Outlier Detection and NHS Icons • controlcharts

Introduction

Statistical Process Control (SPC) charts are powerful tools for detecting signals of change in processes over time. The controlcharts package provides comprehensive outlier detection capabilities to help identify when a process has shifted from its baseline state.

This vignette demonstrates:

Four outlier detection methods: astronomical points, shifts, trends, and 2-in-3 patterns
Custom detection parameters: adjusting sensitivity for shift and trend lengths
Directional flagging: specifying what constitutes improvement vs deterioration
NHS icons: variation and assurance icons for visual interpretation
Combined approaches: using multiple detection methods simultaneously

All outlier detection settings are configured through the outlier_settings parameter, while NHS icons are configured through the nhs_icon_settings parameter.

Outlier Detection Methods

Astronomical Points

Astronomical points (also called astronomical signals or special cause variation) are individual data points that fall outside the statistical control limits. By default, these are points beyond the 3-sigma limits.

# Create data with some extreme outliers
dat_astro <- data.frame(
  month = seq(as.Date("2024-01-01"), length.out = 24, by = "month"),
  infections = c(rnorm(10, mean = 15, sd = 3),
                 28,  # Astronomical point
                 rnorm(5, mean = 15, sd = 3),
                 6,   # Astronomical point
                 rnorm(7, mean = 15, sd = 3))
)

chart_astro <- spc(data = dat_astro,
                   keys = month,
                   numerators = infections,
                   spc_settings = list(chart_type = "i"),
                   outlier_settings = list(
                     astronomical = TRUE,
                     astronomical_limit = "3 Sigma"
                   ))

chart_astro$static_plot

knitr::kable(chart_astro$limits[9:14, ], digits = 2)

	date	value	target	ll99	ll95	ll68	ul68	ul95	ul99	trend_line	astpoint
9	01/09/2024	8.83	14.32	4.01	7.44	10.88	17.76	21.19	24.63	14.50	none
10	01/10/2024	8.83	14.32	4.01	7.44	10.88	17.76	21.19	24.63	14.45	none
11	01/11/2024	28.00	14.32	4.01	7.44	10.88	17.76	21.19	24.63	14.39	improvement
12	01/12/2024	14.84	14.32	4.01	7.44	10.88	17.76	21.19	24.63	14.34	none
13	01/01/2025	12.73	14.32	4.01	7.44	10.88	17.76	21.19	24.63	14.29	none
14	01/02/2025	14.97	14.32	4.01	7.44	10.88	17.76	21.19	24.63	14.24	none

Astronomical points indicate special cause variation - something unusual has occurred that requires investigation. You can adjust the which limit is used for flagging by changing the astronomical_limit parameter to “2 Sigma” or “1 Sigma”:

chart_astro_2s <- spc(data = dat_astro,
                      keys = month,
                      numerators = infections,
                      spc_settings = list(chart_type = "i"),
                      outlier_settings = list(
                        astronomical = TRUE,
                        astronomical_limit = "2 Sigma"
                      ))

chart_astro_2s$static_plot

Shift Detection

A shift occurs when multiple consecutive points fall on the same side of the centerline, suggesting a sustained change in the process. The default threshold is 7 consecutive points. This example shows a 6-point shift, which will not be flagged with the default setting:

# Create data with a 6-point shift at the end
dat_shift <- data.frame(
  month = seq(as.Date("2024-01-01"), length.out = 24, by = "month"),
  satisfaction = c(rnorm(18, mean = 75, sd = 5),  # Baseline
                   rnorm(6, mean = 82, sd = 5))   # 6-point shift at end
)

chart_shift <- spc(data = dat_shift,
                   keys = month,
                   numerators = satisfaction,
                   spc_settings = list(chart_type = "i"),
                   outlier_settings = list(
                     shift = TRUE
                   ))

chart_shift$static_plot

knitr::kable(tail(chart_shift$limits, 10), digits = 2)

	date	value	target	ll99	ll95	ll68	ul68	ul95	ul99	trend_line	shift
15	01/03/2025	78.37	78.58	67.68	71.31	74.95	82.22	85.85	89.49	79.12	none
16	01/04/2025	76.55	78.58	67.68	71.31	74.95	82.22	85.85	89.49	79.34	none
17	01/05/2025	76.60	78.58	67.68	71.31	74.95	82.22	85.85	89.49	79.55	none
18	01/06/2025	74.81	78.58	67.68	71.31	74.95	82.22	85.85	89.49	79.77	none
19	01/07/2025	82.33	78.58	67.68	71.31	74.95	82.22	85.85	89.49	79.98	none
20	01/08/2025	83.81	78.58	67.68	71.31	74.95	82.22	85.85	89.49	80.20	none
21	01/09/2025	85.06	78.58	67.68	71.31	74.95	82.22	85.85	89.49	80.41	none
22	01/10/2025	81.67	78.58	67.68	71.31	74.95	82.22	85.85	89.49	80.63	none
23	01/11/2025	86.41	78.58	67.68	71.31	74.95	82.22	85.85	89.49	80.84	none
24	01/12/2025	80.72	78.58	67.68	71.31	74.95	82.22	85.85	89.49	81.06	none

You can customize the shift detection by changing shift_n. Setting it to 5 or 6 will now detect the 6-point shift:

chart_shift_6 <- spc(data = dat_shift,
                     keys = month,
                     numerators = satisfaction,
                     spc_settings = list(chart_type = "i"),
                     outlier_settings = list(
                       shift = TRUE,
                       shift_n = 6
                     ))

chart_shift_6$static_plot

Trend Detection

Trends detect monotonic increases or decreases over consecutive points, indicating a sustained directional change. The default is 5 consecutive points in a consistent direction. This example shows a 4-point trend, which will not be flagged with the default setting:

# Create data with a 4-point upward trend at the end
dat_trend <- data.frame(
  month = seq(as.Date("2024-01-01"), length.out = 24, by = "month"),
  wait_time = c(rnorm(20, mean = 30, sd = 3),
                31, 33, 35, 37)  # 4-point increasing trend at end
)

chart_trend <- spc(data = dat_trend,
                   keys = month,
                   numerators = wait_time,
                   spc_settings = list(chart_type = "i"),
                   outlier_settings = list(
                     trend = TRUE
                   ))

chart_trend$static_plot

knitr::kable(tail(chart_trend$limits, 10), digits = 2)

	date	value	target	ll99	ll95	ll68	ul68	ul95	ul99	trend_line	trend
15	01/03/2025	32.45	30.84	23.38	25.86	28.35	33.32	35.81	38.3	31.37	none
16	01/04/2025	31.47	30.84	23.38	25.86	28.35	33.32	35.81	38.3	31.58	none
17	01/05/2025	26.24	30.84	23.38	25.86	28.35	33.32	35.81	38.3	31.79	none
18	01/06/2025	33.65	30.84	23.38	25.86	28.35	33.32	35.81	38.3	32.01	none
19	01/07/2025	32.02	30.84	23.38	25.86	28.35	33.32	35.81	38.3	32.22	none
20	01/08/2025	32.55	30.84	23.38	25.86	28.35	33.32	35.81	38.3	32.43	none
21	01/09/2025	31.00	30.84	23.38	25.86	28.35	33.32	35.81	38.3	32.65	none
22	01/10/2025	33.00	30.84	23.38	25.86	28.35	33.32	35.81	38.3	32.86	none
23	01/11/2025	35.00	30.84	23.38	25.86	28.35	33.32	35.81	38.3	33.07	none
24	01/12/2025	37.00	30.84	23.38	25.86	28.35	33.32	35.81	38.3	33.29	none

Setting trend_n to 4 or fewer will now detect the 4-point trend:

chart_trend_4 <- spc(data = dat_trend,
                     keys = month,
                     numerators = wait_time,
                     spc_settings = list(chart_type = "i"),
                     outlier_settings = list(
                       trend = TRUE,
                       trend_n = 4
                     ))

chart_trend_4$static_plot

Two-in-Three Detection

The 2-in-3 rule detects when 2 out of any 3 consecutive points fall outside the 2-sigma (95%) warning limits. This pattern suggests early signs of process change.

# Create data with 2-in-3 pattern
# With mean=120 and sd=5, 2-sigma limits are approximately 120 ± 10
# So values above 130 or below 110 should exceed 2-sigma
dat_2in3 <- data.frame(
  month = seq(as.Date("2024-01-01"), length.out = 24, by = "month"),
  pressure = c(rnorm(8, mean = 120, sd = 5),
               131, 133, 121,  # 2 out of 3 outside 2-sigma (131 and 133 > 130)
               rnorm(13, mean = 120, sd = 5))
)

chart_2in3 <- spc(data = dat_2in3,
                  keys = month,
                  numerators = pressure,
                  spc_settings = list(chart_type = "i"),
                  outlier_settings = list(
                    two_in_three = TRUE,
                    two_in_three_limit = "2 Sigma"
                  ))

chart_2in3$static_plot

knitr::kable(head(chart_2in3$limits, 12), digits = 2)

date	value	target	ll99	ll95	ll68	ul68	ul95	ul99	trend_line	two_in_three
01/01/2024	122.73	121.62	108.66	112.98	117.3	125.94	130.26	134.58	125.42	none
01/02/2024	133.96	121.62	108.66	112.98	117.3	125.94	130.26	134.58	125.09	none
01/03/2024	112.85	121.62	108.66	112.98	117.3	125.94	130.26	134.58	124.76	none
01/04/2024	122.67	121.62	108.66	112.98	117.3	125.94	130.26	134.58	124.43	none
01/05/2024	128.49	121.62	108.66	112.98	117.3	125.94	130.26	134.58	124.09	none
01/06/2024	121.09	121.62	108.66	112.98	117.3	125.94	130.26	134.58	123.76	none
01/07/2024	120.01	121.62	108.66	112.98	117.3	125.94	130.26	134.58	123.43	none
01/08/2024	120.50	121.62	108.66	112.98	117.3	125.94	130.26	134.58	123.10	none
01/09/2024	131.00	121.62	108.66	112.98	117.3	125.94	130.26	134.58	122.77	improvement
01/10/2024	133.00	121.62	108.66	112.98	117.3	125.94	130.26	134.58	122.44	improvement
01/11/2024	121.00	121.62	108.66	112.98	117.3	125.94	130.26	134.58	122.11	none
01/12/2024	118.99	121.62	108.66	112.98	117.3	125.94	130.26	134.58	121.78	none

By default, only the points outside the limits are highlighted. You can highlight all points in the pattern by setting two_in_three_highlight_series = TRUE:

chart_2in3_all <- spc(data = dat_2in3,
                      keys = month,
                      numerators = pressure,
                      spc_settings = list(chart_type = "i"),
                      outlier_settings = list(
                        two_in_three = TRUE,
                        two_in_three_limit = "2 Sigma",
                        two_in_three_highlight_series = TRUE
                      ))

chart_2in3_all$static_plot

Improvement Direction and Flag Types

Improvement Direction

The improvement_direction setting specifies what constitutes an improvement in your process.

# For metrics where lower is better (e.g., infection rates)
dat_infections <- data.frame(
  month = seq(as.Date("2024-01-01"), length.out = 24, by = "month"),
  infections = c(rnorm(16, mean = 10, sd = 1),
                 rnorm(8, mean = 5, sd = 1))  # Improvement (decrease shift)
)

chart_inf <- spc(data = dat_infections,
                 keys = month,
                 numerators = infections,
                 spc_settings = list(chart_type = "i"),
                 outlier_settings = list(
                   shift = TRUE,
                   improvement_direction = "decrease"
                 ))

chart_inf$static_plot

# For metrics where higher is better (e.g., hand hygiene compliance)
dat_compliance <- data.frame(
  month = seq(as.Date("2024-01-01"), length.out = 24, by = "month"),
  compliance = c(rnorm(12, mean = 75, sd = 5),
                 rnorm(12, mean = 85, sd = 5))  # Improvement (increase)
)

chart_comp <- spc(data = dat_compliance,
                  keys = month,
                  numerators = compliance,
                  spc_settings = list(chart_type = "i"),
                  outlier_settings = list(
                    shift = TRUE,
                    improvement_direction = "increase"
                  ))

chart_comp$static_plot

Setting improvement_direction = "neutral" treats all outliers equally, with no distinction between improvement and deterioration.

Process Flag Type

The process_flag_type setting allows you to filter which outliers are flagged. This is useful when you only want to be alerted to deteriorations (not improvements) or vice versa.

First, let’s create data with both an improvement (decrease) and a deterioration (increase) in infection rates:

# Data with both improvement and deterioration
dat_flag_type <- data.frame(
  month = seq(as.Date("2024-01-01"), length.out = 24, by = "month"),
  infections = c(rnorm(5, mean = 8, sd = 1.5),
                 rnorm(8, mean = 5, sd = 1.5),   # Improvement (decrease)
                 rnorm(11, mean = 10, sd = 1.5))  # Deterioration (increase)
)

With the default setting (process_flag_type = "both"), both the improvement and deterioration shifts are flagged:

chart_both <- spc(data = dat_flag_type,
                  keys = month,
                  numerators = infections,
                  spc_settings = list(chart_type = "i"),
                  outlier_settings = list(
                    shift = TRUE,
                    improvement_direction = "decrease",
                    process_flag_type = "both"
                  ))

chart_both$static_plot

Now, setting process_flag_type = "deterioration" will only flag the increase (deterioration), ignoring the improvement:

chart_det_only <- spc(data = dat_flag_type,
                      keys = month,
                      numerators = infections,
                      spc_settings = list(chart_type = "i"),
                      outlier_settings = list(
                        shift = TRUE,
                        improvement_direction = "decrease",
                        process_flag_type = "deterioration"
                      ))

chart_det_only$static_plot

NHS Icons

NHS icons provide standardized visual indicators of process variation and assurance status.

Variation Icons

Variation icons indicate the type of variation present in the chart. By default, icons are only shown if a pattern is detected at the last point:

# Data with shift ending at the last point
dat_var_last <- data.frame(
  month = seq(as.Date("2024-01-01"), length.out = 24, by = "month"),
  readmissions = c(rnorm(10, mean = 12, sd = 2),
                   rnorm(14, mean = 16, sd = 2))  # Shift at end
)

chart_var_last <- spc(data = dat_var_last,
                      keys = month,
                      numerators = readmissions,
                      spc_settings = list(chart_type = "i"),
                      outlier_settings = list(
                        astronomical = TRUE,
                        shift = TRUE,
                        improvement_direction = "decrease"
                      ),
                      nhs_icon_settings = list(
                        show_variation_icons = TRUE,
                        flag_last_point = TRUE,
                        variation_icons_locations = "Top Right"
                      ))

chart_var_last$static_plot

If the pattern occurs earlier in the data but not at the last point, the icon will not be shown with default settings:

# Data with shift in the middle, not at the end
dat_var_middle <- data.frame(
  month = seq(as.Date("2024-01-01"), length.out = 24, by = "month"),
  readmissions = c(rnorm(8, mean = 12, sd = 2),
                   rnorm(8, mean = 16, sd = 2),  # Shift in middle
                   rnorm(8, mean = 12, sd = 2))  # Back to baseline
)

chart_var_middle <- spc(data = dat_var_middle,
                        keys = month,
                        numerators = readmissions,
                        spc_settings = list(chart_type = "i"),
                        outlier_settings = list(
                          astronomical = TRUE,
                          shift = TRUE,
                          improvement_direction = "decrease"
                        ),
                        nhs_icon_settings = list(
                          show_variation_icons = TRUE,
                          flag_last_point = TRUE,
                          variation_icons_locations = "Top Right"
                        ))

chart_var_middle$static_plot

Setting flag_last_point = FALSE will show icons for all relevant points, not just the last observation:

chart_var_all <- spc(data = dat_var_middle,
                     keys = month,
                     numerators = readmissions,
                     spc_settings = list(chart_type = "i"),
                     outlier_settings = list(
                       astronomical = TRUE,
                       shift = TRUE,
                       improvement_direction = "decrease"
                     ),
                     nhs_icon_settings = list(
                       show_variation_icons = TRUE,
                       flag_last_point = FALSE,
                       variation_icons_locations = "Top Right"
                     ))

chart_var_all$static_plot

You can also customize the icon placement and scaling:

chart_var_bl <- spc(data = dat_var_last,
                    keys = month,
                    numerators = readmissions,
                    spc_settings = list(chart_type = "i"),
                    outlier_settings = list(
                      astronomical = TRUE,
                      shift = TRUE,
                      improvement_direction = "decrease"
                    ),
                    nhs_icon_settings = list(
                      show_variation_icons = TRUE,
                      variation_icons_locations = "Bottom Left",
                      variation_icons_scaling = 1.2
                    ))

chart_var_bl$static_plot

Assurance Icons

Assurance icons indicate whether the process is consistently meeting a target threshold. Important: Assurance icons require an alt_target value to be specified in line_settings.

The assurance status depends on the position of the alt_target relative to the 99% control limits:

Consistent Pass: Target is below the lower 99% limit (process consistently exceeds target)
Consistent Fail: Target is above the upper 99% limit (process consistently below target)
Variable: Target is within the 99% limits (process sometimes meets, sometimes doesn’t)

Example: Consistent Pass

When the alt_target is below the lower 99% control limit, the process consistently exceeds the target:

# Process centered around 95%, target at 90% will be below lower 99% limit
dat_assurance_pass <- data.frame(
  month = seq(as.Date("2024-01-01"), length.out = 24, by = "month"),
  vaccination_rate = rnorm(24, mean = 95, sd = 1)
)

chart_assurance_pass <- spc(data = dat_assurance_pass,
                            keys = month,
                            numerators = vaccination_rate,
                            spc_settings = list(chart_type = "i"),
                            nhs_icon_settings = list(
                              show_assurance_icons = TRUE,
                              assurance_icons_locations = "Top Right"
                            ),
                            line_settings = list(
                              show_alt_target = TRUE,
                              alt_target = 90,
                              colour_alt_target = "#E69F00"
                            ))

chart_assurance_pass$static_plot

knitr::kable(tail(chart_assurance_pass$limits, 6), digits = 2)

	date	value	target	alt_target	ll99	ll95	ll68	ul68	ul95	ul99	trend_line
19	01/07/2025	93.76	94.64	90	92.45	93.18	93.91	95.38	96.11	96.84	94.55
20	01/08/2025	94.54	94.64	90	92.45	93.18	93.91	95.38	96.11	96.84	94.53
21	01/09/2025	95.01	94.64	90	92.45	93.18	93.91	95.38	96.11	96.84	94.52
22	01/10/2025	95.00	94.64	90	92.45	93.18	93.91	95.38	96.11	96.84	94.50
23	01/11/2025	94.81	94.64	90	92.45	93.18	93.91	95.38	96.11	96.84	94.49
24	01/12/2025	95.85	94.64	90	92.45	93.18	93.91	95.38	96.11	96.84	94.48

Example: Consistent Fail

When the alt_target is above the upper 99% control limit, the process consistently fails to meet the target:

# Process centered around 82%, target at 90% will be above upper 99% limit
dat_assurance_fail <- data.frame(
  month = seq(as.Date("2024-01-01"), length.out = 24, by = "month"),
  vaccination_rate = rnorm(24, mean = 82, sd = 1.5)
)

chart_assurance_fail <- spc(data = dat_assurance_fail,
                            keys = month,
                            numerators = vaccination_rate,
                            spc_settings = list(chart_type = "i"),
                            nhs_icon_settings = list(
                              show_assurance_icons = TRUE,
                              assurance_icons_locations = "Top Right"
                            ),
                            line_settings = list(
                              show_alt_target = TRUE,
                              alt_target = 90,
                              colour_alt_target = "#E69F00"
                            ))

chart_assurance_fail$static_plot

knitr::kable(tail(chart_assurance_fail$limits, 6), digits = 2)

	date	value	target	alt_target	ll99	ll95	ll68	ul68	ul95	ul99	trend_line
19	01/07/2025	82.68	82.24	90	77.71	79.22	80.73	83.75	85.26	86.77	82.12
20	01/08/2025	81.12	82.24	90	77.71	79.22	80.73	83.75	85.26	86.77	82.10
21	01/09/2025	81.70	82.24	90	77.71	79.22	80.73	83.75	85.26	86.77	82.08
22	01/10/2025	84.68	82.24	90	77.71	79.22	80.73	83.75	85.26	86.77	82.06
23	01/11/2025	80.31	82.24	90	77.71	79.22	80.73	83.75	85.26	86.77	82.04
24	01/12/2025	81.30	82.24	90	77.71	79.22	80.73	83.75	85.26	86.77	82.02

Example: Variable Performance

When the alt_target falls within the 99% control limits, performance is variable:

# Process centered around 90%, target at 90% will be within limits
dat_assurance_variable <- data.frame(
  month = seq(as.Date("2024-01-01"), length.out = 24, by = "month"),
  vaccination_rate = rnorm(24, mean = 90, sd = 2)
)

chart_assurance_variable <- spc(data = dat_assurance_variable,
                                keys = month,
                                numerators = vaccination_rate,
                                spc_settings = list(chart_type = "i"),
                                nhs_icon_settings = list(
                                  show_assurance_icons = TRUE,
                                  assurance_icons_locations = "Top Right"
                                ),
                                line_settings = list(
                                  show_alt_target = TRUE,
                                  alt_target = 90,
                                  colour_alt_target = "#E69F00"
                                ))

chart_assurance_variable$static_plot

knitr::kable(tail(chart_assurance_variable$limits, 6), digits = 2)

	date	value	target	alt_target	ll99	ll95	ll68	ul68	ul95	ul99	trend_line
19	01/07/2025	88.97	90.13	90	83.83	85.93	88.03	92.23	94.33	96.43	90.26
20	01/08/2025	87.33	90.13	90	83.83	85.93	88.03	92.23	94.33	96.43	90.28
21	01/09/2025	91.77	90.13	90	83.83	85.93	88.03	92.23	94.33	96.43	90.30
22	01/10/2025	91.78	90.13	90	83.83	85.93	88.03	92.23	94.33	96.43	90.32
23	01/11/2025	89.15	90.13	90	83.83	85.93	88.03	92.23	94.33	96.43	90.34
24	01/12/2025	88.32	90.13	90	83.83	85.93	88.03	92.23	94.33	96.43	90.36

Combined Variation and Assurance Icons

You can display both icon types simultaneously:

chart_both_icons <- spc(data = dat_assurance_pass,
                        keys = month,
                        numerators = vaccination_rate,
                        spc_settings = list(chart_type = "i"),
                        outlier_settings = list(
                          astronomical = TRUE,
                          shift = TRUE,
                          trend = TRUE,
                          improvement_direction = "increase"
                        ),
                        nhs_icon_settings = list(
                          show_variation_icons = TRUE,
                          show_assurance_icons = TRUE
                        ),
                        line_settings = list(
                          show_alt_target = TRUE,
                          alt_target = 90,
                          colour_alt_target = "#E69F00"
                        ))

chart_both_icons$static_plot

Settings Reference

Outlier Detection Settings

Setting	Type	Default	Options/Range	Purpose
`astronomical`	boolean	FALSE	TRUE/FALSE	Detect points outside limits
`astronomical_limit`	character	“3 Sigma”	“1 Sigma”, “2 Sigma”, “3 Sigma”, “Specification”	Which limit to use
`shift`	boolean	FALSE	TRUE/FALSE	Detect runs on one side
`shift_n`	numeric	7	Min: 1	Points needed for shift
`trend`	boolean	FALSE	TRUE/FALSE	Detect trends
`trend_n`	numeric	5	Min: 1	Points needed for trend
`two_in_three`	boolean	FALSE	TRUE/FALSE	Detect 2-in-3 pattern
`two_in_three_limit`	character	“2 Sigma”	“1 Sigma”, “2 Sigma”, “3 Sigma”, “Specification”	Warning limit
`two_in_three_highlight_series`	boolean	FALSE	TRUE/FALSE	Highlight all vs outliers only
`improvement_direction`	character	“increase”	“increase”, “decrease”, “neutral”	What constitutes improvement
`process_flag_type`	character	“both”	“both”, “improvement”, “deterioration”	Which outliers to flag

NHS Icon Settings

Setting	Type	Default	Options/Range	Purpose
`show_variation_icons`	boolean	FALSE	TRUE/FALSE	Display variation icons
`flag_last_point`	boolean	TRUE	TRUE/FALSE	Icon on last point only
`variation_icons_locations`	character	“Top Right”	“Top Right”, “Bottom Right”, “Top Left”, “Bottom Left”	Icon placement
`variation_icons_scaling`	numeric	1	Min: 0	Icon size multiplier
`show_assurance_icons`	boolean	FALSE	TRUE/FALSE	Display assurance icons
`assurance_icons_locations`	character	“Top Right”	“Top Right”, “Bottom Right”, “Top Left”, “Bottom Left”	Icon placement
`assurance_icons_scaling`	numeric	1	Min: 0	Icon size multiplier

Note: Assurance icons require alt_target to be specified in line_settings.

Additional Resources

For more information, see:

vignette("getting_started") - Basic package usage
vignette("chart_types") - All available chart types
vignette("interactive_charts") - Interactive features with crosstalk