','3'),c(2,2,2))))) -> A
A

##     mm trat grupo
## 1 10.3    a     1
## 2 15.8    a     1
## 3 14.6    a     2
## 4 18.5    b     2
## 5 12.4    b     3
## 6 13.6    b     3

data.frame( cbind(trat =c(rep(c('a','b'),c(20,10))), grupo=c(rep(c('A','B','C','D','E','F'),rep(5,6)) )      )      ) -> B
B

##    trat grupo
## 1     a     A
## 2     a     A
## 3     a     A
## 4     a     A
## 5     a     A
## 6     a     B
## 7     a     B
## 8     a     B
## 9     a     B
## 10    a     B
## 11    a     C
## 12    a     C
## 13    a     C
## 14    a     C
## 15    a     C
## 16    a     D
## 17    a     D
## 18    a     D
## 19    a     D
## 20    a     D
## 21    b     E
## 22    b     E
## 23    b     E
## 24    b     E
## 25    b     E
## 26    b     F
## 27    b     F
## 28    b     F
## 29    b     F
## 30    b     F

c("2", "3")

## [1] "2" "3"

c(2, 3)

## [1] 2 3

c("aa", "bb")

## [1] "aa" "bb"

rep(5,4)

## [1] 5 5 5 5

matrix( c(4,3,6,3,6,5,9,7,9,2,2,0), nrow = 4, ncol = 3 )

##      [,1] [,2] [,3]
## [1,]    4    6    9
## [2,]    3    5    2
## [3,]    6    9    2
## [4,]    3    7    0

m1 <- matrix( c(4,3,6,3,6,5,9,7,9,2,2,0), nrow = 4, ncol = 3 )
m1

##      [,1] [,2] [,3]
## [1,]    4    6    9
## [2,]    3    5    2
## [3,]    6    9    2
## [4,]    3    7    0

matrix( c(4,3,6,3,6,5,9,7,9,2,2,0), nrow = 4, ncol = 3)

##      [,1] [,2] [,3]
## [1,]    4    6    9
## [2,]    3    5    2
## [3,]    6    9    2
## [4,]    3    7    0

mtcars

##                      mpg cyl  disp  hp drat    wt  qsec vs am gear carb
## Mazda RX4           21.0   6 160.0 110 3.90 2.620 16.46  0  1    4    4
## Mazda RX4 Wag       21.0   6 160.0 110 3.90 2.875 17.02  0  1    4    4
## Datsun 710          22.8   4 108.0  93 3.85 2.320 18.61  1  1    4    1
## Hornet 4 Drive      21.4   6 258.0 110 3.08 3.215 19.44  1  0    3    1
## Hornet Sportabout   18.7   8 360.0 175 3.15 3.440 17.02  0  0    3    2
## Valiant             18.1   6 225.0 105 2.76 3.460 20.22  1  0    3    1
## Duster 360          14.3   8 360.0 245 3.21 3.570 15.84  0  0    3    4
## Merc 240D           24.4   4 146.7  62 3.69 3.190 20.00  1  0    4    2
## Merc 230            22.8   4 140.8  95 3.92 3.150 22.90  1  0    4    2
## Merc 280            19.2   6 167.6 123 3.92 3.440 18.30  1  0    4    4
## Merc 280C           17.8   6 167.6 123 3.92 3.440 18.90  1  0    4    4
## Merc 450SE          16.4   8 275.8 180 3.07 4.070 17.40  0  0    3    3
## Merc 450SL          17.3   8 275.8 180 3.07 3.730 17.60  0  0    3    3
## Merc 450SLC         15.2   8 275.8 180 3.07 3.780 18.00  0  0    3    3
## Cadillac Fleetwood  10.4   8 472.0 205 2.93 5.250 17.98  0  0    3    4
## Lincoln Continental 10.4   8 460.0 215 3.00 5.424 17.82  0  0    3    4
## Chrysler Imperial   14.7   8 440.0 230 3.23 5.345 17.42  0  0    3    4
## Fiat 128            32.4   4  78.7  66 4.08 2.200 19.47  1  1    4    1
## Honda Civic         30.4   4  75.7  52 4.93 1.615 18.52  1  1    4    2
## Toyota Corolla      33.9   4  71.1  65 4.22 1.835 19.90  1  1    4    1
## Toyota Corona       21.5   4 120.1  97 3.70 2.465 20.01  1  0    3    1
## Dodge Challenger    15.5   8 318.0 150 2.76 3.520 16.87  0  0    3    2
## AMC Javelin         15.2   8 304.0 150 3.15 3.435 17.30  0  0    3    2
## Camaro Z28          13.3   8 350.0 245 3.73 3.840 15.41  0  0    3    4
## Pontiac Firebird    19.2   8 400.0 175 3.08 3.845 17.05  0  0    3    2
## Fiat X1-9           27.3   4  79.0  66 4.08 1.935 18.90  1  1    4    1
## Porsche 914-2       26.0   4 120.3  91 4.43 2.140 16.70  0  1    5    2
## Lotus Europa        30.4   4  95.1 113 3.77 1.513 16.90  1  1    5    2
## Ford Pantera L      15.8   8 351.0 264 4.22 3.170 14.50  0  1    5    4
## Ferrari Dino        19.7   6 145.0 175 3.62 2.770 15.50  0  1    5    6
## Maserati Bora       15.0   8 301.0 335 3.54 3.570 14.60  0  1    5    8
## Volvo 142E          21.4   4 121.0 109 4.11 2.780 18.60  1  1    4    2

mtcars

##                      mpg cyl  disp  hp drat    wt  qsec vs am gear carb
## Mazda RX4           21.0   6 160.0 110 3.90 2.620 16.46  0  1    4    4
## Mazda RX4 Wag       21.0   6 160.0 110 3.90 2.875 17.02  0  1    4    4
## Datsun 710          22.8   4 108.0  93 3.85 2.320 18.61  1  1    4    1
## Hornet 4 Drive      21.4   6 258.0 110 3.08 3.215 19.44  1  0    3    1
## Hornet Sportabout   18.7   8 360.0 175 3.15 3.440 17.02  0  0    3    2
## Valiant             18.1   6 225.0 105 2.76 3.460 20.22  1  0    3    1
## Duster 360          14.3   8 360.0 245 3.21 3.570 15.84  0  0    3    4
## Merc 240D           24.4   4 146.7  62 3.69 3.190 20.00  1  0    4    2
## Merc 230            22.8   4 140.8  95 3.92 3.150 22.90  1  0    4    2
## Merc 280            19.2   6 167.6 123 3.92 3.440 18.30  1  0    4    4
## Merc 280C           17.8   6 167.6 123 3.92 3.440 18.90  1  0    4    4
## Merc 450SE          16.4   8 275.8 180 3.07 4.070 17.40  0  0    3    3
## Merc 450SL          17.3   8 275.8 180 3.07 3.730 17.60  0  0    3    3
## Merc 450SLC         15.2   8 275.8 180 3.07 3.780 18.00  0  0    3    3
## Cadillac Fleetwood  10.4   8 472.0 205 2.93 5.250 17.98  0  0    3    4
## Lincoln Continental 10.4   8 460.0 215 3.00 5.424 17.82  0  0    3    4
## Chrysler Imperial   14.7   8 440.0 230 3.23 5.345 17.42  0  0    3    4
## Fiat 128            32.4   4  78.7  66 4.08 2.200 19.47  1  1    4    1
## Honda Civic         30.4   4  75.7  52 4.93 1.615 18.52  1  1    4    2
## Toyota Corolla      33.9   4  71.1  65 4.22 1.835 19.90  1  1    4    1
## Toyota Corona       21.5   4 120.1  97 3.70 2.465 20.01  1  0    3    1
## Dodge Challenger    15.5   8 318.0 150 2.76 3.520 16.87  0  0    3    2
## AMC Javelin         15.2   8 304.0 150 3.15 3.435 17.30  0  0    3    2
## Camaro Z28          13.3   8 350.0 245 3.73 3.840 15.41  0  0    3    4
## Pontiac Firebird    19.2   8 400.0 175 3.08 3.845 17.05  0  0    3    2
## Fiat X1-9           27.3   4  79.0  66 4.08 1.935 18.90  1  1    4    1
## Porsche 914-2       26.0   4 120.3  91 4.43 2.140 16.70  0  1    5    2
## Lotus Europa        30.4   4  95.1 113 3.77 1.513 16.90  1  1    5    2
## Ford Pantera L      15.8   8 351.0 264 4.22 3.170 14.50  0  1    5    4
## Ferrari Dino        19.7   6 145.0 175 3.62 2.770 15.50  0  1    5    6
## Maserati Bora       15.0   8 301.0 335 3.54 3.570 14.60  0  1    5    8
## Volvo 142E          21.4   4 121.0 109 4.11 2.780 18.60  1  1    4    2

head(mtcars)

##                    mpg cyl disp  hp drat    wt  qsec vs am gear carb
## Mazda RX4         21.0   6  160 110 3.90 2.620 16.46  0  1    4    4
## Mazda RX4 Wag     21.0   6  160 110 3.90 2.875 17.02  0  1    4    4
## Datsun 710        22.8   4  108  93 3.85 2.320 18.61  1  1    4    1
## Hornet 4 Drive    21.4   6  258 110 3.08 3.215 19.44  1  0    3    1
## Hornet Sportabout 18.7   8  360 175 3.15 3.440 17.02  0  0    3    2
## Valiant           18.1   6  225 105 2.76 3.460 20.22  1  0    3    1

tail(mtcars)

##                 mpg cyl  disp  hp drat    wt qsec vs am gear carb
## Porsche 914-2  26.0   4 120.3  91 4.43 2.140 16.7  0  1    5    2
## Lotus Europa   30.4   4  95.1 113 3.77 1.513 16.9  1  1    5    2
## Ford Pantera L 15.8   8 351.0 264 4.22 3.170 14.5  0  1    5    4
## Ferrari Dino   19.7   6 145.0 175 3.62 2.770 15.5  0  1    5    6
## Maserati Bora  15.0   8 301.0 335 3.54 3.570 14.6  0  1    5    8
## Volvo 142E     21.4   4 121.0 109 4.11 2.780 18.6  1  1    4    2

head(mtcars, 3)

##                mpg cyl disp  hp drat    wt  qsec vs am gear carb
## Mazda RX4     21.0   6  160 110 3.90 2.620 16.46  0  1    4    4
## Mazda RX4 Wag 21.0   6  160 110 3.90 2.875 17.02  0  1    4    4
## Datsun 710    22.8   4  108  93 3.85 2.320 18.61  1  1    4    1

mtcars[3,]

##             mpg cyl disp hp drat   wt  qsec vs am gear carb
## Datsun 710 22.8   4  108 93 3.85 2.32 18.61  1  1    4    1

head(mtcars, 5)

##                    mpg cyl disp  hp drat    wt  qsec vs am gear carb
## Mazda RX4         21.0   6  160 110 3.90 2.620 16.46  0  1    4    4
## Mazda RX4 Wag     21.0   6  160 110 3.90 2.875 17.02  0  1    4    4
## Datsun 710        22.8   4  108  93 3.85 2.320 18.61  1  1    4    1
## Hornet 4 Drive    21.4   6  258 110 3.08 3.215 19.44  1  0    3    1
## Hornet Sportabout 18.7   8  360 175 3.15 3.440 17.02  0  0    3    2

tail(mtcars, 3)

##                mpg cyl disp  hp drat   wt qsec vs am gear carb
## Ferrari Dino  19.7   6  145 175 3.62 2.77 15.5  0  1    5    6
## Maserati Bora 15.0   8  301 335 3.54 3.57 14.6  0  1    5    8
## Volvo 142E    21.4   4  121 109 4.11 2.78 18.6  1  1    4    2

mtcars[  c("mpg","cyl" )   ]

##                      mpg cyl
## Mazda RX4           21.0   6
## Mazda RX4 Wag       21.0   6
## Datsun 710          22.8   4
## Hornet 4 Drive      21.4   6
## Hornet Sportabout   18.7   8
## Valiant             18.1   6
## Duster 360          14.3   8
## Merc 240D           24.4   4
## Merc 230            22.8   4
## Merc 280            19.2   6
## Merc 280C           17.8   6
## Merc 450SE          16.4   8
## Merc 450SL          17.3   8
## Merc 450SLC         15.2   8
## Cadillac Fleetwood  10.4   8
## Lincoln Continental 10.4   8
## Chrysler Imperial   14.7   8
## Fiat 128            32.4   4
## Honda Civic         30.4   4
## Toyota Corolla      33.9   4
## Toyota Corona       21.5   4
## Dodge Challenger    15.5   8
## AMC Javelin         15.2   8
## Camaro Z28          13.3   8
## Pontiac Firebird    19.2   8
## Fiat X1-9           27.3   4
## Porsche 914-2       26.0   4
## Lotus Europa        30.4   4
## Ford Pantera L      15.8   8
## Ferrari Dino        19.7   6
## Maserati Bora       15.0   8
## Volvo 142E          21.4   4

mtcars[  c(1:4)   ]

##                      mpg cyl  disp  hp
## Mazda RX4           21.0   6 160.0 110
## Mazda RX4 Wag       21.0   6 160.0 110
## Datsun 710          22.8   4 108.0  93
## Hornet 4 Drive      21.4   6 258.0 110
## Hornet Sportabout   18.7   8 360.0 175
## Valiant             18.1   6 225.0 105
## Duster 360          14.3   8 360.0 245
## Merc 240D           24.4   4 146.7  62
## Merc 230            22.8   4 140.8  95
## Merc 280            19.2   6 167.6 123
## Merc 280C           17.8   6 167.6 123
## Merc 450SE          16.4   8 275.8 180
## Merc 450SL          17.3   8 275.8 180
## Merc 450SLC         15.2   8 275.8 180
## Cadillac Fleetwood  10.4   8 472.0 205
## Lincoln Continental 10.4   8 460.0 215
## Chrysler Imperial   14.7   8 440.0 230
## Fiat 128            32.4   4  78.7  66
## Honda Civic         30.4   4  75.7  52
## Toyota Corolla      33.9   4  71.1  65
## Toyota Corona       21.5   4 120.1  97
## Dodge Challenger    15.5   8 318.0 150
## AMC Javelin         15.2   8 304.0 150
## Camaro Z28          13.3   8 350.0 245
## Pontiac Firebird    19.2   8 400.0 175
## Fiat X1-9           27.3   4  79.0  66
## Porsche 914-2       26.0   4 120.3  91
## Lotus Europa        30.4   4  95.1 113
## Ford Pantera L      15.8   8 351.0 264
## Ferrari Dino        19.7   6 145.0 175
## Maserati Bora       15.0   8 301.0 335
## Volvo 142E          21.4   4 121.0 109

mtcars["Valiant", ]

##          mpg cyl disp  hp drat   wt  qsec vs am gear carb
## Valiant 18.1   6  225 105 2.76 3.46 20.22  1  0    3    1

mtcars[5, ]

##                    mpg cyl disp  hp drat   wt  qsec vs am gear carb
## Hornet Sportabout 18.7   8  360 175 3.15 3.44 17.02  0  0    3    2

head(mtcars,5)

##                    mpg cyl disp  hp drat    wt  qsec vs am gear carb
## Mazda RX4         21.0   6  160 110 3.90 2.620 16.46  0  1    4    4
## Mazda RX4 Wag     21.0   6  160 110 3.90 2.875 17.02  0  1    4    4
## Datsun 710        22.8   4  108  93 3.85 2.320 18.61  1  1    4    1
## Hornet 4 Drive    21.4   6  258 110 3.08 3.215 19.44  1  0    3    1
## Hornet Sportabout 18.7   8  360 175 3.15 3.440 17.02  0  0    3    2

mtcars$am == 0

##  [1] FALSE FALSE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
## [13]  TRUE  TRUE  TRUE  TRUE  TRUE FALSE FALSE FALSE  TRUE  TRUE  TRUE  TRUE
## [25]  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE

L <- mtcars$am == 0
mtcars[L,]

##                      mpg cyl  disp  hp drat    wt  qsec vs am gear carb
## Hornet 4 Drive      21.4   6 258.0 110 3.08 3.215 19.44  1  0    3    1
## Hornet Sportabout   18.7   8 360.0 175 3.15 3.440 17.02  0  0    3    2
## Valiant             18.1   6 225.0 105 2.76 3.460 20.22  1  0    3    1
## Duster 360          14.3   8 360.0 245 3.21 3.570 15.84  0  0    3    4
## Merc 240D           24.4   4 146.7  62 3.69 3.190 20.00  1  0    4    2
## Merc 230            22.8   4 140.8  95 3.92 3.150 22.90  1  0    4    2
## Merc 280            19.2   6 167.6 123 3.92 3.440 18.30  1  0    4    4
## Merc 280C           17.8   6 167.6 123 3.92 3.440 18.90  1  0    4    4
## Merc 450SE          16.4   8 275.8 180 3.07 4.070 17.40  0  0    3    3
## Merc 450SL          17.3   8 275.8 180 3.07 3.730 17.60  0  0    3    3
## Merc 450SLC         15.2   8 275.8 180 3.07 3.780 18.00  0  0    3    3
## Cadillac Fleetwood  10.4   8 472.0 205 2.93 5.250 17.98  0  0    3    4
## Lincoln Continental 10.4   8 460.0 215 3.00 5.424 17.82  0  0    3    4
## Chrysler Imperial   14.7   8 440.0 230 3.23 5.345 17.42  0  0    3    4
## Toyota Corona       21.5   4 120.1  97 3.70 2.465 20.01  1  0    3    1
## Dodge Challenger    15.5   8 318.0 150 2.76 3.520 16.87  0  0    3    2
## AMC Javelin         15.2   8 304.0 150 3.15 3.435 17.30  0  0    3    2
## Camaro Z28          13.3   8 350.0 245 3.73 3.840 15.41  0  0    3    4
## Pontiac Firebird    19.2   8 400.0 175 3.08 3.845 17.05  0  0    3    2

mtcars[L,]$mpg

##  [1] 21.4 18.7 18.1 14.3 24.4 22.8 19.2 17.8 16.4 17.3 15.2 10.4 10.4 14.7 21.5
## [16] 15.5 15.2 13.3 19.2

read.table("db1.txt")

##     V1   V2   V3
## 1 Col1 Col2 Col3
## 2  100   a1   b1
## 3  200   a2   b2
## 4  300   a3   b3

getwd()

## [1] "C:/Users/fvill/Dropbox/1. MCA_USAC/cursosLibres/2020/Clases_C.Act_FMVZ"

read.table("db1.txt", sep = ",")

##               V1
## 1 Col1 Col2 Col3
## 2      100 a1 b1
## 3     200 a2 b2 
## 4      300 a3 b3

read.table("db1.txt", sep = ",", header = TRUE)

##   Col1.Col2.Col3
## 1      100 a1 b1
## 2     200 a2 b2 
## 3      300 a3 b3

Si es primera vez que se utiliza un paquete, hay que instalarlo

install.packages("readxl")
install.packages("googlesheets4")

Si ya se encuentra instalado, solo se necesita cargarlo

Este comando es cuando no se conoce el nombre exacto del archivo

library(readxl)
read_excel( file.choose(), sheet = 1 )

library(readxl)
read_excel("RPrin_Ejercicio.xlsx", sheet = 1 )

## # A tibble: 19 x 2
##    estat SEXO 
##    <dbl> <chr>
##  1  1.6  f    
##  2  1.72 f    
##  3  1.62 f    
##  4  1.58 f    
##  5  1.63 f    
##  6  1.63 f    
##  7  1.56 f    
##  8  1.7  m    
##  9  1.75 m    
## 10  1.65 m    
## 11  1.8  m    
## 12  1.76 m    
## 13  1.67 m    
## 14  1.68 m    
## 15  1.76 m    
## 16  1.66 m    
## 17  1.77 m    
## 18  1.69 m    
## 19  1.75 m

estatura <- read_excel("RPrin_Ejercicio.xlsx", sheet = 1 )
estatura

## # A tibble: 19 x 2
##    estat SEXO 
##    <dbl> <chr>
##  1  1.6  f    
##  2  1.72 f    
##  3  1.62 f    
##  4  1.58 f    
##  5  1.63 f    
##  6  1.63 f    
##  7  1.56 f    
##  8  1.7  m    
##  9  1.75 m    
## 10  1.65 m    
## 11  1.8  m    
## 12  1.76 m    
## 13  1.67 m    
## 14  1.68 m    
## 15  1.76 m    
## 16  1.66 m    
## 17  1.77 m    
## 18  1.69 m    
## 19  1.75 m

read_excel("RPrin_Ejercicio.xlsx", sheet = 2 )

## # A tibble: 12 x 2
##    growth coat 
##     <dbl> <chr>
##  1    6.6 light
##  2    7.2 light
##  3    6.9 light
##  4    8.3 light
##  5    7.9 light
##  6    9.2 light
##  7    8.3 dark 
##  8    8.7 dark 
##  9    8.1 dark 
## 10    8.5 dark 
## 11    9.1 dark 
## 12    9   dark

growth <- read_excel("RPrin_Ejercicio.xlsx", sheet = 2 )
growth

## # A tibble: 12 x 2
##    growth coat 
##     <dbl> <chr>
##  1    6.6 light
##  2    7.2 light
##  3    6.9 light
##  4    8.3 light
##  5    7.9 light
##  6    9.2 light
##  7    8.3 dark 
##  8    8.7 dark 
##  9    8.1 dark 
## 10    8.5 dark 
## 11    9.1 dark 
## 12    9   dark

library(googlesheets4); gs4_deauth()
ss= "https://docs.google.com/spreadsheets/d/1wUXi0p-RYqpfqxSK_EM_9OPwq0UGlAa8QUJUYxYmkh0/edit?usp=sharing"
hoja="Hoja1"
rango="A3:J471"
dog <- read_sheet(ss,
sheet=hoja,
range=rango,
col_names = TRUE,
col_types = NULL,
na= "NA")

## Reading from "Perros_Bdatos"

## Range "'Hoja1'!A3:J471"

head(dog)

## # A tibble: 6 x 10
##    code comm  region sex   age.m age_cat orig    Rab_Vac time.having func 
##   <dbl> <chr> <chr>  <chr> <dbl> <chr>   <chr>     <dbl>       <dbl> <chr>
## 1  5946 D     A      m         4 1.pup   1.Local       1         0.5 G    
## 2   221 D     A      m         6 juv     Immig         0         6   GT   
## 3   221 D     A      m        12 adult   1.Local       1        12   G    
## 4   221 D     A      h        12 adult   1.Local       1        12   G    
## 5   221 D     A      h        24 adult   Immig         1        24   G    
## 6  5158 D     A      m         8 juv     1.Local       0         8   GT

tail(dog)

## # A tibble: 6 x 10
##    code comm  region sex   age.m age_cat orig    Rab_Vac time.having func 
##   <dbl> <chr> <chr>  <chr> <dbl> <chr>   <chr>     <dbl>       <dbl> <chr>
## 1  7793 B     B      h        36 adult   Immig         1       36    G    
## 2  8399 F     B      m        24 adult   1.Local       1       24    G    
## 3  8399 F     B      h        36 adult   1.Local      NA        6    G    
## 4   539 F     B      h         1 1.pup   1.Local       0        1    <NA> 
## 5   539 F     B      m         1 1.pup   1.Local       0        1    <NA> 
## 6  4497 B     B      h         6 juv     Immig        NA        0.25 G

dim(dog)

## [1] 468  10

str(dog)

## tibble [468 x 10] (S3: tbl_df/tbl/data.frame)
##  $ code       : num [1:468] 5946 221 221 221 221 ...
##  $ comm       : chr [1:468] "D" "D" "D" "D" ...
##  $ region     : chr [1:468] "A" "A" "A" "A" ...
##  $ sex        : chr [1:468] "m" "m" "m" "h" ...
##  $ age.m      : num [1:468] 4 6 12 12 24 8 48 12 12 12 ...
##  $ age_cat    : chr [1:468] "1.pup" "juv" "adult" "adult" ...
##  $ orig       : chr [1:468] "1.Local" "Immig" "1.Local" "1.Local" ...
##  $ Rab_Vac    : num [1:468] 1 0 1 1 1 0 1 1 1 1 ...
##  $ time.having: num [1:468] 0.5 6 12 12 24 8 48 12 12 12 ...
##  $ func       : chr [1:468] "G" "GT" "G" "G" ...

getwd()

## [1] "C:/Users/fvill/Dropbox/1. MCA_USAC/cursosLibres/2020/Clases_C.Act_FMVZ"

read.table("db1.txt")

##     V1   V2   V3
## 1 Col1 Col2 Col3
## 2  100   a1   b1
## 3  200   a2   b2
## 4  300   a3   b3

read.table("db1.txt", header=TRUE)

##   Col1 Col2 Col3
## 1  100   a1   b1
## 2  200   a2   b2
## 3  300   a3   b3

read.table("db1.txt", header=TRUE, sep=",")

##   Col1.Col2.Col3
## 1      100 a1 b1
## 2     200 a2 b2 
## 3      300 a3 b3

read.table("clipboard", header = TRUE)

## Warning in read.table("clipboard", header = TRUE): incomplete final line found
## by readTableHeader on 'clipboard'

## [1] install.packages..googlesheets4..
## <0 rows> (or 0-length row.names)

read.table("clipboard", header = TRUE)

## Warning in read.table("clipboard", header = TRUE): incomplete final line found
## by readTableHeader on 'clipboard'

## [1] install.packages..googlesheets4..
## <0 rows> (or 0-length row.names)

read.table("db1.txt", header=TRUE, sep="")

##   Col1 Col2 Col3
## 1  100   a1   b1
## 2  200   a2   b2
## 3  300   a3   b3

getwd()

## [1] "C:/Users/fvill/Dropbox/1. MCA_USAC/cursosLibres/2020/Clases_C.Act_FMVZ"