1 00:00:01,520 --> 00:00:10,640 Our friend John is backward with a debt problem this time Don is working at a fund raising non-profit 2 00:00:10,640 --> 00:00:13,760 organization where he's working as a marketing head. 3 00:00:14,660 --> 00:00:20,690 And he wants to compare the contribution of the community in different sectors. 4 00:00:20,690 --> 00:00:26,230 So for example he has identified five sectors such as education health and so on. 5 00:00:26,390 --> 00:00:32,720 And he also compared the contribution in each of these sectors and also see the trend of each of these 6 00:00:32,720 --> 00:00:36,540 sectors over the last six years. 7 00:00:36,560 --> 00:00:44,330 So John collects data of contribution in each of these sectors for the last six years and plotted using 8 00:00:44,330 --> 00:00:45,790 a line judge. 9 00:00:47,840 --> 00:00:49,900 This is all John's line. 10 00:00:50,090 --> 00:01:01,110 Looks like you can see in the legions that we have five sectors arts and culture education health one 11 00:01:01,220 --> 00:01:02,830 services and others. 12 00:01:03,290 --> 00:01:13,460 These five industries are plotted with five different lines and on the x axis we have six years these 13 00:01:13,460 --> 00:01:15,990 lines which are tangling with each other. 14 00:01:16,190 --> 00:01:17,760 Looks like spaghetti noodles. 15 00:01:17,780 --> 00:01:22,420 That is why it is also known as a spaghetti plot. 16 00:01:22,490 --> 00:01:30,550 Now as you can see a spaghetti plot is not as informative and due to the crisscrossing of these lines. 17 00:01:30,740 --> 00:01:32,270 It is very confusing. 18 00:01:33,410 --> 00:01:37,420 So how do we handle a spaghetti plot. 19 00:01:37,760 --> 00:01:43,560 That is a combination of multiple data cities which are tangling with each other. 20 00:01:46,610 --> 00:01:54,380 The first solution that I am showing you is highlighting one series at a time. 21 00:01:54,500 --> 00:02:04,100 So as I told you aim of John is to first compare the performance of one sector against the others and 22 00:02:04,100 --> 00:02:08,740 secondly look at its string over the years. 23 00:02:09,380 --> 00:02:17,780 One way of doing this is by deemphasizing on the other cities and picking up one data series on which 24 00:02:17,930 --> 00:02:23,680 we will emphasize that is will focus the attention of the audience. 25 00:02:23,720 --> 00:02:28,590 So in this graph I have big education data cities. 26 00:02:28,850 --> 00:02:38,810 I have highlighted the end point which is the most recent value of these data cities I have named diligent 27 00:02:39,020 --> 00:02:43,310 here only at the end of this state as it is. 28 00:02:43,310 --> 00:02:53,000 So whenever I look at this blog my focus is drawn towards this data sees its trend and its final value 29 00:02:53,840 --> 00:02:56,660 that is 60 percent in the last year. 30 00:02:58,130 --> 00:03:06,620 So one way of controlling the information overflow and the conclusion to do crisscrossing is to deemphasize 31 00:03:06,790 --> 00:03:10,370 a dark data series and emphasize on only one at a time. 32 00:03:12,170 --> 00:03:19,070 And you can imagine how two brothers this we have covered in the formatting options and the lecture 33 00:03:19,070 --> 00:03:22,650 in which we do about how to format a graph. 34 00:03:23,480 --> 00:03:30,170 So if you want to draw this you just used the previous graph select these four data series and color 35 00:03:30,170 --> 00:03:30,880 them late. 36 00:03:30,880 --> 00:03:31,770 Great. 37 00:03:31,790 --> 00:03:38,690 Do not use any data markers or better labeled for this series which you want to emphasize on use of 38 00:03:38,690 --> 00:03:39,730 bright color. 39 00:03:39,840 --> 00:03:41,670 Probably a blue or a black color 40 00:03:44,510 --> 00:03:53,160 and in the end add a text box and highlight this last value and the legit name while highlighting a 41 00:03:53,160 --> 00:03:54,240 particular data series. 42 00:03:54,300 --> 00:03:56,610 You can use your bank colors also. 43 00:03:56,610 --> 00:04:05,490 For example if your company's brand color is raped you can use a red line to show that that particular 44 00:04:05,490 --> 00:04:10,920 data series belongs to your company's performance and the other data it is belongs to other companies 45 00:04:10,920 --> 00:04:11,500 performing it 46 00:04:16,880 --> 00:04:23,510 the second Northerner do of showing the information in a spaghetti graph is to segregate all of these 47 00:04:23,510 --> 00:04:28,100 individual lines so you can look at this example. 48 00:04:29,150 --> 00:04:32,600 I have plotted all of these independently. 49 00:04:32,600 --> 00:04:42,950 These are five different line charts with no access or great lengths to only align with endpoints highlighted 50 00:04:45,260 --> 00:04:54,290 and a corresponding letter name given enough x box to load all these up not using a common primary axis. 51 00:04:54,400 --> 00:05:01,430 Still since the end points are labor and I wanted to highlight the trend it is clearly visible so you 52 00:05:01,430 --> 00:05:08,570 can see the performance of each of these and the trend that following over the six years that I have. 53 00:05:10,730 --> 00:05:21,470 So if you compare this chart with this one clearly clearly the alternative to US bigger deplored are 54 00:05:21,470 --> 00:05:25,450 more informative and more effective in conveying the message.