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尝试添加包括本机min函数的计算值

min r
  •  -1
  • trayvou jba  · 技术社区  · 8 年前

    我想在数据帧中添加一个计算列,以查找每一系列步骤名称(R1,R2…)上的CpK以数据列为度量值

    Cpk的计算为: 

    m <- mean(table1$Data)
s <- sd(table1$Data)

Ts <- max(table1$HIGH_LIMIT)
Ti <- min(table1$LOW_LIMIT) 

CpK <- min((m-Ti)/(3*s), (Ts-m)/(3*s))

    我试过了 

    library(dplyr)
table2 <- table1 %>%
group_by(STEP_NAME) %>%
summarise(m = mean(DATA), s = sd(DATA), Ts = max(High_Limit),
          Ts = max(High_Limit), Ti = min(LOW_LIMIT)) %>%
          mutate(CpK = min((m-Ti)/(3*s), (Ts-m)/(3*s)))

    CpK值总是一个常数,我想min函数不适用于这种格式,知道吗?我还尝试添加Cp=(Ts Ti)/6s的Cp列,效果很好

    这里是我的数据帧示例的dput()导出: 

    structure(list(UUT_NAME = structure(c(31L, 31L, 31L, 31L, 31L, 
12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 30L, 30L, 30L, 
30L, 30L, 30L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 
15L, 15L, 15L, 15L, 15L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 
16L, 16L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 
10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 
10L, 5L, 5L, 5L, 5L, 5L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 
13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 14L, 
14L, 14L, 14L, 14L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 24L, 
24L, 24L, 24L, 24L, 24L, 24L, 24L, 24L, 24L, 18L, 18L, 18L, 18L, 
18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 
18L, 18L, 18L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 
8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 7L, 
7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 
7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 
7L, 7L, 8L, 8L, 8L, 8L, 8L, 20L, 20L, 20L, 20L, 20L, 4L, 4L, 
4L, 4L, 4L, 23L, 23L, 23L, 23L, 23L, 20L, 20L, 20L, 20L, 20L, 
23L, 23L, 23L, 23L, 23L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 
4L, 4L, 4L, 4L, 4L, 4L, 23L, 23L, 23L, 23L, 23L, 21L, 21L, 21L, 
21L, 21L, 9L, 9L, 9L, 9L, 9L, 29L, 29L, 29L, 29L, 29L, 29L, 29L, 
29L, 29L, 29L, 29L, 29L, 29L, 29L, 29L, 29L, 29L, 29L, 29L, 29L, 
29L, 29L, 29L, 29L, 29L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 
22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 11L, 
11L, 11L, 11L, 11L, 28L, 28L, 28L, 28L, 28L, 28L, 28L, 28L, 28L, 
28L, 28L, 28L, 28L, 28L, 28L, 32L, 32L, 32L, 32L, 32L, 32L, 32L, 
32L, 32L, 32L, 25L, 25L, 25L, 25L, 25L, 32L, 32L, 32L, 32L, 32L, 
27L, 27L, 27L, 27L, 27L, 26L, 26L, 26L, 26L, 26L, 19L, 19L, 19L, 
19L, 19L, 1L, 1L, 1L, 1L, 1L, 19L, 19L, 19L, 19L, 19L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 19L, 19L, 
19L, 19L, 19L, 3L, 3L, 3L, 3L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 
3L, 3L, 3L, 3L, 3L, 3L, 3L), .Label = c("CELOGIC.MA-0530", "CELOGIC.MA-0742", 
"CELOGIC.MA-0747", "CELOGIC.MA-0817", "CELOGIC.MA-0887", "CELOGIC.MA-0906", 
"CELOGIC.MA-100", "CELOGIC.MA-102", "CELOGIC.MA-1441", "CELOGIC.MA-1610", 
"CELOGIC.MA-1611", "CELOGIC.MA-2161", "CELOGIC.MA-2189", "CELOGIC.MA-2316", 
"CELOGIC.MA-2374", "CELOGIC.MA-2454", "CELOGIC.MA-2609", "CELOGIC.MA-2610", 
"CELOGIC.MA-2616", "CELOGIC.MA-2641", "CELOGIC.MA-3029", "CELOGIC.MA-3044", 
"CELOGIC.MA-3083", "CELOGIC.MA-3154", "CELOGIC.MA-3238", "CELOGIC.MA-3786", 
"CELOGIC.MA-3826", "CELOGIC.MA-4066", "CELOGIC.MA-4305", "CELOGIC.MA-4329", 
"CELOGIC.MA-6475", "CELOGIC.MA-9014"), class = "factor"), STATION_NUM = structure(c(1L, 
1L, 1L, 1L, 1L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
3L, 3L, 3L, 3L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 4L, 4L, 4L, 
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3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 5L, 5L, 5L, 5L, 5L, 
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5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 3L, 3L, 3L, 3L, 3L, 5L, 5L, 5L, 
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5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 4L, 4L, 4L, 4L, 4L, 4L, 
4L, 4L, 4L, 4L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 
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5L, 1L, 1L, 1L, 1L, 1L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 
4L, 4L, 4L, 4L, 4L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 4L, 4L, 
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 
4L, 4L, 4L, 4L, 4L, 4L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L), .Label = c("00 00297", "00 00323", 
"00 00362", "00 00366", "00 00399"), class = "factor"), START_DATE_TIME = structure(c(1L, 
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5L, 5L, 5L, 5L, 6L, 6L, 6L, 6L, 6L, 7L, 7L, 7L, 7L, 7L, 8L, 8L, 
8L, 8L, 8L, 9L, 9L, 9L, 9L, 9L, 10L, 10L, 10L, 10L, 10L, 11L, 
11L, 11L, 11L, 11L, 12L, 12L, 12L, 12L, 12L, 13L, 13L, 13L, 13L, 
13L, 14L, 14L, 14L, 14L, 14L, 15L, 15L, 15L, 15L, 15L, 16L, 16L, 
16L, 16L, 16L, 17L, 17L, 17L, 17L, 17L, 18L, 18L, 18L, 18L, 18L, 
19L, 19L, 19L, 19L, 19L, 20L, 20L, 20L, 20L, 20L, 21L, 21L, 21L, 
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29L, 29L, 29L, 30L, 30L, 30L, 30L, 30L, 31L, 31L, 31L, 31L, 31L, 
32L, 32L, 32L, 32L, 32L, 33L, 33L, 33L, 33L, 33L, 34L, 34L, 34L, 
34L, 34L, 35L, 35L, 35L, 35L, 35L, 36L, 36L, 36L, 36L, 36L, 39L, 
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63L, 63L, 63L, 63L, 64L, 64L, 64L, 64L, 64L, 65L, 65L, 65L, 65L, 
65L, 66L, 66L, 66L, 66L, 66L, 67L, 67L, 67L, 67L, 67L, 68L, 68L, 
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), class = "factor"), LOW_LIMIT = c(88, 87.9, 2.08, 29.1, 29.1, 
88, 87.9, 2.08, 29.1, 29.1, 88, 87.9, 2.08, 29.1, 29.1, 88, 88, 
87.9, 2.08, 29.1, 29.1, 88, 87.9, 2.08, 29.1, 29.1, 88, 87.9, 
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29.1, 88, 87.9, 2.08, 29.1, 29.1, 88, 87.9, 2.08, 29.1, 29.1, 
88, 87.9, 2.08, 29.1, 29.1, 88, 87.9, 2.08, 29.1, 29.1, 88, 87.9, 
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29.1, 88, 87.9, 2.08, 29.1, 29.1, 88, 87.9, 2.08, 29.1, 29.1, 
88, 87.9, 2.08, 29.1, 29.1), DATA = c(103.83455, 89.64711, 2.13345, 
30.057545, 30.086464, 104.37667, 89.788269, 2.13431, 30.045176, 
30.049561, 104.7235, 89.813126, 2.134059, 30.048182, 30.056608, 
104.03241, 103.96976, 89.63073, 2.141897, 30.035461, 30.104946, 
104.51376, 90.733009, 2.141215, 30.147131, 30.113092, 107.34245, 
90.934967, 2.140769, 30.136806, 30.114983, 105.98895, 90.628517, 
2.141326, 30.142355, 30.122778, 103.49479, 90.358688, 2.141122, 
30.058239, 30.06591, 103.60199, 90.354286, 2.142168, 30.063662, 
30.070904, 102.51865, 90.974564, 2.148871, 29.983458, 30.128607, 
103.016, 90.883461, 2.148184, 29.975309, 30.137098, 102.54838, 
89.871902, 2.142678, 29.947052, 30.094099, 102.50674, 89.974739, 
2.142947, 29.953632, 30.089359, 102.88899, 90.077881, 2.142447, 
29.951406, 30.092203, 105.72658, 89.542099, 2.143392, 29.941921, 
29.980062, 104.56109, 90.826813, 2.13772, 29.979958, 30.028364, 
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"64919", "65348", "65420", "65421", "65422", "65423", "66375", 
"66447", "66448", "66449", "66450", "66656", "66728", "66729", 
"66730", "66731", "67251", "67323", "67324", "67325", "67326", 
"68220", "68292", "68293", "68294", "68295", "68864", "68936", 
"68937", "68938", "68939"))
    1 回复  |  直到 8 年前
        1
  •  0
  •   bouncyball    8 年前

    我必须承认,这有点奇怪。 我认为他们之间有一些意想不到的互动 min 和 group_by ,但我不完全确定。它 几乎 似乎有马车。 last grouping variable is dropped . 这实际上在很多不同的情况下都是有意义的,但在你的情况下是没有意义的。不过,我们可以绕过它: 

    table1 %>%
    group_by(STEP_NAME) %>%
    summarise(m = mean(DATA), s = sd(DATA), Ts = max(HIGH_LIMIT),
              Ts = max(HIGH_LIMIT), Ti = min(LOW_LIMIT)) %>%
    mutate(CpK1 = (m-Ti)/(3*s), 
           CpK2 = (Ts-m)/(3*s)) %>%
    mutate(CpK = ifelse(CpK1 < CpK2, CpK1, CpK2)) %>%
    select(-CpK1, -CpK2)

# A tibble: 5 x 6
  STEP_NAME          m           s     Ts    Ti      CpK
     <fctr>      <dbl>       <dbl>  <dbl> <dbl>    <dbl>
1       C10 104.522294 1.282699532 112.00 88.00 1.943221
2        R1  90.335988 0.327022906  93.90 87.90 2.482994
3        R2   2.140924 0.004407826   2.22  2.08 4.607264
4        R3  30.058242 0.061715734  31.10 29.10 5.175568
5        R4  30.050151 0.065536919  31.10 29.10 4.832650

    另一种方法是添加另一个 分组依据 通话结束后 summarise 呼叫: 

    table1 %>%
    group_by(STEP_NAME) %>%
    summarise(m = mean(DATA), s = sd(DATA), Ts = max(HIGH_LIMIT),
              Ts = max(HIGH_LIMIT), Ti = min(LOW_LIMIT)) %>%
    group_by(STEP_NAME) %>%
    mutate(CpK = min((m-Ti)/(3*s), (Ts-m)/(3*s)))

# A tibble: 5 x 6
# Groups:   STEP_NAME [5]
  STEP_NAME          m           s     Ts    Ti      CpK
     <fctr>      <dbl>       <dbl>  <dbl> <dbl>    <dbl>
1       C10 104.522294 1.282699532 112.00 88.00 1.943221
2        R1  90.335988 0.327022906  93.90 87.90 2.482994
3        R2   2.140924 0.004407826   2.22  2.08 4.607264
4        R3  30.058242 0.061715734  31.10 29.10 5.175568
5        R4  30.050151 0.065536919  31.10 29.10 4.832650
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