2021-09-25
Last updated: 2021-09-30
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Knit directory: Test/
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load data.json
today = "2021-09-25"
cat("data on:", today, "\n")data on: 2021-09-25 json_data = fromJSON(file = paste0("data/json/", today, "/position.json"))
cat("Total collected positions: ", length(json_data), "\n")Total collected positions: 1373090 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: 0db4 2f77 2b9c 19ab 2c57 0da6 28d2 0d82 2c5d 0baf 2e55 2a51 2e5b 2f40 2f7b 2e8d table(tagId_seq)tagId_seq
0baf 0d82 0da6 0db4 19ab 28d2 2a51 2b9c 2c57 2c5d 2e55
1449 1673 1659 427804 85427 1628 1547 422391 3012 1019 1573
2e5b 2e8d 2f40 2f77 2f7b
1416 770 1511 418595 1616 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 | 2021-09-25 00:00:55 | 2021-09-25 23:59:13 | 1547 | 2 | 1.00 | 59.998 | 60.000 | 60.001 |
| 0da6 | BRA1 | 2021-09-25 00:00:59 | 2021-09-25 23:59:42 | 1659 | 58 | 0.97 | 59.951 | 59.999 | 60.002 |
| 2f7b | BRA2 | 2021-09-25 00:00:46 | 2021-09-25 23:59:01 | 1616 | 144 | 0.91 | 59.952 | 60.000 | 60.002 |
| 2f40 | BRA4 | 2021-09-25 00:00:31 | 2021-09-25 23:59:02 | 1511 | 53 | 0.96 | 59.987 | 60.000 | 60.002 |
| 2f77 | BRP1 | 2021-09-25 00:00:00 | 2021-09-25 23:59:59 | 418595 | 5617 | 0.99 | 0.199 | 0.200 | 0.201 |
| 2b9c | BRP2 | 2021-09-25 00:00:00 | 2021-09-25 23:59:59 | 422391 | 1548 | 1.00 | 0.198 | 0.200 | 0.201 |
| 0d82 | CDS1 | 2021-09-25 00:00:33 | 2021-09-25 23:59:33 | 1673 | 28 | 0.98 | 59.952 | 60.000 | 60.002 |
| 2c57 | DYN1 | 2021-09-25 00:00:18 | 2021-09-25 23:59:44 | 3012 | 278 | 0.91 | 0.198 | 0.299 | 60.000 |
| 2e8d | DYN3 | 2021-09-25 01:11:22 | 2021-09-25 23:37:30 | 770 | 87 | 0.89 | 0.197 | 0.245 | 59.950 |
| 0baf | ELC | 2021-09-25 00:00:53 | 2021-09-25 23:59:22 | 1449 | 3 | 1.00 | 59.999 | 60.000 | 60.001 |
| 0db4 | FAU | 2021-09-25 00:00:00 | 2021-09-25 23:59:59 | 427804 | 9626 | 0.98 | 0.198 | 0.200 | 0.201 |
| 28d2 | ORD | 2021-09-25 00:00:21 | 2021-09-25 23:59:41 | 1628 | 136 | 0.92 | 59.953 | 60.000 | 60.002 |
| 2e55 | SCO | 2021-09-25 00:00:06 | 2021-09-25 23:59:13 | 1573 | 13 | 0.99 | 59.991 | 60.000 | 60.002 |
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: 17593 / 1373090 (= 1.281271 %) 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")
}
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