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The former returns a list object and the laner a

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Unformatted text preview: 12 4 Summary of Data Structures Types of structures •  To summarize, the data structures we have encountered so far are: –  vector –  data frame –  list –  matrix Vector – Data frame - List Vector Data Frame List Vector Data frame Matrix Ordered collec9on of primi9ve Ordered collec9on of vectors all same length types Ordered collec9on of objects Indexing data structures •  Vectors: [index] > x[1:10] > x[-3] > x[x>3] •  Data frames: [rowindex, colindex] and $name > family$weight > family[, 3:4] Returns a vector > family[famiy$height > 70, 2] when possible, unless use drop = FALSE •  Note: both $ can index only one element. Indexing data structures •  > > > Lists: $name, [index], [[index]] rain$stationname ! rain[1:2] ! rain[[1]]! •  Matrices: [rowindex, colindex] > m[1, 2] ! > m[1:2, ]! > m[ ,“a”, drop = TRUE]! •  Note: [[ ]] can index only one element. Also we can index a matrix as if it were a vector Apply Func9ons The “Apply” Func9on •  Some9mes we want an opera9on to be applied to each element of a list, to each vector in a data frame, or to individual dimensions of a matrix •  R provides the apply mechanism to do this. •  There are several apply func9ons: –  sapply() and lapply() for lists and data frames –  apply() for matrices –  tapply() for “tables”, i.e. ragged arrays as vectors •  With these func9ons we can avoid looping, and instead write code that is meaningful in a sta9s9cal sekng. •  For example with our list of rainfall data, each element represents the measurements taken at a par9cular weather sta9on and when we think about studying the average rainfall at each sta9on - we don’t think in terms of loops. Rainfall •  Daily rainfall collected...
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