Combine outputs of a function for each index in a for loop in R - r

Question

I have created a function which completes a calculation based on data from two data frames for an individual.
I want to complete that function for each individual and combine all the outputs in a data frame and export to .csv
Currently the output .csv only has data for person 34, none of the other.
I've noted that when I run this it creates an object ID, which is just the numeric 34. It seems to be only holding on to the last ID in data$ID.
How can I create an output with results for all persons?
library(dplyr)
library(lubridate)
library(date)
screen_function = function(x){
# Select each person and get necessary inputs
data = data%>%filter(ID == x)
demogs = demogs %>% filter (P_ID == x)
data$Age = demogs$Age
data$result = data$test * data$Age
data$Date = as.Date(data$Date,format='%d/%m/%Y') # ensures date column is in correct format
# only include tests from most recent 12-24 months and only proceed if test in last 12 months
Recent_12m = data %>% filter(between(Date, today() - years(1), today()))
Recent_24m = data %>% filter(between(Date, today() - years(2), today()))
if ((nrow(Recent_12m)) > 0) {
data = rbind(Recent_12m,Recent_24m)
Recent_12m$min_date = min(Recent_12m$Date)
Recent_12m$Date = as.Date(Recent_12m$Date,format='%d/%m/%Y')
Recent_12m$min_date = as.Date(Recent_12m$min_date,format='%d/%m/%Y')
Recent_24m$min_date = min(Recent_24m$Date)
Recent_24m$Date = as.Date(Recent_24m$Date,format='%d/%m/%Y')
Recent_24m$min_date = as.Date(Recent_24m$min_date,format='%d/%m/%Y')
Recent_12m$Period = interval(Recent_12m$min_date, Recent_12m$Date)
Recent_12m$Years = as.numeric(Recent_12m$Period, unit = "years")
Recent_24m$Period = interval(Recent_24m$min_date, Recent_24m$Date)
Recent_24m$Years = as.numeric(Recent_24m$Period, unit = "years")
# Latest result
Last = filter(Recent_12m, (Recent_12m$Date == max(Date)))
# linear regression model
Reg_12month <- lm(result ~ Years, data=Recent_12m)
Reg_24month <- lm(result ~ Years, data=Recent_24m)
info = c(x, round(Last$result, digits=1), round(Reg_12month$coefficients["Years"], digits = 1), round(Reg_24month$coefficients["Years"], digits = 1))
newdf = data.frame(matrix(0, ncol = 4))
colnames(newdf) = c("ID", "Latest result", "Trend 12month", "Trend 24 month")
newdf= rbind(newdf, info)
write.csv(newdf, "filepath.csv")
}
}
Date= sample(seq(as.Date('2019/11/01'), as.Date('2020/11/01'), by="day"), 12)
ID= c(12,12,12,450,450,450,1,1,1,34,34,34)
test= rnorm(12, mean=150, sd=60)
data= data.frame(ID, Date, test)
P_ID = c(1,12,34,450)
Age = c(50,45,60,72)
demogs = data.frame(P_ID, Age)
persons = unique(data$ID)
for(ID in persons){
screen_function(paste("", ID,"", sep=""))
}
Created on 2020-11-16 by the reprex package (v0.3.0)

Answer 1

So, I've got around this by using a pre-made .csv, instead of creating a new dataframe. The .csv just contains a single row of 4 columns, with random entries in each cell.
newdf= read.csv(file = "filepath.csv")
info = c(x, round(Last$result, digits=1), round(Reg_12month$coefficients["Years"], digits = 1),
round(Reg_24month$coefficients["Years"], digits = 1))
newdf= rbind(Summary, patient_info)
colnames(newdf) = c("ID", "Latest result", "Trend 12month", "Trend 24 month")
newdf= distinct(newdf, ID, .keep_all = TRUE)
write.csv(Summary, "filepath.csv", row.names = FALSE)}}