2021-09-30
Last updated: 2021-10-01
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Knit directory: Test/
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load data.json
today = "2021-09-30"
cat("data on:", today, "\n")data on: 2021-09-30 json_data = fromJSON(file = paste0("data/json/", today, "/position.json"))
cat("Total collected positions: ", length(json_data), "\n")Total collected positions: 1867384 tagId_seq = unlist(lapply(json_data, function(x){x["tag_id"][[1]]}))
tagId = unique(tagId_seq)
nb_tag = length(tagId)
cat("Tags are: ", tagId, "\n")Tags are: 2e55 0db4 2f40 2c57 0d82 2f7b 0baf 28d2 2f77 2a51 19ab 2e8d 2b9c 2dd9 table(tagId_seq)tagId_seq
0baf 0d82 0db4 19ab 28d2 2a51 2b9c 2c57 2dd9 2e55 2e8d
176791 177024 168257 35349 176557 176988 90351 175357 1199 177082 24287
2f40 2f77 2f7b
176694 134872 176576 general analysis
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[order(dat$record_timestamp),]
dat = cbind.data.frame(dat, convert_date(dat$record_timestamp))
dat$x = as.numeric(dat$x)
dat$y = as.numeric(dat$y)
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$label)
list_tag <- split(dat, dat$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 = F)| tag | label | first_record | last_record | number | number_NA | ratio_non_NA | freq_1Q | freq_median | freq_3Q |
|---|---|---|---|---|---|---|---|---|---|
| 2a51 | BLA | 2021-09-30 00:00:00 | 2021-09-30 23:59:59 | 176988 | 17082 | 0.90 | 0 | 0.496 | 1 |
| 2f7b | BRA2 | 2021-09-30 00:00:00 | 2021-09-30 23:59:59 | 176576 | 1711 | 0.99 | 0 | 0.452 | 1 |
| 2f40 | BRA4 | 2021-09-30 00:00:00 | 2021-09-30 23:59:59 | 176694 | 475 | 1.00 | 0 | 0.457 | 1 |
| 2f77 | BRP1 | 2021-09-30 00:00:00 | 2021-09-30 23:59:59 | 134872 | 807 | 0.99 | 0 | 0.546 | 1 |
| 2b9c | BRP2 | 2021-09-30 00:00:00 | 2021-09-30 16:27:06 | 90351 | 118 | 1.00 | 0 | 1.000 | 1 |
| 0d82 | CDS1 | 2021-09-30 00:00:00 | 2021-09-30 23:59:59 | 177024 | 3782 | 0.98 | 0 | 0.452 | 1 |
| 2c57 | DYN1 | 2021-09-30 00:00:00 | 2021-09-30 23:59:59 | 175357 | 2545 | 0.99 | 0 | 0.495 | 1 |
| 2e8d | DYN3 | 2021-09-30 00:00:00 | 2021-09-30 22:51:47 | 24287 | 408 | 0.98 | 0 | 0.566 | 1 |
| 0baf | ELC | 2021-09-30 00:00:00 | 2021-09-30 23:59:59 | 176791 | 2879 | 0.98 | 0 | 0.500 | 1 |
| 0db4 | FAU | 2021-09-30 00:00:00 | 2021-09-30 23:59:59 | 168257 | 15496 | 0.91 | 0 | 0.503 | 1 |
| 28d2 | ORD | 2021-09-30 00:00:00 | 2021-09-30 23:59:59 | 176557 | 9430 | 0.95 | 0 | 0.429 | 1 |
| 2e55 | SCO | 2021-09-30 00:00:00 | 2021-09-30 23:59:59 | 177082 | 43 | 1.00 | 0 | 0.449 | 1 |
plot with NAs
timestamp_breaks = as.numeric(as.POSIXct(paste0(today, " ", sprintf("%02d", 0:23), ":00:00 CEST"))) + 3600*6
p <- ggplot(dat) + theme_bw() +
geom_point(aes(x=record_timestamp, y=label, col=label)) +
scale_x_continuous(breaks = timestamp_breaks, labels = 0:23) +
coord_cartesian(xlim = c(timestamp_breaks[1], timestamp_breaks[23]+3600)) +
theme(legend.position = "None") +
labs(x = "hour", y = "", title = today)
print(p)
plot without NAs
x_na = which(is.na(dat$x))
y_na = which(is.na(dat$y))
cat("if x_na = y_na:", identical(x_na, y_na), "\n")if x_na = y_na: TRUE cat("number of NA positions:", length(x_na), "/", length(tagId_seq), "(=",
length(x_na)/length(tagId_seq)*100, "%)", "\n")number of NA positions: 54776 / 1867384 (= 2.933301 %) if (length(x_na)!=0){
dat = dat[-x_na,]
}timestamp_breaks = as.numeric(as.POSIXct(paste0(today, " ", sprintf("%02d", 0:23), ":00:00 CEST"))) + 3600*6
p <- ggplot(dat) + theme_bw() +
geom_point(aes(x=record_timestamp, y=label, col=label)) +
scale_x_continuous(breaks = timestamp_breaks, labels = 0:23) +
coord_cartesian(xlim = c(timestamp_breaks[1], timestamp_breaks[23]+3600)) +
theme(legend.position = "None") +
labs(x = "hour", y = "", title = today)
print(p)
list_tag <- split(dat, dat$tag)
for (tag in names(list_tag)){
if (!is.null(list_tag[tag][[1]])){
dd = list_tag[tag][[1]]
dd[,c("x","y")] = dd[,c("x","y")]/100
rownames(dd) = 1:nrow(dd)
dd$num = 1:nrow(dd)
dd$timediff = c(0, dd$record_timestamp[-1] - dd$record_timestamp[-nrow(dd)])
list_tag[tag][[1]] = dd
}
}
dat = do.call(rbind.data.frame, list_tag)plot
plan <- read_excel("data/plan/Wall_lignes_firminy.xlsx")
plan = as.data.frame(plan)
plan$`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$label[k]
cat("\n")
cat("## ", label, "\n")
dd = list_tag[tag][[1]]
q <- p +
geom_point(data = dd, aes(x=x,y=y), col="red", size = 1) +
coord_equal(ratio = 1, xlim = c(-35,5), ylim = c(-60,5)) +
labs(x = "", y = "", title = paste0(tag, " - ", names_tag$Matériel[names_tag$id==tag]))
print(q)
cat("\n")
}BLA
BRA2
BRA4
BRP1
BRP2
CDS1
DYN1
DYN3
ELC
FAU
ORD
SCO
sessionInfo()R version 4.0.2 (2020-06-22)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS Catalina 10.15.6
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRblas.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRlapack.dylib
locale:
[1] fr_FR.UTF-8/fr_FR.UTF-8/fr_FR.UTF-8/C/fr_FR.UTF-8/fr_FR.UTF-8
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] readxl_1.3.1 lubridate_1.7.10 dplyr_1.0.6 nnet_7.3-14
[5] kableExtra_1.1.0 rjson_0.2.20 cowplot_1.1.0 gganimate_1.0.7
[9] ggplot2_3.3.3 workflowr_1.6.2
loaded via a namespace (and not attached):
[1] progress_1.2.2 tidyselect_1.1.0 xfun_0.25 purrr_0.3.4
[5] colorspace_1.4-1 vctrs_0.3.8 generics_0.1.0 viridisLite_0.3.0
[9] htmltools_0.5.0 yaml_2.2.1 utf8_1.1.4 rlang_0.4.11
[13] later_1.1.0.1 pillar_1.6.0 glue_1.4.1 withr_2.4.2
[17] DBI_1.1.1 tweenr_1.0.1 lifecycle_1.0.0 stringr_1.4.0
[21] cellranger_1.1.0 munsell_0.5.0 gtable_0.3.0 rvest_1.0.0
[25] evaluate_0.14 labeling_0.3 knitr_1.33 httpuv_1.5.4
[29] fansi_0.4.1 gifski_0.8.6 highr_0.8 Rcpp_1.0.5
[33] readr_1.4.0 promises_1.1.1 scales_1.1.1 backports_1.1.8
[37] webshot_0.5.2 farver_2.0.3 fs_1.5.0 hms_1.0.0
[41] digest_0.6.25 stringi_1.4.6 grid_4.0.2 rprojroot_1.3-2
[45] tools_4.0.2 magrittr_2.0.1 tibble_3.1.1 crayon_1.4.1
[49] whisker_0.4 pkgconfig_2.0.3 ellipsis_0.3.1 xml2_1.3.2
[53] prettyunits_1.1.1 httr_1.4.2 rstudioapi_0.13 assertthat_0.2.1
[57] rmarkdown_2.10 R6_2.4.1 git2r_0.28.0 compiler_4.0.2