1 00:00:01,240 --> 00:00:04,175 In this video, we begin to work with packages in R. 2 00:00:04,175 --> 00:00:06,788 We can think of a package as data sets, 3 00:00:06,788 --> 00:00:11,130 together with ways to operate on those data sets' functions. 4 00:00:13,220 --> 00:00:17,540 In this particular video, we'll try to understand what a package is, and 5 00:00:17,540 --> 00:00:19,879 begin being productive and useful with packages. 6 00:00:20,890 --> 00:00:23,580 We'd like to be able to download a package and 7 00:00:23,580 --> 00:00:26,580 start accessing the data via the functions. 8 00:00:27,700 --> 00:00:29,520 I'm going to take a moment and pull up R. 9 00:00:35,718 --> 00:00:40,020 And you can see that packages are integral to using R. 10 00:00:40,020 --> 00:00:41,940 It has a button right there. 11 00:00:41,940 --> 00:00:43,820 We're going to install a package. 12 00:00:48,880 --> 00:00:49,960 I'm selecting a mirror. 13 00:00:51,640 --> 00:00:55,020 I'm parked in the US, so I'll grab one of our mirrors. 14 00:00:58,636 --> 00:01:02,955 And in just a moment, a set of packages presents itself. 15 00:01:02,955 --> 00:01:08,426 [SOUND] So as you can see, there are very many packages available to us, 16 00:01:08,426 --> 00:01:12,619 greatly extending the functionality and reach of R. 17 00:01:12,619 --> 00:01:14,178 I'm looking for faraway right now. 18 00:01:14,178 --> 00:01:18,765 I'll select it, say OK, and 19 00:01:18,765 --> 00:01:24,636 now R is taking a moment to download and 20 00:01:24,636 --> 00:01:29,420 install the faraway package. 21 00:01:34,642 --> 00:01:38,395 For some very simple ways to access data in this particular package, 22 00:01:38,395 --> 00:01:40,430 we'll look at a couple commands. 23 00:01:40,430 --> 00:01:41,780 First, we'll look at the data command. 24 00:01:45,753 --> 00:01:47,070 I'll take a moment and clear the screen. 25 00:01:49,320 --> 00:01:53,580 If you type data just by itself, and you spell it correctly, 26 00:01:55,900 --> 00:02:01,093 you can see that the base R installation has a number of data sets available. 27 00:02:03,470 --> 00:02:05,610 If we're particular, now, to our package, 28 00:02:11,224 --> 00:02:13,700 And I'd like single quotes around the name of the package, 29 00:02:20,802 --> 00:02:23,494 And evidently, one parenthesis, 30 00:02:23,494 --> 00:02:28,808 you can see that faraway has made a number of data sets available to us. 31 00:02:28,808 --> 00:02:33,920 The data set that we'll be dealing with in this particular lecture is 32 00:02:33,920 --> 00:02:38,800 coagulation, looking at blood coagulation time, in seconds, for 33 00:02:38,800 --> 00:02:40,709 animals fed a variety of diets. 34 00:02:44,987 --> 00:02:53,420 If I type data:coagulation.package='far away', 35 00:02:56,250 --> 00:03:01,540 We bring this particular data set available to us. 36 00:03:01,540 --> 00:03:04,600 The ls command shows you that coagulation is right there. 37 00:03:05,930 --> 00:03:12,930 If we look at coagulation, just by typing the name of the data set on the screen, 38 00:03:15,110 --> 00:03:22,610 you can see that it's stored as 24 cases, where for each case, each animal, 39 00:03:22,610 --> 00:03:26,610 in this case, we look at coagulation time together with diet listed. 40 00:03:28,470 --> 00:03:32,240 If we would like to get a quick numerical summary of our data, 41 00:03:32,240 --> 00:03:34,725 we could type summary(coagulation). 42 00:03:38,630 --> 00:03:42,382 And that gives the popular five number summary, 43 00:03:42,382 --> 00:03:47,741 minimum through maximum, with each of the quartiles represented. 44 00:03:47,741 --> 00:03:53,760 This is for all 24 data points, or 24 cases, listed together. 45 00:03:53,760 --> 00:03:57,594 You can see that there are 24 animals here. 46 00:03:57,594 --> 00:04:01,906 And they're disaggregated by diet, or rather, the frequencies for 47 00:04:01,906 --> 00:04:04,180 diet are available to us right here. 48 00:04:05,740 --> 00:04:09,360 If I were to just naively plot coagulation, 49 00:04:12,195 --> 00:04:17,550 I would obtain a plot which I don't find very useful. 50 00:04:17,550 --> 00:04:22,790 I do see the coagulation times separated out with diet here, but 51 00:04:22,790 --> 00:04:25,150 diet isn't really a numerical variable. 52 00:04:25,150 --> 00:04:27,480 It's more of a qualitative variable. 53 00:04:27,480 --> 00:04:36,205 So instead of plotting like that, I'm going to plot coagulation on diet. 54 00:04:40,318 --> 00:04:43,550 This is probably a more intuitive plot for us. 55 00:04:43,550 --> 00:04:48,390 It shows you a box plot, not for all that data aggregated together, but 56 00:04:48,390 --> 00:04:50,680 rather, spread out by diet. 57 00:04:50,680 --> 00:04:56,100 There are four diets in play, so we have four of these box plots. 58 00:04:56,100 --> 00:04:58,180 And one would, naively, 59 00:04:58,180 --> 00:05:02,850 quickly make an assumption that diets A and D are operating, somehow, 60 00:05:02,850 --> 00:05:08,540 similarly, with B and C, perhaps, increasing coagulation time. 61 00:05:08,540 --> 00:05:11,840 But that's just a quick intuition based on the picture. 62 00:05:11,840 --> 00:05:16,280 We would never draw any conclusions without doing a formal statistical 63 00:05:16,280 --> 00:05:17,060 test first. 64 00:05:27,734 --> 00:05:31,915 In this video, we've learned that R has environments, 65 00:05:31,915 --> 00:05:36,600 called packages, data, together with methods. 66 00:05:36,600 --> 00:05:39,597 And we've been able to download at least one of the packages. 67 00:05:39,597 --> 00:05:42,006 And you'll develop some facility for 68 00:05:42,006 --> 00:05:46,530 downloading other packages through some of the quizzes and readings.