整理“基准测试”列

Question

整理“基准测试”列

3

我有一个数据框架，大部分都是整洁的，但是有两列包含基准测试而不是将基准测试作为观察值。如何整理此数据以便在每个唯一的FYQ和Metric组合下，将"Facility_score"和"TTP"列名添加为"Facility_label"的观察结果？

输入数据：

library(zoo)

dd <- structure(list(Facility_label = structure(c(1L, 1L, 1L, 1L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L), .Label = c("A", "B", "C", 
"D", "Nashville"), class = "factor"), FYQ = structure(c(2017.75, 
2018, 2018.25, 2018.5, 2017.75, 2018, 2018.25, 2018.5, 2018.75, 
2017.75, 2018, 2018.25, 2018.5, 2018.75, 2017.75, 2018, 2018.25, 
2018.5, 2018.75, 2017.75, 2018, 2018.25, 2018.5, 2018.75, 2017.75, 
2018, 2018.25, 2018.5, 2018.75, 2017.75), class = "yearqtr"), 
    Metric = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 
    1L, 1L, 1L, 1L, 1L, 2L), .Label = c("Safety Recall", "Turnaround days", 
    "Consult Active <= 30d", "Consult Pending <- 7d", "Consult Scheduling <- 90d", 
    "ICB Compliance Rate", "FCA Assessment", "Minor construction execution", 
    "NRM funding execution", "Deficincies", "%Deficienceis corrected among corrected or action plan", 
    "%Deficienceis corrected or action plan", "Ratio of Hospital Staff to HR FTE", 
    "Turnover Rate", "GEMS no Action", "Lost time claims", "RTMS risk score", 
    "DOC Control", "Loaner deficiencies", "Pretreatment", "RME rate", 
    "SPS staff vacany rate", "Stock Inactive", "MSPV-NG", "Days to close prosthetis consult", 
    "%Prosthetic PO using national contracts"), class = "factor"), 
    Facility_score = c(84.78802993, 95.59659091, 100, 100, 77.61732852, 
    57.87671233, 81.28898129, 33.33333333, 31.57894737, 10.2, 
    7.902356902, 8.62, 11.71, 13.15, 30.98236776, 33.26086957, 
    31.19584055, 54.54545455, 27.27272727, 11, 17.19132653, 26.02008197, 
    22.29, 30.41, 89.09090909, 93.47826087, 82.10735586, 91.66666667, 
    87.5, 3.2), `Facility mean` = c(85.35550152, 87.31899147, 
    93.11498231, 100, 85.35550152, 87.31899147, 93.11498231, 
    100, 100, 12, 13.06073298, 12.2, 11.51, 10.56, 85.35550152, 
    87.31899147, 93.11498231, 100, 100, 12, 13.06073298, 12.2, 
    11.51, 10.56, 85.35550152, 87.31899147, 93.11498231, 100, 
    100, 12), TTP_score = c(100, 100, 100, 100, 100, 100, 100, 
    100, 100, 5.65, 5.063953488, 4.779310345, 4.47, 4.545, 100, 
    100, 100, 100, 100, 5.65, 5.063953488, 4.779310345, 4.47, 
    4.545, 100, 100, 100, 100, 100, 5.65)), row.names = c(NA, 
-30L), class = c("tbl_df", "tbl", "data.frame"))

期望的输出结果：

dd_output <- structure(list(Facility_label = c("A", "Facility mean", "TTP score", 
"A", "Facility mean", "TTP score", "A", "Facility mean", "TTP score", 
"A", "Facility mean", "TTP score", "B", "B", "B", "B", "B", "B", 
"Facility mean", "TTP score", "B", "Facility mean", "TTP score", 
"B", "Facility mean", "TTP score", "B", "Facility mean", "TTP score", 
"B", "Facility mean", "TTP score", "C", "C", "C", "C", "C", "C", 
"C", "C", "C", "C", "D", "D", "D", "D", "D", "D"), FYQ = c("2017 Q4", 
"2017 Q4", "2017 Q4", "2018 Q1", "2018 Q1", "2018 Q1", "2018 Q2", 
"2018 Q2", "2018 Q2", "2018 Q3", "2018 Q3", "2018 Q3", "2017 Q4", 
"2018 Q1", "2018 Q2", "2018 Q3", "2018 Q4", "2017 Q4", "2017 Q4", 
"2017 Q4", "2018 Q1", "2018 Q1", "2018 Q1", "2018 Q2", "2018 Q2", 
"2018 Q2", "2018 Q3", "2018 Q3", "2018 Q3", "2018 Q4", "2018 Q4", 
"2018 Q4", "2017 Q4", "2018 Q1", "2018 Q2", "2018 Q3", "2018 Q4", 
"2017 Q4", "2018 Q1", "2018 Q2", "2018 Q3", "2018 Q4", "2017 Q4", 
"2018 Q1", "2018 Q2", "2018 Q3", "2018 Q4", "2017 Q4"), Metric = c("Safety Recall", 
"Safety Recall", "safety Recall", "Safety Recall", "Safety Recall", 
"Safety Recall", "Safety Recall", "Safety Recall", "Safety Recall", 
"Safety Recall", "Safety Recall", "Safety Recall", "Safety Recall", 
"Safety Recall", "Safety Recall", "Safety Recall", "Safety Recall", 
"Turnaround days", "Turnaround days", "Turnaround days", "Turnaround days", 
"Turnaround days", "Turnaround days", "Turnaround days", "Turnaround days", 
"Turnaround days", "Turnaround days", "Turnaround days", "Turnaround days", 
"Turnaround days", "Turnaround days", "Turnaround days", "Safety Recall", 
"Safety Recall", "Safety Recall", "Safety Recall", "Safety Recall", 
"Turnaround days", "Turnaround days", "Turnaround days", "Turnaround days", 
"Turnaround days", "Safety Recall", "Safety Recall", "Safety Recall", 
"Safety Recall", "Safety Recall", "Turnaround days"), Facility_score = c(84.78802993, 
85.35550152, 100, 95.59659091, 87.31899147, 100, 100, 93.11498231, 
100, 100, 100, 100, 77.61732852, 57.87671233, 81.28898129, 33.33333333, 
31.57894737, 10.2, 12, 5.65, 7.902356902, 13.06073298, 5.063953488, 
8.62, 12.2, 4.779310345, 11.71, 11.51, 4.47, 13.15, 10.56, 4.545, 
30.98236776, 33.26086957, 31.19584055, 54.54545455, 27.27272727, 
11, 17.19132653, 26.02008197, 22.29, 30.41, 89.09090909, 93.47826087, 
82.10735586, 91.66666667, 87.5, 3.2)), class = c("spec_tbl_df", 
"tbl_df", "tbl", "data.frame"), row.names = c(NA, -48L), spec = structure(list(
    cols = list(X1 = structure(list(), class = c("collector_skip", 
    "collector")), Facility_label = structure(list(), class = c("collector_character", 
    "collector")), FYQ = structure(list(), class = c("collector_character", 
    "collector")), Metric = structure(list(), class = c("collector_character", 
    "collector")), Facility_score = structure(list(), class = c("collector_double", 
    "collector"))), default = structure(list(), class = c("collector_guess", 
    "collector")), skip = 1), class = "col_spec"))

- Ron Sokoloff

1

如果您提供一个简单的可重现示例，其中包含样本输入和期望输出，那么我们更容易帮助您测试和验证可能的解决方案。Dropbox链接并不是很有用，因为它们最终可能会消失。 - MrFlick

我删除了Dropbox链接并添加了dput()。如果有更好（更整洁）的方式来呈现put()，我会进行适当的编辑。 - Ron Sokoloff

1

从你的描述中，我仍然无法确定所期望的输出是什么。 - MrFlick

我已经编辑并添加了所需的输出。感谢您编辑我之前的版本。我认为这次做得更好了。 - Ron Sokoloff

1个回答

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可以查看英文原文，
原文链接

- acylam · Accepted Answer

我们可以使用 tidyr::gather 和一些 dplyr::mutate 操作来完成这个任务：

library(tidyverse)

dd %>%
  mutate(ID = row_number()) %>%
  gather(var, Facility_score, Facility_score:TTP_score) %>%
  group_by(FYQ, Metric,
           temp_ID = case_when(var == "Facility mean" ~ 1, 
                               var == "TTP_score" ~ 2, 
                               TRUE ~ 0)) %>%
  slice(if(any(temp_ID == 0)) row_number() else 1) %>%
  mutate(Facility_label = if_else(var == "Facility_score", as.character(Facility_label), var)) %>%
  ungroup() %>%
  arrange(ID, temp_ID) %>% 
  select(ID, everything(), -var, -temp_ID)

请注意，我已添加了ID列以表示原始行号。这样在合并同一行中的所有分数时，就不会感到困惑了。

注：

gather 将表格从宽格式转换为长格式，将 Facility_score、Facility mean和 TTP_score 的条目视为新的 Facility_score。创建一个变量 var 来临时存储值标签。
然后，我们通过将 Facility mean 设为 1，将 TTP_score 设为 2，并将 var 中的其他所有内容设为 0，来 group_by FYQ、Metric 和一个临时 ID 变量 (temp_ID)。
基于 temp_ID，我们使用 slice 抓取所有行，如果它是 0，否则只抓取第一行。这有效地返回与 Facility_score 相应的所有行，但在每个 FYQ + Metric 组合中仅返回一个 Facility mean 或 TTP_score。
接下来，我们用 var 中对应的标签替换 Facility_label。
最后，ungroup，按 ID 和 temp_ID 排序，重新排列列顺序，同时删除 var 和 temp_ID（当我们想将变量放在前面而保持其他变量不变时，everything 是有用的）。

输出：

# A tibble: 50 x 5
      ID Facility_label FYQ           Metric          Facility_score
   <int> <chr>          <S3: yearqtr> <fct>                    <dbl>
 1     1 A              2017 Q4       Safety Recall             84.8
 2     1 Facility mean  2017 Q4       Safety Recall             85.4
 3     1 TTP_score      2017 Q4       Safety Recall            100  
 4     2 A              2018 Q1       Safety Recall             95.6
 5     2 Facility mean  2018 Q1       Safety Recall             87.3
 6     2 TTP_score      2018 Q1       Safety Recall            100  
 7     3 A              2018 Q2       Safety Recall            100  
 8     3 Facility mean  2018 Q2       Safety Recall             93.1
 9     3 TTP_score      2018 Q2       Safety Recall            100  
10     4 A              2018 Q3       Safety Recall            100  
11     4 Facility mean  2018 Q3       Safety Recall            100  
12     4 TTP_score      2018 Q3       Safety Recall            100  
13     5 B              2017 Q4       Safety Recall             77.6
14     6 B              2018 Q1       Safety Recall             57.9
15     7 B              2018 Q2       Safety Recall             81.3
16     8 B              2018 Q3       Safety Recall             33.3
17     9 B              2018 Q4       Safety Recall             31.6
18     9 Facility mean  2018 Q4       Safety Recall            100  
19     9 TTP_score      2018 Q4       Safety Recall            100  
20    10 B              2017 Q4       Turnaround days           10.2
# ... with 30 more rows