Objects, Class and Attributes

Transcription

1 Objects, Class and Attributes Introduction to objects classes and attributes Practically speaking everything you encounter in R is an object. R has a few different classes of objects. I will talk mainly about S3 objects. If we have time I will talk about S4 classes. Everything your store in your workspace is an object. The built in functions are objects The datasets are objects ggplot2 graphics are objects (base graphics are not) Every object has a class and many objects have attributes The basics You can use the class function to determine an objects class You can use the attributes function to see its attributes Let s start with a few examples class function (x).primitive("class") class(class) [1] "function" attributes(class) NULL What about data? class(mtcars) [1] "data.frame" attributes(mtcars) $names [1] "mpg" "cyl" "disp" "hp" "drat" "wt" "qsec" "vs" "am" "gear" [11] "carb" $row.names [1] "Mazda RX4" "Mazda RX4 Wag" "Datsun 710" [4] "Hornet 4 Drive" "Hornet Sportabout" "Valiant" [7] "Duster 360" "Merc 240D" "Merc 230" [10] "Merc 280" "Merc 280C" "Merc 450SE" [13] "Merc 450SL" "Merc 450SLC" "Cadillac Fleetwood" [16] "Lincoln Continental" "Chrysler Imperial" "Fiat 128" [19] "Honda Civic" "Toyota Corolla" "Toyota Corona" [22] "Dodge Challenger" "AMC Javelin" "Camaro Z28" [25] "Pontiac Firebird" "Fiat X1-9" "Porsche 914-2" [28] "Lotus Europa" "Ford Pantera L" "Ferrari Dino" [31] "Maserati Bora" "Volvo 142E" 1

2 $class [1] "data.frame" Lets create a summary of our data s1 <- summary(mtcars) s1 mpg cyl disp hp Min. :10.40 Min. :4.000 Min. : 71.1 Min. : st Qu.: st Qu.: st Qu.: st Qu.: 96.5 Median :19.20 Median :6.000 Median :196.3 Median :123.0 Mean :20.09 Mean :6.188 Mean :230.7 Mean : rd Qu.: rd Qu.: rd Qu.: rd Qu.:180.0 Max. :33.90 Max. :8.000 Max. :472.0 Max. :335.0 drat wt qsec vs Min. :2.760 Min. :1.513 Min. :14.50 Min. : st Qu.: st Qu.: st Qu.: st Qu.: Median :3.695 Median :3.325 Median :17.71 Median : Mean :3.597 Mean :3.217 Mean :17.85 Mean : rd Qu.: rd Qu.: rd Qu.: rd Qu.: Max. :4.930 Max. :5.424 Max. :22.90 Max. : am gear carb Min. : Min. :3.000 Min. : st Qu.: st Qu.: st Qu.:2.000 Median : Median :4.000 Median :2.000 Mean : Mean :3.688 Mean : rd Qu.: rd Qu.: rd Qu.:4.000 Max. : Max. :5.000 Max. :8.000 class(s1) [1] "table" attributes(s1) $dim [1] 6 11 $dimnames $dimnames[[1]] [1] "" "" "" "" "" "" $dimnames[[2]] [1] " mpg" " cyl" " disp" " hp" " drat" [6] " wt" " qsec" " vs" " am" " gear" [11] " carb" $class [1] "table" What about a graphic library(ggplot2) p1 <- ggplot(mtcars,aes(wt,mpg)) + geom_point() p1 2

3 mpg class(p1) wt [1] "gg" "ggplot" attributes(p1) $names [1] "data" "layers" "scales" "mapping" "theme" [6] "coordinates" "facet" "plot_env" "labels" $class [1] "gg" "ggplot" What about a linear model m1 <- lm(mpg~wt,data=mtcars) m1 Call: lm(formula = mpg ~ wt, data = mtcars) Coefficients: (Intercept) wt class(m1) [1] "lm" 3

4 attributes(m1) $names [1] "coefficients" "residuals" "effects" "rank" [5] "fitted.values" "assign" "qr" "df.residual" [9] "xlevels" "call" "terms" "model" $class [1] "lm" Let s create a summary of our model s1 <- summary(m1) s1 Call: lm(formula = mpg ~ wt, data = mtcars) Residuals: Min 1Q Median 3Q Max Coefficients: Estimate Std. Error t value Pr(> t ) (Intercept) < 2e-16 *** wt e-10 *** --- Signif. codes: 0 '***' '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: on 30 degrees of freedom Multiple R-squared: , Adjusted R-squared: F-statistic: on 1 and 30 DF, p-value: 1.294e-10 class(s1) [1] "summary.lm" attributes(s1) $names [1] "call" "terms" "residuals" "coefficients" [5] "aliased" "sigma" "df" "r.squared" [9] "adj.r.squared" "fstatistic" "cov.unscaled" $class [1] "summary.lm" exercise Compute and assign the anova table for m1. What is its class? What are its attributes. Look at the object. Can you tell what function is served by each attribute? Back to basics for class We know that vectors are everywhere in R. Even a scaler is a vector of length 1. 4

5 Once you store a vector you have created an R object. Every object has a mode and storage mode. Vectors have a inherent class based on their mode. There are also a few implicit classes like matrix, array and integer. There are 5 data types that are stored in basic (atomic) vectors class mode storage.mode logical logical logical integer numeric integer numeric numeric double complex complex complex character character character Besides the basic modes of logical, integer, double, complex and character, we will see mode list and function. x <- 1 class(x) [1] "numeric" mode(x) [1] "numeric" attributes(x) NULL x <- 1L class(x) [1] "integer" mode(x) [1] "numeric" attributes(x) NULL dim(x) <- c(1,1) class(x) [1] "matrix" mode(x) [1] "numeric" attributes(x) $dim [1] 1 1 dim(x) <- c(1,1,1) class(x) [1] "array" 5

6 mode(x) [1] "numeric" attributes(x) $dim [1] Matrix and array are not very informative classes because they do not tell us the real type of data they contain. exercise Assign x the value a. What are its class and mode? Give it a dimension c(1,1). What are its class and mode? The difference between a matrix and data frame carmt <- as.matrix(mtcars) carmt mpg cyl disp hp drat wt qsec vs am gear carb Mazda RX Mazda RX4 Wag Datsun Hornet 4 Drive Hornet Sportabout Valiant Duster Merc 240D Merc Merc Merc 280C Merc 450SE Merc 450SL Merc 450SLC Cadillac Fleetwood Lincoln Continental Chrysler Imperial Fiat Honda Civic Toyota Corolla Toyota Corona Dodge Challenger AMC Javelin Camaro Z Pontiac Firebird Fiat X Porsche Lotus Europa Ford Pantera L Ferrari Dino Maserati Bora

7 Volvo 142E cardf <- mtcars cardf mpg cyl disp hp drat wt qsec vs am gear carb Mazda RX Mazda RX4 Wag Datsun Hornet 4 Drive Hornet Sportabout Valiant Duster Merc 240D Merc Merc Merc 280C Merc 450SE Merc 450SL Merc 450SLC Cadillac Fleetwood Lincoln Continental Chrysler Imperial Fiat Honda Civic Toyota Corolla Toyota Corona Dodge Challenger AMC Javelin Camaro Z Pontiac Firebird Fiat X Porsche Lotus Europa Ford Pantera L Ferrari Dino Maserati Bora Volvo 142E dim(carmt) [1] dim(cardf) [1] carmt[10,4] [1] 123 cardf[10,4] [1] 123 They look the same and feel the same. class(carmt) [1] "matrix" 7

8 class(cardf) [1] "data.frame" attributes(carmt) $dim [1] $dimnames $dimnames[[1]] [1] "Mazda RX4" "Mazda RX4 Wag" "Datsun 710" [4] "Hornet 4 Drive" "Hornet Sportabout" "Valiant" [7] "Duster 360" "Merc 240D" "Merc 230" [10] "Merc 280" "Merc 280C" "Merc 450SE" [13] "Merc 450SL" "Merc 450SLC" "Cadillac Fleetwood" [16] "Lincoln Continental" "Chrysler Imperial" "Fiat 128" [19] "Honda Civic" "Toyota Corolla" "Toyota Corona" [22] "Dodge Challenger" "AMC Javelin" "Camaro Z28" [25] "Pontiac Firebird" "Fiat X1-9" "Porsche 914-2" [28] "Lotus Europa" "Ford Pantera L" "Ferrari Dino" [31] "Maserati Bora" "Volvo 142E" $dimnames[[2]] [1] "mpg" "cyl" "disp" "hp" "drat" "wt" "qsec" "vs" "am" "gear" [11] "carb" attributes(cardf) $names [1] "mpg" "cyl" "disp" "hp" "drat" "wt" "qsec" "vs" "am" "gear" [11] "carb" $row.names [1] "Mazda RX4" "Mazda RX4 Wag" "Datsun 710" [4] "Hornet 4 Drive" "Hornet Sportabout" "Valiant" [7] "Duster 360" "Merc 240D" "Merc 230" [10] "Merc 280" "Merc 280C" "Merc 450SE" [13] "Merc 450SL" "Merc 450SLC" "Cadillac Fleetwood" [16] "Lincoln Continental" "Chrysler Imperial" "Fiat 128" [19] "Honda Civic" "Toyota Corolla" "Toyota Corona" [22] "Dodge Challenger" "AMC Javelin" "Camaro Z28" [25] "Pontiac Firebird" "Fiat X1-9" "Porsche 914-2" [28] "Lotus Europa" "Ford Pantera L" "Ferrari Dino" [31] "Maserati Bora" "Volvo 142E" $class [1] "data.frame" mode(carmt) [1] "numeric" mode(cardf) [1] "list" But they are fundamentally different objects. 8

9 A matrix is a long vector with an attribute (dim) that gives an easier way to access similar items. Each row (same car) and column (same type of measurement) should have something in common. Internally the data stacks the columns. A matrix can be accessed like a vector. length(carmt) [1] 352 carmt[10,4] [1] 123 dim(carmt) [1] carmt[3*32 +10] [1] 123 A data frame is a special type of list. Each element of the list is a vector and each vector must be the same length. The same element number of each vector should have something in common (the same car). A data frame can be accessed like a list. length(cardf) [1] 11 cardf[10,4] [1] 123 cardf[[4]][10] [1] 123 cardf$hp[10] [1] 123 exercise Without using a double index extract the qsec for the Valiant from carmt and cardf The R list object The dataframe$variable notation we are used to more generally a property of lists. You can access an element of a list by listname$elementname. Lists are probably the most important type of objects in R. To prove this lets look at our past objects. class(mtcars) [1] "data.frame" mode(mtcars) [1] "list" class(p1) [1] "gg" "ggplot" 9

10 mode(p1) [1] "list" class(m1) [1] "lm" mode(m1) [1] "list" class(s1) [1] "summary.lm" mode(s1) [1] "list" They all have different classes but internally they are all lists. Most objects other than basic vectors have an attribute that defines their class. Those without a defined class consider their mode to be their class (usually list). Even with a defined class, the object can usually be treated as indicated by their mode. What is a list? It is a more complicated vector. Like a basic vector it can contain any number of elements including zero. Unlike a basic vector, where each element is a single realization of the same basic mode, each element of a list can be any R object and each can be a different type. x <- list() class(x) [1] "list" mode(x) [1] "list" length(x) [1] 0 To assign an element to a list you can type x$function <- lm x$data <- mtcars x$scaler <- 1 length(x) [1] 3 attributes(x) $names [1] "Function" "data" "scaler" A more common way to create the list is x <- list(func=lm,dat=mtcars,onenum=1,model=m1,random=sample(letters,5)) length(x) [1] 5 10

11 attributes(x) $names [1] "func" "dat" "onenum" "model" "random" x$random [1] "A" "H" "Z" "I" "O" Using lists we can create an object containing a variety of different object types. Furthermore we can access the pieces of the list using listname$elementname notation. Also note that mtcars and m1 are objects of mode list. This means a list can contain a list as one of its elements. Since many of the objects we ve created are of mode list, we can access their elements using the list notation. attributes(m1) $names [1] "coefficients" "residuals" "effects" "rank" [5] "fitted.values" "assign" "qr" "df.residual" [9] "xlevels" "call" "terms" "model" $class [1] "lm" m1$coefficients (Intercept) wt exercise use t.test and mtcars, conduct and store an object that tests if mpg differ by transmission type (am). What is the class and mode of the object. Look at its attributes and extract the p value. The pesky factor object in R Lets examine the factor object in R more carefully. It can look a numeric or character but it is really neither. We can use the unclass function to remove the class attribute. A <- gl(3,5,labels=c("red","green","blue")) A [1] red red red red red green green green green green blue [12] blue blue blue blue Levels: red green blue class(a) [1] "factor" mode(a) [1] "numeric" attributes(a) 11

12 $levels [1] "red" "green" "blue" $class [1] "factor" unclass(a) [1] attr(,"levels") [1] "red" "green" "blue" B <- gl(3,1,15,labels=c(4.8,5.3,6.2)) B [1] Levels: unclass(b) [1] attr(,"levels") [1] "4.8" "5.3" "6.2" It is an integer vector with values ranging from 1 to n where n is the number of levels of the factor. It has 2 attributes, levels and class. However it cannot be treated as its mode (numeric) B[1] + B[2] Warning in Ops.factor(B[1], B[2]): '+' not meaningful for factors [1] NA C <- as.numeric(b) C[1] + C[2] [1] 3 D <- as.numeric(as.character(b)) D[1] + D[2] [1] 10.1 Attributes (metadata) What is an attribute? It is a list of additional information that can be attached to an object. class(attributes(m1)) [1] "list" The attributes function can be used to access the whole list. Individual attributes can be accessed using the attr function. attr(b,"levels") [1] "4.8" "5.3" "6.2" 12

13 However you may have noticed there are many attributes that are used repeatedly. These common attributes have conditions and must be set to an acceptable value or an error will occur. Common attributes will have functions that allow you to access them directly. For some common classes, like factor, their attributes will also have a function that can access them directly. levels(b) [1] "4.8" "5.3" "6.2" names(b) NULL The functions not only access the attributes but also allow you to change the value of the attribute. We ve already seen this with dim. names(b) <- paste0("b",1:15) attributes(b) $levels [1] "4.8" "5.3" "6.2" $class [1] "factor" $names [1] "B1" "B2" "B3" "B4" "B5" "B6" "B7" "B8" "B9" "B10" "B11" [12] "B12" "B13" "B14" "B15" names(b)[1] <- "Bob" B Bob B2 B3 B4 B5 B6 B7 B8 B9 B10 B11 B12 B13 B14 B Levels: levels(b) <- letters[25:26] Error in `levels<-.factor`(`*tmp*`, value = c("y", "z")): number of levels differs levels(b) <- letters[24:26] B Bob B2 B3 B4 B5 B6 B7 B8 B9 B10 B11 B12 B13 B14 B15 x y z x y z x y z x y z x y z Levels: x y z Attributes that do not have their own functions can be modified using attr a1 <- anova(m1) a1 Analysis of Variance Table Response: mpg Df Sum Sq Mean Sq F value Pr(>F) wt e-10 *** Residuals Signif. codes: 0 '***' '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 13

14 attr(a1,"heading") [1] "Analysis of Variance Table\n" "Response: mpg" attr(a1,"heading") <- "ANOVA Table for MPG" a1 ANOVA Table for MPG Df Sum Sq Mean Sq F value Pr(>F) wt e-10 *** Residuals Signif. codes: 0 '***' '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 exercise using carsdf rename am " to Trans and vs to Engine. Can you do the same thing to carmt What does class do for an object (Generic Functions) In order to understand the importance of class we need to introduce the generic function. The key to the object oriented nature of R is the a function called UseMethod. UseMethod takes the function name and the class of the first argument, pastes them together and runs a function of the form functionname.class. If no such function exists, it uses the next class listed for an object. If it cannot find a suitable function it runs functionname.default. coef function (object,...) UseMethod("coef") <bytecode: 0x7ff98f54e070> <environment: namespace:stats> The most commonly used generic function is print. Everytime you view an R object, it calls the appropriate print method with default settings. s1 Call: lm(formula = mpg ~ wt, data = mtcars) Residuals: Min 1Q Median 3Q Max Coefficients: Estimate Std. Error t value Pr(> t ) (Intercept) < 2e-16 *** wt e-10 *** --- Signif. codes: 0 '***' '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: on 30 degrees of freedom Multiple R-squared: , Adjusted R-squared:

15 F-statistic: on 1 and 30 DF, p-value: 1.294e-10 print(s1) Call: lm(formula = mpg ~ wt, data = mtcars) Residuals: Min 1Q Median 3Q Max Coefficients: Estimate Std. Error t value Pr(> t ) (Intercept) < 2e-16 *** wt e-10 *** --- Signif. codes: 0 '***' '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: on 30 degrees of freedom Multiple R-squared: , Adjusted R-squared: F-statistic: on 1 and 30 DF, p-value: 1.294e-10 print(s1,signif.stars=false) Call: lm(formula = mpg ~ wt, data = mtcars) Residuals: Min 1Q Median 3Q Max Coefficients: Estimate Std. Error t value Pr(> t ) (Intercept) < 2e-16 wt e-10 Residual standard error: on 30 degrees of freedom Multiple R-squared: , Adjusted R-squared: F-statistic: on 1 and 30 DF, p-value: 1.294e-10 If you want to view the raw object you can call print.default directly or use the unclass function on the object. print.default(s1) $call lm(formula = mpg ~ wt, data = mtcars) $terms mpg ~ wt attr(,"variables") list(mpg, wt) attr(,"factors") wt mpg 0 15

16 wt 1 attr(,"term.labels") [1] "wt" attr(,"order") [1] 1 attr(,"intercept") [1] 1 attr(,"response") [1] 1 attr(,".environment") <environment: R_GlobalEnv> attr(,"predvars") list(mpg, wt) attr(,"dataclasses") mpg wt "numeric" "numeric" $residuals Mazda RX4 Mazda RX4 Wag Datsun Hornet 4 Drive Hornet Sportabout Valiant Duster 360 Merc 240D Merc Merc 280 Merc 280C Merc 450SE Merc 450SL Merc 450SLC Cadillac Fleetwood Lincoln Continental Chrysler Imperial Fiat Honda Civic Toyota Corolla Toyota Corona Dodge Challenger AMC Javelin Camaro Z Pontiac Firebird Fiat X1-9 Porsche Lotus Europa Ford Pantera L Ferrari Dino Maserati Bora Volvo 142E $coefficients Estimate Std. Error t value Pr(> t ) (Intercept) e-19 wt e-10 $aliased (Intercept) wt FALSE FALSE $sigma [1] $df 16

17 [1] $r.squared [1] $adj.r.squared [1] $fstatistic value numdf dendf $cov.unscaled (Intercept) wt (Intercept) wt attr(,"class") [1] "summary.lm" What R shows you and what the real object looks like can be very different. To see all the methods available for a function use methods methods(coef) [1] coef.aov* coef.arima* coef.default* coef.listof* coef.maov* [6] coef.nls* see '?methods' for accessing help and source code To see what generic functions are available to a given class of object we also use methods methods(class="lm") [1] add1 alias anova case.names [5] confint cooks.distance deviance dfbeta [9] dfbetas drop1 dummy.coef effects [13] extractaic family formula fortify [17] hatvalues influence kappa labels [21] loglik model.frame model.matrix nobs [25] plot predict print proj [29] qr residuals rstandard rstudent [33] simulate summary variable.names vcov see '?methods' for accessing help and source code exercise formula is a generic function. How many classes does the formula function have a specific method for. What happens if you call formula with an lm object. What happens if you call formula with a data.frame object. Caution about classes There are no formal definition for an S3 class. You make any object have class data.frame but if they do not have the appropriate elements and attributes, they will cause errors or strange behaviour. class(carmt) <- "data.frame" class(carmt) 17

18 [1] "data.frame" carmt NULL <0 rows> (or 0-length row.names) carmt <- unclass(carmt) carmt mpg cyl disp hp drat wt qsec vs am gear carb Mazda RX Mazda RX4 Wag Datsun Hornet 4 Drive Hornet Sportabout Valiant Duster Merc 240D Merc Merc Merc 280C Merc 450SE Merc 450SL Merc 450SLC Cadillac Fleetwood Lincoln Continental Chrysler Imperial Fiat Honda Civic Toyota Corolla Toyota Corona Dodge Challenger AMC Javelin Camaro Z Pontiac Firebird Fiat X Porsche Lotus Europa Ford Pantera L Ferrari Dino Maserati Bora Volvo 142E Although you can assign any class to an object, the generic functions in R expect a certain structure. If you follow that structure, it will work x <- list(dbl=rnorm(10),char=letters[1:10],fact=gl(2,5)) x $DBL [1] [7] $CHAR [1] "A" "B" "C" "D" "E" "F" "G" "H" "I" "J" 18

19 $FACT [1] Levels: 1 2 attributes(x) $names [1] "DBL" "CHAR" "FACT" class(x) <- "data.frame" x [1] DBL CHAR FACT <0 rows> (or 0-length row.names) attributes(x) $names [1] "DBL" "CHAR" "FACT" $class [1] "data.frame" rownames(x) <- letters[1:10] x DBL CHAR FACT a A 1 b B 1 c C 1 d D 1 e E 1 f F 2 g G 2 h H 2 i I 2 j J 2 attributes(x) $names [1] "DBL" "CHAR" "FACT" $class [1] "data.frame" $row.names [1] "a" "b" "c" "d" "e" "f" "g" "h" "i" "j" S4 objects So far everything we ve talked about has been considering S3 objects. There is another class of object we have not discussed yet, that is S4 objects. S4 objects can trip you up because they operate differently than S3. There is a formal definition of an S4 class. All objects in an S4 class are forced to follow the appropriate definition. 19

20 library(lmertest) Warning: package 'lmertest' was built under R version Loading required package: Matrix Loading required package: methods Loading required package: lme4 Attaching package: 'lmertest' The following object is masked from 'package:lme4': lmer The following object is masked from 'package:stats': step m1 <- lmer(reaction ~ Days + (Days Subject), sleepstudy) class(m1) [1] "mermodlmertest" attr(,"package") [1] "lmertest" mode(m1) [1] "S4" iss4(m1) [1] TRUE names(attributes(m1)) [1] "Gp" "call" "frame" "flist" "cnms" "lower" "theta" [8] "beta" "u" "devcomp" "pp" "optinfo" "resp" "class" To access S4 data a slightly different syntax is used. names(m1) NULL slotnames(m1) [1] "Gp" "call" "frame" "flist" "cnms" "lower" "theta" [8] "beta" "u" "devcomp" "pp" "optinfo" "resp" m1$gp Error in m1$gp: $ operator not defined for this S4 class m1@gp [1] 0 36 class(m1@gp) [1] "integer" 20

21 [1] FALSE Elements of S4 class objects can be S3 class objects. VC <- VarCorr(m1) class(vc) [1] "VarCorr.merMod" iss4(vc) [1] FALSE names(vc) [1] "Subject" class(vc$subject) [1] "matrix" exercise Compute and store the object vcov(m1). What class and mode is it? What elements does it contain? What is the length of x? Thank You for attending this session. 21