how would i run this multiple times?
I have a variable called percent_people, which looks if we have 5000000 people in the variable country, and have a variable called city_share which looks at the percentage share per city, eg London = 40%, the percent variable has different levels of how many of them could potentially become unemployed (ie ranging from 100% to 75% or 50% or 25%), and how these different percentages could affect the how the unemployment rate will change?
However, right now I can only introduce one city_share and one percent_people variables. How could I code it so that I can loop through more than one input of each of the variables?
Right now I have the following:
library(dplyr)
Prediction <- function(city_share,
percent_people) {
unemployed_lon <-5000000 %>%
multiply_by(city_share) %>%
multiply_by(percent_people)
unemp <- 100000 +unemployed_lon
unemprate <- unemp %>% divide_by(5000000)
return(unemprate)
}
# Check -0.4 share + 100% percent_people
Prediction(0.4,1)
I am not sure if this is what you want, but if you are trying to make the function accept more than one percent_people
variable at once, you can loop through it inside of the function so that it can accept a vector of percentages:
library(dplyr)
library(magrittr)
Prediction <- function(city_share,
percent_people) {
unemprates <- c()
for (i in percent_people){
unemployed_lon <-5000000 %>%
multiply_by(city_share) %>%
multiply_by(percent_people)
unemp <- 100000 +unemployed_lon
unemprate <- unemp %>% divide_by(5000000)
}
return(unemprate)
}
# Check -0.4 share + 100% percent_people
Prediction(0.4,c(1,0.5,0.25))
Prediction(0.4,1)
If you want it to also return the result of several city_share
inputs, I think what you probably need is to switch to lists. The code below may not be perfect, but it does the job of returning one list of values per city_share introduced.
library(dplyr)
library(magrittr)
Prediction <- function(city_share,
percent_people) {
unemprates_all<-list()
for (i in city_share){
unemp_share <- c()
for (j in percent_people){
unemployed_lon <-5000000 %>%
multiply_by(i) %>%
multiply_by(j)
unemp <- 100000 + unemployed_lon
unemp <- unemp %>% divide_by(5000000)
unemp_share <- append(unemp_share,unemp)
}
unemprate <- list(unemp_share)
unemprates_all[[length(unemprates_all)+1]] <- unemprate
}
return(unemprates_all)
}
# Check -0.4 share + 100% percent_people
Prediction(c(0.4,0.2),c(1,0.5))
Prediction(0.4,1)
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