Last updated: 2021-09-28

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Knit directory: Test/

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Rmd 5ff2790 cfcforever 2021-09-27 some changes
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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)

list_tag <- split(dat, dat$tag)

quality of collecting data

table_tag <- data.frame(tag = tagId)
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 first_record last_record number number_NA ratio_non_NA freq_1Q freq_median freq_3Q
0db4 2021-09-25 00:00:00 2021-09-25 23:59:59 427804 9626 0.98 0.198 0.200 0.201
2f77 2021-09-25 00:00:00 2021-09-25 23:59:59 418595 5617 0.99 0.199 0.200 0.201
2b9c 2021-09-25 00:00:00 2021-09-25 23:59:59 422391 1548 1.00 0.198 0.200 0.201
19ab 2021-09-25 00:00:00 2021-09-25 23:59:59 85427 842 0.99 0.996 1.000 1.004
2c57 2021-09-25 00:00:18 2021-09-25 23:59:44 3012 278 0.91 0.198 0.299 60.000
0da6 2021-09-25 00:00:59 2021-09-25 23:59:42 1659 58 0.97 59.951 59.999 60.002
28d2 2021-09-25 00:00:21 2021-09-25 23:59:41 1628 136 0.92 59.953 60.000 60.002
0d82 2021-09-25 00:00:33 2021-09-25 23:59:33 1673 28 0.98 59.952 60.000 60.002
2c5d 2021-09-25 00:01:48 2021-09-25 23:59:27 1019 1016 0.00 59.999 60.002 60.281
0baf 2021-09-25 00:00:53 2021-09-25 23:59:22 1449 3 1.00 59.999 60.000 60.001
2e55 2021-09-25 00:00:06 2021-09-25 23:59:13 1573 13 0.99 59.991 60.000 60.002
2a51 2021-09-25 00:00:55 2021-09-25 23:59:13 1547 2 1.00 59.998 60.000 60.001
2e5b 2021-09-25 00:00:11 2021-09-25 23:59:07 1416 799 0.44 59.997 60.000 60.004
2f40 2021-09-25 00:00:31 2021-09-25 23:59:02 1511 53 0.96 59.987 60.000 60.002
2f7b 2021-09-25 00:00:46 2021-09-25 23:59:01 1616 144 0.91 59.952 60.000 60.002
2e8d 2021-09-25 01:11:22 2021-09-25 23:37:30 770 87 0.89 0.197 0.245 59.950
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 invalid positions:", length(x_na), "/", length(tagId_seq), "(=", 
    length(x_na)/length(tagId_seq)*100, "%)", "\n")
number of invalid positions: 20250 / 1373090 (= 1.474776 %) 
if (length(x_na)!=0){
  dat = dat[-x_na,]
}
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

for (tag in names(list_tag)){
  dd = list_tag[tag][[1]]
  
  p <- ggplot(dd) + theme_bw() +
    geom_point(aes(x=x,y=y)) +
    coord_equal(ratio = 1, xlim = c(-30,5), ylim = c(-60,5)) +
    labs(title = tag)
  print(p)
}

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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] lubridate_1.7.10 dplyr_1.0.6      nnet_7.3-14      kableExtra_1.1.0
[5] rjson_0.2.20     cowplot_1.1.0    gganimate_1.0.7  ggplot2_3.3.3   
[9] 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] munsell_0.5.0     gtable_0.3.0      rvest_1.0.0       evaluate_0.14    
[25] labeling_0.3      knitr_1.33        httpuv_1.5.4      fansi_0.4.1      
[29] gifski_0.8.6      highr_0.8         Rcpp_1.0.5        readr_1.4.0      
[33] promises_1.1.1    scales_1.1.1      backports_1.1.8   webshot_0.5.2    
[37] farver_2.0.3      fs_1.5.0          hms_1.0.0         digest_0.6.25    
[41] stringi_1.4.6     grid_4.0.2        rprojroot_1.3-2   tools_4.0.2      
[45] magrittr_2.0.1    tibble_3.1.1      crayon_1.4.1      whisker_0.4      
[49] pkgconfig_2.0.3   ellipsis_0.3.1    xml2_1.3.2        prettyunits_1.1.1
[53] httr_1.4.2        rstudioapi_0.13   assertthat_0.2.1  rmarkdown_2.10   
[57] R6_2.4.1          git2r_0.28.0      compiler_4.0.2