Help with statistical control(histogram ,cp and cpk )for all columns

[danut_horincas]

Min A	Max A	Min B	Max B	Sample	Dif A	Dif B
0.28	0.4	0.29	0.4	1	0.12	0.11
0.28	0.42	0.3	0.39	2	0.14	0.09
0.26	0.39	0.31	0.4	3	0.13	0.09
0.27	0.41	0.32	0.4	4	0.14	0.08
0.25	0.4	0.3	0.39	5	0.15	0.09
0.27	0.42	0.29	0.38	6	0.15	0.09
0.28	0.4	0.29	0.39	7	0.12	0.1
0.26	0.39	0.31	0.42	8	0.13	0.11
0.28	0.37	0.3	0.4	9	0.09	0.1
0.24	0.36	0.34	0.37	10	0.12	0.03
0.25	0.36	0.33	0.39	11	0.11	0.06
0.27	0.38	0.31	0.4	12	0.11	0.09
0.24	0.39	0.34	0.35	13	0.15	0.01
0.26	0.35	0.34	0.4	14	0.09	0.06
0.28	0.37	0.36	0.39	15	0.09	0.03
0.26	0.37	0.3	0.45	16	0.11	0.15
0.23	0.39	0.35	0.39	17	0.16	0.04
0.29	0.36	0.33	0.44	18	0.07	0.11
0.28	0.37	0.35	0.43	19	0.09	0.08
0.3	0.37	0.31	0.4	20	0.07	0.09
0.29	0.39	0.34	0.41	21	0.1	0.07
0.27	0.39	0.32	0.4	22	0.12	0.08
0.23	0.35	0.32	0.35	23	0.12	0.03
0.24	0.32	0.3	0.34	24	0.08	0.04
0.3	0.48	0.27	0.36	25	0.18	0.09
0.3	0.37	0.28	0.35	26	0.07	0.07
0.29	0.4	0.3	0.38	27	0.11	0.08
0.27	0.41	0.31	0.36	28	0.14	0.05
0.26	0.38	0.31	0.37	29	0.12	0.06
0.28	0.37	0.25	0.33	30	0.09	0.08
0.25	0.34	0.27	0.34	31	0.09	0.07
0.25	0.43	0.28	0.35	32	0.18	0.07
0.3	0.39	0.26	0.39	33	0.09	0.13
0.23	0.42	0.26	0.35	34	0.19	0.09
0.29	0.4	0.29	0.33	35	0.11	0.04
0.21	0.43	0.27	0.32	36	0.22	0.05
0.26	0.38	0.29	0.36	37	0.12	0.07
0.28	0.41	0.26	0.33	38	0.13	0.07
0.31	0.38	0.26	0.37	39	0.07	0.11
0.27	0.39	0.3	0.35	40	0.12	0.05
0.28	0.4	0.28	0.35	41	0.12	0.07
0.3	0.38	0.26	0.35	42	0.08	0.09
0.27	0.43	0.28	0.35	43	0.16	0.07
0.28	0.4	0.27	0.36	44	0.12	0.09
0.33	0.41	0.26	0.34	45	0.08	0.08
0.3	0.41	0.26	0.35	46	0.11	0.09
0.27	0.42	0.28	0.35	47	0.15	0.07
0.3	0.39	0.28	0.34	48	0.09	0.06
0.31	0.42	0.28	0.35	49	0.11	0.07
0.33	0.44	0.27	0.39	50	0.11	0.12
0.26	0.38	0.27	0.36	51	0.12	0.09
0.27	0.38	0.27	0.39	52	0.11	0.12
0.24	0.37	0.25	0.38	53	0.13	0.13
0.34	0.44	0.35	0.42	54	0.1	0.07
0.33	0.43	0.35	0.43	55	0.1	0.08
0.3	0.43	0.3	0.42	56	0.13	0.12
0.3	0.42	0.32	0.43	57	0.12	0.11
0.29	0.42	0.32	0.42	58	0.13	0.1
0.3	0.41	0.29	0.4	59	0.11	0.11
0.29	0.43	0.3	0.42	60	0.14	0.12
0.3	0.4	0.32	0.41	61	0.1	0.09
0.29	0.43	0.31	0.41	62	0.14	0.1
0.31	0.45	0.33	0.4	63	0.14	0.07
0.32	0.42	0.31	0.41	64	0.1	0.1
0.29	0.42	0.31	0.41	65	0.13	0.1
0.3	0.43	0.31	0.42	66	0.13	0.11
0.31	0.37	0.3	0.37	67	0.06	0.07

#mydata are above

library(qcc)
require(qcc)
mydata <- read.csv("d:/temp/ARC.csv", header=TRUE)

head(mydata)

mydata <- read.csv("d:/temp/ARC.csv", header=TRUE)
mydata.one<-with(mydata, qcc.groups(Min.A,
Sample))
mydata.xbar.one <- qcc(mydata.one, type="xbar.one")
summary(mydata.xbar.one)
plot(qcc(data = mydata.one,
type = "xbar.one",
center = mydata.xbar.one$center,
limits = mydata.xbar.one$limits))
mydata.xbar.one<-qcc(mydata.xbar.one, type="xbar.one")
process.capability(mydata.xbar.one, spec.limits=c(0.2,0.5))

[FJCC]

I am not sure if this does everything you want. Note at the bottom is an example of getting a summary of the Min_A qcc result.

library(tidyverse)
#> Warning: package 'tibble' was built under R version 4.1.2
library(qcc)
#> Warning: package 'qcc' was built under R version 4.1.2
#> Package 'qcc' version 2.7
#> Type 'citation("qcc")' for citing this R package in publications.
mydata <- read.csv("~/R/Play/Dummy.csv", header=TRUE)
CpFunc <- function(vec){
  qccOut <- qcc(mydata[[vec]], type="xbar.one",title=vec)
  ProcessCap <- process.capability(qccOut, spec.limits=c(0.2,0.5))
  return(list(qcc=qccOut,Process=ProcessCap))
}
Nms <- colnames(mydata)[1:4]
CpList <- map(Nms,CpFunc)

#> 
#> Process Capability Analysis
#> 
#> Call:
#> process.capability(object = qccOut, spec.limits = c(0.2, 0.5))
#> 
#> Number of obs = 67           Target = 0.35
#>        Center = 0.2794          LSL = 0.2
#>        StdDev = 0.02216         USL = 0.5
#> 
#> Capability indices:
#> 
#>        Value    2.5%  97.5%
#> Cp    2.2560  1.8717  2.640
#> Cp_l  1.1942  1.0106  1.378
#> Cp_u  3.3178  2.8381  3.797
#> Cp_k  1.1942  0.9754  1.413
#> Cpm   0.6757  0.5182  0.833
#> 
#> Exp<LSL 0.017%    Obs<LSL 0%
#> Exp>USL 0%    Obs>USL 0%

#> 
#> Process Capability Analysis
#> 
#> Call:
#> process.capability(object = qccOut, spec.limits = c(0.2, 0.5))
#> 
#> Number of obs = 67           Target = 0.35
#>        Center = 0.3975          LSL = 0.2
#>        StdDev = 0.0227          USL = 0.5
#> 
#> Capability indices:
#> 
#>        Value    2.5%  97.5%
#> Cp    2.2026  1.8274  2.577
#> Cp_l  2.8995  2.4791  3.320
#> Cp_u  1.5057  1.2799  1.731
#> Cp_k  1.5057  1.2367  1.775
#> Cpm   0.9504  0.7344  1.166
#> 
#> Exp<LSL 0%    Obs<LSL 0%
#> Exp>USL 0%    Obs>USL 0%

#> 
#> Process Capability Analysis
#> 
#> Call:
#> process.capability(object = qccOut, spec.limits = c(0.2, 0.5))
#> 
#> Number of obs = 67           Target = 0.35
#>        Center = 0.2991          LSL = 0.2
#>        StdDev = 0.01625         USL = 0.5
#> 
#> Capability indices:
#> 
#>        Value    2.5%  97.5%
#> Cp    3.0764  2.5524  3.599
#> Cp_l  2.0325  1.7339  2.331
#> Cp_u  4.1202  3.5265  4.714
#> Cp_k  2.0325  1.6767  2.388
#> Cpm   0.9358  0.7178  1.153
#> 
#> Exp<LSL 0%    Obs<LSL 0%
#> Exp>USL 0%    Obs>USL 0%

#> 
#> Process Capability Analysis
#> 
#> Call:
#> process.capability(object = qccOut, spec.limits = c(0.2, 0.5))
#> 
#> Number of obs = 67           Target = 0.35
#>        Center = 0.3813          LSL = 0.2
#>        StdDev = 0.01786         USL = 0.5
#> 
#> Capability indices:
#> 
#>       Value   2.5%  97.5%
#> Cp    2.799  2.322  3.275
#> Cp_l  3.384  2.895  3.873
#> Cp_u  2.214  1.890  2.538
#> Cp_k  2.214  1.828  2.600
#> Cpm   1.386  1.076  1.695
#> 
#> Exp<LSL 0%    Obs<LSL 0%
#> Exp>USL 0%    Obs>USL 0%

names(CpList) <- Nms
summary(CpList$Min_A$qcc)
#>
#> Call:
#> qcc(data = mydata[[vec]], type = "xbar.one", title = vec)
#>
#> xbar.one chart for mydata[[vec]]
#>
#> Summary of group statistics:
#> Min. 1st Qu. Median Mean 3rd Qu. Max.
#> 0.210000 0.260000 0.280000 0.279403 0.300000 0.340000
#>
#> Group sample size: 1
#> Number of groups: 67
#> Center of group statistics: 0.279403
#> Standard deviation: 0.02216312
#>
#> Control limits:
#> LCL UCL
#> 0.2129136 0.3458923

^{Created on 2022-02-28 by the reprex package (v2.0.1)}