英文:
Using filter to select specific values of ,multiple columns in R
问题
尝试在R中使用filter函数，并选择多列中所需的值，同时包括缺失值。```Rx <- 1:2:3:4:5:NAy <- 3:4:NA:5:6:NAz <- 2:3:4:NA:5:6df <- data.frame(x, y, z)df %>%filter(x > 4 | is.na(x), y > 4 | is.na(y), z > 4 | is.na(z))
我正在尝试筛选x、y和z列中大于4的值，同时保留NA。使用上面的尝试会导致错误：“输入必须是逻辑向量，而不是双精度数”。有什么建议可以纠正上述错误，以及如何将此命令应用于所有三列。
<details><summary>英文:</summary>Trying to use filter function in R and selecting only values needed in multiple columns and also including missing values.
x <- 1:2:3:4:5:NA
y <- 3:4:NA:5:6:NA
z <- 2:3:4:NA:5:6
df <- data.frame(x, y, z)
df %>%
filter(x != 1, 2, 3 | is.na(x))
I am trying to filter to values more than 4 in columns x, y and z keeping NA. Using the attempt above gives an error 'input must be logical vector, not a double'. Any suggestion to rectify the above error and also how to apply this command to all three columns.</details># 答案1**得分**: 0这是使用dplyr的解决方案：```rdf = data.frame(x = c(1,2,3,4,5,NA),y = c(3,4,NA,5,6,NA),z = c(2,3,4,NA,5,6))df %>%filter(x >= 4 | is.na(x),y >= 4 | is.na(y),z >= 4 | is.na(z))
英文:
Here is a solution using dplyr:
df = data.frame(x = c(1,2,3,4,5,NA),y = c(3,4,NA,5,6,NA),z = c(2,3,4,NA,5,6))df %>%filter(x >= 4 | is.na(x),y >= 4 | is.na(y),z >= 4 | is.na(z))
答案2
得分: 0
在基础R中：
subset(df, pmin(x, y, z, na.rm = TRUE) >= 4)x y z4 4 5 NA5 5 6 56 NA NA 6
如果你有很多列而不想通过名称引用它们：
subset(df, do.call(pmin, c(na.rm = TRUE, df)) >= 4)x y z4 4 5 NA5 5 6 56 NA NA 6
英文:
in base R
subset(df, pmin(x, y, z, na.rm = TRUE)>=4)x y z4 4 5 NA5 5 6 56 NA NA 6
in case you have very man columns and do not want to reference them by name:
subset(df, do.call(pmin, c(na.rm = TRUE, df)) >=4)x y z4 4 5 NA5 5 6 56 NA NA 6
答案3
得分: -1
请提供一个可复现的示例：
x <- c(1:5, NA)y <- c(3:4, NA, 5:6, NA)z <- c(2:4, NA, 5:6)
然后，我建议使用 {data.table} 包：
library(data.table)dt <- data.table(x, y, z)
然后，您可以像这样应用筛选条件：
dt[x >= 4 | is.na(x), ]
（意思是，给我表中所有 x 大于或等于 4 或 x 为 NA 的行。）
您还可以进一步组合其他逻辑条件：
dt[(x >= 4 | is.na(x)) | (y >= 4 | is.na(y)) | (z >= 4 | is.na(z)), ]
有关 {data.table} 语法的更多信息可以在此处找到：https://rdatatable.gitlab.io/data.table/
英文:
First of all, please provide a reproducible example:
x <- c(1:5, NA)y <- c(3:4, NA, 5:6, NA)z <- c(2:4, NA, 5:6)
Then I would recommend using the package {data.table}.
library(data.table)dt <- data.table(x, y, z)
And then you can apply filters like so
dt[x >= 4 | is.na(x), ]
(meaning, give me all rows of the table where x is greater or equal to 4 or where x is NA.)
You can further combine other logical constraints:
dt[(x >= 4 | is.na(x)) | (y >= 4 | is.na(y)) | (z >= 4 | is.na(z)), ]
Further information on the {data.table} syntax can be found here: https://rdatatable.gitlab.io/data.table/
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