 So let's import our data file The function that we're going to use is a retable and you see the parentheses there and the argument that it takes The argument takes the name of the comma separated file with a file extension for us. It was ccs.csv It's got to go in these quotation marks And because I'm not putting in the file path there It's got to be in the same folder in Julia box as this Julia file so I can use it directly I'm going to place the content of this data frame if I say retable Julia is going to convert a data frame retable is a function that's part of the data frames package So it's going to create this data frame from this comma separated value file And I'm going to place it in this computer variable that I'm just going to call DFDF for data frame You can call it whatever you want You see the semicolon after After this function that is just to suppress any output to the screen So we won't see the whole data set appear on screen So if I execute that it's going to take a bit just read that file of ours It's got quite a few lines of data entry there And it now lives in data frame and the type of data DF is a data frame now I can use this head function head It's also part of the data frames package and the argument that I pass is the name of my data frame Which is DF and it's just going to print to the screen the first few rows So if I do that there we have I can beautifully see on the left hand side here We have an index that is what the data frames package adds all on its own It's just an index so it knows what row it's talking about We see our patient ID there now We know on a separate piece of paper well away from our data collection What patient number 16 or who patient number 16 is it was never captured as part of our data and data analysis Category one remember a was for minor infections B for major infections category two there Remember, we had our little code our secret code for what refers to male what refers to female variable one was our age to which we added mentally a Value of five years and we've got a subtract five from each of these entries We had our variable two and variable three So if I just wanted to look at the first few rows I call the head function I could also call the tail function, which is going to give me the last few rows Another way just to look at these column headers Remember these will be my variables and the data point values for each of these variables It's just to call the show calls the show columns now look what that does There's a few things we can learn from this first of all We had six columns and then they are column one two three four five six And we see that they are we have a list of the names of those that was patient ID category one category two bar one bar two bar three a Good thing that it does here tells me what data type it finds in that column so patient ID was a 64 bit integer and category one was a utf 8 string a string in other words we automatically know that here we're going to deal with a Categorical data type and if we know it's a categorical data type that limits us to certain types of Proportionals that the skill analysis we can do for instance fishers exact test or using a chi-square test for For proportions there so string tells us what data type we have another string for cat one Remember that was male and female then we had 64 bit floats, which means it's decimal point values So we're talking numerical data here, and I know immediately I can do Use some parametric or non-parametric tests for numerical data types there. So that helps a lot in making Helping us in our decision. What's the test call tests we can do on that on the data points in that in that? Variable and missing is very important now We set up our data and it's good to set your data up that you never have any missing values But missing values do occur Julia handles missing values with the data type in a and it causes havoc you will not be able to calculate a mean of a List of numbers if one or two of them are missing and have an in a type in them There's a special way that we have to deal with that Fortunately for us here. We see we have no missing values in any of those columns of values So that is that's it. We've imported our CSV file and it lives as a data frame now And we had a good look at it in the next section. We're going to change all our coded values We're going to look at changing the variable names We had cat one cat two of our one two and three and changing those coded values into minor major infections and to male and female