Gender earnings gap among younger workers

Now, let's see what happens when we focus on workers in the early stages of their careers. First, we need to load the data. Because the March $2009$ extract is contained in an $.$xIsx file, we will use the read$\_$xlsx function provided by the package. Load the package and read the file, which is named $cps09$mar.$xlsx.(Youneedtogetthisrighttomoveon,$sowewillcheckyourcodehere.$)$

R Code ~~ start over

there is no package called 'readx$1\text{'}$

Next, let's filter down to workers who are between $25-34$ years old. This will allow us to focus on individuals who $($in all likelihood$)$ have completed their educations but are still in the early stage of their careers. To do this we will need the filter function from the dplyr package. To parallel the analysis covered in the class slide deck, we will create a gender indicator using the mutate function from dplyr$.$ We could easily do both things in one chunk but we will split them up$.$

The filtering action will take the original data set, cps$09$mar, filter on the age restriction, and create a new data set containing only the younger workers. The syntax goes like this:

newdataset olddataset $\%>\%$

filter$($age

age $-..--)$

As an example, if we want filter out people younger than $25($i$.$e$.,$ keep people who at least $25),$ the filter command should look like this:

newdataset olddataset $\%>\%$

filter$($age $>=25)$

Note that we have used $\%>\%,$ or the "pipe", to pass the data to the filter function. The pipe is very useful because it allows you to code a sequence of operations that is easy to read and interpret.

Now, use this sample code to complete the filtering operation to create a new data set called containing only the younger workers.