Tag: R

We are often showing participants different stimuli that are randomly ordered to prevent ordering effects. To randomize the stimuli we often used random.org’s list randomizer, but a current limitation of their site is you can only randomize a list once. So, for instance, if we were testing 4 stimuli with four people, we would need to use random.org to randomize that list four different times.

Here is some simple code to create a random list generator that will resample a list as many times as you want. You enter the number of items in your list, the number of times you want to draw down your entire list, and define the items in your list. The code below puts the list items into a single column and then prints the results to a CSV file. The example code has 4 items in the list and will randomize those 4 items 2 separate times. Sampling is done without replacement.

# Enter the number of items in your list
x <- 4

#Enter the number of times you want the list to run
r <- 2

#Add the items that are in your list. Add to or remove the number of items if necessary
mylist <- c('Item 1','Item 2','Item 3','Item 4')

#Below is the randomizer
randommatrix <- matrix(nrow=x,ncol=r)
for (i in 1:r)
{randommatrix[,i]=sample(mylist,x,replace=FALSE)}
y <- x*r
randommatrixlist <- matrix(randommatrix,nrow=y,ncol=1)
print(randommatrixlist)

#Optional: Print your randomization list to a CSV file. Delete the "#" to activate
write.csv(randommatrixlist,'randomlist.csv')

Continue reading “Create a random list generator in R that runs multiple times”

One of the bigger challenges I have had with R is getting output into a shareable format. There are helpful packages, such as gmodels, that can produce nice-looking contingency tables in the console, but getting the results out of the console and into Word is difficult. For example, gmodels can produce a table like this…

   Cell Contents
|-------------------------|
|                       N |
| Chi-square contribution |
|           N / Row Total |
|           N / Col Total |
|         N / Table Total |
|-------------------------|

 
Total Observations in Table:  248 

 
                 | infert$induced 
infert$education |         0 |         1 |         2 | Row Total | 
-----------------|-----------|-----------|-----------|-----------|
          0-5yrs |         4 |         2 |         6 |        12 | 
                 |     1.232 |     0.506 |     9.898 |           | 
                 |     0.333 |     0.167 |     0.500 |     0.048 | 
                 |     0.028 |     0.029 |     0.162 |           | 
                 |     0.016 |     0.008 |     0.024 |           | 
-----------------|-----------|-----------|-----------|-----------|
         6-11yrs |        78 |        27 |        15 |       120 | 
                 |     1.121 |     1.059 |     0.471 |           | 
                 |     0.650 |     0.225 |     0.125 |     0.484 | 
                 |     0.545 |     0.397 |     0.405 |           | 
                 |     0.315 |     0.109 |     0.060 |           | 
-----------------|-----------|-----------|-----------|-----------|
         12+ yrs |        61 |        39 |        16 |       116 | 
                 |     0.518 |     1.627 |     0.099 |           | 
                 |     0.526 |     0.336 |     0.138 |     0.468 | 
                 |     0.427 |     0.574 |     0.432 |           | 
                 |     0.246 |     0.157 |     0.065 |           | 
-----------------|-----------|-----------|-----------|-----------|
    Column Total |       143 |        68 |        37 |       248 | 
                 |     0.577 |     0.274 |     0.149 |           | 
-----------------|-----------|-----------|-----------|-----------|

…which is nice, but doesn’t work well for Word.

I found this wonderful solution as suggested by David Gohel in this forum comment post. (David is also the author of the ReporteRs package).

Continue reading “Create a formatted contingency table for Word using R”

Over the holidays I invested some time in learning R and RStudio, something I have wanted to do for some time now. The opportunity presented itself when needing to help a family member run chi-square tests, something more involved in Excel than I would have guessed (Excel requires you to manually calculate the expected values table). The experience learning R has been fun but there are occasional head-scratching frustrations. As I go through the learning process I’ll be posting here mainly as a reference for me so I can remember how I solved various problems, but hopefully it will provide help to others who have hit stumbling blocks.

One issues I had a problem with was with the barplot function. Take for example this code that contains 100 observations (we’ll say it’s the number of times a person visits a particular store in a month), produces a table from these data, and then creates a barplot from the table.

visits <- c(2, 2, 3, 7, 10, 3, 1, 0, 8, 7, 11, 14, 1, 3, 0, 8, 9, 4, 3, 3, 20, 9, 2, 5, 12, 3, 6, 1, 1, 2, 4, 2, 2, 3, 9, 13, 7 ,11, 15, 15, 19, 7, 8, 7, 6, 0, 6, 0, 1, 4, 2, 9, 0, 6, 12, 7, 6, 14, 5, 0, 4, 0, 0, 8, 0, 4, 4, 1, 3, 5, 6, 15, 1, 6, 13, 2, 1, 3, 5, 3, 19, 12, 3, 0, 7, 0, 2, 4, 2, 2, 2, 5, 1, 4, 3, 0, 6, 11, 0, 3)

visits.table <- table (visits)
barplot(visits.table)

This is the resulting chart.

At first I thought I had a nice looking bar chart, only to notice that, while the data had a maximum frequency of 13, the y-axis cut short at 12. Also when the x-axis legend numbers become double digits they become too wide, leading R to begin skipping every other one. (A low tech way to fix this is widen the plot window.)

Continue reading “Fixing a y-axis that is too short in barplot function in R”