行中的条件标签 [英] conditional labeling in rows
问题描述
我想根据其他行中的条件来标记行。
I would like to label rows based on the condition in other rows.
基本上，我要查找的是如果行是 NA 然后查找具有非NA的行，并使用其 sd_value 列决定是否用其标签标记NA行，否则用NA对其进行标记。我希望这个解释简单明了。
basically, what I look for is if the row is NA then look for row with non-NA and use its sd_value column to decide to whether label the NA row with its label else label it with NA. I hope this explanation is straightforward.
所以可以说我们有
df <- data.frame(value = c(0.5,1,0.6,1.2), sd_value=c(0.1,0.5,0.2,0.8),
label = c("good", "bad",NA,NA))
> df
value sd_value label
1 0.5 0.1 good
2 1.0 0.1 bad
3 0.6 0.5 NA
4 1.2 0.8 NA
例如要标记第3行，我需要检查该行的值，然后检查它们是否位于之间好 或差 值±2 * sd_value。如果这样，则将其标记为好或坏。
to label for example row 3, I need to check that row value and then check whether or not they lie in between 'good' or 'bad' value±2*sd_value. If so label them good or bad.
预期产出
> df
value sd_value label
1 0.5 0.1 good
2 1.0 0.1 bad
3 0.6 0.5 good #because 0.6 is ±2*sd_value of 1st row value
4 1.2 0.8 bad #because 1.2 is ±2*sd_value of 2nd row value
更广泛地说这个问题我们有这样的数据
to generalise the question more lets say we have a data like this
df <- data.frame(value = c(0.5, 1,8, 1.2, 2.4,0.4,6,2,5.7, 9),
sd_value=c(0.1, 0.1,1, 0.2,0.2,0.1,0.4,0.2,0.1,0.1),
label = c("good",NA,"beautiful","bad", NA,NA,"ugly","dirty",NA,NA))
> df
value sd_value label
1 0.5 0.1 good
2 1.0 0.1 <NA>
3 8.0 1.0 beautiful
4 1.2 0.2 bad
5 2.4 0.2 <NA>
6 0.4 0.1 <NA>
7 6.0 0.4 ugly
8 2.0 0.2 dirty
9 5.7 0.1 <NA>
10 9.0 0.1 <NA>
根据条件，预期输出应为
Based on the conditions the expected output should look like
> df
value sd_value label
1 0.5 0.1 good #original label
2 1.0 0.1 bad
3 8.0 1.0 beautiful #original label
4 1.2 0.2 bad
5 2.4 0.2 dirty
6 0.4 0.1 good
7 6.0 0.4 ugly #original label
8 2.0 0.2 dirty #original label
9 5.7 0.1 ugly
10 9.0 0.1 beautiful
根据±2 * sd_value 非NA行值。
推荐答案
我们可以对 NA 行'value's并检查与'good''label对应的'value'，'sd'，通过数字索引或使用<$将逻辑向量（'i2'）更改为'good / bad' c $ c> ifelse 并根据索引（'i1'）将输出分配回该列
We can subset the NA row 'value's and check that with the 'value', 'sd' corresponding to the 'good' 'label, change the logical vector ('i2') to 'good/bad' either with numeric indexing or using ifelse and assign the output back to the column based on the index ('i1')
i1 <- is.na(df$label)
i2 <- df$value[i1] < abs(df$value[1] + 2 * df$sd_value[1])
df$label[i1] <- c("bad", "good")[(i2 + 1)]
可以包装在函数中
It can be wrapped in a function
f1 <- function(data, lblCol, valCol, sdCol){
i1 <- is.na(df[[lblCol]])
gd <- which(df[[lblCol]] == "good")
i2 <- df[[valCol]][i1] < abs(df[[valCol]][gd] + 2 * df[[sdCol]][gd])
df[[lblCol]][i1] <- c("bad", "good")[(i2 + 1)]
df
}
f1(df, "label", "value", "sd_value")
# value sd_value label
#1 0.5 0.1 good
#2 1.0 0.5 bad
#3 0.6 0.2 good
#4 1.2 0.8 bad
更新
使用更新的数据集，我们提取标签为非NA的行，排列升序排列，并在 cut 中使用它来剪切值以获得正确的标签
Update
With the updated dataset, we extract the rows where the 'label' is non-NA, arrange it in ascending order and use that in cut to cut the 'value' to get the correct 'label'
library(dplyr)
df1 <- df %>%
filter(!is.na(label)) %>%
transmute(label, v1 = value + 2 * sd_value) %>%
arrange(v1)
df %>%
mutate(label = cut(value, breaks = c(-Inf, df1$v1), labels = df1$label))
# value sd_value label
#1 0.5 0.1 good
#2 1.0 0.1 bad
#3 8.0 1.0 beautiful
#4 1.2 0.2 bad
#5 2.4 0.2 dirty
#6 0.4 0.1 good
#7 6.0 0.4 ugly
#8 2.0 0.2 dirty
#9 5.7 0.1 ugly
#10 9.0 0.1 beautiful
或 base R
df1 <- transform(na.omit(df), v1 = value + 2 * sd_value)[3:4]
df$label <- cut(df$value, breaks = c(-Inf, df1$v1), labels = df1$label)
这篇关于行中的条件标签的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！
