I have this extremely bad habit of writing 10 lines of code before shelling out the rest of the day learning how to improve these lines in R.  Yes, I learn a lot through the process, but graduate students are responsible for more than just learning. Sometimes, there are those that actually have no interest in what I have learned but only my results.

This idea is nothing new to me, but this post is an attempt to enforce a turning of a new leaf. It was ridiculous for me to waste the previous hour intending to optimize TWO lines of code! I cannot and will not do this anymore. Those two lines of code were not important enough to waste that much time.  Before my bedtime, my whiteboard may be plastered with a repetition of “I will refactor my code later and not now.”  Unless the code is being published, procrastinated optimization is far better than procrastinated results.

Am I alone in this endeavor?

Let’s take a look at an example. First, we start with generating a 3 x 3 identity matrix and assigning the matrix to the variable, mat.

[code]
mat = diag( 3 )
[/code]

By typing mat, we can see the output.

     [,1] [,2] [,3]
[1,]    1    0    0
[2,]    0    1    0
[3,]    0    0    1

Next, we are going to convert this matrix to a data frame called mat_dataframe and output it.

[code] mat_dataframe = data.frame( mat )[/code]

  X1 X2 X3
1  1  0  0
2  0  1  0
3  0  0  1

Notice that the column names are X1, X2, and X3 and that the row names are 1, 2, and 3. Say we want to add more columns and rows to our data frame. Let’s first start by appending a row to the “mat_dataframe.”  We do this with rbind.

[code]
mat_dataframe = rbind(mat_dataframe, c(2,2,2))
[/code]

We have added a vector of twos to the next row of the data frame.  Here’s what mat_dataframe looks like so far.

  X1 X2 X3
1  1  0  0
2  0  1  0
3  0  0  1
4  2  2  2

Now, we should try appending 2 columns to the mat_dataframe using 2 different methods. The first line will create a new data frame from the original data frame and append a column called “City” with “Dallas” as the entry for each row. The second takes this data frame and adds another column called Color with entries blue and green.

[code]
mat_dataframe = data.frame( mat_dataframe, City="Dallas" )
mat_dataframe = cbind( mat_dataframe, Color=c( "blue", "green" ) )
[/code]

Now, the mat_dataframe looks like this.

  X1 X2 X3   City Color
1  1  0  0 Dallas  blue
2  0  1  0 Dallas green
3  0  0  1 Dallas  blue
4  2  2  2 Dallas green

Notice that once blue and green were both used, they were both repeated. Before we move on, let me mention a gotcha when adding columns. On the City column, I simply inserted Dallas for each row, but under the Color column, I added 2 different colors. What happens if we specify three values? Let’s try this with a new column called Country.

[code]
mat_dataframe = data.frame( mat_dataframe, Country=c( "USA", "Canada", "Mexico" ) )
[/code]

We get the following error…

Error in data.frame(mat_dataframe, Country = c(“USA”, “Canada”, “Mexico”)) : arguments imply differing number of rows: 4, 3

A rule of thumb: make sure the number of values being assigned divides into the number of rows (or columns) of the data frame. If our data frame had 6 rows (or 9 or 12 or … ), we could have used the above code.

Our data frame is essentially a matrix with a couple of attached column vectors containing strings. This may not seem very useful at first, but it is a wonderful data structure, making some statistical methods among other things easier to use. Soon, I will post a basic ANOVA example using data frames.