#add the information to the dataset

data_q1final <- data.frame(data_q1)
data_q1final <- cbind (data_q1, nr_detcted = transits, mean_amplitude =  mean_amplitudes,
                  sd_amplitude <- sd_amplitudes, mean_period <- mean_period,
                  sd_period <- mean_period)

mean_peaks <- mean(data_q1final$nr_detcted)
mean_peaks

something <- subset(data_q1final,
                    !(data_q1$confirmed == 0 & data_q1$candidate == 0 & data_q1$falsepositive == 0))
nothing <-   subset(data_q1final,
                    data_q1$confirmed == 0 & data_q1$candidate == 0 & data_q1$falsepositive == 0)
confirmed <- subset (data_q1final, data_q1$confirmed == 1)
candidate <- subset (data_q1final, data_q1$candidate == 1)
fp <- subset (data_q1final, data_q1$falsepositive == 1)


peaks_something <- mean(something$nr_detcted)
peaks_nothing <- mean(nothing$nr_detcted)
peaks_confirmed <- mean(confirmed$nr_detcted)
peaks_candidate <- mean(candidate$nr_detcted)
peaks_fp <- mean(fp$nr_detcted)

peaks_something
peaks_nothing
peaks_confirmed
peaks_candidate
peaks_fp

# Build final dataset for classification
d <- data_q1final
rm(data_q1)
rm(data_q1final)
rm(lctimes)
rm(lcurves)
rm(nothing)
rm(something)
rm(dlc)



data <- data.frame(teff = unlist(d$teff),
                   dist = unlist(d$dist),
                   mass = unlist(d$mass),
                   radius = unlist(d$radius),
                   feh = unlist(d$feh),
                   absmag = unlist(d$absmag),
                   nr_detcted = unlist(d$nr_detcted),
                   mean_amplitude = unlist(d$mean_amplitude),
                   sd_amplitude = unlist(d$`sd_amplitude <- sd_amplitudes`),
                   mean_period = unlist(d$`mean_period <- mean_period`),
                   sd_period = unlist(d$`sd_period <- mean_period`),
                   labelconfirmed = unlist(d$confirmed),
                   labelcandidate = unlist(d$candidate),
                   labelfp = unlist(d$falsepositive))


# Checkpoint
save(data, file = 'data.RData')