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# $ gender "male", "male", "female", "male", "fema. # $ years_smoked NA, NA, NA, NA, NA, NA, NA, NA, NA, 26. # $ vital_status "dead", "dead", "dead", "alive", "alive. # $ tumor_stage "stage ia", "stage ib", "stage ib", "st. Smoke_complete %>% mutate( age_at_death = age_at_diagnosis + days_to_death) %>% glimpse() # Rows: 1,152 8.8.2 More about the Multiple Testing Problem.8.8 Analysis of Variance (ANOVA) (Optional).8.6 How Correlated are the Three Variables?.8.2.2 Googling is StandaRd pRactice foR eRrors.8.2.1 Understanding the difference between warnings and errors.7.5 Making your data long: pivot_longer().7 Part 5: Doing useful things with multiple tables.6.8 Other really useful forcats functions.6.5 fct_rev() - reversing the order of a factor.6.1 Making a factor variable out of disease.5.5 Standardizing variable names: clean_names().5.4.3 group_by()/summarize to calculate mean and standard deviation values.5.3.4 Using mutate to make a continuous variable categorical using case_when.5.3.3 Using mutate to make our character variables into factors.5.3.1 Using mutate to calculate a new variable based on other variables.5.3 mutate() - A confusing name, a powerful dplyr verb.5 Part 4: mutate(), group_by()/summarize().4.7.8 The difference between filter() and select().4.7.6 More about Comparison and Logical Operators.4.7.5 Filtering requires a little logic.4.7.3 Sorting Data Frames using arrange().4.4 Brief Aside: Categorical Data ( factors).4.3.2 Changing visual properties using built in themes.

4 Part 3: More Data Visualization and Data Manipulation.