I have one issue in selecting a value of one variable conditional on the value of another variable in a dataframe.
Dilutionfactor=c(1,3,9,27,80)
Log10Dilutionfactor=log10(Dilutionfactor)
Protection=c(100,81.25,40,10.52,0)
RM=as.data.frame(cbind(Dilutionfactor,Log10Dilutionfactor,Protection))
Now i want to know the value of Log10Dilutionfactor condition on the value of Protection is equal to either 50 (if it appear) or the value immediately just below 50.
when i used subset(RM,Protection<= 50)it gives three rows and when I tried RM[grepl(RM$Protection<=50,Log10Dilutionfactor),] it gives 0 values with warning message. I really appreciate if someone help me.
You can use 2 subset:
subset(RM,Protection==max(subset(RM,Protection<= 50)$Protection))$Log10Dilutionfactor
# [1] 0.954243
You could use
with(RM, Log10Dilutionfactor[which(Protection == max(Protection[Protection <= 50]))])
# [1] 0.9542425
or find the index value of protection that is closest to 50
index = which(abs(RM$Protection-50)<=min(abs(RM$Protection-50)))
and then look it up in what ever column you want. e.g for Dilutionfactor
RM$Dilutionfactor[index]
Related
I am trying to create a new column ($Correct) in a data frame based on values in two other columns ($Condition and $Response).
I realise that there are multiple ways of achieving this (I have since used another method), but I'm interested in the reason why the method below did not work.
training_data.df$Correct<- 0
training_data.df$Correct[training_data.df$Condition==2 & training_data.df$Response==1] <- 1
training_data.df$Correct[(training_data.df$Condition==1|3) & training_data.df$Response==2] <- 1
This method produces the correct values in the output (the new $Correct column), except for cases where $Condition==2 and $Response==2 (the value '1' prints in the $Correct column rather than '0').
This line of code works correctly on its own, but not in combination with the other (last) line for $Condition==1|3.
Can anyone explain why this occurs?
training_data.df$Condition==1|3
reads as:
"(training_data.df$Condition is equal to 1)"
or
"three".
"(training_data.df$Condition is equal to 1)" can be TRUE or FALSE.
"three" not so much.
Whereas what I think you mean is:
"training_data.df$Condition is equal to (either 1 or 3)".
This would be (training_data.df$Condition==1 | training_data.df$Condition==3) or training_data.df$Condition %in% c(1,3).
I have a dataset called college, and one of the columns is 'accepted'. There are two values for this column - 1 (which means student was accepted) and 0 (which means student was not accepted). I was to find the accepted student percentage.
I did this...
table(college$accepted)
which gave me the frequency of 1 and 0. (1 = 44,224 and 0 = 75,166). I then manually added those two values together (119,390) and divided the 44,224/119,390. This is fine and gets me the value I was looking for. But I would really like to know how I could do this with R code, since I'm sure there is a way to do it that I just haven't thought of.
Thanks!
Perhaps you can use prop.table like below
prop.table(table(college$accepted))["1"]
If it's a simple 0/1 column then you only need take the column mean.
mean_accepted <- mean(df$accepted)
you could first sum the column, and the count the total number in the column
sum(college$accepted)/length(college$accepted)
To make the code more explicit and describe your intent better, I suggest using a condition to identify the cases that meet your criteria for inclusion. For example:
college$accepted == 1
Then take the average of the logical vector to compute the proportion (between 0 and 1), multiply by 100 to make it a percentage.
100 * mean(college$accepted == 1, na.rm = TRUE)
I have a dataset named bwght which contains the variable cigs (cigarattes smoked per day)
When I calculate the mean of cigs in the dataset bwght using:
mean(bwght$cigs), I get a number 2.08.
Only 212 of the 1388 women in the sample smoke (and 1176 does not smoke):
summary(bwght$cigs>0) gives the result:
Mode FALSE TRUE NA's
logical 1176 212 0
I'm asked to find the average of cigs among the women who smoke (the 212).
I'm having a hard time finding the right syntax for excluding the non smokers = 0
I have tried:
mean(bwght$cigs| bwght$cigs>0)
mean(bwght$cigs>0 | bwght$cigs=TRUE)
if (bwght$cigs > 0){
sum(bwght$cigs)
}
x <-as.numeric(bwght$cigs, rm="0");
mean(x)
But nothing seems to work! Can anyone please help me??
If you want to exclude the non-smokers, you have a few options. The easiest is probably this:
mean(bwght[bwght$cigs>0,"cigs"])
With a data frame, the first variable is the row and the next is the column. So, you can subset using dataframe[1,2] to get the first row, second column. You can also use logic in the row selection. By using bwght$cigs>0 as the first element, you are subsetting to only have the rows where cigs is not zero.
Your other ones didn't work for the following reasons:
mean(bwght$cigs| bwght$cigs>0)
This is effectively a logical comparison. You're asking for the TRUE / FALSE result of bwght$cigs OR bwght$cigs>0, and then taking the mean on it. I'm not totally sure, but I think R can't even take data typed as logical for the mean() function.
mean(bwght$cigs>0 | bwght$cigs=TRUE)
Same problem. You use the | sign, which returns a logical, and R is trying to take the mean of logicals.
if(bwght$cigs > 0){sum(bwght$cigs)}
By any chance, were you a SAS programmer originally? This looks like how I used to type at first. Basically, if() doesn't work the same way in R as it does in SAS. In that example, you are using bwght$cigs > 0 as the if condition, which won't work because R will only look at the first element of the vector resulting from bwght$cigs > 0. R handles looping differently from SAS - check out functions like lapply, tapply, and so on.
x <-as.numeric(bwght$cigs, rm="0")
mean(x)
I honestly don't know what this would do. It might work if rm="0" didn't have quotes...?
mean(bwght[bwght$cigs>0,"cigs"])
I found the statement failed, returning "argument is not numeric or logical: returning NA"
Converting to matrix solved this:
mean(data.matrix(bwght[bwght$cigs>0,"cigs"]))
cv.uk.df$new.d[2:nrow(cv.uk.df)] <- tail(cv.uk.df$deaths, -1) - head(cv.uk.df$deaths, -1) # this line of code works
I wanted to know why do we -1 in the tail and -1 in head to create this new column.
I made an effort to understand by removing the -1 and "R"(The code is in R studio) throws me this error.
Could anyone shed some light on this? I can't explain how much I would appreciate it.
Look at what is being done. On the left-hand side of the assignment operator, we have:
cv.uk.df$new.d[2:nrow(cv.uk.df)] <-
Let's pick this apart.
cv.uk.df # This is the data.frame
$new.d # a new column to assign or a column to reassign
[2:nrow(cv.uk.df)] # the rows which we are going to assign
Specifically, this line of code will assign a new value all rows of this column except the first. Why would we want to do that? We don't have your data, but from your example, it looks like you want to calculate the change from one line to the next. That calculation is invalid for the first row (no previous row).
Now let's look at the right-hand side.
<- tail(cv.uk.df$deaths, -1) - head(cv.uk.df$deaths, -1)
The cv.uk.df$deaths column has the same number of rows as the data.frame. R gets grouchy when the numbers of elements don't follow sum rules. For data.frames, the right-hand side needs to have the same number of elements, or a number that can be recycled a whole-number of times. For example, if you have 10 rows, you need to have a replacement of 10 values. Or you can have 5 values that R will recycle.
If your data.frame has 100 rows, only 99 are being replaced in this operation. You cannot feed 100 values into an operation that expects 99. We need to trim the data. Let's look at what is happening. The tail() function has the usage tail(x, n), where it returns the last n values of x. If n is a negative integer, tail() returns all values but the first n. The head() function works similarly.
tail(cv.uk.df$deaths, -1) # This returns all values but the first
head(cv.uk.df$deaths, -1) # This returns all values but the last
This makes sense for your calculation. You cannot subtract the number of deaths in the row before the first row from the number in the first row, nor can you subtract the number of deaths in the last row from the number in the row after the last row. There are more intuitive ways to do this thing using functions from other packages, but this gets the job done.
The problem
I would like to find a length of a list.
The expected output
I would like to find the length based on a condition.
Example
Suppose that I have a list of 4 elements as follows:
myve <–list(1,2,3,0)
Here I have 4 elements, one of them is zero. How can I find the length by extracting the zero values? Then, if the length is > 1I would like to substruct one. That is:
If the length is 4 then, I would like to have 4-1=3. So, the output should be 3.
Note
Please note that I am working with a problem where the zero values may be changed from one case to another. For example, For the first list may I have only one 0 value, while for the second list may I have 2 or 3 zero values.
The values are always positive or zero.
You just need to apply the condition to each element. This will produce a list of boolean, then you sum it to get the number of True elements (i.e. validation your condition).
In your case:
sum(myve != 0)
In a more complex case, where the confition is expressed by a function f:
sapply(myve, f)
Use sapply to extract the ones different to zeros and sum to count them
sum(sapply(myve, function(x) x!=0))