sjmisc: Data and Variable Transformation Functions

New functions

• has_na() to check if variables or observations in a data frame contain NA, NaN or Inf values. Convenient shortcuts for this function are complete_cases(), incomplete_cases(), complete_vars() and incomplete_vars().
• total_mean() to compute the overall mean of all values from all columns in a data frame.
• prcn() to convert numeric scalars between 0 and 1 into a character-percentage value.
• numeric_to_factor() to convert numeric variables into factors, using associated value labels as factor levels.

Changes to functions

• set_na() now also replaces different values per variable into NA.
• Changed behaviour of row_sums() and missing values. row_sums() gets a n-argument and now computes row sums if a row has at least n non-missing values.