Last updated: 2021-09-27

Checks: 7 0

Knit directory: Test/

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Repository version: a78cc3d

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File	Version	Author	Date	Message
Rmd	a78cc3d	cfcforever	2021-09-27	make changes

load data.json

today = "2021-09-26"
cat("data on:", today, "\n")

data on: 2021-09-26

json_data = fromJSON(file = paste0("data/json/", today, "/position.json"))
cat("Total collected positions: ", length(json_data), "\n")

Total collected positions:  413097

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 2f40 0da6 0baf 0d82 2e5b 2c5d 2a51 2f7b 2e55 28d2 2e8d

table(tagId_seq)

tagId_seq
  0baf   0d82   0da6   0db4   19ab   28d2   2a51   2b9c   2c57   2c5d   2e55 
   458    606    516 122774  25961    508    567 129721    958    338    448 
  2e5b   2e8d   2f40   2f77   2f7b 
   429    325    503 128505    480

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	2021-09-26 00:00:00	2021-09-26 07:26:57	122774	34891	0.72	0.197	0.200	0.202
2f77	2021-09-26 00:00:00	2021-09-26 07:25:20	128505	6729	0.95	0.198	0.200	0.202
2b9c	2021-09-26 00:00:00	2021-09-26 07:24:37	129721	154	1.00	0.199	0.200	0.201
19ab	2021-09-26 00:00:00	2021-09-26 07:22:25	25961	231	0.99	0.997	1.000	1.003
2c57	2021-09-26 00:00:44	2021-09-26 07:13:07	958	83	0.91	0.198	0.252	59.999
2f40	2021-09-26 00:00:02	2021-09-26 07:12:49	503	4	0.99	59.953	59.999	60.002
0da6	2021-09-26 00:00:42	2021-09-26 07:12:47	516	19	0.96	59.950	59.998	60.002
0baf	2021-09-26 00:00:22	2021-09-26 07:12:43	458	1	1.00	59.996	60.000	60.002
0d82	2021-09-26 00:01:33	2021-09-26 07:12:43	606	8	0.99	0.251	59.986	60.002
2e5b	2021-09-26 00:00:07	2021-09-26 07:12:17	429	226	0.47	59.996	60.000	60.004
2c5d	2021-09-26 00:00:27	2021-09-26 07:12:14	338	336	0.01	59.997	60.001	60.049
2a51	2021-09-26 00:00:13	2021-09-26 07:12:12	567	3	0.99	59.752	59.998	60.003
2f7b	2021-09-26 00:00:01	2021-09-26 07:12:04	480	21	0.96	59.950	59.999	60.008
2e55	2021-09-26 00:00:13	2021-09-26 07:12:02	448	4	0.99	59.951	60.000	60.047
28d2	2021-09-26 00:00:41	2021-09-26 07:12:01	508	25	0.95	59.949	59.999	60.003
2e8d	2021-09-26 01:41:36	2021-09-26 06:41:38	325	7	0.98	0.197	0.202	0.298

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: 42742 / 413097 (= 10.34672 %)

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)
}

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] lubridate_1.7.10 dplyr_1.0.5      nnet_7.3-15      kableExtra_1.3.4
[5] rjson_0.2.20     cowplot_1.1.1    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_0.22         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     munsell_0.5.0     gtable_0.3.0      rvest_1.0.0      
[25] evaluate_0.14     labeling_0.4.2    knitr_1.32        httpuv_1.5.5     
[29] fansi_0.4.2       highr_0.8         Rcpp_1.0.6        promises_1.2.0.1 
[33] scales_1.1.1      webshot_0.5.2     jsonlite_1.7.2    systemfonts_1.0.1
[37] farver_2.1.0      fs_1.5.0          hms_1.0.0         digest_0.6.27    
[41] stringi_1.5.3     grid_4.0.5        rprojroot_2.0.2   tools_4.0.5      
[45] magrittr_2.0.1    sass_0.3.1        tibble_3.1.0      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 svglite_2.0.0     rmarkdown_2.10    httr_1.4.2       
[57] rstudioapi_0.13   R6_2.5.0          git2r_0.28.0      compiler_4.0.5

first-analysis

cfcforever

2021-09-26

load data.json

general analysis

quality of collecting data

plot