How to build a trendline on a graph in R

I've checked everywhere, and people refer to examples that I can't understand (yes I'm kinda slow). Could anyone please explain me how to build a logarithmic trendline in R?

Here's the working example:

myds <- c(23.0415,13.1965,10.4110,12.2560,9.5910,10.7160,9.9665,8.5845,8.9855,8.8920,10.3425,9.3820,9.0860,9.6870,8.5635,9.0755,8.5960,7.9485,8.3235,8.1910)
plot(myds)

I can't find a simple way to apply regression trendlines. I'm interested in particular in the logarithmic and the linear trendlines. Is it possible to do without connecting any new packages?

Good sirs, please be kind to clarify!

since you had some data points missing, I took what you had provided: the six points.

edit - now the full example was available

1. A trendline is just a regression, and regressions are run most simple way like this: a<-lm(outcome~predictor) -- in this example the object a will hold your regression parameters. To get the values of your new trendline model, just use predict(model_name), or in your case predict(a)

2. Adding line to a plot is dead simple. Just say lines(b), where b specifies the line you want to plot after you have used the plot() function.

To wrap it up:

[![myds <- c(23.0415,13.1965,10.4110,12.2560,9.5910,10.7160,9.9665,8.5845,8.9855,8.8920,10.3425,9.3820,9.0860,9.6870,8.5635,9.0755,8.5960,7.9485,8.3235,8.1910)
x <- (1:length(myds))
plot(myds)

#make the main plot
plot(x,myds,ylim=c(5,30),xlim=c(0,20))

#add linear trend
lines(predict(lm(myds~x)),col='green')

#one more trend
lines(predict(lm(myds~log(x))),col='red')][1]][1]