Plot time series linear regression in ggplot2 - r

Question

My question is about the representation of time series analysis from tslm with ggplot2.
I have used forecast package to decompose SST time series in the Mediterranean in trend, seasonal and remainder components. Then I have looked for the slope (trend) of the linear regression for the trend component with tslm. But I can't figure out how to plot the tslm with ggplot2. Should I ggplot SST trend component with geom_smooth(model=lm)? Would lm provide the same results (slope) than tslm?
This is the code used to build and decompose SST time series
library(forecast)
# Loop to calculate trend for any grid point/column
for (i in 2:length(data)){
# read variable/column to analyse
var<-paste("V",i,sep="")
ff<-data$fecha
valor<-data[,i]
datos2<-as.data.frame(cbind(data$fecha,valor))
#Build time series
datos.ts<-ts(datos2$valor, frequency = 365)
datos.stl <- stl(datos.ts,s.window = 365)
# tslm: Save trend component
datos.tslm<-tslm(datos.ts ~ trend)
output[,i-1]<-datos.stl$time.series[,2]
}
# Summarize trends for the whole Mediterranean (mean value to be plotted)
trend<-as.data.frame(rowMeans(output[,1:length(output)]))
And the code to plot with geom_smooth
trend.plot<-ggplot(data=trend, aes(x=fecha, y=trend)) + geom_point(size=0.1) +
geom_smooth(method='lm', data = trend[1:12784,])
EDIT 1
As SST data consists of a bunch of files, I've uploaded trend data to Dropbox and made available in this csv file

Answer 1

I am trying to understand you question and as the first try, I have revised your code as following (the data attached only contains 2 columns, so I removed the for loop, but generalization should not be hard)
library(forecast)
library(ggplot2)
library(zoo)
data <- read.csv('../Downloads/trend_data.csv', header=TRUE)
data$fecha <- as.Date(data$fecha)
i <- 2
# read variable/column to analyse
var<-paste("V",i,sep="")
ff<-data$fecha
valor<-data[,i]
datos2<-as.data.frame(cbind(data$fecha,valor))
#Build time series
datos.ts<-ts(datos2$valor, frequency = 365)
datos.stl <- stl(datos.ts,s.window = 365)
# tslm: Save trend component
datos.tslm<-tslm(datos.ts ~ trend)
output <-datos.stl$time.series[,2]
# Summarize trends for the whole Mediterranean (mean value to be plotted)
# trend<-as.data.frame(rowMeans(output[,1:length(output)]))
ggplot(data=data, aes(x=fecha, y=trend)) + geom_point(size=0.1) +
geom_smooth(method='lm', data = data.frame(fecha=data$fecha, trend=output), aes(x=fecha, y=output))
Let me know if I misinterpret your intention here.
UPDATE: I feel like what you want might be just line plot of the output trend of tslm?
ggplot(data=data, aes(x=fecha, y=trend)) + geom_point(size=0.1) +
geom_line(data = data.frame(fecha=data$fecha, trend=output), aes(x=fecha, y=output))
If you want a smoothed version of the trend,
ggplot(data=data, aes(x=fecha, y=trend)) + geom_point(size=0.1, col="red") +
geom_smooth(data = data.frame(fecha=data$fecha, trend=output), aes(x=fecha, y=output),col="blue",size=0.1)

Answer 2

The data you provided, plotted as a linegraph with one dot per day. Does this solve your problem?
library(dplyr)
library(ggplot2)
trend_data <- read.csv2("../trend_data.csv",
sep = ",",stringsAsFactors = FALSE)
df <- trend_data %>% mutate(fecha = as.Date(fecha), trend = as.numeric(trend))
ggplot(df, aes(x = fecha, y = trend)) +
geom_line() +
geom_point()