sherlock

The {sherlock} R package provides powerful graphical displays and statistical tools to aid structured problem solving and diagnosis. The functions of the package are especially useful for applying the process of elimination as a problem diagnosis technique. {sherlock} was designed to seamlessly work with the tidyverse set of packages.

“That is to say, nature’s laws are causal; they reveal themselves by comparison and difference, and they operate at every multi-variate space-time point” - Edward Tufte

Installation

You can install the development version of sherlock from GitHub with:

# install.packages("devtools")
devtools::install_github("gaborszabo11/sherlock")

Functions

While the package is currently under development, quite a few functions are already available and ready to be used. Available functions are as follows:

Plotting functions

draw_multivari_plot()

draw_categorical_scatterplot()

draw_youden_plot()

draw_small_multiples_line_plot()

draw_cartesian_small_multiples()

draw_polar_small_multiples()

draw_interaction_plot()

draw_process_behavior_chart()

draw_timeseries_scatterplot()

Helper functions

load_file()

save_analysis()

normalize_observations()

theme_sherlock()

scale_color_sherlock()

scale_fill_sherlock()

draw_horizontal_reference_line()

draw_vertical_reference_line()

Examples

Here are a few examples:

# Loading libraries
library(sherlock)
library(ggh4x)
#> Loading required package: ggplot2
multi_vari_data %>% 
  draw_multivari_plot(response = force, 
                      factor_1 = cycle, 
                      factor_2 = fixture, 
                      factor_3 = line)

library(sherlock)
library(ggh4x)

multi_vari_data_2 %>% 
  draw_multivari_plot(response = Length, 
                      factor_1 = Part, 
                      factor_2 = Operator, plot_means = TRUE)

library(sherlock)
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

polar_small_multiples_data %>% 
  filter(Mold_Cavity_Number %in% c(4, 6)) %>% 
  draw_polar_small_multiples(angular_axis   = ID_Measurement_Angle, 
                             x_y_coord_axis = ID_2, 
                             grouping_var   = Tip_Bottom, 
                             faceting_var_1 = Mold_Cavity_Number,
                             point_size     = 0.5, 
                             connect_with_lines = TRUE, 
                             label_text_size = 7)

library(sherlock)
library(dplyr)
library(ggh4x)

polar_small_multiples_data %>% 
  filter(ID_Measurement_Angle %in% c(0, 45, 90, 135)) %>% 
  normalize_observations(response = ID, grouping_var = Tip_Bottom, ref_values = c(0.2075, 0.2225)) %>% 
  draw_multivari_plot(response    = ID_normalized, 
                      factor_1    = ID_Measurement_Angle, 
                      factor_2    = Mold_Cavity_Number, 
                      factor_3    = Tip_Bottom, 
                      x_axis_text = 6) +
  draw_horizontal_reference_line(reference_line = 0)
#> Joining, by = "Tip_Bottom"

youden_plot_data_2 %>% 
  draw_youden_plot(x_axis_var  = gage_1, 
                   y_axis_var  = gage_2, 
                   median_line = TRUE)
#> Smoothing formula not specified. Using: y ~ x

youden_plot_data %>% 
  draw_youden_plot(x_axis_var   = measurement_1, 
                   y_axis_var   = measurement_2, 
                   grouping_var = location, 
                   x_axis_label = "Trial 1", 
                   y_axis_label = "Trial 2")

timeseries_scatterplot_data %>%
  draw_timeseries_scatterplot(y_var = y, 
                              grouping_var_1 = date, 
                              grouping_var_2 = cavity, 
                              faceting       = TRUE, 
                              limits         = TRUE, 
                              alpha          = 0.15,
                              line_size      = 0.5, 
                              x_axis_text    = 7,
                              interactive    = FALSE)
#> Joining, by = c("date", "cavity")
#> Warning: Removed 6 rows containing missing values (geom_point).

References

Diagnosing Performance and Reliability, David Hartshorne and The New Science of Fixing Things, 2019

Statistical Engineering - An Algorithm for Reducing Variation in Manufacturing Processes, Stefan H. Steiner and Jock MacKay, 2005