Chart Types in 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	15.173
01/02/2024	17	120	14.167	15.889	5.877	9.214	12.551	19.226	22.563	25.900	15.247
01/03/2024	10	83	12.048	15.889	3.851	7.863	11.876	19.901	23.914	27.927	15.321
01/04/2024	17	105	16.190	15.889	5.186	8.753	12.321	19.456	23.024	26.591	15.395
01/05/2024	10	81	12.346	15.889	3.703	7.765	11.827	19.951	24.012	28.074	15.469
01/06/2024	13	83	15.663	15.889	3.851	7.863	11.876	19.901	23.914	27.927	15.543

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	23.83
01/02/2024	4	295	13.56	27.35	0.00	8.09	17.72	36.98	46.60	56.23	24.21
01/03/2024	8	238	33.61	27.35	0.00	5.91	16.63	38.07	48.79	59.50	24.58
01/04/2024	7	333	21.02	27.35	0.16	9.22	18.28	36.41	45.47	54.53	24.96
01/05/2024	11	258	42.64	27.35	0.00	6.76	17.05	37.64	47.94	58.23	25.34
01/06/2024	6	394	15.23	27.35	2.35	10.68	19.02	35.68	44.01	52.34	25.72

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	96.37	97.57	98.77	101.17	102.37	103.56	99.52
01/02/2024	99.74	99.97	96.37	97.57	98.77	101.17	102.37	103.56	99.56
01/03/2024	96.38	99.97	96.37	97.57	98.77	101.17	102.37	103.56	99.60
01/04/2024	98.11	99.97	96.37	97.57	98.77	101.17	102.37	103.56	99.64
01/05/2024	97.13	99.97	96.37	97.57	98.77	101.17	102.37	103.56	99.67
01/06/2024	100.52	99.97	96.37	97.57	98.77	101.17	102.37	103.56	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	target	ll99	ll95	ll68	ul68	ul95	ul99
139	Hospital G	19	13.669	0.867	11.242	4.381	6.549	8.719	14.041	17.004	20.751
148	Hospital A	24	16.216	1.765	11.242	4.548	6.677	8.792	13.951	16.811	20.424
166	Hospital I	18	10.843	-0.164	11.242	4.848	6.903	8.922	13.793	16.477	19.856
179	Hospital D	13	7.263	-1.848	11.242	5.040	7.046	9.004	13.695	16.268	19.503
191	Hospital F	19	9.948	-0.582	11.242	5.203	7.167	9.072	13.614	16.096	19.212
215	Hospital B	31	14.419	1.395	11.242	5.492	7.378	9.191	13.472	15.798	18.708
242	Hospital C	23	9.504	-0.888	11.242	5.771	7.580	9.304	13.339	15.519	18.238
260	Hospital E	30	11.538	0.150	11.242	5.935	7.698	9.369	13.263	15.358	17.969
284	Hospital J	34	11.972	0.384	11.242	6.132	7.839	9.447	13.173	15.170	17.652
293	Hospital H	27	9.215	-1.147	11.242	6.200	7.887	9.474	13.142	15.105	17.544

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

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

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

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	target	ll99	ll95	ll68	ul68	ul95	ul99
797	Hospital C	10	12.547	0.567	10.41	3.762	5.459	7.502	14.444	19.851	28.804
800	Hospital H	17	21.250	2.170	10.41	3.768	5.465	7.506	14.436	19.830	28.755
823	Hospital I	5	6.075	-1.659	10.41	3.817	5.509	7.537	14.377	19.670	28.391
921	Hospital E	4	4.343	-2.832	10.41	4.010	5.684	7.658	14.150	19.064	27.024
955	Hospital G	23	24.084	2.762	10.41	4.072	5.740	7.696	14.080	18.878	26.609
1123	Hospital A	15	13.357	0.884	10.41	4.354	5.989	7.863	13.780	18.094	24.888
1405	Hospital D	11	7.829	-1.120	10.41	4.745	6.324	8.084	13.404	17.134	22.837
1530	Hospital F	15	9.804	-0.245	10.41	4.893	6.449	8.165	13.272	16.802	22.146
1657	Hospital B	16	9.656	-0.319	10.41	5.031	6.564	8.238	13.154	16.509	21.538
1805	Hospital J	7	3.878	-4.370	10.41	5.178	6.685	8.315	13.032	16.209	20.925

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	target	ll99	ll95	ll68	ul68	ul95	ul99
797	Hospital C	10	12.547	0.347	10.41	1.977	3.630	6.099	17.766	29.852	54.806
800	Hospital H	17	21.250	1.328	10.41	1.979	3.632	6.101	17.760	29.832	54.749
823	Hospital I	5	6.075	-1.007	10.41	1.995	3.650	6.117	17.716	29.686	54.326
921	Hospital E	4	4.343	-1.665	10.41	2.054	3.719	6.175	17.548	29.136	52.747
955	Hospital G	23	24.084	1.606	10.41	2.073	3.741	6.193	17.497	28.969	52.271
1123	Hospital A	15	13.357	0.489	10.41	2.153	3.832	6.269	17.285	28.280	50.325
1405	Hospital D	11	7.829	-0.575	10.41	2.254	3.945	6.362	17.031	27.469	48.068
1530	Hospital F	15	9.804	-0.122	10.41	2.290	3.984	6.394	16.946	27.199	47.325
1657	Hospital B	16	9.656	-0.155	10.41	2.321	4.019	6.423	16.872	26.964	46.682
1805	Hospital J	7	3.878	-2.052	10.41	2.353	4.054	6.451	16.797	26.730	46.044

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