Download Data Science in R: A Case Studies Approach to Computational by Deborah Nolan, Duncan Temple Lang PDF

By Deborah Nolan, Duncan Temple Lang

This ebook offers case reviews in statistical computing for facts research. each one case research addresses a statistical program with a spotlight on evaluating diverse computational techniques and explaining the reasoning at the back of them. The case reports can function fabric for teachers instructing classes in statistical computing and utilized data. The publication aids readers in realizing the concept technique of info research and the way to cause approximately computing.

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In addition to the offline data, a second set of recordings, called the “online” data, is available for testing models for predicting location. In these data, 60 locations and orientations are chosen at random and 110 signals are measured from them to each access point. 1. In both the offline and online data some of these 110 signal strength values were not recorded. , phone or laptop, in the vicinity of the experimental unit appear in some offline records. The documentation for the data [2] describes the format of the data file.

We discard the lines starting with the comment character ‘#’ and then pass each remaining line to processLine(). = "#" ] tmp = lapply(lines, processLine) When we run this, we get 6 warnings of the form 1: In matrix(tokens[c(2, 4, 6:8, 10)], nrow(tmp), 6, byrow = TRUE) : data length exceeds size of matrix Predicting Location via Indoor Positioning Systems 11 In general, we want to be very cautious about warning messages. We can try to find the rows to which these warning messages correspond by exploring the result, but it is easier to catch them as they occur.

1) that there are only 6 access points. Why are there 8 channels and 12 MAC addresses? Rereading the documentation we find that there are additional access points that are not part of the testing area and so not seen on the floor plan. Let’s check the counts of observations for the various MAC addresses with table(): table(offline$mac) Predicting Location via Indoor Positioning Systems 00:04:0e:5c:23:fc 418 00:0f:a3:39:e1:c0 145862 00:14:bf:b1:97:81 120339 00:14:bf:b1:97:90 122315 00:0f:a3:39:dd:cd 145619 00:0f:a3:39:e2:10 19162 00:14:bf:b1:97:8a 132962 00:30:bd:f8:7f:c5 301 17 00:0f:a3:39:e0:4b 43508 00:14:bf:3b:c7:c6 126529 00:14:bf:b1:97:8d 121325 00:e0:63:82:8b:a9 103 Clearly the first and the last two MAC addresses are not near the testing area or were only working/active for a short time during the measurement process because their counts are very low.

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