I am new to coding and need direction to turn my method into code.
In my lab I am working on a time-series project to discover which gene's in a cell naturally change over the organism's cell cycle. I have a tabular data set with numerical values (originally 10 columns, 27,000 rows). To analyze whether a gene is cycling over the data set I divided the values of one time point (or column) by each subsequent time point (or column), and continued that trend across the data set (the top section of the picture is an example of spread sheet with numerical value at each time-point. The bottom section is an example of what the time-comparisons looked like across the data.
I then imposed an advanced filter with multiple AND / OR criteria that followed the logic (Source Jeeped)
WHERE (column A >= 2.0 AND column B <= 0.5)
OR (column A >= 2.0 AND column C <= 0.5)
OR (column A >= 2.0 AND column D <= 0.5)
OR (column A >= 2.0 AND column E <= 0.5)
(etc ...)
From there, I slid the advanced filter across the entire data set(in the photograph, A on the left -- exanple of the original filter, and B -- the filter sliding across the data)
The filters produced multiple sheets of genes that fit my criteria. To figure how many unique genes met this criteria I merged Column A (Gene_ID's) of all the sheets and removed duplicates to produce a list of unique gene ID's.
The process took me nearly 3 hours due to the size of each spread sheet (37 columns, 27000 rows before filtering). Can this process be expedited? and if so can someone point me in the right direction or help me create the code to do so?
Thank you for your time, and if you need any clarification please don't hesitate to ask.
There are a few ways to do this in R. I think but a common an easy to think about way is to use the any function. This basically takes a series of logical tests and puts an "OR" between each of them, so that if any of them return true then it returns true. You can pass each column to it and then combine it with an AND for the logical test for column a. There are probably other ways to abstract this as well, but this should get you started:
df <- data.frame(
a = 1:100,
b = 1:100,
c = 51:150,
d = 101:200,
value = rep("a", 100)
)
df[ df$a > 2 & any(df$b > 5, df$c > 5, df$d > 5), "value"] <- "Test Passed!"
Related
I am having some trouble coming up with a solution that properly handles classifying a variable number of neighbors for any given observation in a data frame based on some condition. I would like to be able to add a simple, binary indicator variable to a data frame that will equal 1 if the condition is satisfied, and 0 if it is not.
Where I am getting stuck is I am unsure how to iteratively check the condition against neighboring observations only, in either direction (i.e., to check if out of 4 neighboring observations in a given column in my data frame, that at least 3 out of 4 of them contain the same value). I have tried first creating another indicator variable indicating if the condition is satisfied or not (1 or 0 = yes or no). Then, I tried setting up a series of ifelse() statements within a loop to try to assign the proper categorization of the observation where the initial condition is satisfied, +/- 2 observations in either direction. However, when I inspect the dataframe after running the loop, only the observation itself (not its neighbors) where the condition is satisfied is receiving the value, rather than all neighboring observations also receiving the value. Here is my code:
#sample data
sample_dat <- data.frame(initial_ind = c(0,1,0,1,0,0,1,1,0,1,0,0))
sample_dat$violate <- NULL
for(i in 1:nrow(dat_date_ord)){
sample_dat$violate[i] <- ifelse(sample_dat$initial_ind[i]==1 &
((sample_dat$initial_ind[i-2]==1 |
sample_dat$initial_ind[i-1]==1) &
(sample_dat$initial_ind[i+2]==1 |
sample_dat$initial_ind[i+1]==1)),
"trending",
"non-trending"
)
}
This loop correctly identifies one of the four points that needs to be labelled "trending", but it does not also assign "trending" to the correct neighbors. In other words, I expect the output to be "trending for observations 7-10, since 3/4 observations in that group of 4 all have a value of 1 in the initial indicator column. I feel like there might be an easier way to accomplish this - but what I need to ensure is that my code is robust enough to identify and assign observations to a group regardless of if I want 3/4 to indicate a group, 5/6, 2/5, etc.
Thank you for any and all advice.
You can use the rollapply function from the zoo package to apply a function to set intervals in your data. The question then becomes about creating a function that satisfies your needs. I'm not sure if I've understood correctly, but it seems you want a function that checks if the condition is true for at least 3/5 of the observation plus its four closest neighbors. In this case just adding the 1s up and checking if they're above 2 works.
library(zoo)
sample_dat <- data.frame(initial_ind = c(0,1,0,1,0,0,1,1,0,1,0,0))
trend_test = function(x){
ifelse(sum(x) > 2, "trending", "non-trending")
}
sample_dat$violate_new = rollapply(sample_dat$initial_ind, FUN = trend_test, width = 5, fill = NA)
Edit: If you want a function that checks if the observation and the next 3 observations have at least 3 1s, you can do something very similar, just by changing the align argument on rollapply:
trend_test_2 = function(x){
ifelse(sum(x) > 2, "trending", "non-trending")
}
sample_dat$violate_new = rollapply(sample_dat$initial_ind, FUN = trend_test_2, width = 4,
fill = NA, align = "left")
I need to conditionally replace rows in a data frame (x) with rows selected at random from another data frame (y).Some of the rows between the two data frames are the same and so data frame x will contain rows with repeated information. What sort of base r code would I need to achieve this?
I am writing an agent based model in r where rows can be thought of as vectors of attributes pertaining to an agent and columns are attribute types. For agents to transmit their attributes they need to send rows from one data frame (population) to another, but according to conditional learning rules. These rules need to be: conditionally replace values in row n in data frame x if attribute in column 10 for that row is value 1 or more and if probability s is greater than a randomly selected number between 0 and 1. Probability s is itself an adjustable parameter that can take any value from 0 to 1.
I have tried IF function in the code below, but I am new to r and have made a mistake somewhere with it as I get this warning:
"missing value where TRUE/FALSE needed"
I reckon that I have not specified what should happen to a row if the conditions are not satisfied.
I cannot think of an alternative method of achieving my aim.
Note: agent.dat is data frame x and top_ten_percent is data frame y.
s = 0.7
N = nrow(agent.dat)
copy <- runif(N) #to generate a random probability for each row in agent.dat
for (i in 1:nrow(agent.dat)){
if(agent.dat[,10] >= 1 & copy < s){
agent.dat <- top_ten_percent[sample(nrow(top_ten_percent), 1), ]
}
}
The agent.dat data frame should have rows that are replaced with values from rows in the top_ten_percent data frame if the randomly selected value of copy between 0 and 1 for that row is less than the value of parameter s and if the value for that row in column 10 is 1 or more. For each row I need to replace the first 10 columns of agent.dat with the first 10 columns of top_ten_percent (excluding column 11 i.e. copy value).
Assistance with this problem is greatly appreciated.
So you just need to change a few things.
You need to get a particular value for copy for each iteration of the for loop (use: copy[i]).
You also need to make the & in the if statement an && (Boolean operators && and ||)
Then you need to replace a particular row (and columns 1 through 10) in agent.dat, instead of the whole thing (agent.dat[i,1:10])
So, the final code should look like:
copy <- runif(N)
for (i in 1:nrow(agent.dat)){
if(agent.dat[,10] >= 1 && copy[i] < s){
agent.dat[i,1:10] <- top_ten_percent[sample(nrow(top_ten_percent), 1), ]
}
}
This should fix your errors, assuming your data structure fits your code:
copy <- runif(nrow(agent.dat))
s <- 0.7
for (i in 1:nrow(agent.dat)){
if(agent.dat[i,10] >= 1 & copy[i] < s){
agent.dat[i,] <- top_ten_percent[sample(1:nrow(top_ten_percent), 1), ]
}
}
I've already read this question with an approach to counting entries in R:
how to realize countifs function (excel) in R
I'm looking for a similar approach, except that I want to count data that is within a given range.
For example, let's say I have this dataset:
data <- data.frame( values = c(1,1.2,1.5,1.7,1.7,2))
Following the approach on the linked question, we would develop something like this:
count <- data$values == 1.5
sum(count)
Problem is, I want to be able to include in the count anything that varies 0.2 from 1.5 - that is, all possible number from 1.3 to 1.7.
Is there a way to do so?
sum(data$values>=1.3 & data$values<=1.7)
As the explanation in the question you linked to points out, when you just write out a boolean condition, it generates a vector of TRUEs and FALSEs the same length as your original dataframe. TRUE equals 1 and FALSE equals 0, so summing across it gives you a count. So it simply becomes a matter of putting your condition as a boolean phrase. In the case of more than one condition, you connect them with & or | (or) -- much the same way that you could do in excel (only in excel you have to do AND() or OR()).
(For a more general solution, you can use dplyr::between - it's also supposed to be faster since it's implemented in C++. In this case, it would be sum(between(data$values,1.3,1.7).)
Like #doviod writes, you can use a compound logical condition.
My approach is different, I wrote a function that takes the vector and as range the center point value and the distance delta.
After a suggestion by #doviod, I have set a default value delta = 0, so that if only value is passed, the function returns
a count of cases where the values equal the value the user provides.
(doviod, in the comment)
countif <- function(x, value, delta = 0)
sum(value - delta <= x & x <= value + delta)
data <- data.frame( values = c(1,1.2,1.5,1.7,1.7,2))
countif(data$values, 1.5, 0.2)
#[1] 3
which identifies the location of all values in your vector that satisfy your criterion, and length subsequently counts the 'hits'.
length( which(data$values>=1.3 & data$values<=1.7) )
[1] 3
I am trying to count the length of occurrances of a value in a vector such as
q <- c(1,1,1,1,1,1,4,4,4,4,4,4,4,4,4,4,4,4,6,6,6,6,6,6,6,6,6,6,1,1,4,4,4)
Actual vectors are longer than this, and are time based. What I would like would be an output for 4 that tells me it occurred for 12 time steps (before the vector changes to 6) and then 3 time steps. (Not that it occurred 15 times total).
Currently my ideas to do this are pretty inefficient (a loop that looks element by element that I can have stop when it doesn't equal the value I specified). Can anyone recommend a more efficient method?
x <- with(rle(q), data.frame(values, lengths)) will pull the information that you want (courtesy of d.b. in the comments).
From the R Documentation: rle is used to "Compute the lengths and values of runs of equal values in a vector – or the reverse operation."
y <- x[x$values == 4, ] will subset the data frame to include only the value of interest (4). You can then see clearly that 4 ran for 12 times and then later for 3.
Modifying the code will let you check whatever value you want.
Lets say I have this data. My objective is to extraxt combinations of sequences.
I have one constraint, the time between two events may not be more than 5, lets call this maxGap.
User <- c(rep(1,3)) # One users
Event <- c("C","B","C") # Say this is random events could be anything from LETTERS[1:4]
Time <- c(c(1,12,13)) # This is a timeline
df <- data.frame(User=User,
Event=Event,
Time=Time)
If want to use these sequences as binary explanatory variables for analysis.
Given this dataframe the result should be like this.
res.df <- data.frame(User=1,
C=1,
B=1,
CB=0,
BC=1,
CBC=0)
(CB) and (CBC) will be 0 since the maxGap > 5.
I was trying to write a function for this using many for-loops, but it becomes very complex if the sequence becomes larger and the different number of evets also becomes larger. And also if the number of different User grows to 100 000.
Is it possible of doing this in TraMineR with the help of seqeconstraint?
Here is how you would do that with TraMineR
df.seqe <- seqecreate(id=df$User, timestamp=df$Time, event=df$Event)
constr <- seqeconstraint(maxGap=5)
subseq <- seqefsub(df.seqe, minSupport=0, constraint=constr)
(presence <- seqeapplysub(subseq, method="presence"))
which gives
(B) (B)-(C) (C)
1-(C)-11-(B)-1-(C) 1 1 1
presence is a table with a column for each subsequence that occurs at least once in the data set. So, if you have several individuals (event sequences), the table will have one row per individual and the columns will be the binary variable you are looking for. (See also TraMineR: Can I get the complete sequence if I give an event sub sequence? )
However, be aware that TraMineR works fine only with subsequences of length up to about 4 or 5. We suggest to set maxK=3 or 4 in seqefsub. The number of individuals should not be a problem, nor should the number of different possible events (the alphabet) as long as you restrict the maximal subsequence length you are looking for.
Hope this helps