I would like to import the data into R as intervals, then I would like to count all the numbers falling within these intervals and draw a histogram from this counts.
Example:
start end freq
1 8 3
5 10 2
7 11 5
.
.
.
Result:
number freq
1 3
2 3
3 3
4 3
5 5
6 5
7 10
8 10
9 7
10 7
11 5
Some suggestions?
Thank you very much!
Assuming your data is in df, you can create a data set that has each number in the range repeated by freq. Once you have that it's trivial to use the summarizing functions in R. This is a little roundabout, but a lot easier than explicitly computing the sum of the overlaps (though that isn't that hard either).
dat <- unlist(apply(df, 1, function(x) rep(x[[1]]:x[[2]], x[[3]])))
hist(dat, breaks=0:max(df$end))
You can also do table(dat)
dat
1 2 3 4 5 6 7 8 9 10 11
3 3 3 3 5 5 10 10 7 7 5
Related
guys:
I have two matrix as following:
d <- cbind(c(1,2,3,4),c(1,1,1,1),c(1,2,4,8))
v <- cbind(c(2,2,2,2),c(3,3,3,3))
But I want to get a matrix consisted of divj as following:
d1v1 d1v2 d2v1 d2v2 d3v1 d3v2
2 3 2 3 2 3
4 6 2 3 4 6
6 9 2 3 8 12
8 12 2 3 16 24
This is an example of my question,I wonder if you can tell me how to write codes to solve this question.Many thanks.
matrix(apply(v,2,function(x){x*d}),4,6)
I have a dataframe df with two columns, which are plotted in a scatterplot using ggplot. Now I have parted the curve into intervalls. The sectioning points of the intervalls are in a vector r. I now want to highlight these points to improve the visualization of the intervalls. I was thinking about coloring these intervall points or even to section the intervalls in adding vertical lines into the plot...I have tried some commands, but they didnt work for me.
Here is an idea of how my data frame looks like:
d is first colume, e is the second with number of instances.
d e
1 4
2 4
3 5
4 5
5 5
6 4
7 2
8 3
9 1
10 3
11 2
12 3
13 3
14 3
15 3
16 3
17 3
18 4
My vector r shows, where the intervall borders were set.
7
8
9
10
11
12
18
Any ideas how to do so? Thanks!
You can try a tidyverse. The idea is to find overlapping points using ´mutateand%in%, then color by the resutling logical vectorgr`. I also added vertical lines to illustrate the "intervals".
library(tidyverse)
d %>%
mutate(gr=d %in% r) %>%
ggplot(aes(d,e, color=gr)) +
geom_vline(xintercept=r, alpha=.1) +
geom_point()
Edit: Without tidyverse you can add gr using
d$gr <- d$d %in% r
ggplot(d, aes(d,e, color=gr)) ...
The data
d <- read.table(text=" d e
1 4
2 4
3 5
4 5
5 5
6 4
7 2
8 3
9 1
10 3
11 2
12 3
13 3
14 3
15 3
16 3
17 3
18 4", header=T)
r <- c(7:12,18)
This question already has answers here:
Filtering a data frame by values in a column [duplicate]
(3 answers)
Closed 3 years ago.
I have the following data with the ID of subjects.
V1
1 2
2 2
3 2
4 2
5 2
6 2
7 2
8 2
9 2
10 2
11 2
12 2
13 2
14 2
15 2
16 4
17 4
18 4
19 4
20 4
21 4
22 4
23 4
24 4
I want to subset all the rows of the data where V1 == 4. This way I can see which observations relate to subject 4.
For example, the correct output would be
16 4
17 4
18 4
19 4
20 4
21 4
22 4
23 4
24 4
However, the output I'm given after subsetting does not give me the correct rows . It simply gives me.
V1
1 4
2 4
3 4
4 4
5 4
6 4
7 4
8 4
I'm unable to tell which observations relate to subject 4, as observations 1:8 are for subject 2.
I've tried the usual methods, such as
condition<- df == 4
df[condition]
How can I subset the data so I'm given back a dataset that shows the correct row numbers for subject 4.
You can also use the subset function:
subset(df,df$V1==4)
I've managed to find a solution since posting.
newdf <- subset(df, V1 == 4).
However i'm still very interested in other solutions to this problems, so please post if you're aware of another method.
I'm trying to merge 7 complete data frames into one great wide data frame. I figured I have to do this stepwise and merge 2 frames into 1 and then that frame into another so forth until all 7 original frames becomes one.
fil2005: "ID" "abr_2005" "lop_2005" "ins_2005"
fil2006: "ID" "abr_2006" "lop_2006" "ins_2006"
But the variables "abr_2006" "lop_2006" "ins_2006" and 2005 are all either 0,1.
Now the things is, I want to either merge or do a dcast of some sort (I think) to make these two long data frames into one wide data frame were both "abr_2005" "lop_2005" "ins_2005" and abr_2006" "lop_2006" "ins_2006" are in that final file.
When I try
$fil_2006.1 <- merge(x=fil_2005, y=fil_2006, by="ID__", all.y=T)
all the variables with _2005 at the end if it is saved to the fil_2006.1, but the variables ending in _2006 doesn't.
I'm apparently doing something wrong. Any idea?
Is there a reason you put those underscores after ID__? Otherwise, the code you provided will work
An example:
dat1 <- data.frame("ID"=seq(1,20,by=2),"varx2005"=1:10, "vary2005"=2:11)
dat2 <- data.frame("ID"=5:14,"varx2006"=1:20, "vary2006"=21:40)
# create data frames of differing lengths
head(dat1)
ID varx2005 vary2005
1 1 1 2
2 3 2 3
3 5 3 4
4 7 4 5
5 9 5 6
6 11 6 7
head(dat2)
ID varx2006 vary2006
1 5 1 21
2 6 2 22
3 7 3 23
4 8 4 24
5 9 5 25
6 10 6 26
merged <- merge(dat1,dat2,by="ID",all=T)
head(merged)
ID varx2006 vary2006 varx2005 vary2005
1 1 NA NA 1 2
2 3 NA NA 2 3
3 5 1 21 3 4
4 5 11 31 3 4
5 7 13 33 4 5
6 7 3 23 4 5
I printed out the summary of a column variables as such:
Please see below the summary table printed out from R:
I would like to generate it into a data.frame. However, there are too many subject names that it's very difficult to list out all, also, the term "OTHER" with number 31 means that there are 319 subjects which appear only 1 time in the original data.frame.
So, the new data.frame I hope to produce would look like below:
Here is one possible solution.
Table<-table(rpois(100,5))
as.data.frame(Table)
Var1 Freq
1 1 2
2 2 11
3 3 9
4 4 18
5 5 13
6 6 20
7 7 14
8 8 8
9 9 3
10 10 1
11 11 1