Multiply list values by 100 using apply function-CodePudding

I would like to multiply list values by 100. That is, 0.0549 would be 5.49, 0.5719 would be 57.19, 0.0166 would be 1.66, etc.

#example data
x <- list(structure(list(conf.int = structure(c(0.054, 0.57), conf.level = 0.95)), 
                    class = "htest"),
          structure(list( conf.int = structure(c(0.01638, 0.4003372 ), conf.level = 0.95)), 
                    class = "htest"))

x
# [[1]]
# data:  
# 
# 95 percent confidence interval:
#  0.054 0.570
# 
# [[2]]
# data:  
# 
# 95 percent confidence interval:
#  0.0163800 0.4003372

How would I get this? thanks in advance!!

CodePudding user response：

Using example data of a list...

#Make list
n <- rnorm(12, mean = 0.00791, sd = 0.4)
n2 <- c(0.010, 0.0223, 0.0890, 0.034, 0.078, 0.0902, 0.034, 0.0211, 0.01007,0.0209, 0.0330, 0.0446)
data <- list(n, n2)

# apply x 100 to the data list
new_dataframe <- lapply(data,"*",100)

This will X100 to all components of the list. Without seeing your code, it is hard to give any more advice.

CodePudding user response：

The apply family is not needed in this instance I think. You seem to have a list of lists, and each sub-list contains a single named value. EDIT: This assumption is wrong, I misread the screenshot, leaving the error here for posterity) :EDIT

mocking up data:

a <- list(list(conf.int = 1.2), list(conf.int = 0.0166), list( conf.int = 0.95))

if we try a * 100 we get an error:

> a*100
Error in a * 100 : non-numeric argument to binary operator

So we need to simplify the list into a numeric vector using unlist():

> unlist(a)*100
conf.int conf.int conf.int 
  120.00     1.66    95.00

If you really want an apply() variant, perhaps:

> sapply(a, FUN = \(x) {x$conf.int * 100})
[1] 120.00   1.66  95.00

CodePudding user response：

We can use double lapply, and somewhat keep the original data structure:

lapply(x, function(i) lapply(i, function(j) j  * 100))
# [[1]]
# [[1]]$conf.int
# [1]  5.4 57.0
# attr(,"conf.level")
# [1] 0.95
# 
# 
# [[2]]
# [[2]]$conf.int
# [1]  1.63800 40.03372
# attr(,"conf.level")
# [1] 0.95

Or we can avoid manipulating the output and fix the problem from the start, by multiplying data by 100 before computing the confidence intervals, for example:

t.test(mtcars$mpg, conf.level = 0.95)
#   One Sample t-test
# 
# data:  mtcars$mpg
# t = 18.857, df = 31, p-value < 2.2e-16
# alternative hypothesis: true mean is not equal to 0
# 95 percent confidence interval:
#  17.91768 22.26357
# sample estimates:
# mean of x 
#  20.09062 

t.test(mtcars$mpg * 100, conf.level = 0.95) 
#   One Sample t-test
# 
# data:  mtcars$mpg * 100
# t = 18.857, df = 31, p-value < 2.2e-16
# alternative hypothesis: true mean is not equal to 0
# 95 percent confidence interval:
#  1791.768 2226.357
# sample estimates:
# mean of x 
#  2009.062