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this is data manipulation using the player package in r so the first thing that we need to do is to set our working directory in this case i have set mine to a location where i have saved my data so to run this command then it will be able now to set my location as you can see from the console down here the command has run without any error next we need to load the library and we are going to make good use of the 3d first package the beauty about this package is that it comes in with other several sub packages for instance we have the ggplot which is normally used for data visualization we have the read r package which is used to read data into r we have the deployer package which is used to conduct data manipulation and several other packages that you can see which are different functionalities now just in case you don't have this package installed then you'll need to use the dot packages function to this package and then later load it using the library function now when you load this function you'll be able to see in your console a window resembling this one now loading the library that has enabled me to load the required packages now we want to import data into r and we are making good use of read underscore csv function this is a function which is in read our package so in this function we need to specify the name of the data and this name should correspond to the name that you had used to save your data in your computer then followed by this option this will tell us that the first rows in your data are column names then you also need to give a name to your data running this command will now bring in the data into r as you can see we have the data now imported and the column names are given as follows with their respective data types for instance report date is a double data type or card date is a character data type now continuing let's say that we want to select variables that are of interest to us or before that let's say that you need to view this data and see whether we really imported the correct data and then you need to use the view function now as you can see this is now the data frame that we have imported and you can now validate or ascertain whether it was really the data that you intended to import now say that we want to select some variables because at times you'll find that in a data set that you've imported it might contain several variables and some of those variables are not of interest to your analysis so we need to just select those variables that we want to do our analysis with to do that we need to use the select function this is a function in the deployer package now inside this function you need to specify the name of the data set and then followed by those variable names that you are interested in and remember to put the names with the codes running this command will now enable us to select those variables that we are interested in we can view and see whether that selection was done as you can see from the data frame the number of variables now have been reduced that means that we have been able to select the variables that we are interested in let's say again that we need to rename variables say for example some of these variable names are not as good as they look for instance crime subcategory we need to rename it as maybe category primary offense description we need to name it as just description to do that we will need to use again the select function but now incorporating the assignment operator so we use the select function specify the name of the data and then now use the assignment operator to rename these variables for instance we have renamed reported date to date reported sub i mean crime subcategory to just category we run this command and we will be able to see that the names have been renamed we can view and see that so you can see that the names have actually been renamed category spinner names description has been renamed now let's say again that we need to filter rows for instance say because this data is uh i mean it it contains information of crimes committed in some locations now let's say we only need to have this data containing crimes committed in the neighborhood of queen anne to do that you will need to filter our data and we will be using the filter function which is a function in the deploy package so in this function we need to specify the name of the data and then followed by now the variable name that we need to filter our data with for instance here we are filtering our data using the neighborhood variable beside that we only need those crimes which were committed in the queen and neighborhood you can run this command and you will see that the command is run correctly then we can view and see if we have filtered our data you can see from the data frame that in the variable of neighborhood neighborhood it has been filtered to those crimes which were committed only in the queen and neighborhood continuing let's say or before that we can as well filter our data not only by one variable but by more than one variable let's say again we need to filter our data and looking at this variable called the category we only need those crimes which are of buccalary nature to do that what we need to add we need to use the filter function specify the name of the data and then now the name forward as queen and as we had done earlier and then add the category variable again and specify the type of the crime and then we can run this command and we'll be able to see that the function has already filtered you can see from the data frame that we have filtered our data now based on the bucklery uh nature of the crime and then also based on queen and neighborhood now let's say again that we need to create new variables in our data and in this case we are creating new variables based on the existing variables to do that we will need to use the mutate function the mutate function is a function in the deployer package which will allow us to create new variables based on the existing variables now i have also included here a library called lubridate this will allow me to manipulate date variables now looking at the data we have a variable called date reported now we need to create a new variable called here and that variable will just have to take the year of which the crime was committed and not the old date just the year one thing again to note is that this date variable is of character type so we need to transform this date variable into date type to do that we will need to use as date function as date function now to transform this variable called date reported into date type and then now specify the format to which now the date will take and then now this year function from the from the library function i mean library will be able now to pick the year from this variable and then now the mutate function will be able to create a new variable called ear and add it to our crime data now running this command it does run without any error then we can see whether we have created a new variable you can see that at the far end we have not created a new variable called here and this variable has only picked the year in the date reported variable next let's say that you want to conduct some summary statistics and you want to conduct based on some certain variables for instance say you want only to count maybe crimes committed per year so you'll need to group your data uh based on the year variable and do that summary statistics so what you need to do you will need to use the group by function this will be able to group your data based on a specified variable so in this group by function you need to specify the name of your data and also the variable name that you would want your data to be grouped by now running this command will be able now to group our data now next now you need to do a summary of your data and depending on the kind of the statistic that you want it could be one mean variance or any other summary statistic but in our case we have chosen to maybe half count we need counts of crimes committed by and that one we are using the end function the end function is going to count those creates crime rates based on the year so running this command the command has run correctly then we can now view and see whether we did the summary correctly as you can see from the data frame here the group by function was able to group our data based on the year and then now we have used the summary function to count the number of crimes committed per year that is the end of this discussion hope it was helpful