R 在不使用汇总或过滤器的情况下查找观测子集中的最大值

发布于 2025-01-11 03:14:11 字数 1169 浏览 0 评论 0原文

大家，

我有以下数据框示例，其中包含患者的 ID（1 和 2）、其类别 X（是或否）以及参数的值

df <- data.frame (ID = c (1, 1, 1, 1, 2, 2, 2),
                  X = c ("YES", "YES", "NO", "NO", "YES", "NO", "NO"),
                  Value = c (10, 15, 12, 13, 18, 16, 17))

df

这提供了下表：

  ID   X Value
1  1 YES    10
2  1 YES    15
3  1  NO    12
4  1  NO    13
5  2 YES    18
6  2  NO    16
7  2  NO    17

我想获得新的列结果这将给出每个患者的最大值，对 X 列中的“是”做出如下响应

  ID   X Value Result
1  1 YES    10     15
2  1 YES    15     15
3  1  NO    12     15
4  1  NO    13     15
5  2 YES    18     18
6  2  NO    16     18
7  2  NO    19     18

我知道我可以使用 group_by 并汇总来获取值，但我想使用 mutate 以便我可以跟踪所有变量我为这个项目构建的原因，避免过滤功能。

以下解决方案为我提供了结果列，但我再次希望每个 ID 仅有一个值。

df %>%
  group_by(ID,X)%>%
  mutate (Result = max(Value))

     ID X     Value Result
  <dbl> <chr> <dbl>  <dbl>
1     1 YES      10     15
2     1 YES      15     15
3     1 NO       12     13
4     1 NO       13     13
5     2 YES      18     18
6     2 NO       16     19
7     2 NO       19     19

非常感谢您的帮助

原文

everyone,

I have the following dataframe example, with the ID of patients (1 and 2), their category X (YES or NO), and the values of a parameter

df <- data.frame (ID = c (1, 1, 1, 1, 2, 2, 2),
                  X = c ("YES", "YES", "NO", "NO", "YES", "NO", "NO"),
                  Value = c (10, 15, 12, 13, 18, 16, 17))

df

This provides the following table:

  ID   X Value
1  1 YES    10
2  1 YES    15
3  1  NO    12
4  1  NO    13
5  2 YES    18
6  2  NO    16
7  2  NO    17

I would like to get a new column result that would give the maximum value, per patient, responding to "YES" in the column X as follows

  ID   X Value Result
1  1 YES    10     15
2  1 YES    15     15
3  1  NO    12     15
4  1  NO    13     15
5  2 YES    18     18
6  2  NO    16     18
7  2  NO    19     18

I know that I can use group_by and summarise to obtain the values, but I would like to use mutate so that I can follow all the variables that I build for this project, and for the same reason, avoid the filter function.

The following solution provides me the result column, but again I would like only one value per ID.

df %>%
  group_by(ID,X)%>%
  mutate (Result = max(Value))

     ID X     Value Result
  <dbl> <chr> <dbl>  <dbl>
1     1 YES      10     15
2     1 YES      15     15
3     1 NO       12     13
4     1 NO       13     13
5     2 YES      18     18
6     2 NO       16     19
7     2 NO       19     19

Thank you very much for your help

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随心而道 2025-01-18 03:14:11

您可以使用此代码：

df %>% group_by(ID) %>% mutate(result = max(Value[X == "YES]))

You can use this code:

df %>% group_by(ID) %>% mutate(result = max(Value[X == "YES]))

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衣神在巴黎 2025-01-18 03:14:11

使用data.table

library(data.table)
setDT(df)[, result := max(Value[X == "YES"], na.rm = TRUE), ID]

Using data.table

library(data.table)
setDT(df)[, result := max(Value[X == "YES"], na.rm = TRUE), ID]

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过潦 2025-01-18 03:14:11

这又如何呢？

> transform(df, Result = ave(Value, ID, X, FUN = max))
  ID   X Value Result
1  1 YES    10     15
2  1 YES    15     15
3  1  NO    12     13
4  1  NO    13     13
5  2 YES    18     18
6  2  NO    16     19
7  2  NO    19     19

What about this?

> transform(df, Result = ave(Value, ID, X, FUN = max))
  ID   X Value Result
1  1 YES    10     15
2  1 YES    15     15
3  1  NO    12     13
4  1  NO    13     13
5  2 YES    18     18
6  2  NO    16     19
7  2  NO    19     19

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