Part 2- Quick Plotting for Simple Analysis

Where We Are:

• • We have some properly formatted data in the data frame "samples"

  • We are aware that Funk and its ilk is the most represented sampled genre

What To Do:

• • Condition our data a bit, and explore the data a bit further with some quick plotting

Analysis:

After our simple pie chart, its time to investigate a bit further with the qplot() function, allowing us to make some simple plots to see some clear results. First, using our structure "samples" and the summary() function from Part 1, you can see that the year of original publishing is stored as a factor, and includes unknowns as "?".

> 

        Sampled.Genre          Sampled.Artist

 Soul,Funk,R&B :3761   Honey Drippers : 117  

 Rock/Pop      :1068   Collins, Lyn   : 114  

 Jazz          : 898   Bliss, Melvin  :  94  

 Old School Rap: 173   Clinton, George:  82  

 Reggae        :  90   James, Bob     :  82  

 Soundtracks   :  88   J. B.'s, The   :  69  

 (Other)       : 388   (Other)        :5908  

                       Sampled.Song           Sampled.Album 

 Innocent 'till Proven Guilty: 118   single          : 671  

 Think (About It)            : 112   ?               : 258  

 Birth                       :  95   Think (About It): 114  

 Atomic Dog                  :  80   Computer Games  :  82  

 Nautilus                    :  62   One             :  82  

 Easier to Love              :  61   Food for Thought:  69  

 (Other)                     :5938   (Other)         :5190  

     Sampled.Label  Sampled.Publishing.Date         Sampling.Artist

 ?          : 277   1973   : 647            Beastie Boys    : 102  

 Capitol    : 227   1974   : 514            De la Soul      :  99  

 Warner Bros: 220   1972   : 486            Public Enemy    :  78  

 Atlantic   : 210   1970   : 403            Eric B and Rakim:  68  

 People     : 207   1971   : 397            Ice Cube        :  66  

 Columbia   : 151   1969   : 392            Big Daddy Kane  :  63  

 (Other)    :5174   (Other):3627            (Other)         :5990  

           Sampling.Song 

 ?                :  45  

 In/Flux          :  10  

 Move the Crowd   :  10  

 The Number Song  :  10  

 Jackin' for Beats:   9  

 Shake Your Rump  :   9  

 (Other)          :6373  

This limits the ways we can display and analyze the data. R has a Date class for calendar dates, but it's not really easy to use, and we have just years and not a full calendar date. Instead, converting to a numeric will be sufficient, as well as eliminating the entries with unknown years. We can quickly eliminate the unknown entries and save the resulting subset with the subset() function:

> definite.dates <- subset(samples,Sampled.Publishing.Date!="?")

Then, we can set the class for the publishing date to a numeric from a factor. First, to preserve the dates, we have to convert to the character class, and then into a numeric:

> definite.dates$Sampled.Publishing.Date = as.numeric(as.character(definite.dates$Sampled.Publishing.Date))

Now to the graphing!

Graphs

As the two smallest and most discreet information available currently are our newly numeric publishing year and genre, we can begin by creating a scatterplot of these two categories. However, because of the data's density, using the "jitter" geom and lowering the alpha can make our overlapping data points a little more readable:

    > qplot(x=Sampled.Publishing.Date, y=Sampled.Genre, data=definite.dates, color=Sampled.Genre, geom ="jitter", alpha = I(1/2))

We can right away see the Soul, Funk, and R&B predominance, and the years of original publishing matches up to times of their popularity and large scale release. Sampled Rap and Comedy also seems to be strongly associated with a few years of large releases post 'Rapper's Delight' in 1979, and Comedy with Eddie Murphy and Richard Pryor's big mid-1970's hits.

We can inspect these figures in more of a global context by creating a histogram, allowing us to compare genres to each other directly, as well as the whole population:

> qplot(Sampled.Publishing.Date, data=definite.dates, color=Sampled.Genre, fill=Sampled.Genre, geom ="histogram",binwidth=2)

The late 1970s peak is even more apparent in this graph, and you can observe that this is greatly contributed to by a large amount of Jazz samples as well. 

What's Next:

Unfortunately due to the nature of this data, it is difficult to portray much in the way of global information- a lot of the information, such as songs and artists, is extremely granular. Figuring out how to summarize and analyze that data would be an important step. In addition, if more data could be located about the sampling artists and songs, such as their publication dates and popularity, that would allow us to start drawing meaningful conclusions from this data set.