How to subset data in R without losing NA rows?
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I have some data that I am looking at in R. One particular column, titled "Height", contains a few rows of NA.
I am looking to subset my data-frame so that all Heights above a certain value are excluded from my analysis.
df2 <- subset ( df1 , Height < 40 )
However whenever I do this, R automatically removes all rows that contain NA values for Height. I do not want this. I have tried including arguments for na.rm
f1 <- function ( x , na.rm = FALSE ) {
df2 <- subset ( x , Height < 40 )
}
f1 ( df1 , na.rm = FALSE )
but this does not seem to do anything; the rows with NA still end up disappearing from my data-frame. Is there a way of subsetting my data as such, without losing the NA rows?
r dataframe subset na
edited Nov 6 '16 at 5:45
李哲源
49.1k1498153
asked Nov 6 '16 at 5:02
Ryan RothmanRyan Rothman
58210
add a comment |
I have some data that I am looking at in R. One particular column, titled "Height", contains a few rows of NA.
I am looking to subset my data-frame so that all Heights above a certain value are excluded from my analysis.
df2 <- subset ( df1 , Height < 40 )
However whenever I do this, R automatically removes all rows that contain NA values for Height. I do not want this. I have tried including arguments for na.rm
f1 <- function ( x , na.rm = FALSE ) {
df2 <- subset ( x , Height < 40 )
}
f1 ( df1 , na.rm = FALSE )
but this does not seem to do anything; the rows with NA still end up disappearing from my data-frame. Is there a way of subsetting my data as such, without losing the NA rows?
r dataframe subset na
edited Nov 6 '16 at 5:45
李哲源
49.1k1498153
asked Nov 6 '16 at 5:02
Ryan RothmanRyan Rothman
58210
Alternately, we can usesubset (df1 , Height < 40 | is.na(Height))
– Zach
Nov 6 '16 at 5:07
For completeness sake, similar option fromdplyrpackage isfilter(df1, Height < 40 | is.na(Height))
– Simon Jackson
Nov 6 '16 at 5:57
add a comment |
I have some data that I am looking at in R. One particular column, titled "Height", contains a few rows of NA.
I am looking to subset my data-frame so that all Heights above a certain value are excluded from my analysis.
df2 <- subset ( df1 , Height < 40 )
However whenever I do this, R automatically removes all rows that contain NA values for Height. I do not want this. I have tried including arguments for na.rm
f1 <- function ( x , na.rm = FALSE ) {
df2 <- subset ( x , Height < 40 )
}
f1 ( df1 , na.rm = FALSE )
but this does not seem to do anything; the rows with NA still end up disappearing from my data-frame. Is there a way of subsetting my data as such, without losing the NA rows?
r dataframe subset na
edited Nov 6 '16 at 5:45
李哲源
49.1k1498153
asked Nov 6 '16 at 5:02
Ryan RothmanRyan Rothman
58210
I have some data that I am looking at in R. One particular column, titled "Height", contains a few rows of NA.
I am looking to subset my data-frame so that all Heights above a certain value are excluded from my analysis.
df2 <- subset ( df1 , Height < 40 )
However whenever I do this, R automatically removes all rows that contain NA values for Height. I do not want this. I have tried including arguments for na.rm
f1 <- function ( x , na.rm = FALSE ) {
df2 <- subset ( x , Height < 40 )
}
f1 ( df1 , na.rm = FALSE )
but this does not seem to do anything; the rows with NA still end up disappearing from my data-frame. Is there a way of subsetting my data as such, without losing the NA rows?
r dataframe subset na
r dataframe subset na
edited Nov 6 '16 at 5:45
李哲源
49.1k1498153
asked Nov 6 '16 at 5:02
Ryan RothmanRyan Rothman
58210
edited Nov 6 '16 at 5:45
李哲源
49.1k1498153
asked Nov 6 '16 at 5:02
Ryan RothmanRyan Rothman
58210
edited Nov 6 '16 at 5:45
李哲源
49.1k1498153
edited Nov 6 '16 at 5:45
李哲源
49.1k1498153
edited Nov 6 '16 at 5:45
李哲源
49.1k1498153
49.1k1498153
asked Nov 6 '16 at 5:02
Ryan RothmanRyan Rothman
58210
asked Nov 6 '16 at 5:02
Ryan RothmanRyan Rothman
58210
asked Nov 6 '16 at 5:02
Ryan RothmanRyan Rothman
58210
58210
Alternately, we can usesubset (df1 , Height < 40 | is.na(Height))
– Zach
Nov 6 '16 at 5:07
For completeness sake, similar option fromdplyrpackage isfilter(df1, Height < 40 | is.na(Height))
– Simon Jackson
Nov 6 '16 at 5:57
add a comment |
Alternately, we can usesubset (df1 , Height < 40 | is.na(Height))
– Zach
Nov 6 '16 at 5:07
For completeness sake, similar option fromdplyrpackage isfilter(df1, Height < 40 | is.na(Height))
– Simon Jackson
Nov 6 '16 at 5:57
Alternately, we can use
subset (df1 , Height < 40 | is.na(Height))– Zach
Nov 6 '16 at 5:07
Alternately, we can use
subset (df1 , Height < 40 | is.na(Height))– Zach
Nov 6 '16 at 5:07
For completeness sake, similar option from
dplyr package is filter(df1, Height < 40 | is.na(Height))– Simon Jackson
Nov 6 '16 at 5:57
For completeness sake, similar option from
dplyr package is filter(df1, Height < 40 | is.na(Height))– Simon Jackson
Nov 6 '16 at 5:57
add a comment |
2 Answers
2
active
oldest
votes
If we decide to use subset function, then we need to watch out:
For ordinary vectors, the result is simply ‘x[subset & !is.na(subset)]’.
So only non-NA values will be retained.
If you want to keep NA cases, use logical or condition to tell R not to drop NA cases:
subset(df1, Height < 40 | is.na(Height))
# or `df1[df1$Height < 40 | is.na(df1$Height), ]`
Don't use directly (to be explained soon):
df2 <- df1[df1$Height < 40, ]
Example
df1 <- data.frame(Height = c(NA, 2, 4, NA, 50, 60), y = 1:6)
subset(df1, Height < 40 | is.na(Height))
# Height y
#1 NA 1
#2 2 2
#3 4 3
#4 NA 4
df1[df1$Height < 40, ]
# Height y
#1 NA NA
#2 2 2
#3 4 3
#4 NA NA
The reason that the latter fails, is that indexing by NA gives NA. Consider this simple example with a vector:
x <- 1:4
ind <- c(NA, TRUE, NA, FALSE)
x[ind]
# [1] NA 2 NA
We need to somehow replace those NA with TRUE. The most straightforward way is to add another "or" condition is.na(ind):
x[ind | is.na(ind)]
# [1] 1 2 3
This is exactly what will happen in your situation. If your Height contains NA, then logical operation Height < 40 ends up a mix of TRUE / FALSE / NA, so we need replace NA by TRUE as above.
answered Nov 6 '16 at 5:05
李哲源李哲源
49.1k1498153
add a comment |
You could also do:
df2 <- df1[(df1$Height < 40 | is.na(df1$Height)),]
answered Apr 20 '17 at 14:00
dededede
4111819
add a comment |
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2 Answers
2
active
oldest
votes
2 Answers
2
active
oldest
votes
active
oldest
votes
active
oldest
votes
If we decide to use subset function, then we need to watch out:
For ordinary vectors, the result is simply ‘x[subset & !is.na(subset)]’.
So only non-NA values will be retained.
If you want to keep NA cases, use logical or condition to tell R not to drop NA cases:
subset(df1, Height < 40 | is.na(Height))
# or `df1[df1$Height < 40 | is.na(df1$Height), ]`
Don't use directly (to be explained soon):
df2 <- df1[df1$Height < 40, ]
Example
df1 <- data.frame(Height = c(NA, 2, 4, NA, 50, 60), y = 1:6)
subset(df1, Height < 40 | is.na(Height))
# Height y
#1 NA 1
#2 2 2
#3 4 3
#4 NA 4
df1[df1$Height < 40, ]
# Height y
#1 NA NA
#2 2 2
#3 4 3
#4 NA NA
The reason that the latter fails, is that indexing by NA gives NA. Consider this simple example with a vector:
x <- 1:4
ind <- c(NA, TRUE, NA, FALSE)
x[ind]
# [1] NA 2 NA
We need to somehow replace those NA with TRUE. The most straightforward way is to add another "or" condition is.na(ind):
x[ind | is.na(ind)]
# [1] 1 2 3
This is exactly what will happen in your situation. If your Height contains NA, then logical operation Height < 40 ends up a mix of TRUE / FALSE / NA, so we need replace NA by TRUE as above.
answered Nov 6 '16 at 5:05
李哲源李哲源
49.1k1498153
add a comment |
If we decide to use subset function, then we need to watch out:
For ordinary vectors, the result is simply ‘x[subset & !is.na(subset)]’.
So only non-NA values will be retained.
If you want to keep NA cases, use logical or condition to tell R not to drop NA cases:
subset(df1, Height < 40 | is.na(Height))
# or `df1[df1$Height < 40 | is.na(df1$Height), ]`
Don't use directly (to be explained soon):
df2 <- df1[df1$Height < 40, ]
Example
df1 <- data.frame(Height = c(NA, 2, 4, NA, 50, 60), y = 1:6)
subset(df1, Height < 40 | is.na(Height))
# Height y
#1 NA 1
#2 2 2
#3 4 3
#4 NA 4
df1[df1$Height < 40, ]
# Height y
#1 NA NA
#2 2 2
#3 4 3
#4 NA NA
The reason that the latter fails, is that indexing by NA gives NA. Consider this simple example with a vector:
x <- 1:4
ind <- c(NA, TRUE, NA, FALSE)
x[ind]
# [1] NA 2 NA
We need to somehow replace those NA with TRUE. The most straightforward way is to add another "or" condition is.na(ind):
x[ind | is.na(ind)]
# [1] 1 2 3
This is exactly what will happen in your situation. If your Height contains NA, then logical operation Height < 40 ends up a mix of TRUE / FALSE / NA, so we need replace NA by TRUE as above.
answered Nov 6 '16 at 5:05
李哲源李哲源
49.1k1498153
add a comment |
If we decide to use subset function, then we need to watch out:
For ordinary vectors, the result is simply ‘x[subset & !is.na(subset)]’.
So only non-NA values will be retained.
If you want to keep NA cases, use logical or condition to tell R not to drop NA cases:
subset(df1, Height < 40 | is.na(Height))
# or `df1[df1$Height < 40 | is.na(df1$Height), ]`
Don't use directly (to be explained soon):
df2 <- df1[df1$Height < 40, ]
Example
df1 <- data.frame(Height = c(NA, 2, 4, NA, 50, 60), y = 1:6)
subset(df1, Height < 40 | is.na(Height))
# Height y
#1 NA 1
#2 2 2
#3 4 3
#4 NA 4
df1[df1$Height < 40, ]
# Height y
#1 NA NA
#2 2 2
#3 4 3
#4 NA NA
The reason that the latter fails, is that indexing by NA gives NA. Consider this simple example with a vector:
x <- 1:4
ind <- c(NA, TRUE, NA, FALSE)
x[ind]
# [1] NA 2 NA
We need to somehow replace those NA with TRUE. The most straightforward way is to add another "or" condition is.na(ind):
x[ind | is.na(ind)]
# [1] 1 2 3
This is exactly what will happen in your situation. If your Height contains NA, then logical operation Height < 40 ends up a mix of TRUE / FALSE / NA, so we need replace NA by TRUE as above.
answered Nov 6 '16 at 5:05
李哲源李哲源
49.1k1498153
If we decide to use subset function, then we need to watch out:
For ordinary vectors, the result is simply ‘x[subset & !is.na(subset)]’.
So only non-NA values will be retained.
If you want to keep NA cases, use logical or condition to tell R not to drop NA cases:
subset(df1, Height < 40 | is.na(Height))
# or `df1[df1$Height < 40 | is.na(df1$Height), ]`
Don't use directly (to be explained soon):
df2 <- df1[df1$Height < 40, ]
Example
df1 <- data.frame(Height = c(NA, 2, 4, NA, 50, 60), y = 1:6)
subset(df1, Height < 40 | is.na(Height))
# Height y
#1 NA 1
#2 2 2
#3 4 3
#4 NA 4
df1[df1$Height < 40, ]
# Height y
#1 NA NA
#2 2 2
#3 4 3
#4 NA NA
The reason that the latter fails, is that indexing by NA gives NA. Consider this simple example with a vector:
x <- 1:4
ind <- c(NA, TRUE, NA, FALSE)
x[ind]
# [1] NA 2 NA
We need to somehow replace those NA with TRUE. The most straightforward way is to add another "or" condition is.na(ind):
x[ind | is.na(ind)]
# [1] 1 2 3
This is exactly what will happen in your situation. If your Height contains NA, then logical operation Height < 40 ends up a mix of TRUE / FALSE / NA, so we need replace NA by TRUE as above.
answered Nov 6 '16 at 5:05
李哲源李哲源
49.1k1498153
edited Nov 6 '16 at 5:39
answered Nov 6 '16 at 5:05
李哲源李哲源
49.1k1498153
answered Nov 6 '16 at 5:05
李哲源李哲源
49.1k1498153
answered Nov 6 '16 at 5:05
李哲源李哲源
49.1k1498153
49.1k1498153
add a comment |
add a comment |
You could also do:
df2 <- df1[(df1$Height < 40 | is.na(df1$Height)),]
answered Apr 20 '17 at 14:00
dededede
4111819
add a comment |
You could also do:
df2 <- df1[(df1$Height < 40 | is.na(df1$Height)),]
answered Apr 20 '17 at 14:00
dededede
4111819
add a comment |
You could also do:
df2 <- df1[(df1$Height < 40 | is.na(df1$Height)),]
answered Apr 20 '17 at 14:00
dededede
4111819
You could also do:
df2 <- df1[(df1$Height < 40 | is.na(df1$Height)),]
answered Apr 20 '17 at 14:00
dededede
4111819
answered Apr 20 '17 at 14:00
dededede
4111819
answered Apr 20 '17 at 14:00
dededede
4111819
answered Apr 20 '17 at 14:00
dededede
4111819
4111819
add a comment |
add a comment |
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Alternately, we can use
subset (df1 , Height < 40 | is.na(Height))– Zach
Nov 6 '16 at 5:07
For completeness sake, similar option from
dplyrpackage isfilter(df1, Height < 40 | is.na(Height))– Simon Jackson
Nov 6 '16 at 5:57