Introduction to 'dataMojo

In dataMojo: Reshape Data Table

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)

Built on the top of 'data.table', 'dataMojo' is a grammar of data manipulation with 'data.table', providing a consistent a series of utility functions that help you solve the most common data manipulation challenges:

• Calculate the row wise percentage
• Calculate the survey type percentage table
• Select columns
• Split one column to multiple columns based on patterns
• Filter cases based on their values
• Fill missing values
• Summarize and reduces multiple values down to a single summary
• Reshape long to wide or wide to long

Calculate the row wise percentage

Calculate the row wise percentage of a frequency table

library(dataMojo)
library(data.table)
test_df <- data.frame(
      Group = c("A", "B", "C"),
      Female = c(2,3,5),
      Male = c(10,11, 13)
    )
print(test_df)
dataMojo::row_percent_convert(test_df, cols_rowsum = c("Female", "Male"))

Calculate the survey type percentage table for single question

library(dataMojo)
library(data.table)
   test_dt <- data.table::data.table(
      Question = c(rep("Good", 3), rep("OK", 3), rep("Bad", 3)),
      Gender = c(rep("F", 4), rep("M", 5))
    )
   print(test_dt)
   dataMojo::pivot_percent_at(test_dt, 
                                 question_col = "Question", aggregated_by_cols = "Gender")

Calcuate the survey type percentage table for multiple question

library(dataMojo)
library(data.table)
test_dt <- data.table(
      Question1 = c(rep("Good", 3), rep("OK", 3), rep("Bad", 3)),
      Question2 = c(rep("Good", 2), rep("OK", 2), rep("Bad", 5)),
      Gender = c(rep("F", 4), rep("M", 5))
    )
print(test_dt)
dataMojo::pivot_percent_at_multi(test_dt, 
                                    question_col = c("Question1","Question2") , aggregated_by_cols = "Gender") 

Calculate the column wise percentage with desired numerator and denominator

This function is to calculate column-wise percentage in a new column with desired numerator columns and denominator columns. If denominator is 0, the percentage will be N/A.

library(dataMojo)
test_df <- data.frame(
      hc1 = c(2, 0, 1, 5, 6, 7, 10),
      hc2 = c(1, 0, 10, 12, 4, 1, 9 ),
      total = c(10, 2, 0, 39, 23, 27, 30)
    )
print(test_df)
dataMojo::col_cal_percent(test_df, 
                          new_col_name = "hc_percentage", 
                          numerator_cols = c("hc1", "hc2"), 
                          denominator_cols = "total"
                          ) 

Select columns

Select variables in a data table. You can also use predicate functions like is.numeric to select variables based on their properties (e.g. 1:3 selects the first column to the third column).

library(dataMojo)
library(data.table)
data("dt_dates")
dt_dates <- setDT(dt_dates)
dataMojo::select_cols(dt_dates, c("Start_Date", "Full_name"))

Split a column

Split a column with its special pattern, and assign to multiple columns respectively. For example, split full name column to first name and last name column.

data("dt_dates")
library(data.table)
data("dt_dates")
dataMojo::str_split_col(dt_dates,
              by_col = "Full_name",
              by_pattern = ", ",
              match_to_names = c("First Name", "Last Name"))

Filter cases based on values

filter_all() is to return a data table with ALL columns (greater than/ less than/ equal to) a desired value.

data("dt_values")
dataMojo::filter_all(dt_values, operator = "l", .2)

filter_any() is to return a data table with ANY columns (greater than/ less than/ equal to) a desired value.

data("dt_values")
dataMojo::filter_any(dt_values, operator = "l", .1)

Similarly, filter_all_at() is to return a data table with ALL selected columns (greater than/ less than/ equal to) a desired value.

data("dt_values")
dataMojo::filter_all_at(dt_values, operator = "l", .1, c("A1", "A2"))

Similarly, filter_any_at() is to return a data table with ANY selected columns (greater than/ less than/ equal to) a desired value.

data("dt_values")
dataMojo::filter_any_at(dt_values, operator = "l", .1, c("A1", "A2"))

Fill missing values

fill_NA_with() will fill NA value with a desired value in the selected columns. If fill_cols is All (same columns type), it will apply to the whole data table.

data("dt_missing")
dataMojo::fill_NA_with(dt_missing, fill_cols = c("Full_name"), fill_value = "pending")

Group by and summarize

dt_group_by() is to group by desired columns and summarize rows within groups.

data("dt_groups")
print(head(dt_groups))

Now we see the dt_groups data table has A1, A2 as numeric columns, and group1, group2 as group infomation.

data("dt_groups")
dataMojo::dt_group_by(dt_groups, 
            group_by_cols = c("group1", "group2"), 
            summarize_at = "A1", 
            operation = "mean")

Now we want to group by group1 and group2, then fetch the first within each group, we can use get_row_group_by() function.

data("dt_groups")
dataMojo::get_row_group_by(dt_groups, 
                 group_by_cols = c("group1", "group2"), 
                 fetch_row = "first")

or last row with same example.

data("dt_groups")
dataMojo::get_row_group_by(dt_groups, 
                 group_by_cols = c("group1", "group2"), 
                 fetch_row = "last")

Reshape long to wide or wide to long

Here is an example of reshaping a data table from wide to long.

data("dt_dates")
print(head(dt_dates))
dataMojo::reshape_longer(dt_dates, 
               keep_cols = "Full_name", 
               label_cols = c("Date_Type"), 
               value_cols = "Exact_date")

Here is an example of reshaping a data table from long to wide.

data("dt_long")
print(head(dt_long))
dataMojo::reshape_wider(dt_long, 
              keep_cols = c("Full_name"), 
              col_label = c("Date_Type"), 
              col_value = "Exact_date")

Advanced Topic: expand row based on pattern

row_expand_pattern() is to expand rows based on a desired column.

data("starwars_simple")
starwars_simple[]
row_expand_pattern(starwars_simple, "films", ", ", "film")[]

Advanced Topic: expand row given start and end dates

row_expand_dates() is to expand rows to each date given start and end dates.

dt_dates_simple <- data.table(
  Start_Date = as.Date(c("2020-02-03", "2020-03-01") ),
  End_Date = as.Date(c("2020-02-05", "2020-03-02") ),
  group = c("A", "B")
)
dt_dates_simple[]
row_expand_dates(dt_dates_simple, "Start_Date", "End_Date", "Date")[]

dataMojo documentation built on March 7, 2023, 8:10 p.m.