Adding a slope(s) line to a linear model(s) that's in a loop in R

Question

Apologies if this doesn't make any sense, this is my first experience using R (and coding in general) and I'm lost. Any help would be greatly appreciated.

I have a data set of case numbers from various countries and years for measles.

So far, I have (I'm assuming correctly but am not completely sure):

• subsetted that data set to only contain info for after 1998 (to 2018), called All_after

• using a loop (1-194 being the different countries), run a linear model of the effect of year on the natural log during this time period

What I should have is slope for each country from the linear model (which I can then plot as histograms). However, with my current code I am 'slope-less'. I've tried abline functions and lm functions inside and outside the loop but get the answer that whatever I put in the brackets isn't finite.

Here's my code so far:

All_after <- subset(Measles, year >= 1998, year < 2018)

attach(All_after)

All_after$Cname <- "1":"194"

log_All_after <- log(All_after[,7]+1)

i <- c(Cname="1":"194")

for (val in i) {

with(All_after[All_after$Cname==1,] plot(year,log_All_after))

model5 <- lm(log_All_after~year, data=All_after, subset=i)

} 

Again, apologies if this is all gibberish!

Edit:

Using the summary function, this is what appears for All_after :

> summary(All_after)
       X        WHO_REGION     ISO_CODE                    Cname     
 Min.   :3493   AFR : 987   AFG    :  21   Afghanistan        :  21  
 1st Qu.:4511   AMR : 735   AGO    :  21   Albania            :  21  
 Median :5530   EMR : 441   ALB    :  21   Algeria            :  21  
 Mean   :5530   EUR :1113   AND    :  21   Andorra            :  21  
 3rd Qu.:6548   SEAR: 231   ARE    :  21   Angola             :  21  
 Max.   :7566   WPR : 567   ARG    :  21   Antigua and Barbuda:  21  
                            (Other):3948   (Other)            :3948  
    Disease          year          cases         
 measles:4074   Min.   :1998   Min.   :     0.0  
                1st Qu.:2003   1st Qu.:     0.0  
                Median :2008   Median :    20.5  
                Mean   :2008   Mean   :  2402.6  
                3rd Qu.:2013   3rd Qu.:   446.5  
                Max.   :2018   Max.   :217151.0  
                               NA's   :294     

Answer 1

what I have written below can get you started. I am roughly guessing what you data is like, and simulate some values for 3 countries:

n = 21 

All_after <- data.frame(
  X = sample(3000:4000,n*3),
  WHO_REGION = rep(c("EMR","EUR","EUR"),each=n),
  ISO_CODE = rep(c("AFG","AND","ALB"),each=n),
  Cname = rep(c("Afghanistan","Andorra","Albania"),each=n),
  Disease="measles",
  year = sample(1998:2018,n*3,replace=TRUE),
  cases = rnbinom(n*3,mu=400,size=0.1)
)

We do log as you have on the 7th column. I add it to the data.frame

All_after$log_All_after <- log(All_after[,7]+1)

Below is the part I made changes, you need to create a unique vector of country names to iterate through and also a list to store your results:

# create a vector of all your countries to iterate through
all_countries <- unique(All_after$Cname)

# to store the output
output <- vector("list",length(all_countries))
names(output) <- all_countries

Here i plot 3 per page using par(mfrow=c(1,3)), you can remove this if you prefer one plot per page. The key is to store your plots in a pdf, so it will not be lost.

pdf("regress.pdf",width=12,height=8)
par(mfrow=c(1,3))

for (val in all_countries) {

# you need to subset using Cname == val
countryData <- All_after[All_after$Cname==val,]
with(countryData, plot(year,log_All_after,main=val))

model5 <- lm(log_All_after~year, data=countryData)
abline(model5,lty=8,col="blue")
output[[val]] <- coefficients(model5)

} 
dev.off()

And you can collect all your results

 do.call(rbind,output)
            (Intercept)        year
Afghanistan  -202.79141  0.10199578
Andorra       -41.21576  0.02161816
Albania       115.88641 -0.05598124