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3. Wk2_DataTypesVectorsAndSubsets2013

# lengthfweight 1 14 headfweight 1 175 125 185

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Unformatted text preview: stands for “Not Available” •  NA can be an element of a vector of any type •  NA is diﬀerent from the character string “NA” •  You can check for the presence of NA values using the is.na() func6on. Special Values •  Other special values are NaN, for “not a number,” which typically arises when you try to compute an indeterminate form such as 0/0. > 0/0 [1] NaN •  The result of dividing a non- zero number by zero is Inf (or -Inf). > 12/0 [1] Inf Special Values •  NULL is a special value that denotes an empty vector > names(fweight) NULL •  Here we asked for the names of the elements of the vector fweight. The func6on names returns a character vector of element names. Since this vector has no element names, the return value is a NULL vector Finding out more informa6on •  Retrieve the number of elements in the vector •  Examine the ﬁrst 6 elements in the vector •  Elements can have names – height has names •  Are any of the elements in the vector missing? > length(fweight) [1] 14 > head(fweight) [1] 175 125 185 156 105 190 > names(4eight) [1] "a" "b" "c" "d" "e" "f" "g" "h" "i" "j" "k" "l" "m" "n” > is.na(fweight) [1] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE … Finding out more informa6on •  Aggregator func6ons operate on the elements of the vector •  Func6ons can tell us the about the data type •  Check if a vector is empty •  Convert a vector to a speciﬁed data type > min(fweight) [1] 105 > is.logical(fweight) [1] FALSE > is.null(4eight) [1] FALSE > as.numeric(fgender) [1] 1 2 1 1 2 2 1 2 1 1 2 1 1 2 How to manage variables in the workspace •  Give names of all variables •  Remove one or more variables •  Save objects for future use •  Restore saved variables •  Save an en6re workspace, and it will automa6cally load when you start R again > objects() [1] "age" "bmi" "desiredWt” … > rm(x) > save(age, bmi, desiredWt, weight, height, gender, ﬁle="cdc200.rda") > load("cdc200.rda") > q() Save workspace image? [y/n/c]: BUT IT KEEPS EVERYTHING!! Subse4ng Suppose we want the: •  BMI of the 10th person in the family > umi[10] Subset by posiFon [1] 30.04911 •  Ages of all but the ﬁrst person in the family > fage[- 1] [1] 33 79 47 27 33 67 52 59 27 55 24 46 48 Subset by exclusion Suppose we want the: •  Height of person “j” > veight["j"] S...
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