Editing Statistics and Statistical Programming (Fall 2020)/pset2
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===PC5. Cleanup/tidy your data=== | ===PC5. Cleanup/tidy your data=== | ||
Once again, some cleanup and recoding is needed for this week's data. It turns out that the variables <code>i</code> and <code>j</code> are really dichotomous "true/false" variables that have been coded as 0 and 1 respectively in this dataset. Recode these columns as <code>logical</code> (i.e., "TRUE" or "FALSE" values). The variable <code>k</code> is really a categorical variable. Recode <code>k</code> as a factor and change the numbers so that they are replaced with the following values or levels: 0="none", 1="some", 2="lots", 3="all" | Once again, some cleanup and recoding is needed for this week's data. It turns out that the variables <code>i</code> and <code>j</code> are really dichotomous "true/false" variables that have been coded as 0 and 1 respectively in this dataset. Recode these columns as <code>logical</code> (i.e., "TRUE" or "FALSE" values). The variable <code>k</code> is really a categorical variable. Recode <code>k</code> as a factor and change the numbers so that they are replaced with the following values or levels: 0="none", 1="some", 2="lots", 3="all". The goal is to end up with a factor (so the command <code>class(k)</code> should return the value <code>TRUE</code>) where those text strings are the levels of the factor. | ||
===PC6. Calculate conditional summary statistics=== | ===PC6. Calculate conditional summary statistics=== |