ACTL1101 Lecture Notes - Lecture 6: Margarine
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Understanding your data - variables - pages 339- 13 variables (columns) of 226 individuals (rows: qualitative: gender, situation and fat, ordinal: meat, fish, raw_fruit, cooked_fruit_veg and chocol, discrete quantitative: tea, coffee, continuous quantitative: height, weight and age. Frequency table - discrete variables - pages 343- ## butter duck isio4 margarine olive peanut repesseed. A similar table can be obtained using dplyr: sample_data %>% group_by(fat) %>% summarise(n = n()) Frequency table - continuous variables - page 344. For the continuous variable height", using dplyr functions (and function cut() to create intervals of height"), we get: sample_data %>% mutate(ints = cut(height, breaks= c(140,150, 160, 170, 180, 190), right = f)) %>% group_by(ints) %>% summarise(n = n()) Frequency table - joint observations - pages 344- For the paired observations of gender and situation: (mytable
