If I have a dataframe like the one below which has numerical column names
example = data.frame(1=c(1,8,3,9), 2=c(3,2,3,3), 3=c(5,2,5,4), 4=c(1,2,3,4), 5=c(2,5,7,8))
Which looks like this:
1 2 3 4 5
1 3 5 1 2
8 2 2 2 5
3 3 5 3 7
9 3 4 4 8
And I want to arrange it so that the column names start with three and proceed through five and back to one, like this:
3 4 5 1 2
5 1 2 1 3
2 2 5 8 2
5 3 7 3 3
4 4 8 9 3
I know how to rearrange the position of a single column in a dataset, but I'm not sure how to do this with more than one column in this particular order.
We can use the column index concatenated (c) based on the sequence (:) on a range of values
example[c(3:5, 1:2)]
# 3 4 5 1 2
#1 5 1 2 1 3
#2 2 2 5 8 2
#3 5 3 7 3 3
#4 4 4 8 9 3
As the column names are all numeric, just convert to numeric and use that for ordering
v1 <- as.numeric(names(example))
example[c(v1[3:5], v1[1:2])]
Or simply do
example[c(names(example)[3:5], names(example)[1:2])]
Or another way is with head and tail
example[c(tail(names(example), 3), head(names(example), 2))]
data
example <- data.frame(`1`=c(1,8,3,9), `2`=c(3,2,3,3),
`3`=c(5,2,5,4), `4`=c(1,2,3,4), `5`=c(2,5,7,8), check.names = FALSE)
R will not easily let you create columns with numbers as name. If somehow, you are able to create columns with numbers you can use match to get order in which you want the column names.
example[match(c(3:5, 1:2), names(example))]
# 3 4 5 1 2
#1 5 1 2 1 3
#2 2 2 5 8 2
#3 5 3 7 3 3
#4 4 4 8 9 3
Related
I have a data set that will ultimately be about ~30,000 observations. I have formatted a variable in such a way that the numerical values 1:4 are of interest, while the value 5 is a place holder and was not able to be collected by our testing instrument for one reason or another (not worried about why or missingness etc).
I am looking to turn any observation of 5, or series of observations of 5, into the next number in the observations. As can be seen in the example data set below, the first four observations have the number 5 while the next four observations are the number 4. In this situation I would like the first 4 observations to be changed from 5 to 4.
Note that after the 8th observation another series of 5's occur, follow by a series of 3s. In this case the 5s should be changed to 3s.
In the code block below I have provided an example of what the current data look like, delineated by the column "Current." I have also provided a column of the desired output, delineated by the column name "Desired." The obs variable was helpful to create just to show the row number of the changes in values for the case of this post.
df <- data.frame(Current = c(5,5,5,5,4,4,4,4,5,5,3,3,3,5,3,3,5,5,2,5,5,5,1),
Desired = c(4,4,4,4,4,4,4,4,3,3,3,3,3,3,3,3,2,2,2,1,1,1,1))
df$obs = seq(1,nrow(df), by = 1)
You could use
library(tidyr)
library(dplyr)
df %>%
mutate(new_column = na_if(Current, 5)) %>%
fill(new_column, .direction = "up")
This returns
Current Desired new_column
1 5 4 4
2 5 4 4
3 5 4 4
4 5 4 4
5 4 4 4
6 4 4 4
7 4 4 4
8 4 4 4
9 5 3 3
10 5 3 3
11 3 3 3
12 3 3 3
13 3 3 3
14 5 3 3
15 3 3 3
16 3 3 3
17 5 2 2
18 5 2 2
19 2 2 2
20 5 1 1
21 5 1 1
22 5 1 1
23 1 1 1
We use dplyr's na_if function to convert the 5 into missing values.
Next we use tidyr's fill function to replace the NA's by the following values.
You can use the following solution. I made use of zoo::na.locf function which takes the most non-NA value and replace all NAs on the way down. However, to fit this to your data set I first replaced all values equal to 5 with NA and then reverse the vector and after I replaced all the values with the desired values, I again reversed it back to its original order:
library(dplyr)
library(zoo)
library(zoo)
df %>%
mutate(Desired2 = ifelse(Current == 5, NA, Current),
Desired2 = rev(na.locf(rev(Desired2))))
Current Desired Desired2
1 5 4 4
2 5 4 4
3 5 4 4
4 5 4 4
5 4 4 4
6 4 4 4
7 4 4 4
8 4 4 4
9 5 3 3
10 5 3 3
11 3 3 3
12 3 3 3
13 3 3 3
14 5 3 3
15 3 3 3
16 3 3 3
17 5 2 2
18 5 2 2
19 2 2 2
20 5 1 1
21 5 1 1
22 5 1 1
23 1 1 1
I want to subset rows which do not contain special values. For example:
df <- data.frame(a=c(1,2,2,3,4,4),b=c(-9999,2,3,4,5,6),c=c(2,3,4,-9999,2,4))
a b c
1 1 -9999 2
2 2 2 3
3 2 3 4
4 3 4 -9999
5 4 5 2
6 4 6 4
df has many rows and columns , I want to subset the rows which don't contain -9999. Expect result as follow codes:
df[which(df$a!=-9999,df$b!=-9999,df$c!=-9999),]
a b c
2 2 2 3
3 2 3 4
5 4 5 2
6 4 6 4
when columns are to many to type above logical judge, how to subset it?
You can try this one:
temp <- which(df == "-9999",arr.ind = T)
df[-unique(temp[,1]),]
a b c
2 2 2 3
3 2 3 4
5 4 5 2
6 4 6 4
I have a dataframe as described below. Now I want to reverse the order of column B without hampering the total order of the dataframe. So now the column B has 5,4,3,2,1. I want to change it to 1,2,3,4,5. I don't want to sort as it will hamper the total ordering.
A B C
1 5 6
2 4 8
3 3 5
4 2 5
5 1 3
You can replace just that column:
x$B <- rev(x$B)
On your data:
> x$B <- rev(x$B)
> x
A B C
1 1 1 6
2 2 2 8
3 3 3 5
4 4 4 5
5 5 5 3
transform is also handy for this:
> transform(x, B = rev(B))
A B C
1 1 1 6
2 2 2 8
3 3 3 5
4 4 4 5
5 5 5 3
This doesn't modify x so you need to assign the result to something (perhaps back to x).
I have a dataframe with an id variable, which may be duplicated. I want to split this into two dataframes, one which contains only the entries where the id's are duplicated, the other which shows only the id's which are unique. What is the best way of doing this?
For example, say I had the data frame:
dataDF <- data.frame(id = c(1,1,2,3,4,4,5,6),
a = c(1,2,3,4,5,6,7,8),
b = c(8,7,6,5,4,3,2,1))
i.e. the following
id a b
1 1 1 8
2 1 2 7
3 2 3 6
4 3 4 5
5 4 5 4
6 4 6 3
7 5 7 2
8 6 8 1
I want to get the following dataframes:
id a b
1 1 1 8
2 1 2 7
5 4 5 4
6 4 6 3
and
id a b
3 2 3 6
4 3 4 5
7 5 7 2
8 6 8 1
I am currently doing this as follows
dupeIds <- unique(subset(dataDF, duplicated(dataDF$id))$id)
uniqueDF <- subset(dataDF, !id %in% dupeIds)
dupeDF <- subset(dataDF, id %in% dupeIds)
which seems to work but it seems a bit off to subset three times, is there a simpler way of doing this? Thanks
Use duplicated twice, once top down, and once bottom up, and then use split to get it all in a list, like this:
split(dataDF, duplicated(dataDF$id) | duplicated(dataDF$id, fromLast = TRUE))
# $`FALSE`
# id a b
# 3 2 3 6
# 4 3 4 5
# 7 5 7 2
# 8 6 8 1
#
# $`TRUE`
# id a b
# 1 1 1 8
# 2 1 2 7
# 5 4 5 4
# 6 4 6 3
If you need to split this out into separate data.frames in your workspace (not sure why you would need to do that), assign names to the list items (eg names(mylist) <- c("nodupe", "dupe")) and then use list2env.
For instance, I have a data frame like this, there is no duplicated number for each row, numbers are sorted by each row.
W1 W2 W3 W4
1 1 3 4 7
2 4 5 6 7
3 1 2 5 8
4 2 5 9 10
5 4 7 10 13
6 1 2 6 9
I want to get the row ID when 1/2/3.... appears, since 1 in row 1,3,6; 2 in row 3,4,6; 3 in row 1 only, ...; So my result would like this
1 1 3 6
2 3 4 6
3 1
4 1 2 5
5 2 3 4
......
I would do:
split(t(row(df)), unlist(t(df)))
And if you need empty levels to show up:
split(t(row(df)), factor(unlist(t(df)), 1:max(df)))
This should be a lot faster than looping via for example:
lapply(1:max(df), function(i) which(rowSums(df == i) > 0))