# Load main data file, including all indices
df = readRDS("~/cropmonitor/cropmonitor.rds")
# screen for test sites in the US / EU
df = df[which(df$longitude > 70),]
df = df[, -which(names(df) %in% "questionnaireresult")]
df = df[which(as.Date(df$date) > as.Date("2016-10-01")),]
# read in the crop cutting data (metric version g/m )
crop_data = readstata13::read.dta13("~/cropmonitor/crop_cutting_master_metric.dta")
# read questionnaire data and merges with the original
# data
if (file.exists("~/cropmonitor/questionaire.xlsx")){
quest = readxl::read_excel("~/cropmonitor/questionaire.xlsx")
quest = quest[,-(2:9)]
df = merge(df, quest, by = 'reportid', all.x = TRUE)
}
# generate strings for thumbs
df$thumbs = sprintf("~/cropmonitor/thumbs/%s/%s/%s-%s-%s.jpg",
df$old_uniqueuserid,
df$old_uniquecropsiteid,
df$old_uniqueuserid,
df$old_uniquecropsiteid,
df$pic_timestamp)
# create unique field vector
df$userfield = paste(df$uniqueuserid,df$uniquecropsiteid,sep = "-")
# grab auc data
field_metrics = lapply(unique(df$userfield),function(x){
sub = df[which(df$userfield == x),]
auc_results = auc(sub)
userid = sub$uniqueuserid[1]
weeks = ceiling(as.numeric(difftime(max(sub$date),
min(sub$date),
units = c("weeks"))))
return(list("FarmerID" = userid,
"auc" = auc_results$auc,
"auc_left" = auc_results$auc_left,
"auc_right" = auc_results$auc_right,
"image_count" = nrow(sub),
"nr_weeks" = weeks))
})
# summarize metrics
field_metrics = data.frame(do.call(rbind.data.frame, field_metrics))
field_metrics$images_week = field_metrics$image_count / field_metrics$nr_weeks
# grab the area under the curve for all sites merge the data
#test = merge(crop_data, field_metrics, by = 'FarmerID', all.x = TRUE)
# summary stats for a gcc time series
#time_series_summary = function(df, metric = "gcc"){
# summary stats
res = df %>%
group_by(date) %>%
summarise_(mean = mean(as.name(metric), na.rm = TRUE),
sd = sd(as.name(metric),na.rm = TRUE))
# plotting
ggplot(data = res, aes(x = date, y = mean)) +
geom_smooth(method = "loess", span = 0.3) +
geom_point()
#}
#time_series_summary(df, "grvi")
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