JORGE LUIS VILLALBA ACEVEDO
Una gran ventaja de los data frames, es que R tiene diversas funciones para leer y guardar las tablas que representan, en archivos de texto, y otros formatos.
Gender=c("M","F","M","F","M","F")
Height=c(1.83,1.76,1.82,1.60,1.90,1.66)
Weight=c(67,58,66,48,75,55)
df <- data.frame(Gender,Height,Weight);df
Gender Height Weight
1 M 1.83 67
2 F 1.76 58
3 M 1.82 66
4 F 1.60 48
5 M 1.90 75
6 F 1.66 55
class(df)
[1] "data.frame"
str(df)
'data.frame': 6 obs. of 3 variables:
$ Gender: Factor w/ 2 levels "F","M": 2 1 2 1 2 1
$ Height: num 1.83 1.76 1.82 1.6 1.9 1.66
$ Weight: num 67 58 66 48 75 55
Gender=c("M","F","M","F","M","F")
Height=c(1.83,1.76,1.82,1.60,1.90,1.66)
Weight=c(67,58,66,48,75,55)
df1 <- data.frame(Gender,Height,Weight,stringsAsFactors=FALSE);df1
Gender Height Weight
1 M 1.83 67
2 F 1.76 58
3 M 1.82 66
4 F 1.60 48
5 M 1.90 75
6 F 1.66 55
class(df1)
[1] "data.frame"
str(df1)
'data.frame': 6 obs. of 3 variables:
$ Gender: chr "M" "F" "M" "F" ...
$ Height: num 1.83 1.76 1.82 1.6 1.9 1.66
$ Weight: num 67 58 66 48 75 55
dim(df)
[1] 6 3
nrow(df)
[1] 6
length(df)
[1] 3
df$Weight
[1] 67 58 66 48 75 55
class(df$Weight)
[1] "numeric"
df[3]
Weight
1 67
2 58
3 66
4 48
5 75
6 55
df[[3]]
[1] 67 58 66 48 75 55
df[3, 2]
[1] 1.82
df[3, 2] <- 1.06
df
Gender Height Weight
1 M 1.83 67
2 F 1.76 58
3 M 1.06 66
4 F 1.60 48
5 M 1.90 75
6 F 1.66 55
df$BMI <- df$Weight/(df$Height)^2
df <- cbind(df, jorgeR=c(1,2,3,4,5,6))
df
Gender Height Weight BMI jorgeR
1 M 1.83 67 20.00657 1
2 F 1.76 58 18.72417 2
3 M 1.06 66 58.73977 3
4 F 1.60 48 18.75000 4
5 M 1.90 75 20.77562 5
6 F 1.66 55 19.95936 6
[1] "Gender" "Height" "Weight" "BMI" "jorgeR"
[1] "1" "2" "3" "4" "5" "6"
[1] "Gender" "Height" "Weight" "BMI" "jorgeR"
rownames(df) <- c("Jack","Julia","Henry","Emma","William","Elsa")
names(df)[3] <- c("Peso")
colnames(df)[2] <- c("Altura")
df
Gender Altura Peso BMI jorgeR
Jack M 1.83 67 20.00657 1
Julia F 1.76 58 18.72417 2
Henry M 1.06 66 58.73977 3
Emma F 1.60 48 18.75000 4
William M 1.90 75 20.77562 5
Elsa F 1.66 55 19.95936 6
tabla <- read.table("DF.txt",header=TRUE);tabla
Precio Piso Area Cuartos Edad Calentador
1 52.00 111 830 5 6.2 no
2 54.75 128 710 5 7.5 no
3 57.50 101 1000 5 4.2 no
4 57.50 131 690 6 8.8 no
5 59.75 93 900 5 1.9 si
class(tabla)
[1] "data.frame"
is.data.frame(tabla)
[1] TRUE
str(tabla)
'data.frame': 5 obs. of 6 variables:
$ Precio : num 52 54.8 57.5 57.5 59.8
$ Piso : num 111 128 101 131 93
$ Area : int 830 710 1000 690 900
$ Cuartos : int 5 5 5 6 5
$ Edad : num 6.2 7.5 4.2 8.8 1.9
$ Calentador: Factor w/ 2 levels "no","si": 1 1 1 1 2