英文:
R: Get values from separate dataframes based on the running values of a group_by function
问题
我有以下的数据框。
Col1 = c("A1", "A1", "A2", "A2")
Col2 = c("B1", "B1", "B2", "B2")
Value = c(10, 20, 30, 40)
df = data.frame(Col1, Col2, Value)
这是一个包含各种观测值的数据框。有两个因子列和一个值列。可以有相同组观测的多行，但具有不同的值。有多个类似观测的数据框。
MinCol1 = c("A1", "A2")
MinCol2 = c("B1", "B2")
MinValue = c(1, 1)
mins = data.frame(MinCol1, MinCol2, MinValue)
MaxCol1 = c("A1", "A2")
MaxCol2 = c("B1", "B2")
MaxValue = c(100, 100)
maxes = data.frame(MaxCol1, MaxCol2, MaxValue)
上面的两个数据框是所有数据框（如第一个数据框 df）中所有组（Col1 和 Col2）的最小值和最大值。
我想要对类似第一个数据框的数据框进行每个组的值标准化。我希望新的值在0到1之间，但我希望标准化的范围从mins和maxes数据框中获取。
normalizeDataForAllBenchmarks = function(df, mins, maxes) {
### 标准化指标 [0,1]
df_normal = df %>%
group_by(Col1, Col2) %>%
mutate(Value = rescale(Value, to = c(0,1), from = c(mins$MinValue, maxes$MaxValue)))
return(df_normal)
}
我有上面的函数，但我不确定在range函数中应该放什么，以便执行对mins和maxes数据框的每个组查找。
英文:
I have the following dataframe.
Col1 = c("A1", "A1", "A2", "A2")
Col2 = c("B1", "B1", "B2", "B2")
Value = c(10, 20, 30, 40)
df = data.frame(Col1, Col2, Value)
This is a dataframe with various observations. Two factor columns and a value column. There can be multiple rows of the same group of observations with different values. There are multiple such dataframes with similar observations.
MinCol1 = c("A1", "A2")
MinCol2 = c("B1", "B2")
MinValue = c(1, 1)
mins = data.frame(MinCol1, MinCol2, MinValue)
MaxCol1 = c("A1", "A2")
MaxCol2 = c("B1", "B2")
MaxValue = c(100, 100)
maxes = data.frame(MaxCol1, MaxCol2, MaxValue)
The above two dataframes are the minimum and maximum values for all groups (Col1 and Col2) across all dataframes (like the 1st one, df).
I want to normalize the values of dataframes like the 1st one per group. I want the new values to be between 0 to 1 but I want the range to be normalized against to be taken from the mins and maxes dataframes.
normalizeDataForAllBenchmarks = function(df, mins, maxes) {
### Normalize metrics [0,1]
df_normal = df %>%
group_by(Process, Category, Metric) %>%
mutate(Value = rescale(Value, to = c(0,1), from = range(...)))
return(df_normal)
}
I have the above function bun I'm not sure what goes in the range function in order to do a per group lookup into the mins and maxes dataframes.
答案1
得分: 2
库(tidyverse)
为所有基准规范化数据 = 函数(df, 最小值, 最大值) {
左连接(df, 最小值, by = c("Col1" = "MinCol1", "Col2" = "MinCol2"))|
左连接(最大值, by = c("Col1" = "MaxCol1", "Col2" = "MaxCol2")) |
变异(跨(Value:MaxValue, as.numeric),
Value = (Value - MinValue)/(MaxValue-MinValue))|
选择(-c(MinValue, MaxValue))
}
为所有基准规范化数据(df, 最小值, 最大值)
#> Col1 Col2 Value
#> 1 A1 B1 0.09090909
#> 2 A1 B1 0.19191919
#> 3 A2 B2 0.29292929
#> 4 A2 B2 0.39393939
英文:
All you need to do is join the data by the ids and then calculate the norm:
library(tidyverse)
normalizeDataForAllBenchmarks = function(df, mins, maxes) {
left_join(df, mins, by = c("Col1" = "MinCol1", "Col2" = "MinCol2"))|>
left_join(maxes, by = c("Col1" = "MaxCol1", "Col2" = "MaxCol2")) |>
mutate(across(Value:MaxValue, as.numeric),
Value = (Value - MinValue)/(MaxValue-MinValue))|>
select(-c(MinValue, MaxValue))
}
normalizeDataForAllBenchmarks(df, mins, maxes)
#> Col1 Col2 Value
#> 1 A1 B1 0.09090909
#> 2 A1 B1 0.19191919
#> 3 A2 B2 0.29292929
#> 4 A2 B2 0.39393939
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