Primary functions for data analysis:

• read_csv() : imports text files in CSV format into the R environment.

  • ex: read_csv("path/filename.csv")

• filter() : select only certain rows from a table.

  • ex: filter(text_column=="value") or filter(number_column > 0)

• select() : select only certain columns from a table.

  • ex: select(column1, column2, column6)

• group_by() : group the rows in a dataset based on the values in one or more columns.

  • ex: group_by(column) or group_by(column_a, column_b, …)

• summarise() : apply summary functions (see below) to the whole table, or to groups if group_by() is used.

  • ex: summarise(total = sum(number_column) or summarise(average = mean(number_column))

• arrange() : sort results based on one or more columns; ascending is the default, use desc() for descending.

  • ex: arrange(column) or arrange(desc(column))

• count() : group the data based on a column or columns and apply the n() summary function to those groups.

  • ex: count(text_column)

• mutate() : create a new column in a table, or overwrite an existing column with new information.

  • ex: mutate(new_column = number_column1 + number_column2)

Summary functions for analysis, used in summarise() or summarize():

• n() : count rows or observations (no arguments in the parentheses).
  • ex: summarise(num = n())
• n_distinct() : counts unique values in any column.
  • ex: summarise(employees = n_distinct(name))
• sum() : sum a numbers column. If NAs are present will return NA, unless na.rm=T is used.
  • ex: summarise(total = sum(number_column, na.rm=T))
• mean() : average a numbers column. If NAs are present will return NA, unless na.rm=T is used.
• median() : get the median of a numbers column. If NAs are present will return NA, unless na.rm=T is used.
• range() : get the highest and lowest values in a column. If NAs are present will return NA, unless na.rm=T is used. This is best used with reframe() instead of summarise():
  • ex: reframe(range(column))

Supplementary functions for manipulating data:

• clean_names() : from the janitor package, standardizes column names in a dataset to all lowercase, and replaces non-letter characters and spaces to underscores (_).
  • ex: tibble <- clean_names(tibble)
• is.na() : used within filter() to find NAs (NULLs).
  • find nulls: filter(is.na(column))
  • exclude nulls: filter(!is.na(column))
• as.numeric() : converts strings to numbers; will coerce non-numeric values into NA.
• as.character() : converts non-string values to strings.