Chart Types in controlcharts • controlcharts

Overview

The controlcharts package supports a comprehensive range of Statistical Process Control (SPC) charts and funnel plots. This vignette demonstrates all available chart types with examples of their usage.

SPC Chart Types

The package supports 14 different SPC chart types, each designed for specific data characteristics and monitoring scenarios.

Basic Charts

Run Chart

A run chart displays data over time without statistical control limits. It’s useful for visualizing trends and patterns in data.

# Simulate 24 months of data
dat_run <- data.frame(
  month = seq(as.Date("2024-01-01"), length.out = 24, by = "month"),
  y = rnorm(24, mean = 100, sd = 10)
)

chart_run <- spc(data = dat_run,
                 keys = month,
                 numerators = y,
                 spc_settings = list(chart_type = "run"))

chart_run$static_plot

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

date	value	target	trend_line
01/01/2024	100.67	100.29	94.37
01/02/2024	107.95	100.29	94.74
01/03/2024	115.18	100.29	95.11
01/04/2024	78.35	100.29	95.48
01/05/2024	97.86	100.29	95.85
01/06/2024	101.29	100.29	96.22

Individuals Chart (i-chart)

The individuals chart (also called an XmR chart) is the default chart type. It monitors individual measurements with control limits based on the moving range.

dat_i <- data.frame(
  month = seq(as.Date("2024-01-01"), length.out = 24, by = "month"),
  y = rnorm(24, mean = 50, sd = 5)
)

chart_i <- spc(data = dat_i,
               keys = month,
               numerators = y,
               spc_settings = list(chart_type = "i"))

chart_i$static_plot

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

date	value	target	ll99	ll95	ll68	ul68	ul95	ul99	trend_line
01/01/2024	49.13	50.86	43.35	45.85	48.36	53.36	55.86	58.36	51.48
01/02/2024	54.28	50.86	43.35	45.85	48.36	53.36	55.86	58.36	51.43
01/03/2024	51.43	50.86	43.35	45.85	48.36	53.36	55.86	58.36	51.37
01/04/2024	50.12	50.86	43.35	45.85	48.36	53.36	55.86	58.36	51.32
01/05/2024	49.71	50.86	43.35	45.85	48.36	53.36	55.86	58.36	51.26
01/06/2024	53.72	50.86	43.35	45.85	48.36	53.36	55.86	58.36	51.21

Individuals Chart with Ratios

i-charts can also monitor ratios by providing both numerators and denominators. The chart will automatically calculate and plot the ratio with appropriate control limits.

# Example: monitoring average processing time per case
dat_ratio <- data.frame(
  month = seq(as.Date("2024-01-01"), length.out = 24, by = "month"),
  total_time = rpois(24, lambda = 120),
  num_cases = rpois(24, lambda = 25)
)

chart_ratio <- spc(data = dat_ratio,
                   keys = month,
                   numerators = total_time,
                   denominators = num_cases,
                   spc_settings = list(chart_type = "i"))

chart_ratio$static_plot

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

date	numerator	denominator	value	target	ll99	ll95	ll68	ul68	ul95	ul99	trend_line
01/01/2024	127	31	4.10	5.03	2.49	3.34	4.18	5.88	6.73	7.58	4.93
01/02/2024	114	23	4.96	5.03	2.49	3.34	4.18	5.88	6.73	7.58	4.94
01/03/2024	123	23	5.35	5.03	2.49	3.34	4.18	5.88	6.73	7.58	4.95
01/04/2024	127	26	4.88	5.03	2.49	3.34	4.18	5.88	6.73	7.58	4.96
01/05/2024	109	21	5.19	5.03	2.49	3.34	4.18	5.88	6.73	7.58	4.97
01/06/2024	109	26	4.19	5.03	2.49	3.34	4.18	5.88	6.73	7.58	4.98

Individuals Chart with Median Centerline (i_m)

Uses the median instead of the mean for the centerline, making it more robust to outliers.

dat_im <- data.frame(
  month = seq(as.Date("2024-01-01"), length.out = 24, by = "month"),
  y = c(rnorm(20, mean = 50, sd = 5), 75, 78, 72, 71)  # Some outliers
)

chart_im <- spc(data = dat_im,
                keys = month,
                numerators = y,
                spc_settings = list(chart_type = "i_m"))

chart_im$static_plot

Individuals Chart with Median Limits (i_mm)

You can use both median centerline and median-based moving range limits.

chart_imm <- spc(data = dat_im,
                 keys = month,
                 numerators = y,
                 spc_settings = list(chart_type = "i_mm"))

chart_imm$static_plot

Moving Range Chart (mr)

Plots the moving range between consecutive measurements.

dat_mr <- data.frame(
  month = seq(as.Date("2024-01-01"), length.out = 24, by = "month"),
  y = rnorm(24, mean = 10, sd = 2)
)

chart_mr <- spc(data = dat_mr,
                keys = month,
                numerators = y,
                spc_settings = list(chart_type = "mr"))

chart_mr$static_plot

Proportion Charts

p-chart (Proportions)

Monitors proportions when the numerator cannot exceed the denominator (e.g., defect rates, complication rates).

dat_p <- data.frame(
  month = seq(as.Date("2024-01-01"), length.out = 24, by = "month"),
  denominator = sample(80:120, 24, replace = TRUE)
)
dat_p$numerator <- rbinom(24, size = dat_p$denominator, prob = 0.15)

chart_p <- spc(data = dat_p,
               keys = month,
               numerators = numerator,
               denominators = denominator,
               spc_settings = list(chart_type = "p"))

chart_p$static_plot

knitr::kable(head(chart_p$limits), digits = 3)

date	numerator	denominator	value	target	ll99	ll95	ll68	ul68	ul95	ul99	trend_line
01/01/2024	14	110	12.727	15.889	5.432	8.918	12.403	19.374	22.860	26.345	0.152
01/02/2024	17	120	14.167	15.889	5.877	9.214	12.551	19.226	22.563	25.900	0.152
01/03/2024	10	83	12.048	15.889	3.851	7.863	11.876	19.901	23.914	27.927	0.153
01/04/2024	17	105	16.190	15.889	5.186	8.753	12.321	19.456	23.024	26.591	0.154
01/05/2024	10	81	12.346	15.889	3.703	7.765	11.827	19.951	24.012	28.074	0.155
01/06/2024	13	83	15.663	15.889	3.851	7.863	11.876	19.901	23.914	27.927	0.155

p-prime Chart (pp)

Proportion chart with large-sample correction for better accuracy with varying sample sizes.

chart_pp <- spc(data = dat_p,
                keys = month,
                numerators = numerator,
                denominators = denominator,
                spc_settings = list(chart_type = "pp"))

chart_pp$static_plot

Rate Charts

u-chart (Rates)

Monitors rates (events per unit exposure), such as infections per 1000 patient-days.

dat_u <- data.frame(
  month = seq(as.Date("2024-01-01"), length.out = 24, by = "month"),
  infections = rpois(24, lambda = 8),
  patient_days = sample(200:400, 24, replace = TRUE)
)

chart_u <- spc(data = dat_u,
               keys = month,
               numerators = infections,
               denominators = patient_days,
               spc_settings = list(chart_type = "u", multiplier = 1000))

chart_u$static_plot

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

date	numerator	denominator	value	target	ll99	ll95	ll68	ul68	ul95	ul99	trend_line
01/01/2024	5	305	16.39	27.35	0.00	8.41	17.88	36.82	46.28	55.75	0.02
01/02/2024	4	295	13.56	27.35	0.00	8.09	17.72	36.98	46.60	56.23	0.02
01/03/2024	8	238	33.61	27.35	0.00	5.91	16.63	38.07	48.79	59.50	0.02
01/04/2024	7	333	21.02	27.35	0.16	9.22	18.28	36.41	45.47	54.53	0.02
01/05/2024	11	258	42.64	27.35	0.00	6.76	17.05	37.64	47.94	58.23	0.03
01/06/2024	6	394	15.23	27.35	2.35	10.68	19.02	35.68	44.01	52.34	0.03

u-prime Chart (up)

Rate chart with large-sample correction.

chart_up <- spc(data = dat_u,
                keys = month,
                numerators = infections,
                denominators = patient_days,
                spc_settings = list(chart_type = "up", multiplier = 1000))

chart_up$static_plot

Count Charts

c-chart (Counts)

Monitors count data where each observation represents the number of events in a fixed area of opportunity.

dat_c <- data.frame(
  month = seq(as.Date("2024-01-01"), length.out = 24, by = "month"),
  defects = rpois(24, lambda = 12)
)

chart_c <- spc(data = dat_c,
               keys = month,
               numerators = defects,
               spc_settings = list(chart_type = "c"))

chart_c$static_plot

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

date	value	target	ll99	ll95	ll68	ul68	ul95	ul99	trend_line
01/01/2024	18	13.67	2.58	6.27	9.97	17.36	21.06	24.76	14.51
01/02/2024	11	13.67	2.58	6.27	9.97	17.36	21.06	24.76	14.43
01/03/2024	17	13.67	2.58	6.27	9.97	17.36	21.06	24.76	14.36
01/04/2024	11	13.67	2.58	6.27	9.97	17.36	21.06	24.76	14.29
01/05/2024	10	13.67	2.58	6.27	9.97	17.36	21.06	24.76	14.21
01/06/2024	21	13.67	2.58	6.27	9.97	17.36	21.06	24.76	14.14

Sample-Based Charts

xbar Chart (Sample Means)

Monitors the mean of samples when multiple measurements are taken at each time point.

dat_xbar <- data.frame(
  month = seq(as.Date("2024-01-01"), length.out = 24, by = "month"),
  sample_mean = rnorm(24, mean = 100, sd = 3),
  sample_size = rep(5, 24),
  sample_sd = rchisq(24, df = 4) / 2
)

chart_xbar <- spc(data = dat_xbar,
                  keys = month,
                  numerators = sample_mean,
                  denominators = sample_size,
                  xbar_sds = sample_sd,
                  spc_settings = list(chart_type = "xbar"))

chart_xbar$static_plot

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

date	value	target	ll99	ll95	ll68	ul68	ul95	ul99	trend_line
01/01/2024	99.11	99.97	95.62	97.07	98.52	101.42	102.87	104.32	99.52
01/02/2024	99.74	99.97	95.62	97.07	98.52	101.42	102.87	104.32	99.56
01/03/2024	96.38	99.97	95.62	97.07	98.52	101.42	102.87	104.32	99.60
01/04/2024	98.11	99.97	95.62	97.07	98.52	101.42	102.87	104.32	99.64
01/05/2024	97.13	99.97	95.62	97.07	98.52	101.42	102.87	104.32	99.67
01/06/2024	100.52	99.97	95.62	97.07	98.52	101.42	102.87	104.32	99.71

s Chart (Sample Standard Deviations)

Monitors the standard deviation of samples.

chart_s <- spc(data = dat_xbar,
               keys = month,
               numerators = sample_sd,
               denominators = sample_size,
               spc_settings = list(chart_type = "s"))

chart_s$static_plot

Event-Based Charts

Event-based charts use sequential event numbers on the x-axis rather than dates.

g Chart (Geometric)

Monitors the number of non-events between events (e.g., days between accidents).

dat_g <- data.frame(
  event = 1:20,
  opportunities_between = rpois(20, lambda = 15)
)

chart_g <- spc(data = dat_g,
               keys = event,
               numerators = opportunities_between,
               spc_settings = list(chart_type = "g"))

chart_g$static_plot

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

date	value	target	ul68	ul95	ul99	trend_line
0	12	14.5	29.89	45.08	60.28	12.77
1	17	14.5	29.89	45.08	60.28	12.97
2	13	14.5	29.89	45.08	60.28	13.18
3	11	14.5	29.89	45.08	60.28	13.38
4	11	14.5	29.89	45.08	60.28	13.58
5	19	14.5	29.89	45.08	60.28	13.79

t Chart (Time Between Events)

Monitors the time intervals between events.

dat_t <- data.frame(
  event = 1:20,
  days_between = rexp(20, rate = 1 / 30)
)

chart_t <- spc(data = dat_t,
               keys = event,
               numerators = days_between,
               spc_settings = list(chart_type = "t"))

chart_t$static_plot

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

date	value	target	ll99	ll95	ll68	ul68	ul95	ul99	trend_line
0	14.40	16.59	0.92	3.17	7.97	30.5	51.34	80.9	25.67
1	68.34	16.59	0.92	3.17	7.97	30.5	51.34	80.9	25.09
2	12.76	16.59	0.92	3.17	7.97	30.5	51.34	80.9	24.52
3	10.45	16.59	0.92	3.17	7.97	30.5	51.34	80.9	23.95
4	32.05	16.59	0.92	3.17	7.97	30.5	51.34	80.9	23.37
5	15.34	16.59	0.92	3.17	7.97	30.5	51.34	80.9	22.80

Funnel Plot Types

Funnel plots are used to compare performance across multiple organizations or groups, accounting for natural variation due to different sample sizes.

PR - Proportion Funnel

The default funnel plot type for proportion data. Uses arc-sine transformation.

# Simulate complication rates across 10 hospitals
denoms_pr <- sample(100:300, 10)
dat_pr <- data.frame(
  hospital = paste0("Hospital ", LETTERS[1:10]),
  complications = rbinom(10, size = denoms_pr, prob = 0.12),
  procedures = denoms_pr
)

funnel_pr <- funnel(data = dat_pr,
                    keys = hospital,
                    numerators = complications,
                    denominators = procedures,
                    funnel_settings = list(chart_type = "PR"))

funnel_pr$static_plot

knitr::kable(funnel_pr$limits, digits = 3)

denominator	group	numerator	value	z	ll99	ll95	ll68	ul68	ul95	ul99	target
139	Hospital G	19	13.669	0.867	4.381	6.549	8.719	14.041	17.004	20.751	11.242
148	Hospital A	24	16.216	1.765	4.548	6.677	8.792	13.951	16.811	20.424	11.242
166	Hospital I	18	10.843	-0.164	4.848	6.903	8.922	13.793	16.477	19.856	11.242
179	Hospital D	13	7.263	-1.848	5.040	7.046	9.004	13.695	16.268	19.503	11.242
191	Hospital F	19	9.948	-0.582	5.203	7.167	9.072	13.614	16.096	19.212	11.242
215	Hospital B	31	14.419	1.395	5.492	7.378	9.191	13.472	15.798	18.708	11.242
242	Hospital C	23	9.504	-0.888	5.771	7.580	9.304	13.339	15.519	18.238	11.242
260	Hospital E	30	11.538	0.150	5.935	7.698	9.369	13.263	15.358	17.969	11.242
284	Hospital J	34	11.972	0.384	6.132	7.839	9.447	13.173	15.170	17.652	11.242
293	Hospital H	27	9.215	-1.147	6.200	7.887	9.474	13.142	15.105	17.544	11.242

PR Funnel with Overdispersion Adjustment

When data shows more variation than expected, overdispersion adjustment widens the control limits.

funnel_pr_od <- funnel(data = dat_pr,
                       keys = hospital,
                       numerators = complications,
                       denominators = procedures,
                       funnel_settings = list(chart_type = "PR",
                                              od_adjust = "yes"))

funnel_pr_od$static_plot

knitr::kable(funnel_pr_od$limits, digits = 3)

denominator	group	numerator	value	z	ll99	ll95	ll68	ul68	ul95	ul99	target
139	Hospital G	19	13.669	0.867	4.381	6.549	8.719	14.041	17.004	20.751	11.242
148	Hospital A	24	16.216	1.765	4.548	6.677	8.792	13.951	16.811	20.424	11.242
166	Hospital I	18	10.843	-0.164	4.848	6.903	8.922	13.793	16.477	19.856	11.242
179	Hospital D	13	7.263	-1.848	5.040	7.046	9.004	13.695	16.268	19.503	11.242
191	Hospital F	19	9.948	-0.582	5.203	7.167	9.072	13.614	16.096	19.212	11.242
215	Hospital B	31	14.419	1.395	5.492	7.378	9.191	13.472	15.798	18.708	11.242
242	Hospital C	23	9.504	-0.888	5.771	7.580	9.304	13.339	15.519	18.238	11.242
260	Hospital E	30	11.538	0.150	5.935	7.698	9.369	13.263	15.358	17.969	11.242
284	Hospital J	34	11.972	0.384	6.132	7.839	9.447	13.173	15.170	17.652	11.242
293	Hospital H	27	9.215	-1.147	6.200	7.887	9.474	13.142	15.105	17.544	11.242

SR - Standardised Ratio Funnel

Used for indirectly standardized ratios (e.g., HSMR - Hospital Standardised Mortality Ratio).

# Simulate observed and expected deaths
dat_sr <- data.frame(
  hospital = paste0("Hospital ", LETTERS[1:10]),
  observed_deaths = rpois(10, lambda = sample(30:80, 10)),
  expected_deaths = sample(30:80, 10)
)

funnel_sr <- funnel(data = dat_sr,
                    keys = hospital,
                    numerators = observed_deaths,
                    denominators = expected_deaths,
                    funnel_settings = list(chart_type = "SR"))

funnel_sr$static_plot

knitr::kable(funnel_sr$limits, digits = 3)

denominator	group	numerator	value	z	ll99	ll95	ll68	ul68	ul95	ul99	target
35	Hospital I	43	1.229	1.153	0.558	0.697	0.833	1.198	1.391	1.641	1
38	Hospital J	68	1.789	3.876	0.572	0.708	0.839	1.189	1.373	1.610	1
42	Hospital F	55	1.310	1.733	0.590	0.721	0.847	1.178	1.352	1.575	1
54	Hospital B	63	1.167	1.075	0.632	0.751	0.865	1.155	1.305	1.496	1
56	Hospital A	35	0.625	-3.216	0.637	0.755	0.867	1.151	1.299	1.486	1
57	Hospital D	65	1.140	0.928	0.640	0.757	0.869	1.150	1.296	1.481	1
59	Hospital G	60	1.017	0.043	0.645	0.761	0.871	1.147	1.290	1.471	1
62	Hospital H	82	1.323	2.229	0.653	0.767	0.874	1.143	1.282	1.458	1
63	Hospital C	70	1.111	0.769	0.655	0.768	0.875	1.142	1.279	1.454	1
71	Hospital E	49	0.690	-2.902	0.673	0.781	0.882	1.133	1.261	1.424	1

SR Funnel with Overdispersion Adjustment

funnel_sr_od <- funnel(data = dat_sr,
                       keys = hospital,
                       numerators = observed_deaths,
                       denominators = expected_deaths,
                       funnel_settings = list(chart_type = "SR",
                                              od_adjust = "yes"))

funnel_sr_od$static_plot

knitr::kable(funnel_sr_od$limits, digits = 3)

denominator	group	numerator	value	z	ll99	ll95	ll68	ul68	ul95	ul99	target
35	Hospital I	43	1.229	0.738	0.298	0.507	0.729	1.314	1.659	2.115	1
38	Hospital J	68	1.789	2.328	0.304	0.512	0.732	1.309	1.649	2.097	1
42	Hospital F	55	1.310	1.010	0.312	0.518	0.736	1.304	1.638	2.078	1
54	Hospital B	63	1.167	0.580	0.328	0.532	0.744	1.294	1.615	2.036	1
56	Hospital A	35	0.625	-1.522	0.331	0.533	0.745	1.292	1.612	2.031	1
57	Hospital D	65	1.140	0.494	0.332	0.534	0.746	1.292	1.610	2.028	1
59	Hospital G	60	1.017	0.062	0.333	0.536	0.747	1.290	1.608	2.024	1
62	Hospital H	82	1.323	1.103	0.336	0.538	0.748	1.289	1.604	2.017	1
63	Hospital C	70	1.111	0.398	0.337	0.539	0.748	1.288	1.603	2.015	1
71	Hospital E	49	0.690	-1.262	0.343	0.543	0.751	1.284	1.595	2.000	1

RC - Rate Funnel

Used for rate data with log transformation.

# Simulate infection rates across hospitals
dat_rc <- data.frame(
  hospital = paste0("Hospital ", LETTERS[1:10]),
  infections = rpois(10, lambda = sample(5:20, 10)),
  patient_days = sample(500:2000, 10)
)

funnel_rc <- funnel(data = dat_rc,
                    keys = hospital,
                    numerators = infections,
                    denominators = patient_days,
                    funnel_settings = list(chart_type = "RC",
                                           multiplier = 1000))

funnel_rc$static_plot

knitr::kable(funnel_rc$limits, digits = 3)

denominator	group	numerator	value	z	ll99	ll95	ll68	ul68	ul95	ul99	target
797	Hospital C	10	12.547	0.775	8.918	9.437	9.904	10.941	11.483	12.151	10.41
800	Hospital H	17	21.250	3.114	8.920	9.438	9.905	10.940	11.481	12.148	10.41
823	Hospital I	5	6.075	-1.088	8.940	9.451	9.912	10.932	11.465	12.121	10.41
921	Hospital E	4	4.343	-1.698	9.014	9.501	9.938	10.903	11.405	12.021	10.41
955	Hospital G	23	24.084	4.161	9.038	9.517	9.947	10.894	11.386	11.990	10.41
1123	Hospital A	15	13.357	1.119	9.137	9.584	9.982	10.856	11.307	11.859	10.41
1405	Hospital D	11	7.829	-0.831	9.264	9.668	10.026	10.807	11.208	11.696	10.41
1530	Hospital F	15	9.804	-0.109	9.310	9.698	10.042	10.791	11.174	11.640	10.41
1657	Hospital B	16	9.656	-0.183	9.350	9.725	10.056	10.775	11.143	11.589	10.41
1805	Hospital J	7	3.878	-2.598	9.392	9.752	10.071	10.760	11.111	11.537	10.41

RC Funnel with Overdispersion Adjustment

funnel_rc_od <- funnel(data = dat_rc,
                       keys = hospital,
                       numerators = infections,
                       denominators = patient_days,
                       funnel_settings = list(chart_type = "RC",
                                              od_adjust = "yes",
                                              multiplier = 1000))

funnel_rc_od$static_plot

knitr::kable(funnel_rc_od$limits, digits = 3)

denominator	group	numerator	value	z	ll99	ll95	ll68	ul68	ul95	ul99	target
797	Hospital C	10	12.547	0.450	2.776	4.501	6.803	15.928	24.073	39.039	10.41
800	Hospital H	17	21.250	1.523	2.776	4.501	6.803	15.928	24.072	39.037	10.41
823	Hospital I	5	6.075	-0.753	2.777	4.502	6.804	15.927	24.069	39.027	10.41
921	Hospital E	4	4.343	-1.229	2.779	4.505	6.806	15.922	24.054	38.991	10.41
955	Hospital G	23	24.084	1.820	2.780	4.506	6.806	15.921	24.050	38.980	10.41
1123	Hospital A	15	13.357	0.570	2.783	4.509	6.809	15.915	24.033	38.936	10.41
1405	Hospital D	11	7.829	-0.468	2.787	4.513	6.812	15.908	24.013	38.886	10.41
1530	Hospital F	15	9.804	-0.056	2.788	4.514	6.812	15.906	24.007	38.869	10.41
1657	Hospital B	16	9.656	-0.091	2.789	4.515	6.813	15.904	24.001	38.855	10.41
1805	Hospital J	7	3.878	-1.662	2.790	4.516	6.814	15.902	23.996	38.841	10.41

Chart Selection Guide

SPC Chart Selection

Data Type	Chart Type	When to Use
Individual measurements	`i`, `i_m`, `i_mm`	Single measurement per time point
Ratios/efficiency metrics	`i` with denominators	Monitoring rates calculated from num/denom
Moving ranges	`mr`	Monitoring variation between consecutive points
Proportions	`p`, `pp`	Numerator ≤ denominator (e.g., defect rates)
Rates	`u`, `up`	Events per unit exposure (any ratio)
Counts	`c`	Fixed area of opportunity
Sample means	`xbar`	Multiple measurements per time point
Sample SDs	`s`	Monitoring within-sample variation
Opportunities between events	`g`	Count of non-events between events
Time between events	`t`	Continuous time intervals
Trend visualization only	`run`	No statistical limits needed

Funnel Plot Selection

Data Type	Chart Type	When to Use
Proportions	`PR`	Numerator ≤ denominator (complication rates, readmissions)
Standardised ratios	`SR`	Observed/expected (mortality, morbidity ratios)
Rates	`RC`	Events per exposure (infections per patient-days)

Overdispersion Adjustment

Use od_adjust = "yes" when: - Data shows more variation than expected from binomial/Poisson models - There is clustering or hierarchical structure in the data - Groups have different underlying risks not captured by standardization - Control limits appear too narrow for the observed variation

Data Requirements Summary

SPC Charts

Chart Type	Numerator	Denominator	Standard Deviation
run, i, i_m, i_mm, mr	✓	Optional*	-
p, pp	✓	✓	-
u, up	✓	✓	-
c	✓	-	-
xbar	✓	✓	✓
s	✓**	✓	-
g, t	✓	-	-

*Optional denominator creates a ratio chart **For s-chart, numerator contains standard deviation values

Funnel Plots

All funnel plot types require: - Keys (organization/group identifier) - Numerators (event counts or observed values) - Denominators (sample sizes or expected values)

Additional constraints: - PR: Numerator must be ≤ denominator - SR: Typically observed/expected counts or deaths - RC: Any rate data (events/exposure)

Additional Resources

For more information, see: - vignette("getting_started") - Basic package usage - vignette("interactive_charts") - Interactive features with crosstalk