2021-10-14-0000-0100

workflowr

• Summary
• Checks
• Past versions

Last updated: 2021-10-14

Checks: 7 0

Knit directory: Test/

This reproducible R Markdown analysis was created with workflowr (version 1.6.2). The Checks tab describes the reproducibility checks that were applied when the results were created. The Past versions tab lists the development history.

---

R Markdown file: up-to-date

Great! Since the R Markdown file has been committed to the Git repository, you know the exact version of the code that produced these results.

Environment: empty

Great job! The global environment was empty. Objects defined in the global environment can affect the analysis in your R Markdown file in unknown ways. For reproduciblity it’s best to always run the code in an empty environment.

Seed: set.seed(20210926)

The command set.seed(20210926) was run prior to running the code in the R Markdown file. Setting a seed ensures that any results that rely on randomness, e.g. subsampling or permutations, are reproducible.

Session information: recorded

Great job! Recording the operating system, R version, and package versions is critical for reproducibility.

Cache: none

Nice! There were no cached chunks for this analysis, so you can be confident that you successfully produced the results during this run.

File paths: relative

Great job! Using relative paths to the files within your workflowr project makes it easier to run your code on other machines.

Repository version: 5e3499c

Great! You are using Git for version control. Tracking code development and connecting the code version to the results is critical for reproducibility.

The results in this page were generated with repository version 5e3499c. See the Past versions tab to see a history of the changes made to the R Markdown and HTML files.

Note that you need to be careful to ensure that all relevant files for the analysis have been committed to Git prior to generating the results (you can use wflow_publish or wflow_git_commit). workflowr only checks the R Markdown file, but you know if there are other scripts or data files that it depends on. Below is the status of the Git repository when the results were generated:

Ignored files:
    Ignored:    .DS_Store
    Ignored:    .Rhistory
    Ignored:    .Rproj.user/
    Ignored:    analysis/figure/
    Ignored:    data/.DS_Store
    Ignored:    data/Stabiliseur/
    Ignored:    data/json/
    Ignored:    data/plan/
    Ignored:    fig/
    Ignored:    workflowr.R

Untracked files:
    Untracked:  analysis/2021-10-12b.Rmd

Note that any generated files, e.g. HTML, png, CSS, etc., are not included in this status report because it is ok for generated content to have uncommitted changes.

---

These are the previous versions of the repository in which changes were made to the R Markdown (analysis/2021-10-14-0000-0100.Rmd) and HTML (docs/2021-10-14-0000-0100.html) files. If you’ve configured a remote Git repository (see ?wflow_git_remote), click on the hyperlinks in the table below to view the files as they were in that past version.

File	Version	Author	Date	Message
Rmd	5e3499c	cfcforever	2021-10-14	add new analysis
html	a06d3d2	cfcforever	2021-10-14	Build site.
Rmd	17a5a62	cfcforever	2021-10-14	add new analysis

---

load data.json

today = "2021-10-14"
cat("data on:", today, "\n")

data on: 2021-10-14 

json_data = fromJSON(file = paste0("data/json/", today, "/position.json"))
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[which(dat$record_timestamp>=1634162400 & dat$record_timestamp<=1634166000),]
cat("Total collected positions: ", nrow(dat), "\n")

Total collected positions:  108897 

dat = dat[order(dat$record_timestamp),]
dat = cbind.data.frame(dat, convert_date(dat$record_timestamp))
dat$x = as.numeric(dat$x)/100
dat$y = as.numeric(dat$y)/100

tagId = unique(dat$tag)

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$label)
dat$tagn = as.numeric(factor(dat$tag, levels = names_tag$id, labels = 1:nb_tag))

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 = T)

tag	label	first_record	last_record	number	number_NA	ratio_non_NA
0da6	BRA1	2021-10-14 00:00:00	2021-10-14 00:59:59	17999	11230	0.38
2f40	BRA4	2021-10-14 00:00:00	2021-10-14 00:59:59	18000	0	1.00
2f77	BRP1	2021-10-14 00:00:00	2021-10-14 00:59:59	17999	1860	0.90
2b9c	BRP2	2021-10-14 00:00:00	2021-10-14 00:59:59	18000	77	1.00
2e8d	DYN3	2021-10-14 00:44:44	2021-10-14 00:52:53	2446	4	1.00
0baf	ELC	2021-10-14 00:00:00	2021-10-14 00:54:44	16424	144	0.99
19ab	FIX	2021-10-14 00:00:00	2021-10-14 00:59:59	18029	141	0.99

list_tag = lapply(list_tag, function(x){cbind(x, data.frame(diff_ts = c(0, x$record_timestamp[-1]-x$record_timestamp[-nrow(x)])))})

nq = 10
table_diff_ts = matrix(NA, nrow = nb_tag, ncol = nq+1)
colnames(table_diff_ts) = paste0(c(0:10)/10*100, "%")
rownames(table_diff_ts) = tagId
for (k in 1:nb_tag){
  tag = tagId[k]
  table_diff_ts[k,] = round(quantile(list_tag[tag][[1]]$diff_ts[-1], c(0:10)/10), 3)
}
table_diff_ts = cbind.data.frame(label = names_tag$label, table_diff_ts)

kable(table_diff_ts) %>%
  kable_styling(bootstrap_options = "striped", full_width = T)

	label	0%	10%	20%	30%	40%	50%	60%	70%	80%	90%	100%
0da6	BRA1	0.141	0.199	0.200	0.2	0.2	0.2	0.2	0.2	0.200	0.201	0.401
2f40	BRA4	0.159	0.199	0.200	0.2	0.2	0.2	0.2	0.2	0.200	0.201	0.242
2f77	BRP1	0.153	0.199	0.199	0.2	0.2	0.2	0.2	0.2	0.201	0.201	0.400
2b9c	BRP2	0.155	0.199	0.200	0.2	0.2	0.2	0.2	0.2	0.200	0.201	0.245
2e8d	DYN3	0.195	0.199	0.200	0.2	0.2	0.2	0.2	0.2	0.200	0.201	0.205
0baf	ELC	0.142	0.158	0.199	0.2	0.2	0.2	0.2	0.2	0.201	0.242	0.258
19ab	FIX	0.000	0.199	0.199	0.2	0.2	0.2	0.2	0.2	0.201	0.201	0.400

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]]
  if (!is.null(dd)){
    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)
  }else{
    cat("NO DATA for plot!!")
  }
  cat("\n")
}

BRA1

Warning: Removed 11230 rows containing missing values (geom_point).

Past versions of unnamed-chunk-6-1.png

Version	Author	Date
a06d3d2	cfcforever	2021-10-14

BRA4

Past versions of unnamed-chunk-6-2.png

Version	Author	Date
a06d3d2	cfcforever	2021-10-14

BRP1

Warning: Removed 1860 rows containing missing values (geom_point).

Past versions of unnamed-chunk-6-3.png

Version	Author	Date
a06d3d2	cfcforever	2021-10-14

BRP2

Warning: Removed 77 rows containing missing values (geom_point).

Past versions of unnamed-chunk-6-4.png

Version	Author	Date
a06d3d2	cfcforever	2021-10-14

DYN3

Warning: Removed 4 rows containing missing values (geom_point).

Past versions of unnamed-chunk-6-5.png

Version	Author	Date
a06d3d2	cfcforever	2021-10-14

ELC

Warning: Removed 144 rows containing missing values (geom_point).

Past versions of unnamed-chunk-6-6.png

Version	Author	Date
a06d3d2	cfcforever	2021-10-14

FIX

Warning: Removed 141 rows containing missing values (geom_point).

Past versions of unnamed-chunk-6-7.png

Version	Author	Date
a06d3d2	cfcforever	2021-10-14

data from serveur

rm(list = ls())
source("code/fun_convert_date.R")

today = "2021-10-14"
json_data = fromJSON(file = paste0("data/json/", today, "/20211014_0000_0100.json"))
dat <- data.frame(tag = unlist(lapply(json_data, function(x){x["tag_id"][[1]]})),
                  x = rep(NA, length(json_data)),
                  y = rep(NA, length(json_data)),
                  record_timestamp = as.numeric(unlist(lapply(json_data, function(x){x["record_timestamp"][[1]]}))))
dat$x[unlist(lapply(json_data, function(x){length(unlist(x))==9}))] = unlist(lapply(json_data, function(x){x["x"][[1]]}))
dat$y[unlist(lapply(json_data, function(x){length(unlist(x))==9}))] = unlist(lapply(json_data, function(x){x["y"][[1]]}))

range(dat$record_timestamp)

[1] 1634162400 1634166000

dat = dat[which(dat$record_timestamp>=1634162400 & dat$record_timestamp<=1634166000),]
cat("Total collected positions: ", nrow(dat), "\n")

Total collected positions:  108897 

dat = dat[order(dat$record_timestamp),]
dat = cbind.data.frame(dat, convert_date(dat$record_timestamp))
dat$x = as.numeric(dat$x)/100
dat$y = as.numeric(dat$y)/100

tagId = unique(dat$tag)

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$label)
dat$tagn = as.numeric(factor(dat$tag, levels = names_tag$id, labels = 1:nb_tag))

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 = T)

tag	label	first_record	last_record	number	number_NA	ratio_non_NA
0da6	BRA1	2021-10-14 00:00:00	2021-10-14 00:59:59	17999	11230	0.38
2f40	BRA4	2021-10-14 00:00:00	2021-10-14 00:59:59	18000	0	1.00
2f77	BRP1	2021-10-14 00:00:00	2021-10-14 00:59:59	17999	1860	0.90
2b9c	BRP2	2021-10-14 00:00:00	2021-10-14 00:59:59	18000	77	1.00
2e8d	DYN3	2021-10-14 00:44:44	2021-10-14 00:52:53	2446	4	1.00
0baf	ELC	2021-10-14 00:00:00	2021-10-14 00:54:44	16424	144	0.99
19ab	FIX	2021-10-14 00:00:00	2021-10-14 00:59:59	18029	141	0.99

list_tag = lapply(list_tag, function(x){cbind(x, data.frame(diff_ts = c(0, x$record_timestamp[-1]-x$record_timestamp[-nrow(x)])))})

nq = 10
table_diff_ts = matrix(NA, nrow = nb_tag, ncol = nq+1)
colnames(table_diff_ts) = paste0(c(0:10)/10*100, "%")
rownames(table_diff_ts) = tagId
for (k in 1:nb_tag){
  tag = tagId[k]
  table_diff_ts[k,] = round(quantile(list_tag[tag][[1]]$diff_ts[-1], c(0:10)/10), 3)
}
table_diff_ts = cbind.data.frame(label = names_tag$label, table_diff_ts)

kable(table_diff_ts) %>%
  kable_styling(bootstrap_options = "striped", full_width = T)

	label	0%	10%	20%	30%	40%	50%	60%	70%	80%	90%	100%
0da6	BRA1	0.141	0.199	0.200	0.2	0.2	0.2	0.2	0.2	0.200	0.201	0.399
2f40	BRA4	0.185	0.199	0.200	0.2	0.2	0.2	0.2	0.2	0.200	0.201	0.215
2f77	BRP1	0.153	0.199	0.200	0.2	0.2	0.2	0.2	0.2	0.200	0.201	0.400
2b9c	BRP2	0.155	0.199	0.200	0.2	0.2	0.2	0.2	0.2	0.200	0.201	0.245
2e8d	DYN3	0.196	0.199	0.200	0.2	0.2	0.2	0.2	0.2	0.200	0.201	0.205
0baf	ELC	0.136	0.158	0.199	0.2	0.2	0.2	0.2	0.2	0.201	0.242	0.264
19ab	FIX	0.000	0.199	0.199	0.2	0.2	0.2	0.2	0.2	0.201	0.201	0.400

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]]
  if (!is.null(dd)){
    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)
  }else{
    cat("NO DATA for plot!!")
  }
  cat("\n")
}

BRA1

Warning: Removed 11230 rows containing missing values (geom_point).

Past versions of unnamed-chunk-11-1.png

Version	Author	Date
a06d3d2	cfcforever	2021-10-14

BRA4

Past versions of unnamed-chunk-11-2.png

Version	Author	Date
a06d3d2	cfcforever	2021-10-14

BRP1

Warning: Removed 1860 rows containing missing values (geom_point).

Past versions of unnamed-chunk-11-3.png

Version	Author	Date
a06d3d2	cfcforever	2021-10-14

BRP2

Warning: Removed 77 rows containing missing values (geom_point).

Past versions of unnamed-chunk-11-4.png

Version	Author	Date
a06d3d2	cfcforever	2021-10-14

DYN3

Warning: Removed 4 rows containing missing values (geom_point).

Past versions of unnamed-chunk-11-5.png

Version	Author	Date
a06d3d2	cfcforever	2021-10-14

ELC

Warning: Removed 144 rows containing missing values (geom_point).

Past versions of unnamed-chunk-11-6.png

Version	Author	Date
a06d3d2	cfcforever	2021-10-14

FIX

Warning: Removed 141 rows containing missing values (geom_point).

Past versions of unnamed-chunk-11-7.png

Version	Author	Date
a06d3d2	cfcforever	2021-10-14

Session information

sessionInfo()

R version 4.0.2 (2020-06-22)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS Catalina 10.15.7

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] scales_1.1.1     DT_0.17          readxl_1.3.1     lubridate_1.7.10
 [5] dplyr_1.0.6      nnet_7.3-14      kableExtra_1.1.0 rjson_0.2.20    
 [9] cowplot_1.1.0    gifski_0.8.6     gganimate_1.0.7  ggplot2_3.3.3   
[13] 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] htmlwidgets_1.5.1 evaluate_0.14     labeling_0.3      knitr_1.33       
[29] httpuv_1.5.4      fansi_0.4.1       highr_0.8         Rcpp_1.0.5       
[33] readr_1.4.0       promises_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