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Code:
data(tips)
tips%>%
group_by(sex)%>%
summarize(variance=var(tip))
Output:
variance
1 1.914455
The output isn't the desired one. The result should be a tibble with variance computed against each group (in this case, sex). The summarize function is computing the variance of the entire tip column, rather than calculating the variance of each group.
Tried executing the code and restarting RStudio several times, but it didn't work.
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I working with a csv that has 234 rows. I thought the length() function was supposed to return the number of rows in my csv, but it returns a much larger number. It gives me 1046860. Does the length function not do what I think it does? What is going on?
I just wanted to double check the length and haven't run any other code yet other than setting up my working directory and attaching a file (Cat6 <- read.csv("Category6.csv")).
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Closed 4 years ago.
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I am doing an exercise to create a function. One of the questions is:
"We can estimate the cumulative risk of an certain event using the
exponential formula
1-exp(-1/10000*t) where t is the time to the event. Create a function ans(t), which returns the risk at time t.
and I am using this command:
function(t){ans(t)<-1-exp(-1/10000*t)return(ans(t))}
but it is giving wrong answer. Can someone help me to understand this please?
The proper format to define a function is this:
ans<-function(t){
answer<-1-exp(-1/10000*t)
return(answer)
}
ans(1)
#[1] 9.9995e-05
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I would like to calculate Mann Attached imageKendall statistics in R. i have an excel sheet with rainfall and years. how would i best get it
Looking at the attached image you should not put Book1$Mean in quotes. Try using:
MannKendall(Book1$Mean)
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I'm trying to do a simple calculation with my mean function. When I input mean(1, 10, 100), R returns 1, which is obviously not the correct average. It always returns the first entry of my vector. What went wrong?
You should use mean(c(1,10,100))
See http://www.rdocumentation.org/packages/base/functions/c
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Closed 8 years ago.
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Generating a Random Walk in R is pretty easy. It's done by the following code:
x <- rnorm(100)
y <- cumsum(x)
But how do I generate/simulate a Random Walk with Trend and / or Drift?
A slightly more compact/efficient version of the code from here:
cumsum(rnorm(n=100, mean=drift, sd=sqrt(variance)))
should give you a realization of a random walk with variance t*variance and mean t*drift, where t is the index (starting from 1; prepend a zero or add a constant to the whole series if you like).