So far, we’ve covered
print("Dataframes are collections of lists of vectors that require items of the same length. id only has 8 items")## [1] "Dataframes are collections of lists of vectors that require items of the same length. id only has 8 items"df <- data.frame(id = c("Jason", "Paul", "Mary", "Robert", "Toby", "Nina", "Robin", "James"), x = 1:10, y = rnorm(10))## Error: arguments imply differing number of rows: 8, 10# 1
x <- mtcars[mtcars$cyl == 4, ]
# 2 Exclude only rows 1 through 4
x <- mtcars[-c(1:4), ]
# 3 Return only rows for cylinders less than 5
x <- mtcars[mtcars$cyl <= 5, ]
# 4 Return only rows for cylinders that are 4 or 6.
x <- mtcars[mtcars$cyl == 4 | mtcars$cyl == 6, ]mtcars[1:20] return a error? How does it differ from the similar mtcars[1:20, ]?print("mtcars[1:20] calls the first 20 positions, but does not specify any columns")## [1] "mtcars[1:20] calls the first 20 positions, but does not specify any columns"ggplot2 library. There should be a dataset called diamonds. You can verify that by typing in data(diamonds)data.frame called small_diamonds that only contains rows 1 through 9 and 19 through 23. You can do this in one or two steps. *How many records of “Ideal” cut diamonds?library(ggplot2)
data(diamonds)
# 1
dim(diamonds)## [1] 53940 10str(diamonds)## 'data.frame': 53940 obs. of 10 variables:
## $ carat : num 0.23 0.21 0.23 0.29 0.31 0.24 0.24 0.26 0.22 0.23 ...
## $ cut : Ord.factor w/ 5 levels "Fair"<"Good"<..: 5 4 2 4 2 3 3 3 1 3 ...
## $ color : Ord.factor w/ 7 levels "D"<"E"<"F"<"G"<..: 2 2 2 6 7 7 6 5 2 5 ...
## $ clarity: Ord.factor w/ 8 levels "I1"<"SI2"<"SI1"<..: 2 3 5 4 2 6 7 3 4 5 ...
## $ depth : num 61.5 59.8 56.9 62.4 63.3 62.8 62.3 61.9 65.1 59.4 ...
## $ table : num 55 61 65 58 58 57 57 55 61 61 ...
## $ price : int 326 326 327 334 335 336 336 337 337 338 ...
## $ x : num 3.95 3.89 4.05 4.2 4.34 3.94 3.95 4.07 3.87 4 ...
## $ y : num 3.98 3.84 4.07 4.23 4.35 3.96 3.98 4.11 3.78 4.05 ...
## $ z : num 2.43 2.31 2.31 2.63 2.75 2.48 2.47 2.53 2.49 2.39 ...nrow(diamonds)## [1] 53940ncol(diamonds)## [1] 10## [1] 10# 2
small_diamonds <- diamonds[c(1:9, 19:23), ]
# 3
nrow(diamonds[diamonds$cut == "Ideal", ])## [1] 21551Bonus: Using the str() command, investigate the following brand new data type. *Given a linear model - Extract the residuals for the fitted values
mod <- lm(mpg ~ wt, data = mtcars)
str(mod)## List of 12
## $ coefficients : Named num [1:2] 37.29 -5.34
## ..- attr(*, "names")= chr [1:2] "(Intercept)" "wt"
## $ residuals : Named num [1:32] -2.28 -0.92 -2.09 1.3 -0.2 ...
## ..- attr(*, "names")= chr [1:32] "Mazda RX4" "Mazda RX4 Wag" "Datsun 710" "Hornet 4 Drive" ...
## $ effects : Named num [1:32] -113.65 -29.116 -1.661 1.631 0.111 ...
## ..- attr(*, "names")= chr [1:32] "(Intercept)" "wt" "" "" ...
## $ rank : int 2
## $ fitted.values: Named num [1:32] 23.3 21.9 24.9 20.1 18.9 ...
## ..- attr(*, "names")= chr [1:32] "Mazda RX4" "Mazda RX4 Wag" "Datsun 710" "Hornet 4 Drive" ...
## $ assign : int [1:2] 0 1
## $ qr :List of 5
## ..$ qr : num [1:32, 1:2] -5.657 0.177 0.177 0.177 0.177 ...
## .. ..- attr(*, "dimnames")=List of 2
## .. .. ..$ : chr [1:32] "Mazda RX4" "Mazda RX4 Wag" "Datsun 710" "Hornet 4 Drive" ...
## .. .. ..$ : chr [1:2] "(Intercept)" "wt"
## .. ..- attr(*, "assign")= int [1:2] 0 1
## ..$ qraux: num [1:2] 1.18 1.05
## ..$ pivot: int [1:2] 1 2
## ..$ tol : num 1e-07
## ..$ rank : int 2
## ..- attr(*, "class")= chr "qr"
## $ df.residual : int 30
## $ xlevels : Named list()
## $ call : language lm(formula = mpg ~ wt, data = mtcars)
## $ terms :Classes 'terms', 'formula' length 3 mpg ~ wt
## .. ..- attr(*, "variables")= language list(mpg, wt)
## .. ..- attr(*, "factors")= int [1:2, 1] 0 1
## .. .. ..- attr(*, "dimnames")=List of 2
## .. .. .. ..$ : chr [1:2] "mpg" "wt"
## .. .. .. ..$ : chr "wt"
## .. ..- attr(*, "term.labels")= chr "wt"
## .. ..- attr(*, "order")= int 1
## .. ..- attr(*, "intercept")= int 1
## .. ..- attr(*, "response")= int 1
## .. ..- attr(*, ".Environment")=<environment: R_GlobalEnv>
## .. ..- attr(*, "predvars")= language list(mpg, wt)
## .. ..- attr(*, "dataClasses")= Named chr [1:2] "numeric" "numeric"
## .. .. ..- attr(*, "names")= chr [1:2] "mpg" "wt"
## $ model :'data.frame': 32 obs. of 2 variables:
## ..$ mpg: num [1:32] 21 21 22.8 21.4 18.7 18.1 14.3 24.4 22.8 19.2 ...
## ..$ wt : num [1:32] 2.62 2.88 2.32 3.21 3.44 ...
## ..- attr(*, "terms")=Classes 'terms', 'formula' length 3 mpg ~ wt
## .. .. ..- attr(*, "variables")= language list(mpg, wt)
## .. .. ..- attr(*, "factors")= int [1:2, 1] 0 1
## .. .. .. ..- attr(*, "dimnames")=List of 2
## .. .. .. .. ..$ : chr [1:2] "mpg" "wt"
## .. .. .. .. ..$ : chr "wt"
## .. .. ..- attr(*, "term.labels")= chr "wt"
## .. .. ..- attr(*, "order")= int 1
## .. .. ..- attr(*, "intercept")= int 1
## .. .. ..- attr(*, "response")= int 1
## .. .. ..- attr(*, ".Environment")=<environment: R_GlobalEnv>
## .. .. ..- attr(*, "predvars")= language list(mpg, wt)
## .. .. ..- attr(*, "dataClasses")= Named chr [1:2] "numeric" "numeric"
## .. .. .. ..- attr(*, "names")= chr [1:2] "mpg" "wt"
## - attr(*, "class")= chr "lm"mod$residuals## Mazda RX4 Mazda RX4 Wag Datsun 710
## -2.2826106 -0.9197704 -2.0859521
## Hornet 4 Drive Hornet Sportabout Valiant
## 1.2973499 -0.2001440 -0.6932545
## Duster 360 Merc 240D Merc 230
## -3.9053627 4.1637381 2.3499593
## Merc 280 Merc 280C Merc 450SE
## 0.2998560 -1.1001440 0.8668731
## Merc 450SL Merc 450SLC Cadillac Fleetwood
## -0.0502472 -1.8830236 1.1733496
## Lincoln Continental Chrysler Imperial Fiat 128
## 2.1032876 5.9810744 6.8727113
## Honda Civic Toyota Corolla Toyota Corona
## 1.7461954 6.4219792 -2.6110037
## Dodge Challenger AMC Javelin Camaro Z28
## -2.9725862 -3.7268663 -3.4623553
## Pontiac Firebird Fiat X1-9 Porsche 914-2
## 2.4643670 0.3564263 0.1520430
## Lotus Europa Ford Pantera L Ferrari Dino
## 1.2010593 -4.5431513 -2.7809399
## Maserati Bora Volvo 142E
## -3.2053627 -1.0274952