{"id":1679,"date":"2013-12-14T17:41:28","date_gmt":"2013-12-14T17:41:28","guid":{"rendered":"http:\/\/memetechnology.org\/?p=2971"},"modified":"2013-12-14T17:41:28","modified_gmt":"2013-12-14T17:41:28","slug":"using-r-in-anger","status":"publish","type":"post","link":"https:\/\/generic.wordpress.soton.ac.uk\/archaeology\/2013\/12\/14\/using-r-in-anger\/","title":{"rendered":"Using R in anger"},"content":{"rendered":"

I’m expecting to be emailedl the link to my final exam in the Coursera statistics course this weekend, and by way of revision I’m using my own data in R for the first time.<\/p>\n

The first challenge is making sure my data is R “fit” – I entered it into Excel the first time, and though I made sure that even category data was entered numerically, I did some foolish things. The first of which was entering the data for the recommendation rating in catagories rather than just putting the raw score in. So I’ve just gone back to my original collected data and re-inputted the raw numbers.<\/p>\n

Then I needed to get the excel data into R. I like this post<\/a> on that subject, which starts off \u00a0with “don’t put it into excel in the first place.” It seems the quickest way is [Ctrl]+[C] copying the relevant data in Excel, then in R entering > gh <- read.table(“clipboard”) lets try that.<\/p>\n

Warning message:
\nIn read.table(“clipboard”) :
\nincomplete final line found by readTableHeader on ‘clipboard’<\/p><\/blockquote>\n

Oh dear, that doesn’t look good, what does the table look like in R?<\/p>\n

V1 V2 V3
\n1 gh <- read.table(“clipboard”)<\/p><\/blockquote>\n

Hmmm no. So then I use the not-lazy method, exporting the Excel sheet into a tab delimited text file. Then reading the table (remembering this time to add the argument “header = T”). Well it seems to work, but the last column (my carefully inputed actual values for recommend) isn’t there. Why? I still don’t know, having tried again and again, adding extra blank buffer columns and reordering columns. All I get is:<\/p>\n

Error in scan(file, what, nmax, sep, dec, quote, skip, nlines, na.strings, :
\nline 1 did not have 57 elements<\/p><\/blockquote>\n

So lets try a third<\/em> method, exporting the excel sheet to the csv (comma separated values) file and using\u00a0read.csv(“gh.csv”, header = T) rather than read.table.<\/p>\n

Aha success! Though some (but not all) of the blank fields come up “NA” rather than empty. I’ll work out if I need to worry about that later.<\/p>\n

Lesson one learned.\u00a0”Don’t put it into excel in the first place.”<\/p>\n

But now I have my data in R, I can at least have a look at it by using the describe() function (as long as I have loaded the package library(sm), which I have). The table that produces tells me that some of my category date is easily identifiable as such, the “Very enjoyable” five category Likert question is full of NAs (and NaNs – don’t know what that means). Same with the \u00a0categories describing the groups that the respondents visited with, and the weather. That’s fine, I don’t need to worry about those yet. More importantly the continuous numerical data has returned a number of descriptive statistics.<\/p>\n

I asked a number of questions scored on a seven point Likert scale, about how much respondents agreed with statements that: the experience added to their enjoyment of the visit, and added value to their visit; \u00a0their choices changed the story; they felt an emotional impact; it was a great story; they learned about the history of site; they were inspired to learn more; and, they enjoyed listening as a group. I also asked them to score on a ten point scale how likely they were to recommend the experience to friends and family.<\/p>\n

You could argue that being a Likert scale, this is categorical data, but I think there’s a strong case for arguing that it is continuous, especially if you collect the data with something like QuickTap survey<\/a>. That’s not the case with other Likert scale questions, like the one I asked based on a National Trust survey question about how much they enjoyed their visit. Those five options are clearly categorical.<\/p>\n

The describe function shows a number of summary statistics for all the data I collected. For each question I asked, I can see (among other things):<\/p>\n