R: dplyr - Removing Empty Rows

I’m still working my way through the exercises in Think Bayes and in Chapter 6 needed to do some cleaning of the data in a CSV file containing information about the Price is Right.

I downloaded the file using wget:

wget http://www.greenteapress.com/thinkbayes/showcases.2011.csv

And then loaded it into R and explored the first few rows using dplyr

library(dplyr)
df2011 = read.csv("~/projects/rLearning/showcases.2011.csv")

> df2011 %>% head(10)

           X Sep..19 Sep..20 Sep..21 Sep..22 Sep..23 Sep..26 Sep..27 Sep..28 Sep..29 Sep..30 Oct..3
1              5631K   5632K   5633K   5634K   5635K   5641K   5642K   5643K   5644K   5645K  5681K
2                                                                                                  
3 Showcase 1   50969   21901   32815   44432   24273   30554   20963   28941   25851   28800  37703
4 Showcase 2   45429   34061   53186   31428   22320   24337   41373   45437   41125   36319  38752
5                                                                                                  
...

As you can see, we have some empty rows which we want to get rid of to ease future processing. I couldn’t find an easy way to filter those out but what we can do instead is have empty columns converted to ‘NA’ and then filter those.

First we need to tell read.csv to treat empty columns as NA:

df2011 = read.csv("~/projects/rLearning/showcases.2011.csv", na.strings = c("", "NA"))

And now we can filter them out using na.omit:

df2011 = df2011 %>% na.omit()

> df2011  %>% head(5)
             X Sep..19 Sep..20 Sep..21 Sep..22 Sep..23 Sep..26 Sep..27 Sep..28 Sep..29 Sep..30 Oct..3
3   Showcase 1   50969   21901   32815   44432   24273   30554   20963   28941   25851   28800  37703
4   Showcase 2   45429   34061   53186   31428   22320   24337   41373   45437   41125   36319  38752
6        Bid 1   42000   14000   32000   27000   18750   27222   25000   35000   22500   21300  21567
7        Bid 2   34000   59900   45000   38000   23000   18525   32000   45000   32000   27500  23800
9 Difference 1    8969    7901     815   17432    5523    3332   -4037   -6059    3351    7500  16136
...

Much better!