If I told you that your audience would have three seconds to look at a visual of your data, how would you design that visual? What would you do differently than what you normally do with your data? Well, I'd like to argue that maybe that's what we should do with our data, because the ability to interpret a graph quickly also relates to the ability to interpret a graph easily. And we want to make things as easy as possible for our audience. How do we do this? Well, this comes back to a concept that's called pre-attentive attributes. Never heard of those before? Well, stay tuned for today's episode of Code Club. Hey, folks, I'm Pat Schloss and obviously this is Code Club. In today's episode, I'm going to continue talking about some of the elements that were in the rubric that I shared with you from my last episode and talking about a concept that's called pre-attentive attributes. This seems like kind of mumbo jumbo or some just jargon, but I think it's really important for helping us to think about how we design our data visuals. We want things to be easy on our reader. We want to have empathy on our audience. We want it to be easy for them to understand what's going on. One of the things that I like to think about are my eyeballs. And so as I'm looking at a figure, how much do my eyeballs twitch back and forth up and down trying to understand what's going on in the visual? If my eyeballs don't move much, then it's easy to read. If they're moving all over the place, left and right to the legend and to the data and trying to figure out what's going on, it's not easy to interpret. We want things to be easy to interpret for our audience. What are pre-attentive attributes? Again, these are the attributes or in GG plot terms, the aesthetics of your plot, that your audience is going to see within the first second, maybe first half second, and that they're going to use to form an impression of your data of what's going on. So what are pre-attentive attributes? Well, position on the axis, right? So is something higher or lower on the y-axis? Where is it positioned on the x-axis, right, to the left or right of other points? Those are some of the strongest pre-attentive attributes, the spatial positioning, because we're really good at comparing relative positions of different points. We might also think about color and how different colors could indicate categorical variables or perhaps continuous variables. We might use shape as a pre-attentive attribute to indicate different groups or categories of samples that we're plotting. Shape and color are kind of, I think, seen as being equal pre-attentive attributes for categorical data. We might also think about length, right? If you have a bar plot, you have a length of that bar or perhaps the length of a line or a line segment to indicate distance or variation, right? We might also think about width, of a width of that line. And so if a line is wider than another, then perhaps that line has more emphasis or more whatever it is we're measuring behind it. But another attribute that I want to spend a fair amount of time talking about today is grouping. That if we can draw a circle or some kind of ellipsoid around a group of points and lasso those together, we can say these points all go together. And so yeah, while perhaps I'm showing you those individual points, what you really need to focus on is that grouping, since we can compare this grouping to another grouping. So if you were with me in the last episode, you know that we built a scatter plot of an NMDS ordination of some data that I published in a supplemental figure many years ago that wasn't very good. And so what I'd like to do is think about how we can use grouping within that ordination to make it easier for the audience to understand what's going on. So if you look at this ordination, one of the things you perhaps begin to notice is that your eyes are going all over the place, right? There's a couple hundred points here in the visual. And there's color indicating different groups of patients. And there's position x and y and where they are, right? And so what my eye does when I look at this visual is I'm looking back and forth, I'm looking up and down, I'm looking back up to the legend and back, trying to interpret what's going on. But if I were to group those points somehow, perhaps by drawing some type of lasso, if you will, around the three different groups of patients, it would become much easier for my audience to understand what's going on. No doubt, this has become a fairly common approach that people have taken to looking at ordinations in the field of microbiome research. So there's a couple different variations on that ellipsoid approach that we will talk about today. We'll go about building four different variations on the plot that we made in the last episode. As always, if you'd like to get caught up, I have a video from a couple of weeks ago where I showed how to install our studio and our four windows, as well as Mac, how to install the tidyverse and how to get the data that I'm using in these Code Club episodes. Also, if you look down below in the notes, there's a link to a blog post for today that includes the code that I'm going to be generating as we work through today's content. If you're excited about the possibility of learning more about R, you're in the right place. Down below in the notes, I've got a couple links to some tutorials that I've made. The minimal R tutorial is all about generating data visuals using microbiome data. Also, I do teach workshops, three-day workshops that go over the material in those tutorials. So be in touch if you're interested in learning more. But by all means, please be sure to like this video, subscribe to the channel, hit that bell icon so you know when the next video drops, where we'll continue to build out our skills in visualizing data. Let's go ahead to RStudio and I will fire up RStudio by launching that Rproj file again. So I'm in my correct working directory. I'm going to go ahead and open up that Schubert nmds.r file and I will go ahead and run everything to get to the point that we were at in the previous episode. And the the figure that has generated is not the figure that I ended with in the last episode. You'll notice that for some reason my legend is truncated here to the right. I'm not I'm not quite sure what's going on with that. This is really a point of frustration that I have with RStudio. And perhaps it's also my fault. And so what gg save which I'm using here does is it saves the image as it sees it down here in the lower right corner. What I need to do is if I set the dimensions better, I think it'll look good. So if I do width equals five, height equals four, and that looks a little better, but it still looks kind of funky. So again, this is just kind of a frustration of mine with RStudio that it's really hard in a reproducible way to get a figure made the way you want it to look unless you are working off of the TIFF. I'm really not sure what happened there, whatever. So this is an opportunity to show you a few things. And I think one of the problems I have is this chord fixed function. Let's add some arguments to this. Let's do XLM. And I'll do C. Let's do from negative 0.8 to 0.8. And then YLM from also negative 0.8 to 0.8. Give that a run. So we could mess with the margins later. But for now, I'm happy that I've got my legend here. And we can move on without worrying about it too much. But this kind of underscores the need that if you're trying to make a figure that is going to be going out for more public consumption, it pays to use GGSafe to save it directly and to set the width and the height. I think part of the reason mine looks so weird is that I've got things zoomed in so that you can see it more easily. But anyway, I think we'll be in good shape. And I don't want to spend a whole lot of time messing with that. I want to get to thinking about how we can group our data. So again, as we look at this ordination, and as I'm looking at it, my eyes are kind of really attracted to looking at all of the points and trying to, again, interpret what's going on here in the legend with the points to try to see, like, are there more gray points down here? Are there more red points up here? Are there more blue points up here? What's going on? And so I'm spending a lot of time with my eyes going back and forth trying to interpolate the data and see where the centroid is. So I think the first thing we can do to help our audience is let's put down a point in the middle of these clouds, the three clouds to indicate where the centroid is for each of these three clouds. How do we do that? Well, I'm glad you asked, we're in the right spot. So we've got this metadata NMDS data frame, which as we've seen from previous episode, has our metadata, and then at the very end joined to it are the two axes columns that we're plotting. And what I'd like to do is I'm going to group by disease stat. And again, that's the variable that we're using for our color. And to get the centroid, what I will do is a summarize. And I will say axis one equals mean axis one. And then axis two is mean axis two. And I'll go ahead and do dot groups equals drop. So I don't get those annoying error messages or warning messages. And so we see the centroid coordinates that we can imagine a point being plopped down into those different spots. Good. So I need to define this as a data frame that I'll call centroid. Save that. So then I can add points, extra points, extra data to my ggplot. So this geom point is using data from metadata NMDS. I can actually add another geom point that will use data from centroid. Right. And then my mapping will be AES. And I'll do x equals axis one, y equals axis two, color equals disease stat. And I'll add that. And let's go ahead and run that. Now the problem is that it's plotting it with the same plotting simple as everything else. So I have no idea which of those points are the new points I added. So I need to change that. And I can I can I can use a different plotting symbol by adding the plotting symbol outside of the mapping argument. So I can do shape equals 15. And so if we look at that, again, it's not obvious to me where that is. So what I'll do is make the size bigger. And so let's do size equals five. And bam, there we go. We see that we now have these large squares to indicate the centroids of the three different clouds. And so we say, yeah, the grays are down here at the bottom, the reds are over here on the left, and the blues are more towards the right. Now we can do a statistical test later to figure out whether or not those are significant or not. We'll probably save that for a future episode. One of the things I don't like about this, though, is that it makes the squares and those legends much larger. And I don't really want the legend to be the square, I still want it to be those circles. So I can show I can add another argument to the second genome point, which will be show legend equals false. And so that way, then, and be sure I run gg save, I now have my previous legend with the circles, and I then have those squares to indicate where the centroid is. Good. So that's helpful. And so that's that's approach one, right, plopping in the centroids to make it much easier for our audience to see what's going on. If they're familiar with this type of plot, it will be easier for them to see that. And so what I now do is I'm comparing these three squares rather than the couple hundred points that are behind them. Okay, so that's that's helpful, but still my eye might want to kind of wander to compare this red square to all the other squares. So a second approach that's commonly used, I don't know what it's what it's properly called, I'm going to call it a star plot. Tell me down below and then comments if you know what the actual name for this is, is a plot and I call it a star plot because if we take the red points, we'll draw a line from the square out to each of the red points and from the blue square out to each of the blue points and same for the gray. So let's give that a shot and see how it looks. I kind of have a feeling that we have so many points that it might just look really bad. So how do we do that? Well, let's start like we had back here. And I'm going to copy this and I'm not going to do summarize, I'm actually going to do mutate. And I'm going to then call this centroid one and centroid two. And and so what we're doing is we're taking our big data frame, metadata and MDS, we're grouping by disease stats of the three different disease statuses. And then for each of those, we're creating a column that is the mean of the axis one columns, the axis two columns, and that's then our centroid. And then I'll add an ungroup to the end of that. And again, at the end here, that output, we see the two centroid columns. So I will save this as star. And you know what, so that we can save this, I'm going to go ahead and bring this down below. And I'm going to save Schubert and MDS as centroid. And then I'm going to copy down my code so that so that when I put it up in the notes, it's easier for you to see what's going on. Okay. And so again, what we want to do then is plot a line segment from the centroid out to each point to do this. Again, we're not going to use centroid, we are going to be using, when I didn't copy everything I needed, I also need that GG plot line. So I'm going to change some things. So instead of metadata and MDS, I'm going to put star. So I'm going to add a third geome actually, which will be geome segment. And what geome segment needs is it needs x and y and also needs x and and y and so x and will be centroid one. And then y and will be centroid two. And I'll go ahead and put these on different lines. So it's easier for you all to see. And let's go ahead and see what this does. I'm kind of curious. We get an error. All right, so I'm getting an error of centroid one not found. And what that is coming from is this geome point where I'm plotting the centroid. And so I need to also add in here inherit dot AES equals false, because what it's trying to do is it's trying to find centroid one and centroid two in the centroid data frame, which only has axis one and axis two. So I think we're in good shape. Now, let's go ahead and run this. And so yeah, that looks pretty trippy. You can't see the squares in there. Not a big deal. Again, that looks pretty wild. It's a little bit distracting to have all that going on. I think what I've seen this be perhaps a bit more effective is when you have fewer points. At the same time, you know, I don't know that you really need the lines. I'm going to show you another way that we can perhaps think about grouping these variables together. We might also think about removing those points at the tips. And so we could, we could do that by commenting out geome point around the whole thing. And so now we have the stars without the exterior points, we also don't have those centroids showing up very clearly in here. We could see where they are. Actually, you know what, we've got the segments on top of the points. So let's go ahead and change the order here and see if that makes it easier to see. So we can kind of see that we have those there. What happened if we make the color of that centroid black? So let's remove color equals disease stat from there. And go ahead and do color equals black. And one thing to remember is that if it's in the mapping, then we're mapping the data from a column in the data frame to some aesthetic or again, pre attentive attribute. If it's outside of it, then everything is going to get the same treatment, right? So the three centroids will all have the same color. They'll all have the same size and the same shape. And the black black isn't doing it for me. Maybe we could make it the same color as the lines, but make the border black. And so let's let's see if we did fill equals disease stat that. And then we've got the default colors. So one thing we might want to do is copy that down and then make this scale fill manual. And then that gives you kind of a clear square for where the centroid is. Again, I'm not totally sold that I like this visualization. I think it's kind of busy with all those spikes going around. And I think what we're going to come to is the benefit of perhaps drawing an ellipse around the three different clouds of points. So let's give that a shot. I think this is useful. And what again, I think this might be a little bit more effective when you have fewer points. And perhaps, you know, it's not it's not so noisy. It doesn't look like fireworks going off here. So the next plot that I want to make with you, the third plot, I'm going to go ahead and copy from what we had earlier. Bring that down. And again, we've got our metadata and MDS and to remind you what that looked like, this was with our three centroid points. I'm actually going to remove those centroid points to get a clean ordination like so. And we're good. Let me go ahead and copy down a gg save. And I'm going to call this ellipse. And what we're going to do is we're going to draw ellipses around the three different clouds of points. How do we do this? It's actually not so bad. So what you can do is stat ellipse, and add that to the gg plot flow. Let's go ahead and give that a run and see what it looks like. And I will open this. And what you see is, well, big ellipses around our points. And one of the things I don't totally like about this is that the ellipse is much larger than the cloud of points. So this red ellipse goes way out beyond where the points are. Now, the ellipse is actually a statistical transformation. That's why it's stat ellipse. It's assuming a normal distribution in the data to draw these ellipses. Our data are not normally distributed. I really just want the ellipse to make it look pretty. So what I can perhaps do instead, one of the arguments is type, and the default is norm. That gives you the normal distribution. You can also do a t for a t distribution. That actually I think will get you maybe wider tail. So it'll give you a larger ellipse. Let's stick with with norm, the default. And what you can do is level. And so this ellipse is drawn is a 95% confidence interval. So let's do say 0.7. Because again, I'm not interested in the statistical transformation so much, I want kind of a general idea of where the points are. And that's maybe that's better and that it brings it in and constrains it a bit. Let's maybe make it a little bit larger with like, say, 0.8. Give that a shot. I think that let's let's split the difference and do 0.75. So I think that looks okay. Again, I'm not going for some precise fitting of the data. There's something called a convex hole. And so maybe in a future episode we'll talk about building those. And that's something you can easily do in a package called ggforce. But for now, let's work with pure tidyverse. And those ellipses I think look fairly decent. What I'd like to do is shade the ellipse. So fill the ellipse, but put it behind the points. So how do we do that? Well, if we want to put it behind the points, well, we have to put it behind or in front of geome point, we want to add geome equals polygon. All right. And so that then fills the ellipse with this dark color, which is not what I want.  And so then we get the default color schemes, which again, isn't what I want. But like we saw earlier, when we were making those those centroids where we had a different fill from the the border the color, I'm going to do scale fill manual. And this then will give us ellipses that are the same color as the points, which isn't exactly what I want. So what I'm going to do is I'm going to pick colors that are actually a muted version of those colors that I already use. So instead of gray, I'll use light gray. Set of blue, I'll use Dodger blue. Instead of red, I'll use pink. And so again, this gives us ellipses that for the most part encompass all of our points. These are opaque ellipses. And so you can't see what's behind it. What we could do is up here in stat ellipse, we could go ahead and add alpha equals 0.2. That then gives us an ellipse that's shaded but it's not so intrusive, right? And I think this does a pretty nice job of showing the general distribution of the data. One thing I am noticing is this legend again has a box to indicate the geometry for a polygon. We can remove that as we saw earlier. Do you remember what we used? Yell it so I can hear you. Just show legend equals false. And that will then give us back our legend as we had it before with those three points. Perhaps what we might do is kind of fill that with white and then that looks pretty decent, right? And so I think that looks fairly attractive. Tell me what you think down below in the comments. I think this is a good way of thinking about having ellipses to again bring that pre-attentive attribute to group our data so it's easier for our audience to see what's going on. If we had more time, something that I might do is kind of get rid of this legend entirely and maybe over here in red text put C to facility positive over here in blue text diarrhea and down here in gray text healthy, right? That way I don't have to be going back and forth trying to figure out what color corresponds to each variable. I know where it is. So maybe again that's something that we can tackle in a future episode or maybe you can use some, you know, some of your Google skills to see if you can figure out how you would do that. All right, so I like this plot. There's one other thing that I want to try that is a little bit different. So again, I'm going to go ahead and copy this ggplot chunk down and I'm going to get rid of stat ellipse. And so let's again see what this all looks like. And we need to add our gg save line. And I will call this density. And so we've got what we've been working with. And I can also get rid of the scale fill manual. So I hope you can also see that so much of building plots is can be doesn't have to be but can be taking something you've done before copying it, pasting it, modifying it and seeing if you like it better. Anyway, so what I will add then is geome and then density 2d. And what this should do is create a density map for each of the three variables or three diagnosis groups on our ordination. So let's see what this looks like. And wow, that looks pretty wild. But I think you can kind of see it, right? Like the the gray points are focused right around here, which is kind of about where our centroids were. Our red points are clustered more up kind of at like 11 o'clock. If you think of this as like a clock face. And then our our blue, our diaries are more towards like one or one o'clock or so on the face. And so I kind of like this. I feel guilty saying that. But think I need to turn off the show legend here. And then I also need to get rid of the fill. And then that gives us an interesting plot, right? What happens if we turn off the points? I mean, I think I need the points. I think there's something called Xeno graphics, which is data visualizations that are very different from what you're used to. I think this is kind of up there. Normally you would have a density plot, I think of kind of one variable. So I think if I were to present this, this might be hard to really justify, perhaps it would be better with like even more points. But I don't know, I don't know that what I really feel about this, it's kind of out there might be something that would be cool to plot and hang on my wall. Who knows? Anyway, there's also a geom density 2d fill filled. Let's see what that looks like. Yeah, and I think it's only doing one variable. It's not doing two variables. So anyway, let's go ahead and go back to geom density 2d and add the geom point. And so something else we might think about doing with this actually might be turning off the points. So depending on your audience, you may or may not want to confuse them with the points, right? So if it's going for a scientific publication, I think you should show all the points. If it's something that you're showing for more of a lay audience, then maybe you turn off the points. And you can kind of show that, you know, healthy people have a community structure that's very different from people with diarrhea and C. difficile. But it appears that people with C diff are a little bit different than people with diarrhea. So how would we do that? We would obviously we could come back up here. We could turn off geom point and and see this, right? This is almost kind of a cartoon example of what the visualization might look like. And I'm going to go ahead and put those back in there and and call it good. And again, I'm pretty happy with how this looks. And I don't know, tell me what you think of the ellipses. Sometimes I feel like they're a bit of chart junk. You know, sometimes I'll see the ellipses around like four points. And it's kind of like, really, do I need an ellipse to tell me that those four points are all grouped together? I think that's kind of obvious. But I think in something like this, where you have more points, it is helpful to have the ellipses to make it easier to understand what's going on. Again, let me know what you think down below in the notes. And anyway, I hope this has been helpful showing you four different ways that again, we can think about pre attentive attributes of grouping our data. And if you will, drawing a lasso around our points to make it more clear to our audience, what's going on in the data and what we ultimately want them to see. Now, are the diarrhea samples and the are the are the diarrhea samples in blue, and the C difficile samples in red significantly different? I don't know. I'm going to assume that the healthies are, but we'll save that for another episode where we think about using vegan, another are packaged to test the significance of that. Anyway, see if you can give this a shot with your own data. It doesn't have to be ordination data. You could also do it with any type of scatter plot data. Again, drawing these ellipses to help group our data to make it instantly clear to our audience what they should be focusing on and what comparisons they should be making in their in their analysis. And of course, we want to make things easier for our audience, because they're going to be much more likely to keep reading our paper and being engaged and ultimately, you know, getting the most out of our work. All right. Well, that's enough for today. Hope you found this interesting. Please practice with this. Check out the code that I'm posting and tweak it and kind of modify it to make it your own. And again, see if you can apply it to your own data to see if you can draw ellipses or contour maps or whatever around your data. Anyway, keep practicing and we'll see you next time for another episode of Code Club.


Hello! Hi, I'm Ethel from Zambia. I'm Zalimbe from Zambia. In case you're wondering, it's Zimimbe.  Yeah, Costa Rica. Yeah, very far from here. Very far. 23 hours inside. And so far it's been a great experience to be here because we've learned a lot from other people. Seeing that we are facing the same problems in Zambia, I hope we'll have a lot of solutions. Yes, yes. So far I have attended a session on Negotiating for Success, which is very wonderful. I didn't know that much about negotiating, so I feel very empowered right now. And right now we're going to attend another session, volunteer. Hopefully you've attended the hackathon. Yeah. But the fact that I don't have to speak tonight, I think the hackathon is in my mind. Yeah, I'm supporting her in case you're wondering. I'm supporting her 100%. Yeah, I'll bring her a blanket. Okay, thank you.


Go! Six, all the way down. Forty-seven, forty-eight, forty-nine, five, four, five, seven, nine, four, nine, six, seven, sixty-one, sixty-three, forty-four, forty-one, forty-one, watch the crowd, watch the crowd.


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In the realm of content creation, originality and consistency are your keys to success. Now let's delve into the profitable world of e-commerce and online selling. E-commerce or electronic commerce is a business model that involves transactions taking place on the internet. These transactions can be between businesses, between businesses and consumers, or between consumers themselves. This model has revolutionized the way we shop, sell, and do business, opening up a world of opportunities for anyone with an internet connection. One of the most popular forms of e-commerce is online selling, which is essentially retailing over the internet. This can be done through established platforms like Amazon, eBay, and Etsy, or through individual websites. These platforms allow sellers to reach a global audience 24 hours a day, seven days a week. Imagine having your store open around the clock without worrying about rent, utilities, or staff salaries. That's the beauty of online selling. But how do you get started? First, you need to choose what you're going to sell. This is a critical step as your product choice will influence many other aspects of your business. Whether it's handmade crafts, vintage items, or high-tech gadgets, make sure it's something you're passionate about and that there's a market for. Once you've picked your product, it's time to list it on your chosen platform. This is where you need to consider things like pricing, shipping, and most importantly, customer service. Remember, in the online world, your customers can't physically touch or see your product. They rely on your descriptions, photos, and reviews. Therefore, good customer service is paramount. It's not just about answering queries and handling complaints. It's about creating an experience for your customers that's so good, they not only come back for more, but also recommend you to others. In e-commerce, your product and your customer service are your brand ambassadors. They represent you in the digital marketplace, so make sure they're top-notch and watch your online business thrive. In the next scene, we'll explore another exciting avenue for making money online, online tutoring and coaching. Stay tuned. Ever thought about sharing your knowledge online? Let's talk about online tutoring and coaching. It's a realm brimming with opportunities. With the digital wave taking over, the demand for online education is on a high tide.  There's a universe of platforms, from Coursera to Udemy, where you can offer your services. These platforms are the digital classrooms of the 21st century, where knowledge transcends geographical boundaries. They allow you to create courses, conduct live sessions, and interact with students from all around the globe. But remember, the key to success in this domain is not just what you know, but how well you can communicate it. It's about making complex concepts accessible, sparking curiosity, and fostering a love for learning. In the world of online tutoring, your knowledge and your ability to communicate effectively are your strengths. Lastly, let's take a quick look at online surveys and micro tasks. These are simple, small tasks or surveys you can complete online, often on platforms like Swagbucks or Amazon's Mechanical Turk. The tasks can range from answering a quick questionnaire to identifying images. While the pay for individual tasks can be small, it's the collective effort that can add up over time. Whether you have a few spare minutes or a few spare hours, your time can be turned into earnings. Remember, every little bit counts when it comes to online surveys and micro tasks. So, are you ready to dive into the world of making money online in 2024? We've traversed through an array of avenues that can lead you to the treasure trove of online income. To recap, we started with freelancing your skills. Remember, everyone has a skill that someone else needs.  We then dived into the realm of content creation and sharing. Yes, it's the age of the creators, and it's time for you to shine. Whether you choose to create engaging videos, write captivating blog posts, or create killer podcasts, the internet is your oyster. E-commerce and online selling was our next stop. With the digital marketplace booming, there's never been a better time to sell your products or services online. Whether it's handmade crafts, digital products, or even your old stuff, the online marketplace is a goldmine waiting to be tapped. Our journey then took us to the world of online tutoring and coaching. Share your knowledge, be it academic, fitness, music, or any other field, and earn while you learn. The world is eager to learn and you could be the one to teach them. Finally, we explored online surveys and micro tasks. Although they might not make you a millionaire overnight, these tasks can provide a steady income stream and are perfect for those who have a few spare minutes throughout the day. Each of these avenues offers a unique path to financial freedom. The key is to explore multiple avenues, find what resonates with you, and go for it wholeheartedly. Remember, the only limit to your success is your imagination and willingness to put in the effort. So go ahead, pick a method that resonates with you and start your journey towards making money online in 2024. Don't forget to like, share, and subscribe for more content like this.


You're very good with public speaking, I can tell. I can only imagine times he was in Meshika, how much he got. But I get you a story, it's really nice. Tell me, when was the first time you interacted with drugs? And which was the first drug you interacted with? It was in high school, I was in Form 3, and it was marijuana. Marijuana? The first drug you have interacted with was marijuana. Mwutu, nili yona, nili kaingi atua po ibo. Nani nani, nili kufanya ukai choma. Jusia ti... Seia ti uliteme ukai yokata?  Ya, I found my friend an Isanif in class. Kamu liza ini ni. Kania ambia, em, jamaa. Bukitaku jesu ni pati jio ni pali ibo, njumaya cho. Kama, miya sawa, nakuja. Miya went, path one. Ayosiku un kakana kani, mechizi kido go. Bakini siku chizi, thank God. Then the jamia kutumiwa matrikanza. So when you start one, the cravings, imoja yunona yizi kutoshileza, you go to another one. So from weed, ni kind of to, I think, called tobacco. Chavi su komtaan, indivu ni itua. I went to Jabba. And zikana tu ibo ziki yongezeka. Ziki yongezeka. After Jabba, what kind of? After Jabba, koni ingi ni itua wada. Wada. So I use those things. And to my life, they were not paving way. They were not paving the way. Alright, did you graduate high school? Yeah, I graduated. You graduated high school? Yeah, I graduated. Still under the influence? Still under the influence. Nika tokau kunje, households are making my own money. Now I can buy what I want. You can buy or you can get any high that you want? You can get any high that I want. Alright, so what was this high for? Nika kuno atu se mi na get high ndo, ni fusa ushida zangu. Ukuno atu na se mi na get high. Ndo ni kwe creative mi na get high ndo. Budango ni pay attention. Nde utushaski aizo adi kenya saizim hefika. Yako ilkwanini? Yango fostajas ilikwa fofan. Yeah. Ukianza? Ukianza ilikwa fofan. Alright. Is it any fun apu mbele? Yako na fun apu ilkwa. Alright. Ukianza ulianza fofan. Apu mbele ilikwa na tanina kwanini. What goes through your mind? Inakwa dikwamba. Inakwa kitwingine you can't get away from. You can't do without it. Azini mingi ya kwa you have bloodstreams. Nika ma food. Well, you can't do without food. So na yu kitwiko tuweva. You can't do without it. You can't do without it. Alright. You journey with drugs. What was the first drug you interacted with? Yango pili kwandom. An experience yango almost similar to his in the sense that he ends up in high school. So from one at was in a private school. Uko kututu barbi barbi. Urui li kwa kuhepachu. No na butru kamaizu. Fom 3 nikenda high. Now high was a public school day at day school. Na uko sondun kapata na wa rui wenyewe. And now in the private school we didn't, atukwa in, I had never known, drum in a kaji. And so me, mse tufla ni uko. Nimi mse mwenye. Actually, kwa 8 na yoni lingia fom 3. And it was just the year after post-election violence. So after that, first time, kwa na mada rumingi mamode wa ko. So that was this guy, kwa ito Booster. So he's got an EoG na 2 neza pa. Booster? I hope you're watching. And if you know Booster, we won't say his ID names. But okay. Booster ni aline introduced kwa kwa ile weed. And that very first feeling yani li have ya kuget high, kwangu li kwa neza semel kwa scary. Siku juani ni li happenu afternoon. Kwa itul kwa ile kufika ibi madaro ni kenda, mi li kwa 3G kuski ni mienda R. Nani kwa Blake yadi 5. So li kwa experience ni mi li stuka toz like Issa, isi kitu neza kwa kwa engaging. So the rest of high school siku kwa iru di historia drugs enimo. Ili kujatena sasa Kampo now in 2010 joined a hospitality school. And there yo time pia ni kwa on manza. Ile social drink ni ya. Ile kujibamba on weekends.  You wait for a fright. But now what ile kitu li fanya tip over from the social drinking to now irresponsible drinking was a broken relationship. Ya. So this something So kuna triggers zai zivit. Ya, there are triggers. Kilamsa kuna jani ya kia drug. Ya, exactly. And so for me yo kusabu koleli kwa nmengiana zile Haif mingi sana. So kilamsa kwa nani jua. Wase wengi wengiana Haif mingi sana. Ulemsa kwa nama highlights sana juli kana ulemsa ma swag ni kuna. Sasa vibe ya kwa reputation ya kwa ina change na kwa ulemsa li achuana de mi ya. So yo kitu li affect estimi yangu. And that's how now got into drinking to like forget my problems. Da kuna swa li tumuli zachats na nkata katu piya ni nichangi that road. Nandi nanzangas fun and I'll tell you because mi na kumuka niki ingiakampo atatukuna rumetwangu tuli kwa na tissues ina fana na one of the alcohol brands. So kila time tu na doku drink tu na dungaisu tisho so it was fun namazili, ili kwa fun but none. Na stories asuboim na checka nini mlejimamba. And that time again we see it was kind of responsible drinking muadara sana so ina it was fun na niki kitu eniata Personali I'll be okay to tell my folks lewa by there to na bash flanitumenda nini nini because at the end of the day mi ngejuwa it's responsible na kuna kitu udu tu ngepania.  Lakin ni the road apuchi mi. Yo salumuli za nini. That's the main thing kitu na faongelia. U kumbele u kumbele mi disaster. Alright so how do you know na juwaji sayi nisha pita speed flan? Okay one thing for me one indicator was that na bestewako na bestewako na para tu vele mkuivi msa na kunyo na kunyo mi na kunyo na tu na pata ma bestewako anza kumbele mi tuwango maze maze kuna vele punguza tu kiyati siyati ni maho li jo na pata siyati ni waso kuku judge no that they're judging you pa tu na kari fiyako kumbele maze ni kunguze mi fiyati na e pia na kunyo e pia anona e e e e e e e e e e anangali e pia na na maze ume poteza simungapin e e e for example ume poteza simungapin in like one man mi tukama iso e de aros kuizia tu na anza pata ma supplement na anza pata tu bestewako anza kwa de ma science too waja waja alafu piya home na juwaji mi tukamingi zineza penoma bestewako a juwibari home una pata kuna iso na anza korofishana na na maparo ni tu zundu iso science ni anona tu mazo when it was responsible drinking you were conscious a uta ceilewa home unona kuna fula okorada to take care of your behavior of your behavior Of your theater Ya, your image as well Ya, ya your self as price and your body and your image All right so apu umbele na pia in terms of your choice of drinks na juwaji PPP taking care of image na juwapuna zile brings zine utaji yeka class, na ni nina punyo anga ii lage ni kuadiksho Niki tiyo yote, niki na kami kama Okay, so, we'll answer We'll answer you in some place, right? Yeah All right, wukachi wana tu Maina shuka, maina shuka Maina shuka, maina shuka So those are some of the signs you know that you're going in the wrong path All right Now you start mixing the drugs Exactly Okay What makes somebody One of the one thing about addiction nia tiyo na fukanga pwenda piata asa deliver neza Shuka pwenti addiction umekwa emtinda niya kuh So hauna esteem you don't feel yani Let me even speak about it myself Niki pwenti yani nimi upatu kama zombie yani nona So the more high the better The more things you experiment the better for you Times ingine una inga tu pwazogo yu upigu at least at least kusikia, kusikia, nimi tu nona Because by the time we were addicted kwa so atakani story So the number one umekwa mse not use food As a friend, you don't trust me So I'm not nimi simse monineza I can't count on you I can't count on you I can't trust you with my money I can't trust you with my stuff Ukotuna mimi tu kosho Sili wu trust na ki nia ki yangi All right So it's going to happen All right I'd like to know kwa ii jani ako ya addiction Elifika je point ukasema Wao it's too much I need to find help Na uli jambia manin se aliku pelika Well it was a process kwa zwene started nili kwa nafanyama job job So I was making money moving out of home and now starting experiencing life out there But now the kahae life it comes a circuit in me because the more you're using these things the more you're not also productive And the money becomes less when you're less productive Yeah and the money becomes less the more you're less productive So I did the jobs in Ajipata na fanya It is hard work but less pay Say na bidi wu sweat for this little cash na bidi wu sweat na bidi wu sweat na bidi wu sweat na bidi wu sweat na bidi wu sweat na bidi wu sweat What happened it's my mom ya li nili liya sana So after I've shifted from home Ya nili kwa na ishina I love sasin mehama So I'm not experiencing life out there So after after ya li kwa na follow up na yangu li kwa na tumabbro kana kumkana snoop kana kumkana unae umjama ae wu kwa sa wu unae umjama ae umjama kumkana kumkana kumkana kumkana kumkana but it was good it was good for them because it helped me a lot big time So when mom now heard that my life is at rock bottom ya kwa unaa pana So she came kana nipata ni kwa jobo zili na jumuni meva man kwa na expect ni kwa nikoivo kwa ni kwa job but kia tu ina check you can't even afford kwa na do job gani kwa do job sato hoteli to small scale businesses So for you to get the high for me to get the high I'm not even getting food for my own nili eto na eza kulipa rent na kina broa kwa na stay 2 pwa na mother kwa na stay 2 feet na mother na nini na fanya nini na fanya nini na fanya In forata ayibu ndi on the better you feel some sü herbs ya ya feel sü herbs man Nana homes a kufanya welcome How are these people passiving you stepo Back to you my brother more tea kwa nini, it teping Lisa so gives mother so how do you Miofiluza kwa mikiwa hindi na ito ngaumanya ni. Chigiwa na naumanya na watapa na, kusia kwa wikwini wikiwa kwa higiwa. Obama za na Heroic Bird, na naka kwa hiri kwa weiwa ahawe. Laza na mikiwa hindi alexa na tanaka ni. Kwani wa maikunani, amatiwa mikiwa hindi, hindi na rika wa mikiwa kwa wikiwa. goto kwaita na Self-Destruction and Extremity flown fasa K 29 Kautre diarrhea, sdakin areWhast you never have thought of drinking and why was ntilzhunt ion wouz groups Ya, unona. So kuhu hab maikuliku kwa wiki wiku ma Ohio. Uza, kwa kiko ni pati doia, kung finanza j Omega ya k kloutini ku kwa wiki your kwa zia. Kwa hiwa ku basi yu kiliku unona. Kupiki wiko kika wiki wiko kwa ayu kwa kabisa yonole. Una jia kwenya na yu ua kwa yu kya omio wiki wiki yu kue mave. na kwa napuk'u na kwenanda, katia ni nashih entrini nama. So, ni kwa chuwa, kwa kwenanda, kwa kwenanda kiwa gand liaison, ya kwa ni nisimula. Baby, takio yatao yatao ito, na wendi, kwa kwenanda kenida, ya kwa kwenanda. She muzumia.  Lafiza mkwa kwa kwa kwa sewa nili yukua Itowe nukua stabbed 7 martyrs Ya e mahi yukua kwa normal Ngee ni ni kwa 2 wiki leita nikiwa mahi Kwa miskini kiwe moka kwana yukufu ni mendo Kuma suko naimu moka niti ni ni injury ni mikwe sewa nili 9wiki ni nikiwa behi yukuhu 2 wiki ni nilikuu kua nili nili 2 weeks nili kwa home ya nili kwa 2 tormenting kuka home siyongenam tu yo time da etiangu kuna boiflani stone kwa momba toaster ya kwa fikriya nomba 2 yosubu emojiya nili rudishi bati nili tumiya, shangoji ya masi ya nili rudishi sada eti li kwa tumu kate bibi, bibi na toaster after yo seisho ningini handom, azisayo nili kwa 2 da etiangu for like 2 weeks and so one day waswa kwa ni kamia kwa hao na pili kwa canceling ilu kwa subu e kabisa kambi ue relax, ue yata kitando standi kwa yata brekos kunyutu na rudi 2 sai sai so that was my dad and later 2a seiflani kwa hao wakani ambiya na enda canceling and by that time I was reducing like 2 psychologists emojiya li kwa kenyata wengine high ridge so na shangai canceling ni nini and one of these doctors at the community taji estoria rehab esa yo asubu e na shangai so you guys were lucky to have parents who cared about you na waka come back for you guys when you guys hit rock bottom e ome sama point 2 shu wakabisa ili lavi yawazazi waski kabisa hapa ewe leo ni world international day against draga buz what is triumf ministry doing to commemorate this day okay on this day we are just kind of doing an online campaign just an awareness because you and ome put forward a message which is better knowledge for better care so for us just giving out some 2 information to which we think can be helpful since triumf ministry was birthed that has been a main foundation of triumf ministry what message are you giving to the people the people who are watching right now and maybe are dealing with drug addiction ama bradha kia kuna akua addicted to drugs bia kwa shemdo bradha kia kia kuna connected atana bradha kia what message could you give to a drug addict wepia na message to a drug addict wepia na message to family wana ishina drug addict transe na wep my message to you i tell you can we in this world like christ through his drug god's love he gave us his son that we may have a life eternal same atana he disease because we term this drug as a disease to do away with it has to have medicine yakuza idea for me my medicine was christ and the love of family so me ta kusho upendo onesha that guy upendo it seems you talked to the people who are living with the drug addict so you will talk to the drug addicts as we wrap this up to the drug addict there is a short film i did in it what they did not tell me I am just talking about our influencers or maybe those role models onesha love da kukunya kusho ma I just want to tell you your life in a little distraction we are here like me kukam kuni ambiaizi story but for me it's a privilege that I'm here to just share my life the thing is in life there are two paths you can just choose the path of sobriety but it's a more sure way you are guaranteed certain promises in life and there is the path onesha chukua drugs it can be fun for one year two years probably for ten years but you never know and so many of you can encourage to stay triumphant, focus on your goals that's the best thing you can do social media social media is at Triumph NGO which is on Instagram, Twitter and Facebook personal liya kwa siniswewe nimseni chakchali Facebook chakchali Twitter chakchali Instagram films akwa tapata VP it's still on the YouTube channel now you can search Triumph Limited Triumph Limited is where we do our film productions both on YouTube and on Vimeo alright, thank you very much for taking your time to come have this conversation with us I never even knew today is world what again? international drugs thank you very much for celebrating this with us and remember true power comes from you can control over yourself you can control come and you can control you have not attained true power yet true power inakam kwa kujikontrol women nyewe na kujijua let's head straight to akumashu break then we'll be back with some more of social Friday


Now, Senate President Ahmed Lawan has constituted a 13-member adult committee to investigate oil theft in Nigeria and its impact on petroleum production and oil revenues. Lawan announced on the floor at the plenary that Senator Basi Abbot Akman is the chairman of the committee, while members on the panel include Senators Yusuf A. Yusuf, Salomon Alamele Kondiola, Kabiru Gaya, Mohamed Adamu Aleru, George Thompson, Sekibo, and Gabriel Susan, among others. The adult committee was given one month to conclude work on investigations and report to the chamber in plenary. The following is the membership of the committee. Senator Basi Abbot Akman, chairman, Senator Yusuf A. Yusuf, Salomon Alamele Kondiola, Kabiru Gaya, Mohamed Adamu Aleru, George Thompson, Sekibo, Gabriel Susan, Kashim Shatima, Sabi Abdullah, Stella Odua, and Sanu Musa member and Senator Ibrahim Gwabir member. This committee has a month to bring us the report on how the oil theft is going on, how we can mitigate it, and of course the resolutions that will help stop this. Please do subscribe to our YouTube channel and don't forget to hit the notification button so you get notified about fresh news updates.


Hello everyone, my name is Danny and I'm here for the British Library, so thank you for coming to join us. Now I want to have a look and see who's watching from home, so I'm going to take out my binoculars. I'm going to have a look and see, I can see Soha. I can see Adam and Sam and Max and Amina and Wahid and Maya and Tolu. I can see you. So thank you for coming to join me. Now today we're going to have a fairy tale adventure and on the British Library website there are links to some collections for fairy tales. Also there'll be some links for some craft activities that maybe afterwards you can get a bit crafty. I'll tell you more about those at the end. For now let's get ready to have a story adventure. We need to warm ourselves up. So let's do some claps. Can you give your hands a bit of a rub? Can you show me a fast clap? Can you show me a slow clap? Like you're in slow motion. Fantastic. Can you show me a nose clap? And now can you make up your own clap? You might clap on a different part of your body. You might do a marshmallow clap. I don't mind. Any idea is good. And I'll watch. Ready to show me your clap? One, two, three, show me. Those claps, they're fantastic. Well done you. Now we've warmed up. Let's jump inside our story. Now our story comes from a fairy tale. It might be a fairy tale you know. But what we've done is we've shaken it up, shaken it up. We've turned it on its side. So we've changed it. You might know some of the characters but it's a little bit different. So look out for the differences. Are we ready? Let's go. Put on your listening ears. Wiggle those eyes. And let's begin the story. This is a story about a little girl who loved to wear red. In fact she liked it so much they called her Little Red. One day she got a riding hood and her name became Little Red Riding Hood. That's right. So our story is about her and a wolf. Not a scary wolf but a nice wolf. A hungry wolf. One day Little Red Riding Hood was in the woods by her house playing with her friends. There was mouse. Squeaks. Let me hear mouse. Good. There was fox. And let me see fox sniffing. They used to say I love honey. Honey so much that he made his own from the bees buzzing around the trees in the wood. But he made too much to eat so he would give it to his friends. One day.  I made it myself. You can take it home to your family. Little Red Riding Hood was so delighted. She took the honey and as she was taking it home she heard a call. Little Red, Little Red.  Little Red Riding Hood ran home and when she got there she heard some sad news. Her grandma was poorly achoo achoo achoo.  I wonder what you would put in a basket to cheer someone up. I wonder. Little Red Riding Hood looked around. She found a banana. Grandma's favourite.  And oh just the other day Little Red Riding Hood had drawn a picture of her and her grandma. So that went in too. There was space for just one more thing. Hmm the honey bears honey. Oh she could imagine that grandma would find that very yummy. So in it went. And off went Little Red Riding Hood through the wood. Her family telling her be careful of that wolf.  Little Red said she would. As she walked along she waved to her friends. Mouse squeak squeak. And fox. And bear. What does bear say? I love honey. As she walked past her animal friends someone was listening. As she told them that she was on her way to her grandma's house on the other side of the wood. Wolf was hungry. Wolf loved the sound. The smell of Little Red Riding Hood's basket. And wolf wanted some of that. So he began to run as fast as he could to the other side of the wood where grandma lived. Wolf got there. The door was already open. He pushed it open with his paw and stepped inside. There was no sign of Granny but he could hear the shower running. She must be in the bathroom. He carried on looking trying to find something, anything to eat when through the wood came the sound of singing. It was Little Red Riding Hood and she got to the house and she pushed open the door and crept inside. Granny! Oh Grandma! Had leapt into action. Scared that Little Red Riding Hood would be scared of him. He jumped into Grandma's bed and put on her hat to try to disguise himself. Little Red Riding Hood went over to Granny and He's you have, she said. Oh the better to see you with my dear, said wolf. Granny! The better to hear you with my dear. Red Riding Hood thought that the wolf was going to jump up and eat her. He instead jumped out of bed, jumped on her voice. At that moment Granny came in from the shower and she began to scream. She began to run around the kitchen. It was very loud and very crazy until everything stopped.  So Little Red Riding Hood asked wolf and Granny to sit around the table and they decided to share what was in the basket. They shared the banana. For me, here we are wolf, some nice banana. Delicious. They shared the bread, Granny, for you. There we are. And for you there, here we are wolf. Delicious. And they shared bears sticky honey. And they had a lovely afternoon sharing food.  It wasn't really scary at all. It was just a little hungry. And that brings us to the end of a slightly different Little Red Riding Hood. Thank you for being brilliant listeners and brilliant helpers. Thank you for helping me with the story. Now maybe you'd like to make up your own fairytale. Maybe you'd like to follow our craft and make yourself a riding hood or maybe a wolf mask. Maybe you can have your own picnic, maybe with your toys. Whatever you do, we hope you have a wonderful time using your imagination. Thank you for listening. And me and everyone at the British Library hope you are well and we hope to see you again soon. Goodbye. Give me a wave. Bye-bye.