# Investigate and clean the data set sample.trial # Use the function, str( ) to list: # the class of sample.trial # the number of observations and variables # the variable names and types # The variables gender and treatment should be factors # (categorical variables). If these variables are not factors, # please change both variables to factors. # Use the str() function to check that the changes have been # made correctly. # List the first 11 lines of the data set # Summarize the data set using summary( ) ?summary() # Convert the numeric variable age into the factor age.groups # using the function cut( ) and the following # breaks: 17, 25, 39, 60 # Assign the new variable, age.groups, to the data frame, sample.trial # NOTE: Review Lecture 6 Class Notes - # Converting continuous variables to categorical variables ?cut() # Check: Print the first 11 observations # Create a subset of males only and assign this subset to the # object, males ?subset() # Use the object males to create a frequency table for the # variable, age.group
| age | gender | treatment |
| 26 | F | A |
| 32 | F | B |
| 18 | M | B |
| 29 | F | A |
| 35 | F | B |
| 35 | M | A |
| 38 | F | B |
| 55 | M | B |
| 56 | M | A |
| 34 | F | A |
| 22 | M | A |
| 22 | F | A |
| 23 | F | B |
| 35 | F | B |
| 34 | F | A |
| 22 | F | B |
| 34 | M | B |
| 56 | F | A |
| 59 | F | B |
| 29 | M | A |
| 45 | F | A |
| 43 | F | B |
| 33 | F | B |
| 23 | M | A |
| 49 | F | A |
| 51 | F | A |
| 23 | F | B |
| 38 | F | A |
| 34 | M | B |
| 19 | F | A |
| 39 | F | B |
| 40 | M | B |
# Investigate and clean the data set sample.trial # Use the function, str( ) to list: # the class of sample.trial # the number of observations and variables # the variable names and types # The variables gender and treatment should be factors # (categorical variables). If these variables are not factors, # please change both variables to factors. # Use the str() function to check that the changes have been # made correctly. # List the first 11 lines of the data set # Summarize the data set using summary( ) ?summary() # Convert the numeric variable age into the factor age.groups # using the function cut( ) and the following # breaks: 17, 25, 39, 60 # Assign the new variable, age.groups, to the data frame, sample.trial # NOTE: Review Lecture 6 Class Notes - # Converting continuous variables to categorical variables ?cut() # Check: Print the first 11 observations # Create a subset of males only and assign this subset to the # object, males ?subset() # Use the object males to create a frequency table for the # variable, age.group
To investigate and clean the data set sample.trial, we can follow these steps:
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