qexplore

The goal of qexplore is to streamline the process of exploring and interrogating data. It allows users to quickly list rows of a data frame that meet certain criteria or tabulate data based on specific conditions.

There are two functions qlist and qtab and they allow you to quickly list or tabulate your data.

qlist is tidyverse compatiable as the input and output is a data.frame.
qtab is also compatible with tidyverse as the input is a data.frame and the output is a tabyl
This also has the unintended consequence that it can be combined with gt and flextable.

Installation

You can install qexplore from GitHub with:

# install.packages("remotes")
remotes::install_github("jarvisc1/qexplore")

qexplore is not currently available on CRAN.

Usage

Here are some examples of how to use qexplore. First we look at qlist and then qtab.

The real power of these commands comes from three arguments.

.if: this is like filter in dplyr, or the df[**here**, ] in base or data.table or the if option in Stata where the command would be written as list var1 if var2 == 1
.in: this allows you to select the rows that you’d like to look at very similar to the in option in Stata or filtering by rows with base or data.table.
.by: Only for qtab this allow for a 2x2 table to be printing several times by a third variable. Similar to doing a table() by three variables but I prefer this syntax as I think it’s cleaner. Note: using .by will result in the output being a list of tabyl so I doubt it will be compatible with gt or flex.table.

Listing Rows

Use qlist to filter and explore subsets of data:

library(qexplore)

# Example dataset
data <- data.frame(
  drink_type = c("Latte", "Espresso", "Cappuccino", "Americano"),
  size = c("Small", "Large", "Medium", "Large"),
  day_of_week = c("Mon", "Tue", "Wed", "Thu")
)

# List specific rows
data |>
  qlist(drink_type, size, day_of_week, .in = 1:2)
#>  drink_type        size day_of_week 
#>           1           2           3
#> 
#> ── List Details: ───────────────────────────────────────────────────────────────
#> Columns: drink_type, size, day_of_week
#> Filter: None
#> Rows 1:2
#>   drink_type  size day_of_week
#> 1      Latte Small         Mon
#> 2   Espresso Large         Tue

# Filter and list
data |>
  qlist(drink_type, size, .if = size == "Large")
#> drink_type       size 
#>          1          2
#> 
#> ── List Details: ───────────────────────────────────────────────────────────────
#> Columns: drink_type, size
#> Filter: size == "Large"
#> Rows: All
#>   drink_type  size
#> 1   Espresso Large
#> 2  Americano Large

Relation to tidyverse

qlist takes a dataframe as input and outputs a dataframe. Therefore it can be connected to other tidyverse functions.

library(qexplore)
library(dplyr)
#> 
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#> 
#>     filter, lag
#> The following objects are masked from 'package:base':
#> 
#>     intersect, setdiff, setequal, union

# Example dataset
data <- data.frame(
  drink_type = c("Latte", "Espresso", "Cappuccino", "Americano"),
  size = c("Small", "Large", "Medium", "Large"),
  day_of_week = c("Mon", "Tue", "Wed", "Thu")
)

# List specific rows
data |> 
  filter(size %in% c("Small", "Large")) |> 
  mutate(fav_drink = if_else(drink_type == "Latte", "Fav Drink", "Rubbish Drinks")) |> 
  qlist(drink_type, fav_drink, size, day_of_week, .in = 1:2) |> 
  arrange(fav_drink)
#>  drink_type   fav_drink        size day_of_week 
#>           1           4           2           3
#> 
#> ── List Details: ───────────────────────────────────────────────────────────────
#> Columns: drink_type, fav_drink, size, day_of_week
#> Filter: None
#> Rows 1:2
#>   drink_type      fav_drink  size day_of_week
#> 1      Latte      Fav Drink Small         Mon
#> 2   Espresso Rubbish Drinks Large         Tue

Tabulating Data

Use qtab to create summary tables:

larger_data <- data.frame(
  drink_type = sample(data$drink_type, 200, replace = TRUE),
  size = sample(data$size, 200, replace = TRUE),
  day_of_week = sample(c("Mon", "Tue", "Wed", "Thu", "Fri", "Sat", "Sun"), 200, replace = TRUE),
  quantity = sample(1:5, 200, replace = TRUE),  # Add variability for quantity
  price = round(runif(200, 2.5, 6.5), 2)       # Add a random price column
)



# Tabulate a single variable
larger_data |>
  qtab(drink_type)
#> 
#> ── Table Details: ──────────────────────────────────────────────────────────────
#> Columns: drink_type
#> Filter: None
#> Rows: All
#> Group: None
#>  drink_type   n percent
#>   Americano  51   25.5%
#>  Cappuccino  54   27.0%
#>    Espresso  53   26.5%
#>       Latte  42   21.0%
#>       Total 200  100.0%

# Tabulate two variables
larger_data |>
  qtab(drink_type, size)
#> 
#> ── Table Details: ──────────────────────────────────────────────────────────────
#> Columns: drink_type, size
#> Filter: None
#> Rows: All
#> Group: None
#>  drink_type       Large     Medium      Small        Total
#>   Americano 16.5%  (33)  3.5%  (7)  5.5% (11)  25.5%  (51)
#>  Cappuccino 16.5%  (33)  5.5% (11)  5.0% (10)  27.0%  (54)
#>    Espresso 14.0%  (28)  8.5% (17)  4.0%  (8)  26.5%  (53)
#>       Latte 12.5%  (25)  4.0%  (8)  4.5%  (9)  21.0%  (42)
#>       Total 59.5% (119) 21.5% (43) 19.0% (38) 100.0% (200)

# Grouped tabulations
larger_data |> 
  filter(day_of_week %in% c("Mon", "Tues")) |> 
  qtab(drink_type, size, .by = day_of_week)
#> 
#> ── Table Details: ──────────────────────────────────────────────────────────────
#> Columns: drink_type, size
#> Filter: None
#> Rows: All
#> Group By: day_of_week
#> $Mon
#>  drink_type      Large    Medium     Small       Total
#>   Americano 23.1%  (9)  7.7% (3)  7.7% (3)  38.5% (15)
#>  Cappuccino 23.1%  (9)  0.0% (0)  5.1% (2)  28.2% (11)
#>    Espresso 12.8%  (5)  0.0% (0)  2.6% (1)  15.4%  (6)
#>       Latte  7.7%  (3)  7.7% (3)  2.6% (1)  17.9%  (7)
#>       Total 66.7% (26) 15.4% (6) 17.9% (7) 100.0% (39)

Tabulating Data with different percentages

Use qtab with different percentages:

# Tabulate two variables
larger_data |>
  qtab(drink_type, size, per = "all")
#> 
#> ── Table Details: ──────────────────────────────────────────────────────────────
#> Columns: drink_type, size
#> Filter: None
#> Rows: All
#> Group: None
#>  drink_type       Large     Medium      Small        Total
#>   Americano 16.5%  (33)  3.5%  (7)  5.5% (11)  25.5%  (51)
#>  Cappuccino 16.5%  (33)  5.5% (11)  5.0% (10)  27.0%  (54)
#>    Espresso 14.0%  (28)  8.5% (17)  4.0%  (8)  26.5%  (53)
#>       Latte 12.5%  (25)  4.0%  (8)  4.5%  (9)  21.0%  (42)
#>       Total 59.5% (119) 21.5% (43) 19.0% (38) 100.0% (200)

# column percentages
larger_data |>
  qtab(drink_type, size, per = "col")
#> 
#> ── Table Details: ──────────────────────────────────────────────────────────────
#> Columns: drink_type, size
#> Filter: None
#> Rows: All
#> Group: None
#>  drink_type        Large      Medium       Small        Total
#>   Americano  27.7%  (33)  16.3%  (7)  28.9% (11)  25.5%  (51)
#>  Cappuccino  27.7%  (33)  25.6% (11)  26.3% (10)  27.0%  (54)
#>    Espresso  23.5%  (28)  39.5% (17)  21.1%  (8)  26.5%  (53)
#>       Latte  21.0%  (25)  18.6%  (8)  23.7%  (9)  21.0%  (42)
#>       Total 100.0% (119) 100.0% (43) 100.0% (38) 100.0% (200)

# row percentages
larger_data |>
  qtab(drink_type, size, per = "row")
#> 
#> ── Table Details: ──────────────────────────────────────────────────────────────
#> Columns: drink_type, size
#> Filter: None
#> Rows: All
#> Group: None
#>  drink_type       Large     Medium      Small        Total
#>   Americano 64.7%  (33) 13.7%  (7) 21.6% (11) 100.0%  (51)
#>  Cappuccino 61.1%  (33) 20.4% (11) 18.5% (10) 100.0%  (54)
#>    Espresso 52.8%  (28) 32.1% (17) 15.1%  (8) 100.0%  (53)
#>       Latte 59.5%  (25) 19.0%  (8) 21.4%  (9) 100.0%  (42)
#>       Total 59.5% (119) 21.5% (43) 19.0% (38) 100.0% (200)
# no percentages
larger_data |>
  qtab(drink_type, size, per = "none")
#> 
#> ── Table Details: ──────────────────────────────────────────────────────────────
#> Columns: drink_type, size
#> Filter: None
#> Rows: All
#> Group: None
#>  drink_type Large Medium Small Total
#>   Americano    33      7    11    51
#>  Cappuccino    33     11    10    54
#>    Espresso    28     17     8    53
#>       Latte    25      8     9    42
#>       Total   119     43    38   200

# Grouped tabulations
data |>
  qtab(drink_type, size, .by = day_of_week, per = "none")
#> 
#> ── Table Details: ──────────────────────────────────────────────────────────────
#> Columns: drink_type, size
#> Filter: None
#> Rows: All
#> Group By: day_of_week
#> $Mon
#>  drink_type Small Total
#>       Latte     1     1
#>       Total     1     1
#> 
#> $Tue
#>  drink_type Large Total
#>   Americano     1     1
#>       Total     1     1
#> 
#> $Wed
#>  drink_type Large Total
#>    Espresso     1     1
#>       Total     1     1
#> 
#> $Thu
#>  drink_type Medium Total
#>  Cappuccino      1     1
#>       Total      1     1

Advanced Tabulations

# Tabulate with a filter condition
data |>
  qtab(drink_type, size, .if = size == "Large")
#> 
#> ── Table Details: ──────────────────────────────────────────────────────────────
#> Columns: drink_type, size
#> Filter: size == "Large"
#> Rows: All
#> Group: None
#>  drink_type      Large      Total
#>   Americano  50.0% (1)  50.0% (1)
#>    Espresso  50.0% (1)  50.0% (1)
#>       Total 100.0% (2) 100.0% (2)

# Tabulate specific rows
data |>
  qtab(drink_type, size, .in = 1:3) 
#> 
#> ── Table Details: ──────────────────────────────────────────────────────────────
#> Columns: drink_type, size
#> Filter: None
#> Rows 1:3
#> Group: None
#>  drink_type     Large    Medium     Small      Total
#>  Cappuccino  0.0% (0) 33.3% (1)  0.0% (0)  33.3% (1)
#>    Espresso 33.3% (1)  0.0% (0)  0.0% (0)  33.3% (1)
#>       Latte  0.0% (0)  0.0% (0) 33.3% (1)  33.3% (1)
#>       Total 33.3% (1) 33.3% (1) 33.3% (1) 100.0% (3)

# Group and filter tabulations
data |>
  qtab(drink_type, size, .by = day_of_week, .if = day_of_week %in% c("Mon", "Tue"))
#> 
#> ── Table Details: ──────────────────────────────────────────────────────────────
#> Columns: drink_type, size
#> Filter: day_of_week %in% c("Mon", "Tue")
#> Rows: All
#> Group By: day_of_week
#> $Mon
#>  drink_type      Small      Total
#>       Latte 100.0% (1) 100.0% (1)
#>       Total 100.0% (1) 100.0% (1)
#> 
#> $Tue
#>  drink_type      Large      Total
#>    Espresso 100.0% (1) 100.0% (1)
#>       Total 100.0% (1) 100.0% (1)

Relation to tidyverse

qtab outputs a tabyl data.frame object. Therefore qtab can be piped into anything that tabyl can (I think?)

library(qexplore)
library(dplyr)
library(janitor)
#> 
#> Attaching package: 'janitor'
#> The following objects are masked from 'package:stats':
#> 
#>     chisq.test, fisher.test
library(gt)
library(flextable)

# Tabulate specific rows
data |>
  qtab(drink_type, size, .in = 1:3) |> 
   adorn_title(col_name = "Drink size", row_name = "Drink Type")
#> 
#> ── Table Details: ──────────────────────────────────────────────────────────────
#> Columns: drink_type, size
#> Filter: None
#> Rows 1:3
#> Group: None
#>             Drink size                               
#>  Drink Type      Large    Medium     Small      Total
#>  Cappuccino   0.0% (0) 33.3% (1)  0.0% (0)  33.3% (1)
#>    Espresso  33.3% (1)  0.0% (0)  0.0% (0)  33.3% (1)
#>       Latte   0.0% (0)  0.0% (0) 33.3% (1)  33.3% (1)
#>       Total  33.3% (1) 33.3% (1) 33.3% (1) 100.0% (3)

Relation to gt and flextable

Due to qtab being a tabyl, this has the consequence that it can be piped into gt and flextable though this is the purpose as qexplore is about quickly exploring data via the console. This is a nice feature maybe this will have some unintended consequences.

library(qexplore)
library(dplyr)
library(janitor)
library(gt)

# Tabulate specific rows
gt_table <- data |>
  qtab(drink_type, size, .in = 1:3) |> 
  gt() |> 
    tab_header(
    title = "Drink Types by Day of the Week",
    subtitle = "Percentage Breakdown with Totals"
  )
#> 
#> ── Table Details: ──────────────────────────────────────────────────────────────
#> Columns: drink_type, size
#> Filter: None
#> Rows 1:3
#> Group: None

library(qexplore)
library(dplyr)
library(janitor)
library(flextable)

# Tabulate specific rows
tabyl_data <- data |>
  qtab(drink_type, size, .in = 1:3) 
#> 
#> ── Table Details: ──────────────────────────────────────────────────────────────
#> Columns: drink_type, size
#> Filter: None
#> Rows 1:3
#> Group: None

# Convert tabyl to flextable
flex_table <- tabyl_data |> 
  flextable() |> 
  set_header_labels(
    drink_type = "Drink Type",
    Total = "Weekly Total"
  ) |> 
  add_header_row(
    values = c("", "Day of the Week"), colwidths = c(1, ncol(tabyl_data) - 1)
  ) |> 
  theme_vanilla() |> 
  autofit()

# View the flextable
flex_table

Acknowledgements

This package brings together if, in, and by arguments from Stata. It replicates something similar that you can do in data.table. It also fits a tidyverse framework and uses tidyverse especially dplyr and janitor.

I’d like to thank everyone that was involved in all of the software above. Especially tidyplots for the idea that you can take something as well established as ggplot2 and still make something possibly quicker and maybe more accessible that might help enable more people to use R.

qexplore leverages the following amazing packages to do the heavy lifting: cli, dplyr, rlang, tidyselect, and tidyr.

Name		Name	Last commit message	Last commit date
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inst		inst
man		man
tests		tests
.Rbuildignore		.Rbuildignore
.gitignore		.gitignore
DESCRIPTION		DESCRIPTION
LICENSE		LICENSE
NAMESPACE		NAMESPACE
README.Rmd		README.Rmd
README.md		README.md
qexplore.Rproj		qexplore.Rproj

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qexplore

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Tabulating Data with different percentages

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Relation to gt and flextable

Acknowledgements

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qexplore

Installation

Usage

Listing Rows

Relation to tidyverse

Tabulating Data

Tabulating Data with different percentages

Advanced Tabulations

Relation to tidyverse

Relation to gt and flextable

Acknowledgements

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