 we wanted to plot. Maybe we just want to focus on September 8th. And so before we can get into that, we need to create a new column. So if we create new columns in data frames, we just need to specify it within quotes and square brackets. And in particular, we're going to use the PD on to date time. And you can see it's telling me to click it. And what this does is it takes hours or it takes dates, and it takes time and combines them into a single date time object. And so in order to do that, we need to provide it what our date is. And then we tell it quote space quote. And then we tell it what our time is. And that space just allows the date time object to not have not have the time running against the date, but to space it out. And so if we look at the top five rows, we can now see that we've got this date time object, which is a combination of the date and the time. And additionally, if we look at the data types of our new data set, we can now see that date time is this special type of data called a date time object, which means that we can use it in Boolean or conditional statements to find dates that are greater than our date of interest or less than. And so to go ahead and get that data, we can create a new variable called solar subset in which we want the solar date time that is greater than 2021 September 08. And and we want similar data in which the date time is less than the ninth. And so what this will do is it will extract the data that is above the September 8 at midnight. But before September 9 the midnight, so we'll effectively get all 24 hours of September 8. And then we don't really need all of the subset. So I'm going to extract a few columns. So we want, we still want all the rules, but we really only want, well to show you, we can really just extract date time and produced kilowatt hour. And so I forgot this print statement here. And so there is how we could extract just the date time and just the produced kilowatt hour. But it's not necessary because we can always just call out specific values within our ggplot. So this is what our new data set looks like and we can see that now it's just September 8. So then we can create our line plot. So we can say ggplot with the subset of data because we're just plotting the 8th at this point. And we can say geomline aes x equals date time as before and y equals produced kilowatt hour. And so here we can see that this is looking a lot better. We can see that this is the early hours of September 8. This is the late hours. We can see that it's spiked sometime in the middle of the day as solar power is likely to do. But we can improve how this looks. I'm just going to copy it, paste it down here. And we can use the theme command as we did with the box plots early on to actually change this axis. So we can say axis text x. And now before we said element blank because we were removing it, but in this case we're changing it. So we say element underscore text and we tell it what we're changing. And so we can just change the angle to 90. And so now we can see that this is a little bit more readable and we can see that this spike happens. It hasn't attached the time to it so it's not terribly useful because it's cut off that little bit. But we could assume that if the time was here it would be an even better representation.