So, now that you know a little bit more about the notebook, I want to just take this time to show you this document. So, for chapter four, just a demonstration of what is possible. What you're going to see here is what we are going to recreate at the end of this course, you'll be able to do all of this. So, what I've done here, just create some simulated data, and show you what a research project report would look like in my unit. So, imagine this was data from a project, I'm going to import it, analyze it, and show you how I present this in our research group. So, have a look at this. I've put in chapter four there at the top just because this is part of the course and a nice little title there. But what I would usually have is the following. I would have a title here or subtitle. This that would say, "Research questions." It will say something like this: "The research projects considered data from an Intensive Care Unit admissions for patients with cellulitis and necrotizing fasciitis." Now, if you go online and you search for these terms, you might come across quite a few published articles that did exactly this. So, I read a few articles that inspired me and I simulated some data and I created this notebook. So, something that was actually published right here in this document. What I'm trying to see here, at the end of this course, you will be able to create a document that will have analysis in it, from which you can write a manuscript which you can submit to a journal for publication. At the end of this course, you will have enough knowledge to do that. There are actual published journal articles out there that used nothing more than the fundamental data analysis for the scores. So, let's have a look. I would get something like this, and then I would say, "The main research question was, is there difference between in-hospital mortality for these two groups?" So, we'll have a group of ICU patients with cellulitis and a group with necrotizing fasciitis, and we're just going to look at the mortality rates, and is there a statistically significant difference in the mortality rates? There'll be secondary research questions, and we'll look at the differences between these two groups for the following: the age, the body mass index, presence of serious chronic illness, the Referral Unit from which they came, that they've septic shock, that they need a mechanical ventilation, what was the admission white cell count, what was their admission lactate, admission CRP and the length of stay. So, you can see here there's a mix of numerical data variables and categorical data variables. So, a nice, concise representation there of our research projects that we're putting out our main research question. We're putting out the secondary research questions nicely in the document. Everyone who sees this document that's involved in the research can see that, know what this is about. We'll go about importing our data. Our data is going to live in a spreadsheet and we'll import as such. We'll view the first patients data entry like that. We can view the first ten patients data entry like that just so that we can see that everything imported correctly. We're going to create lists. We're going to extract some values from our dataset. We're going to save that inside of our computer's memory so we can use that for data analysis, and then we're good to go. I'm going to write some lines of code just to so that we can see how many patients they were. You can see here in the yellow, I've done my analysis, but in the yellow actually write the normal English sentence. I said, "There were 56, that's 47 percent, patients with necrotizing fasciitis and 64, that's 53 percent of the patients with cellulitis. So, necrotizing fasciitis and the cellulitis, the actual values there, and I got that from just writing a line of code to extract that for me, and I write another two lines of code, and that gave me the percentages. So, here we go, we'll see the main research question is the difference in mortality. I do some analysis. I can see how many there were in each. I create the contingency table. So, this is a chi-square test for independence, and I see my chi-squared value there and my p-value. Now, I write a line of code here, I've colored it in green so when we go through the documents, we can immediately see these green ones were no statistical significant difference between these two groups. I write it there in normal English. So, a total number of 38, that's 31.7% of patients succumb to their disease, that's related to 18 deaths for those with necrotizing fasciitis, and 20 deaths for those with cellulitis. There was therefore no significant difference in mortality between these two groups, chi-squared, 0.01, p-value, 0.92. So, beautiful there in green. The data analysis is there. The report clearly states there in normal English which I put in my document just as normal text. We'll go to the secondary questions. I'll do some data analysis. We'll see the mean. We'll see the minimum and the maximums there. I'll draw a nice little chart there so we can see a box-and-whisker chart, and I'll do a t-test, a student's t-test, and we'll see the t-statistic come out there and the p value. Once again, I put that in green here because it was not statistically significant. We can see the overall average age there was 48.3 years with the youngest patient and oldest patient, and when we break down for the two groups, and eventually, this was not significantly different from the cellulitis group with an average of 50.4 years and p-value 0.16, all from the data analysis. So, I could save this as a nice plot if I want that in my manuscript. Everyone who reads this report can just read this normal English sentence if they don't want to see all of this. So, you can see this computational essay come together, and it's a fantastic report. We go on to body mass. If it was a significant result, I always put that in red. So, I changed the background to red. Remember, if I am in that cell, let's select that cell. I can go up to format, and I can say "format background." I can choose a color, and you can see it was light red. As simple as that. Chronic illness, this was a categorical variable, we'll do a chi-square test for independence, and then we'll see our chi-squared value and our p-value for that. That constitutes if I go down, this whole computational essay, we can even see a nice little table there. Absolutely fantastic. We did our research. Now, remember this is simulated data, I just used actual published articles. This is an inspiration for this. So, I simulated some data and I save that in a spreadsheet, imported that spreadsheet, and we created this document that I can work from, that I can share with my colleagues those are collaborating with me in the research group, with whom I'm going to publish this if this was real data. Just a beautiful document, beautiful environment. At the end of this course, you'll be able to create all of this no problems whatsoever. So, I really hope you're looking forward to creating your own documents such as this doing your own data analysis.
