2021-09-28
Last updated: 2021-09-30
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Knit directory: Test/
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| Rmd | cc39597 | cfcforever | 2021-09-28 | some changes |
load data.json
today = "2021-09-28"
cat("data on:", today, "\n")data on: 2021-09-28 json_data = fromJSON(file = paste0("data/json/", today, "/position.json"))
cat("Total collected positions: ", length(json_data), "\n")Total collected positions: 443484 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: 28d2 2f77 2a51 2e55 2c57 0da6 0baf 2f40 2b9c 0db4 0d82 2f7b 19ab 2e8d table(tagId_seq)tagId_seq
0baf 0d82 0da6 0db4 19ab 28d2 2a51 2b9c 2c57 2e55 2e8d 2f40 2f77
35991 36012 35844 35516 7158 35543 35860 35859 35981 36013 6688 35828 35344
2f7b
35847 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 | 2021-09-28 00:00:00 | 2021-09-28 06:05:48 | 35860 | 4572 | 0.87 | 0.544 | 0.593 | 0.651 |
| 0da6 | BRA1 | 2021-09-28 00:00:00 | 2021-09-28 06:05:17 | 35844 | 6 | 1.00 | 0.583 | 0.594 | 0.639 |
| 2f7b | BRA2 | 2021-09-28 00:00:00 | 2021-09-28 06:04:42 | 35847 | 203 | 0.99 | 0.544 | 0.593 | 0.649 |
| 2f40 | BRA4 | 2021-09-28 00:00:00 | 2021-09-28 06:04:50 | 35828 | 680 | 0.98 | 0.539 | 0.593 | 0.680 |
| 2f77 | BRP1 | 2021-09-28 00:00:00 | 2021-09-28 06:05:50 | 35344 | 830 | 0.98 | 0.556 | 0.595 | 0.644 |
| 2b9c | BRP2 | 2021-09-28 00:00:00 | 2021-09-28 06:04:47 | 35859 | 1751 | 0.95 | 0.550 | 0.593 | 0.646 |
| 0d82 | CDS1 | 2021-09-28 00:00:00 | 2021-09-28 06:04:44 | 36012 | 846 | 0.98 | 0.542 | 0.594 | 0.652 |
| 2c57 | DYN1 | 2021-09-28 00:00:00 | 2021-09-28 06:05:18 | 35981 | 8000 | 0.78 | 0.550 | 0.594 | 0.644 |
| 2e8d | DYN3 | 2021-09-28 00:00:00 | 2021-09-28 05:54:56 | 6688 | 14 | 1.00 | 0.585 | 0.639 | 0.699 |
| 0baf | ELC | 2021-09-28 00:00:00 | 2021-09-28 06:05:16 | 35991 | 24 | 1.00 | 0.548 | 0.593 | 0.647 |
| 0db4 | FAU | 2021-09-28 00:00:00 | 2021-09-28 06:04:45 | 35516 | 3038 | 0.91 | 0.548 | 0.594 | 0.647 |
| 28d2 | ORD | 2021-09-28 00:00:00 | 2021-09-28 06:08:37 | 35543 | 4598 | 0.87 | 0.541 | 0.595 | 0.679 |
| 2e55 | SCO | 2021-09-28 00:00:00 | 2021-09-28 06:05:39 | 36013 | 93 | 1.00 | 0.552 | 0.594 | 0.644 |
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: 24655 / 443484 (= 5.559389 %) 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 4.0.5 (2021-03-31)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS Big Sur 10.16
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] zh_CN.UTF-8/zh_CN.UTF-8/zh_CN.UTF-8/C/zh_CN.UTF-8/zh_CN.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_1.0.5 nnet_7.3-15
[5] kableExtra_1.3.4 rjson_0.2.20 cowplot_1.1.1 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_0.22 bslib_0.2.4
[5] purrr_0.3.4 colorspace_2.0-0 vctrs_0.3.7 generics_0.1.0
[9] viridisLite_0.4.0 htmltools_0.5.1.1 yaml_2.2.1 utf8_1.2.1
[13] rlang_0.4.10 jquerylib_0.1.3 later_1.1.0.1 pillar_1.6.0
[17] glue_1.4.2 withr_2.4.1 tweenr_1.0.2 lifecycle_1.0.0
[21] stringr_1.4.0 cellranger_1.1.0 munsell_0.5.0 gtable_0.3.0
[25] rvest_1.0.0 evaluate_0.14 labeling_0.4.2 knitr_1.32
[29] httpuv_1.5.5 fansi_0.4.2 highr_0.8 Rcpp_1.0.6
[33] promises_1.2.0.1 scales_1.1.1 webshot_0.5.2 jsonlite_1.7.2
[37] systemfonts_1.0.1 farver_2.1.0 fs_1.5.0 hms_1.0.0
[41] digest_0.6.27 stringi_1.5.3 grid_4.0.5 rprojroot_2.0.2
[45] tools_4.0.5 magrittr_2.0.1 sass_0.3.1 tibble_3.1.0
[49] crayon_1.4.1 whisker_0.4 pkgconfig_2.0.3 ellipsis_0.3.1
[53] xml2_1.3.2 prettyunits_1.1.1 svglite_2.0.0 rmarkdown_2.10
[57] httr_1.4.2 rstudioapi_0.13 R6_2.5.0 git2r_0.28.0
[61] compiler_4.0.5