I am currently making my first steps using R with RStudio and right now I am struggling with the following problem:
I got some test data about a marathon with four columns, where the third column is a factor with 15 levels representing different age classes.
One age class randomAgeClass will be randomly selected at the beginning, and an object is created holding the data that matches this age class.
set.seed(12345678)
attach(marathon)
randomAgeClass <- sample(levels(marathon[,3]), 1)
filteredMara <- subset(marathon, AgeClass == randomAgeClass)
My goal is to store a second object that holds the data matching the next higher level, meaning that if age class 'kids' was randomly selected, I now want to access the data relating to 'teenagers', which is the next higher level. Looking something like this:
nextAgeClass <- .... randomAgeClass+1 .... ?
filteredMaraAgeClass <- subset(marathon, AgeClass == nextAgeClass)
Note that I already found this StackOverflow question, which seems to partially match my situation, but the accepted answer is not understandable to me, thus I wasn't able to apply it to my needs.
Thanks a lot for any patient help!
First you have to make sure thar the levels of your factor are ordered by age:
factor(marathon$AgeClass,levels=c("kids","teenagers",etc.))
Then you almost got there in your example:
next_pos<-which(levels(marathon$AgeClass)==randomAgeClass)+1 #here you get the desired position in the level vector
nextAgeClass <- levels(marathon$AgeClass) [next_pos]
filteredMaraAgeClass <- subset(marathon, AgeClass == nextAgeClass)
You might have a problem if the randomAgeClass is the last one, so make sure to avoid that problem
Related
Hey guys I am trying to calculate the p value of individual variables to see if they have an impact when the other variable is set to 0. Here is my code:
quiet_result = aov(overbearing ~ as.factor(Intention)*as.factor(quiet_only), data=df)
summary(quiet_result)
loud_result = aov(overbearing ~ as.factor(Intention)*as.factor(loud_only), data = df)
summary(loud_result)
For context, the intention variable only has the values of -1 and 1. -1 is intentional and 1 is intentional. Quiet_only and loud_only are new columns created from a data set. quiet_only only has the values of 0 and 2 and it is the original column of sound + 1, and loud_only only has the values of -2 and 0 because it is only the original column of sound - 1. Therefore these are all ordered variables and they are not supposed to be assessed by their actual numerical value like a class variable. However, my code keeps reading it as a class variable even though I changed all the variables to factors to make them ordered variables. Therefore, when I run anova on them, they all return the same result. I am wondering how I can change the variables to make them into ordered variables because the anova is only reading the change between the intention and quiet_only/loud_only columns, which would obviously return the same anovas because there is no actual change if you subtract or add 1 to a column. Therefore, I'm trying to find the p value of the intention variable with loud_only and quiet_only and this p value should change depending on whether I use loud_only or quiet_only.
Sorry if this doesn't make any sense lol. This is research work for a graduate professor so it uses concept that I don't fully understand (I'm undergrad) so I don't think I explained it very well. Anyways, if any of you have any ideas that would be great.
I have to include participants into a dataframe(or existing data frame) if they have higher score in invalid conditions relative to valid conditions. But I have two times of (T1-T3) data.
I have tried this one: data_new <- subset(data_raw, T1_invalid > T1_valid & T3_invalid > T3_valid)
However, it did not work because, for instance, some participants may have higher invalid score in just one time (T1), not in the second time (T3), or vice versa.
For example, a person can have higher invalid in one of the times, let's say T1_invalid > T1_valid. This should be included to the new data frame, it is okay. But, T3_invalid - T3_valid should be excluded because the invalid score is not higher than the valid score. But when you use AND operator, it excludes the person because, they have to have higher invalid scores in both T1 and T3. So, we over exclude in that case.
When you use OR operator it is the same. For example, a person has a higher score in T1_invalid > T1_valid, but not in the T3_invalid - T3_valid. Then, since one of the conditions is okay, it includes the person, but this person failed at T3. So, we should exclude T3_invalid - valid scores.
So basically, I was looking for something can check them separately. Then, I decided to make it null one by one like this:
data_raw[data_raw$T1_invalid < data_raw$T1_valid, c("T1_invalid", "T1_valid")] <- NA
data_raw[data_raw$T3_invalid < data_raw$T3_valid, c("T3_invalid", "T3_valid")] <- NA
However, it did not let me do this because I use the variables two times, for the condition part (>) and for make it null.
Does anyone have any idea? By the way they have to be in the same data frame for using in the model.
Here I provide a normal data.table solution. You can have a try.
library(data.table)
setDT(data_raw)
data_raw[, T1_invalid := ifelse(T1_invalid < T1_valid,NA,T1_invalid)]
data_raw[, T1_valid := ifelse(T1_invalid < T1_valid,NA,T1_valid)]
data_raw[, T3_invalid := ifelse(T3_invalid < T3_valid,NA,T3_valid)]
data_raw[, T3_valid := ifelse(T3_invalid < T3_valid,NA,T3_valid)]
I am a relatively novice R user, though familiar with dplyr and tidy verse. I still can't seem to figure out how to pull in the actual data from one column if it meets certain condition, into a new column.
Here is what I'm trying to do. Participants have ranked specific practices (n=5) and provided responses to questions that represent their beliefs about these practices. I want to have five new columns that assign their beliefs about the practices to their ranks, rather than the practices.
For example, they have a score for "beliefs about NI" called ni.beliefs, if a participant ranked NI as their first choice, I want the value for ni.beliefs to be pulled into the new column for first.beliefs. The same is true that if a participant put pmii as their first choice practice, their value for pmii.beliefs should be pulled into the first.beliefs column.
So, I need five new columns called: first.beliefs, second.beliefs, third.beliefs, fourth.beliefs, last.beliefs and then I need each of these to have the data pulled in conditionally from the practice specific beliefs (ni.beliefs, dtt.beliefs, pmi.beliefs, sn.beliefs, script.beliefs) dependent on the practice specific ranks (rank assigned of 1-5 for each practice, rank.ni, rank.dtt, rank.pmi, rank.sn, rank.script).
Here is what I have so far but I am stuck and aware that this is not very close. Any help is appreciated!!!
`
Diss$first.beliefs <-ifelse(rank.ni==1, ni.beliefs,
ifelse(rank.dtt==1, dtt.beliefs,
ifelse(rank.pmi==1, pmi.beliefs,
ifelse(rank.sn, sn.beliefs,
ifelse(rank.script==1, script.beliefs)))))
`
Thank you!!
I'm not sure if I understood correctly (it would help if you show how your data looks like), but this is what I'm thinking:
Without using additional packages, if the ranking columns are equivalent to the index of the new columns you want (i.e. they rank each practice from 1 to 5, without repeats, and in the same order as the new columns "firsts belief, second belief, etc"), then you can use that data as the indices for the second set of columns:
for(j in 1:nrow(people_table)){
people_table[j,]$first.belief[[1]] <- names(beliefs)[(people_table[j,c(A:B)]) %in% 1]
people_table[j,]$second.belief[[1]] <- names(beliefs)[(people_table[j,c(A:B)]) %in% 2]
...
}
Where
A -> index of the first preference rank column
B -> index of the last preference rank column
(people_table[j,c(A:B)] %in% 1) -> this returns something like (FALSE FALSE TRUE FALSE FALSE)
beliefs -> vector with the names of each belief
That should work. It's simple, no need for packages, and it'll be fast too. Just make sure you've initialized/created the new columns first, otherwise you'll get some errors. If
This is done very easily with the case_when() function. You can improve on the code below.
library(dplyr)
Diss$first.beliefs <- case_when(
rank.ni == 1 ~ ni.beliefs,
rank.dtt == 1 ~ dtt.beliefs,
rank.pmi == 1 ~ pmi.beliefs,
rank.sn ~ sn.beliefs,
rank.script == 1 ~ script.beliefs
)
this sounds pretty basic but every time I try to make a histogram, my code is saying x needs to be numeric. I've been looking everywhere but can't find one relating to my problem. I have data with 240 obs with 5 variables.
Nipper length
Number of Whiskers
Crab Carapace
Sex
Estuary location
There is 3 locations and i'm trying to make a histogram with nipper length
I've tried making new factors and levels, with the 80 obs in each location but its not working
Crabs.data <-read.table(pipe("pbpaste"),header = FALSE)##Mac
names(Crabs.data)<-c("Crab Identification","Estuary Location","Sex","Crab Carapace","Length of Nipper","Number of Whiskers")
Crabs.data<-Crabs.data[,-1]
attach(Crabs.data)
hist(`Length of Nipper`~`Estuary Location`)
Error in hist.default(Length of Nipper ~ Estuary Location) :
'x' must be numeric
Instead of correct result
hist() doesn't seem to like taking more than one variable.
I think you'd have the best luck subsetting the data, that is, making a vector of nipper lengths for all crabs in a given estuary.
crabs.data<-read.table("whatever you're calling it")
names<-(as you have it)
Estuary1<-as.vector(unlist(subset(crabs.data, `Estuary Loc`=="Location", select = `Length of Nipper`)))
hist(Estuary1)
Repeat the last two lines for your other two estuaries. You may not need the unlist() command, depending on your table. I've tended to need it for Excel files, but I don't know what format your table is in (that would've been helpful).
So I'm extracting data from a rasterbrick I made using the method from this question: How to extract data from a RasterBrick?
In addition to obtaining the data from the layer given by the date, I want to extract the data from months prior. In my best guess I do this by doing something like this:
sapply(1:nrow(pts), function(i){extract(b, cbind(pts$x[i],pts$y[i]), layer=pts$layerindex[i-1], nl=1)})
So it the extracting should look at layerindex i-1, this should then give the data for one month earlier. So a point with layerindex = 5, should look at layer 5-1 = 4.
However it doesn't do this and seems to give either some random number or a duplicate from months prior. What would be the correct way to go about this?
Your code is taking the value from the layer of the previous point, not the previous layer.
To see that imagine we are looking at the point in row 2 (i=2). your code that indicates the layer is pts$layerindex[i-1], which is pts$layerindex[1]. In other words, the layer of the point in row 1.
The fix is easy enough. For clarity I will write the function separetely:
foo = function(i) extract(b, cbind(pts$x[i],pts$y[i]), layer=pts$layerindex[i]-1, nl=1)
sapply(1:nrow(pts), foo)
I have not tested it, but this should be all.