#Codebook in r code#

This works, so lets apply it to the real data set: This is an easy way to check that you have closed all functions, useful for debugging problems. If you place your cursor after it, the corresponding opening parentheses will be highlighted. ) this final parentheses closes the mutate function.Note that NA refers to missing data and is not in quotes.levels=c('None','Beginner', 'Intermediate', 'Expert', NA )) set the level names and orders to these.factor(qr_experience, make a factor from “qr_experience”.Note: we could have done mutate(qr_experience) and overwritten our column, but if we create a new variable, we can compare them side by side (for demonstration purposes).qr_experience2 = we will create a new variable “qr_experience2” equal to the next command.mutate( means we are going to make a change.select(qr_experience) %>% means we are only going to keep this variable in our data (to make demonstration easier), and.recoded % tells R to use the “rsurvey” data object and….Now our factors are in the correct order and if we wanted to do some statistical test, it would be much more accurate. Recoded %>% count(qr_experience2) # qr_experience2 n mat=TRUE) - Show a matrix in the output instead of a more complicated list.group=age$qr_experience - The grouping variable.describeBy(age$age, - The numeric variable.2020-rsurvey$qyear_born - Subtract rsurvey$qyear_born from the current year, 2020.rsurvey$age # trimmed mad min max range skew kurtosis #create an age variable rsurvey $age <- 2020 -rsurvey $qyear_born #Describe experience by age describeBy(rsurvey $age, group=rsurvey $qr_experience, mat= TRUE) # item group1 vars n mean sd median If you just want to examine a single variable, you can use the $ operator: The first 10 numbers are all missing ( NA). For example, “Beginner” might be 1 or 2 or 3.