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An easy way to check what order they appear in the data is to look what the unique continents are. This output tells us that Asia is the first continent that appears in the data meaning that the first row is an Asian country , and Europe is next meaning that the first non-Asian row is European. Notice how the order of the continents does not match the order of the levels in the case of gapminder.
If we run the same command on dejavu , we expect to see the same order of continents in the data since the data. Yup, just as we suspected! See how in the case of dejavu , the order in which the continents are seen in the data is the same as the levels order. So now we know that in order to make our two data. Remember that we often want to reorder factors for plotting, as we learned in hw Since that is the default when creating a new factor, we just need to convert continent to character and back again.
We were able to find all the differences between the original dataset and the written-to-files dataset. Some common tools are to use the output from all.
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Hopefully this can make you more comfortable in tackling a similar problem with different data in the future. How could we have eliminated all this faffing around? By deliberately sorting dejavu on country and only converting continent to factor after the aggregation was done. Always check or force things to be the way you need them to be. Lesson 2: it is often easiest to keep things as character for data munging THEN convert to factor.
As mentioned before, identical checks for exact identity, and often times will return FALSE even when two objects seem completely similar to you. So we know that all. Oh, I do see a difference! Alright, still not identical. No more easy answers. It looks like the pop and gdpPercap columns are the ones that are causing non-identity of the two data,frames. Those two columns are numeric - could it be that the floating-point number issue described above is the cause of this?
Is the difference tiny as we suspect? Yup, both are tiny differences that could be attributable to computer storage. There might be better ways, but this is what I can think of immediately. One potential problem you might run into when creating files with filenames based on data is that not all filenames are legal or desirable. One possibility is to simply strip spaces, but another solution would be to replace spaces with underscores. You can get even fancier and more advanced if you need. Explanation : Because of the way computers work and store information, only integers and fractions whose denominator is a power of 2 can be represented exactly.
As a result, two fractions that should be equal might not be equal in R, simply because different algorithms are used to compute them, so they may be rounded a little bit differently.
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As you can see, the difference is tiny. You can Google for this to learn more, or read this StackOverflow thread to see a nice detailed discussion that is tailored to R. Though, granted, there are many other reasons to hate on R if you so wish. According to the all. For example, all.
Another example is that all. As shown above, 1 - 0. But all. You can actually change the error tolerance of all.