Convert Multiple Choice Data to Numeric - r

Question

I have data that looks like this:
+-------------+------------+------------------+-------------------+------------------+
| gender | age | income | ate_string_cheese | tech_familiarity |
+-------------+------------+------------------+-------------------+------------------+
| A. Female | D. 45-54 | B. $50K - $80K | B. Once or twice | A. Low |
| A. Female | C. 35-44 | A. $35K - $49K | B. Once or twice | B. Medium |
| B. Male | B. 25-34 | B. 50k - 79,999 | B. Once or twice | C. High |
| A. Female | A. 18-24 | D. $100k - $149k | B. Once or twice | B. Medium |
+-------------+------------+------------------+-------------------+------------------+
I want to try to find correlations between different observations. I need the values to be numerical. I'm wondering if there's an easy way to do this in R?
To be clear the result from above would look like this:
+--------+-----+--------+-------------------+------------------+
| gender | age | income | ate_string_cheese | tech_familiarity |
+--------+-----+--------+-------------------+------------------+
| 1 | 4 | 2 | 2 | 1 |
| 1 | 3 | 1 | 2 | 2 |
| 2 | 2 | 2 | 2 | 3 |
| 1 | 1 | 4 | 2 | 2 |
+--------+-----+--------+-------------------+------------------+
I'm assuming there must be a package for this, but I can't find the Google incantation that will conjure it. Please know that I'm a complete statistic newbie who's just poking around. So if you prod me for more details, I likely won't have an educated answer to return.

Answer 1

To answer your question about converting categorical data into numerical data in R:
You can convert character data into factor using as.factor()
factor returns an object of class "factor" which has a set of integer codes the length of x with a "levels" attribute of mode character.
Pros:
This will encode your data numerically with an attribute that maps the character value for reference.
Factors can be ordered which can capture important information about ordinal data (such as age bands in your case)
Cons:
Beware converting categorical data into numeric for the purposes of performing statistical analysis on the data. The numerical values are probably not on the interval or ratio scale for all questions, so taking things like the mean or difference between levels may not make sense. e.g. consider if the distance between each level is actually constant, does it have a natural zero point etc.

Answer 2

You need to just extract first character, convert it to lowercase and map it with number:
# Your original data frame
df=read.table(text="gender;age;income;ate_string_cheese;tech_familiarity
A. Female;D.45-54;B.$50K - $80K;B.Once or twice;A.Low
A. Female;C.35-44;A.$35K - $49K;B.Once or twice;B. Medium
B. Male;B.25-34;B.50k - 79,999;B.Once or twice;C. High
A. Female;A. 18-24;D.$100k - $149k;B.Once or twice;B. Medium",header=T,sep=";")
myLetters <- letters[1:26]
# Apply match function to df, convert to lowercase and map it with number
sapply(df, function(x) match(tolower(gsub("([A-Za-z]+).*", "\\1", x)), myLetters))
Output:
gender age income ate_string_cheese tech_familiarity
[1,] 1 4 2 2 1
[2,] 1 3 1 2 2
[3,] 2 2 2 2 3
[4,] 1 1 4 2 2

Answer 3

You could trim the whitepace, and just grab the A,B,C,D parts and call factor on each column with level=LETTERS[1:4] and labels=1:4.
structure(factor(sub('\\..*','',trimws(as.matrix(df))),labels=1:4),.Dim=dim(df),dimnames=dimnames(df))
gender age income ate_string_cheese tech_familiarity
1 1 4 2 2 1
2 1 3 1 2 2
3 2 2 2 2 3
4 1 1 4 2 2
This is a matrix. You can convert to a dataframe

Answer 4

We can convert the columns to factor and coerce it to numeric
df[] <- lapply(df, function(x) as.integer(factor(x)))