Last updated: 2021-10-15

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Knit directory: Test/

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File	Version	Author	Date	Message
Rmd	e8f889a	cfcforever	2021-10-15	add new analysis

load data.json

today = "2021-10-14"
cat("data on:", today, "\n")

data on: 2021-10-14

json_data = fromJSON(file = paste0("data/json/", today, "/position.json"))
dat <- data.frame(tag = unlist(lapply(json_data, function(x){x["tag_id"][[1]]})),
                  x = unlist(lapply(json_data, function(x){x["x"][[1]]})),
                  y = unlist(lapply(json_data, function(x){x["y"][[1]]})),
                  record_timestamp = unlist(lapply(json_data, function(x){x["record_timestamp"][[1]]})))
dat = dat[which(dat$record_timestamp>=1634162400 & dat$record_timestamp<1634241600),]
cat("Total collected positions: ", nrow(dat), "\n")

Total collected positions:  2244003

dat = dat[order(dat$record_timestamp),]
dat = cbind.data.frame(dat, convert_date(dat$record_timestamp))
dat$x = as.numeric(dat$x)/100
dat$y = as.numeric(dat$y)/100

tagId = unique(dat$tag)

names_tag <- read.table(file = "data/tag_names_20210924.txt", header = T, sep = "\t")
names_tag = names_tag[names_tag$id%in%tagId, ]

tagId = names_tag$id
nb_tag = length(tagId)

dat = dat[dat$tag%in%tagId,]
dat$label = factor(dat$tag, levels = names_tag$id, labels = names_tag$label)
dat$tagn = as.numeric(factor(dat$tag, levels = names_tag$id, labels = 1:nb_tag))

list_tag <- split(dat, dat$tag)

quality of collecting data

table_tag <- data.frame(tag = names_tag$id, label = names_tag$label)
table_tag$first_record = NA
table_tag$last_record = NA
table_tag$number = NA
table_tag$number_NA = NA
table_tag$ratio_non_NA = NA
# table_tag$freq_1Q = NA
# table_tag$freq_median = NA
# table_tag$freq_3Q = NA


for (k in 1:nb_tag){
  tag = table_tag$tag[k]
  temp = list_tag[tag][[1]]
  temp$diff_ts = c(0, temp$record_timestamp[-1]-temp$record_timestamp[-nrow(temp)])
  
  table_tag$first_record[k] = head(as.character(temp$date),1)
  table_tag$last_record[k] = tail(as.character(temp$date),1)
  table_tag$number[k] = nrow(temp)
  table_tag$number_NA[k] = sum(is.na(temp$x))
  table_tag$ratio_non_NA[k] = round(1-table_tag$number_NA[k]/table_tag$number[k],2)
  # table_tag$freq_1Q[k] = round(quantile(temp$diff_ts, 0.25), 3)
  # table_tag$freq_median[k] = round(quantile(temp$diff_ts, 0.5), 3)
  # table_tag$freq_3Q[k] = round(quantile(temp$diff_ts, 0.75), 3)
}

kable(table_tag) %>%
  kable_styling(bootstrap_options = "striped", full_width = T)

tag	label	first_record	last_record	number	number_NA	ratio_non_NA
0da6	BRA1	2021-10-14 00:00:00	2021-10-14 21:59:59	394842	23231	0.94
2f7b	BRA2	2021-10-14 10:15:13	2021-10-14 21:59:59	210992	2180	0.99
2f40	BRA4	2021-10-14 00:00:00	2021-10-14 21:59:59	395879	351	1.00
2f77	BRP1	2021-10-14 00:00:00	2021-10-14 21:59:59	390160	9390	0.98
2b9c	BRP2	2021-10-14 00:00:00	2021-10-14 21:59:59	390979	2939	0.99
2e8d	DYN3	2021-10-14 00:44:44	2021-10-14 21:33:29	49032	945	0.98
0baf	ELC	2021-10-14 00:00:00	2021-10-14 00:54:44	16424	144	0.99
19ab	FIX	2021-10-14 00:00:00	2021-10-14 21:59:59	395695	3455	0.99

list_tag = lapply(list_tag, function(x){cbind(x, data.frame(diff_ts = c(0, x$record_timestamp[-1]-x$record_timestamp[-nrow(x)])))})

nq = 10
table_diff_ts = matrix(NA, nrow = nb_tag, ncol = nq+1)
colnames(table_diff_ts) = paste0(c(0:10)/10*100, "%")
rownames(table_diff_ts) = tagId
for (k in 1:nb_tag){
  tag = tagId[k]
  table_diff_ts[k,] = round(quantile(list_tag[tag][[1]]$diff_ts[-1], c(0:10)/10), 3)
}
table_diff_ts = cbind.data.frame(label = names_tag$label, table_diff_ts)

kable(table_diff_ts) %>%
  kable_styling(bootstrap_options = "striped", full_width = T)

	label	0%	10%	20%	30%	40%	50%	60%	70%	80%	90%	100%
0da6	BRA1	0.000	0.199	0.200	0.2	0.2	0.2	0.2	0.2	0.200	0.201	55.901
2f7b	BRA2	0.000	0.199	0.200	0.2	0.2	0.2	0.2	0.2	0.200	0.201	14.799
2f40	BRA4	0.157	0.199	0.200	0.2	0.2	0.2	0.2	0.2	0.200	0.201	17.501
2f77	BRP1	0.000	0.199	0.200	0.2	0.2	0.2	0.2	0.2	0.200	0.201	906.702
2b9c	BRP2	0.000	0.199	0.200	0.2	0.2	0.2	0.2	0.2	0.200	0.201	454.301
2e8d	DYN3	0.144	0.199	0.200	0.2	0.2	0.2	0.2	0.2	0.200	0.201	22372.547
0baf	ELC	0.142	0.158	0.199	0.2	0.2	0.2	0.2	0.2	0.201	0.242	0.258
19ab	FIX	0.000	0.199	0.200	0.2	0.2	0.2	0.2	0.2	0.200	0.201	19.500

plot

plan <- read_excel("data/plan/Wall_lignes_firminy.xlsx")
plan = as.data.frame(plan)
plan$`Start X` <- as.numeric(plan$`Start X`)/100
plan$`Start Y` <- as.numeric(plan$`Start Y`)/100
plan$`End X` <- as.numeric(plan$`End X`)/100
plan$`End Y` <- as.numeric(plan$`End Y`)/100
colnames(plan) = c("Name", "Length", "Linetype Scale", "Angle", "Delta X",
                   "Delta Y", "Delta Z", "EndX", "EndY", "EndZ", 
                   "StartX", "StartY", "StartZ")
p <- ggplot(plan) + theme_bw() + 
  geom_segment(aes(x=StartX, y=StartY, xend=EndX, yend=EndY))

for (k in 1:nb_tag){
  tag = names_tag$id[k]
  label = names_tag$label[k]
  cat("\n")
  cat("### ", label, "\n")
  dd = list_tag[tag][[1]]
  if (!is.null(dd)){
    q <- p + 
      geom_point(data = dd, aes(x=x,y=y), col="red", size = 1) +
      coord_equal(ratio = 1, xlim = c(-35,5), ylim = c(-60,5)) + 
      labs(x = "", y = "", title = paste0(tag, " - ", names_tag$Matériel[names_tag$id==tag]))
    print(q)
  }else{
    cat("NO DATA for plot!!")
  }
  cat("\n")
}

BRA1

Warning: Removed 23231 rows containing missing values (geom_point).

BRA2

Warning: Removed 2180 rows containing missing values (geom_point).

BRA4

Warning: Removed 351 rows containing missing values (geom_point).

BRP1

Warning: Removed 9390 rows containing missing values (geom_point).

BRP2

Warning: Removed 2939 rows containing missing values (geom_point).

DYN3

Warning: Removed 945 rows containing missing values (geom_point).

ELC

Warning: Removed 144 rows containing missing values (geom_point).

FIX

Warning: Removed 3455 rows containing missing values (geom_point).

data from serveur

rm(list = ls())
source("code/fun_convert_date.R")

today = "2021-10-14"
cat("data on:", today, "\n")

data on: 2021-10-14

json_data = fromJSON(file = paste0("data/json/", today, "/20211014.json"))
dat <- data.frame(tag = unlist(lapply(json_data, function(x){x["tag_id"][[1]]})),
                  x = rep(NA, length(json_data)),
                  y = rep(NA, length(json_data)),
                  record_timestamp = as.numeric(unlist(lapply(json_data, function(x){x["record_timestamp"][[1]]}))))
dat$x[unlist(lapply(json_data, function(x){length(unlist(x))==9}))] = unlist(lapply(json_data, function(x){x["x"][[1]]}))
dat$y[unlist(lapply(json_data, function(x){length(unlist(x))==9}))] = unlist(lapply(json_data, function(x){x["y"][[1]]}))

dat = dat[which(dat$record_timestamp>=1634162400 & dat$record_timestamp<1634241600),]
cat("Total collected positions: ", nrow(dat), "\n")

Total collected positions:  2244003

range(dat$record_timestamp)

[1] 1634162400 1634241600

dat = dat[order(dat$record_timestamp),]
dat = cbind.data.frame(dat, convert_date(dat$record_timestamp))
dat$x = as.numeric(dat$x)/100
dat$y = as.numeric(dat$y)/100

tagId = unique(dat$tag)

names_tag <- read.table(file = "data/tag_names_20210924.txt", header = T, sep = "\t")
names_tag = names_tag[names_tag$id%in%tagId, ]

tagId = names_tag$id
nb_tag = length(tagId)

dat = dat[dat$tag%in%tagId,]
dat$label = factor(dat$tag, levels = names_tag$id, labels = names_tag$label)
dat$tagn = as.numeric(factor(dat$tag, levels = names_tag$id, labels = 1:nb_tag))

list_tag <- split(dat, dat$tag)