In this Link, I have 4 dropdowns each. First two dropdowns (Fitting-1 and Fitting-2) have 5 values and remaining two dropdowns (meter and centimeter) have 4 values..
Now, we have to show all options. I don't know how many variations will come?
Please suggest me how to calculate variations
In your link Fitting 1 only has 3 choices, but regardless, the math is always the same, you multiply the number of outcomes for each attribute by each other.
Variations = n attribute 1 x n attribute 2 x n attribute 3 x n attribute 4
Where n is the number of possible outcomes. If the first two attributes have 5 possible outcomes each and the second two attributes have 4 outcomes each then your total is
Variations = 5 x 5 x 4 x 4 = 400
Related
I'm working on a unique NFT generator and I need a mathematical equation to help me calculate how many combinations I can create from an X amount of layers of traits/properties.
For example, say I have 4 layers, with 5 traits in each layer.
The expression for this is simple, its 5 x 5 x 5 x 5 = 625 possible combinations.
Now, what if I want one of the traits in one of the layers to only be used in, for example, lets say, 10 combinations.
What would be the expression to calculate the possible combinations of something like that, while taking these limits for specific traits into account?
Separate the case into combinations with that trait:
1 x 5 x 5 x 5 = 125
and combinations without that trait:
4 x 5 x 5 x 5 = 500
Limit the combinations with that trait to 10, and you get:
10 + 500 = 510
If you have multiple restricted traits, it makes sense to combine them, so as to get the greatest possible use of the unrestricted traits.
I am not very good at R, and I have a problem. As I want to do a linear regression between two variables from different datasets, i run into the proble, that one dataset is way bigger than the other. So, in order to bypass that problem, I want to create a smaller variable with an equal population, randomly selected from the greater datasets variable. What is the command for that? And if any specification is needed for that, please let me know! Thank you so much for your help!
Tried to make a liner regression out of two datasets, but as one is bigger than the other, it did not help, and the line (error)
Error in model.frame.default(formula = lobby_expenditure$expend \~ compustat$lct, :
variable lengths differ (found for 'compustat$lct')
appeared
Here is a simple example; y comes from d2 and a sample of rows from d1 are selected for x
d1=data.frame(x=rnorm(100))
d2=data.frame(y=rnorm(10))
lm(d2$y~d1[sample(1:nrow(d1),nrow(d2)),"x"])
To get any sample rows, use dplyr::sample_n
Example : dataset :
df2 <- read_table('Individual Site
1 A
2 B
3 A
4 C
5 C
6 B
7 A
8 B
9 C')
with sample_n(df2,2) where 2 is number of samples you want, you can get random rows. The following output may differ in your case since its random.
#A tibble: 2 x 2
Individual Site
<dbl> <chr>
1 4 C
2 5 C
I am trying to simulate a game in R. For that I need to choose a random player out of n_players who begins in the first round. Then the other n_players follow in a random order in the first round. However, in the next rounds the same order of players as in the first round must be kept. Does anyone have an idea on how to do this?
Create a sequence of numbers, say n=10, from 1 up to n.
x<-1:10
Think of this to be the tag number of players. You can then use the sample function of R (read the documentation using ?sample command or visit here) to create another sequence of numbers whose order have been shuffled randomly.
y<-sample(x,10,replace=F)
Now your y variable is the order in which your players are selected one by one.
Also, you can access each individual chosen player just like you choose an element from a vector.
Finally, the vector y is the sequence in which these players are selected in the subsequent rounds.
Test run:
x<-1:10
#[1] 1 2 3 4 5 6 7 8 9 10
y<-sample(x,10,replace=F)
#[1] 2 4 1 8 9 7 5 6 10 3
Suppose I have the dataset that has the following information:
1) Number (of products bought, for example)
1 2 3
2) Frequency for each number (e.g., how many people purchased that number of products)
2 5 10
Let's say I have the above information for each of the 2 groups: control and test data.
How do I format the data such that it would look like this:
controldata<-c(1,1,2,2,2,2,2,3,3,3,3,3,3,3,3,3,3)
(each number * frequency listed as a vector)
testdata<- (similar to above)
so that I can perform the two independent sample t-test on R?
If I don't even need to make them a vector / if there's an alternative clever way to format the data to perform the t-test, please let me know!
It would be simple if the vector is small like above, but I can have the frequency>10000 for each number.
P.S.
Control and test data have a different sample size.
Thanks!
Use rep. Using your data above
rep(c(1, 2, 3), c(2, 5, 10))
# [1] 1 1 2 2 2 2 2 3 3 3 3 3 3 3 3 3 3
Or, for your case
control_data = rep(n_bought, frequency)
We are working on a social capital project so our data set has a list of an individual's organizational memberships. So each person gets a numeric ID and then a sub ID for each group they are in. The unit of analysis, therefore, is the group they are in. One of our variables is a three point scale for the type of group it is. Sounds simple enough?
We want to bring the unit of analysis to the individual level and condense the type of group it is into a variable signifying how many different types of groups they are in.
For instance, person one is in eight groups. Of those groups, three are (1s), three are (2s), and two are (3s). What the individual level variable would look like, ideally, is 3, because she is in all three types of groups.
Is this possible in the least?
##simulate data
##individuals
n <- 10
## groups
g <- 5
## group types
gt <- 3
## individuals*group membership
N <- 20
## inidividuals data frame
di <- data.frame(individual=sample(1:n,N,replace=TRUE),
group=sample(1:g,N, replace=TRUE))
## groups data frame
dg <- data.frame(group=1:g, type=sample(1:gt,g,replace=TRUE))
## merge
dm <- merge(di,dg)
## order - not necessary, but nice
dm <- dm[order(dm$individual),]
## group type per individual
library(plyr)
dr <- ddply(dm, "individual", function(x) length(unique(x$type)))
> head(dm)
group individual type
2 2 1 2
8 2 1 2
20 5 1 1
9 3 3 2
12 3 3 2
17 4 3 2
> head(dr)
individual V1
1 1 2
2 3 1
3 4 2
4 5 1
5 6 1
6 7 1
I think what you're asking is whether it is possible to count the number of unique types of group to which an individual belongs.
If so, then that is certainly possible.
I wouldn't be able to tell you how to do it in R since I don't know a lot of R, and I don't know what your data looks like. But there's no reason why it wouldn't be possible.
Is this data coming from a database? If so, then it might be easier to write a SQL query to compute the value you want, rather than to do it in R. If you describe your schema, there should be lots of people here who could give you the query you need.