epla-muspad-interactive-report.Rmd

---
title: '`r paste0("EPLA MuSPAD ", format(Sys.time(),  "%Y-%m-%d"))`'
output: 
  flexdashboard::flex_dashboard:
    orientation: rows
    vertical_layout: scroll
---

```{r setup, include=FALSE}
library(flexdashboard)
library(tidyr)
library(ggplot2)
library(forcats)
library(janitor)
library(lubridate)
library(DT)
library(ggiraph)
library(pdftools)
library(readr)
library(dplyr)
library(purrr)
library(stringr)
library(readxl)

# Set paths to data files
source("R/data-paths.R")

# Load data processing functions
source("R/LoqTaq.R")
source("R/blutp.R")
source("R/labResults.R")

# Report theme
theme_set(theme_light())
my_facet_theme <- theme(strip.background = element_rect(fill = "#001540"),
  strip.text = element_text(colour = 'white', face = "bold"),
  legend.position = "bottom")

# Load and arrange data
tabellen <- read_log_pdfs(logs_path)
blutproben <- read_blut(bloodsamples_path)
laborergebnisse <- read_lab_results(labresults_path)

# Colors
vega_colors <- c("#1f77b4", "#ff7f0e", "#2ca02c", "#d62728", "#9467bd",
  "#8c564b", "#e377c2", "#7f7f7f", "#bcbd22", "#17becf")

city_vec <- sort(unique(c(blutproben$stadt, laborergebnisse$stadt)))
city_colors <- sapply(
  1:length(city_vec), 
  function (i) vega_colors[1 + (i-1) %% length(vega_colors)]
)
names(city_colors) <- city_vec

ID_format_colors <- c("correct" = vega_colors[1], "pseudonym" = vega_colors[2],
  "comma" = vega_colors[3], "other" = vega_colors[4])
```


```{r}
# Arrange data sets and annotate
ArrangeAnnotateDataSets <- function (data_df, sampleID_name, dataset) {
  
  ## DEBUG
  # data_df <- blutproben; sampleID_name <- "monovette"; dataset <- "sz"
  
  # Identify duplicated and count IDs and duplicated rows irrespective of file 
  # name. N.B. for Blutporben (centers sending samples), sending date and file 
  # name are the same, so that it doesn't make a difference to ignore the file 
  # name.
  data_df <- data_df %>% 
    mutate(dataset = dataset) %>%
    add_count_(vars = names(data_df)[names(data_df) != "dateinname"]) %>%
    rename(data_multiplicity = n, sampleID = all_of(sampleID_name)) %>%
    add_count(sampleID, name = "sampleID_multiplicity")
  
  # Correct IDs should be a string of 12 or 14 digits starting with "777" (e.g. 
  #   "777555705610").
  data_df <- data_df %>%
    mutate(ID_format = case_when(
      nchar(sampleID) %in% c(12, 14) &
        nchar(gsub("[[:digit:]]", "", sampleID)) == 0 &
        substr(sampleID, 1, 3) == "777" ~ "correct",
      grepl(",", sampleID) ~ "comma", # e.g. "777090000000,00"
      TRUE ~ "other" # e.g. "Blutproben Entnahme 17.07.2020"
    ))
  # Couldn't get the test for "pseudonym" (of the form: letters-"-"-numbers, e.g.
  # "hiubz-176865718") to work in the tidyverse framework.
  data_df$ID_format <- sapply(
    1:nrow(data_df),
    function(i) 
      # print(i)
      if (grepl("-", data_df$sampleID[i])) {
        if (
          nchar(gsub("[[:alpha:]]", "", 
            str_split(data_df$sampleID[i], "-")[[1]][1])) == 0 & 
            nchar(gsub("[[:digit:]]", "", 
              str_split(data_df$sampleID[i], "-")[[1]][2])) == 0
        ) {
          "pseudonym"
        } else {
          data_df$ID_format[i]
        }
      } else {
        data_df$ID_format[i]
      }
  )
  # Suggest corrected IDs:
  # - remove characters after "," in IDs with a comma
  # - add leading "777" and trailing "00" to numbers in pseudonyms
  # - adhoc suggestions
  data_df$sampleID_corrected <- sapply(
    1:nrow(data_df),
    function(i) 
      if (data_df$ID_format[i] == "correct") {
        data_df$sampleID[i] 
      } else if (data_df$ID_format[i] == "comma") {
        strsplit(data_df$sampleID[i], ",")[[1]][1]
      } else if (data_df$ID_format[i] == "pseudonym") {
        paste0("777", strsplit(data_df$sampleID[i], "-")[[1]][2], "10")
      } else if (data_df$sampleID[i] == "hiubz101942238") {
        "77710194223810"
      } else if (data_df$sampleID[i] == paste0("ezdve-1594692 ( fälschlicher ",
        "weise statt Bioprobenetikett angebrachtes Barcode  Etikett)")) {  
        "777159469210"
      } else {
        NA
      }
  )
  
  # Add row number as data point ID
  data_df <- data_df %>% mutate(ID = as.character(row_number()))
  
  # Rearrange columns
  first_columns <- c("dataset", "ID", "sampleID", "sampleID_corrected", 
    "ID_format", "sampleID_multiplicity", "data_multiplicity", "stadt", "datum",
    "dateinname")
  data_df <- data_df %>% 
    select(all_of(
      c(first_columns, names(data_df)[!names(data_df) %in% first_columns])
    )) %>%
    arrange(stadt, datum)
  
  return(data_df)
}

blutproben_arranged <- ArrangeAnnotateDataSets(blutproben, "monovette", "sz")
laborergebnisse_arranged <- ArrangeAnnotateDataSets(laborergebnisse, 
  "Proben.ID", "lab")
```

```{r}
ReplaceNAWithChar <- function (vec) {
  vec <- sapply(vec, function (v) ifelse (is.na(v), "NA", v))
  return(vec)
}
```

QS / SZ
=======================================================================

Row
-------------------------------------------------------------------------------

### Tabelle

```{r}
datatable(
  blutproben_arranged %>% 
    mutate(across(where(is_character), ReplaceNAWithChar)) %>% 
    mutate(across(where(is_character), as_factor)),
  rownames = FALSE, 
  filter = list(position = "top", clear = FALSE),
  class = "cell-border stripe nowrap compact hover",
  extensions = c("Scroller", "Buttons"),
  options = list(
    dom = "Bfrtip",
    buttons = c("copy", "csv", "excel"), 
    scrollY = 400, 
    scroller = TRUE, 
    fixedHeader = TRUE, 
    deferRender = TRUE
  )
)
```

Row
-------------------------------------------------------------------------------

```{r}
ID_format_sz_df <- blutproben_arranged %>% count(datum, ID_format, stadt)
plot_ID_format_sz <- ggplot(ID_format_sz_df, aes(datum, n, fill = ID_format, color = ID_format)) +
  # geom_bar(stat = "identity", position = "stack", width = 1) +
  geom_bar_interactive(aes(data_id = 1:nrow(ID_format_sz_df), 
    tooltip = paste0(stadt, "\n", ID_format, "\n", datum, "\n", n)), 
    stat = "identity", position = "stack", width = 0.9, size = 0.1) +
  facet_wrap(~stadt) +
  scale_color_manual(values = ID_format_colors) +
  scale_fill_manual(values = ID_format_colors) +
  labs(x = "Tagesdatum", y = "Anzahl von Proben", fill = "Proben-ID-Format") +
  theme(aspect.ratio = 9/16, plot.margin = margin(b = 2, unit = "cm"),
    legend.position = "bottom") +
  guides(color = FALSE)

dist_ID_format_sz_df <- blutproben_arranged %>% count(ID_format, stadt)
plot_dist_ID_format_sz <- ggplot(dist_ID_format_sz_df,
  aes(x = ID_format, y = n, color = ID_format, fill = ID_format)) +
  # geom_bar(stat = "identity") +
  geom_bar_interactive(aes(data_id = 1:nrow(dist_ID_format_sz_df),
    tooltip = paste0(stadt, "\n", ID_format, "\n", n)), 
    stat = "identity") +
  facet_wrap(~stadt) +
  scale_color_manual(values = ID_format_colors) +
  scale_fill_manual(values = ID_format_colors) +
  labs(x = "Proben-ID-Format", y = "Anzahl von Proben", fill = "Proben-ID-Format") +
  theme(aspect.ratio = 9/16, legend.position = "none", 
    plot.margin = margin(b = 2, unit = "cm"))

dist_ID_multiplicity_sz_df <- blutproben_arranged %>%
  select(sampleID, sampleID_multiplicity, stadt) %>%
  unique() %>%
  count(sampleID_multiplicity, stadt)
plot_dist_ID_multiplicity_sz <- ggplot(dist_ID_multiplicity_sz_df,
  aes(x = sampleID_multiplicity, y = n)) +
  geom_bar_interactive(aes(data_id = 1:nrow(dist_ID_multiplicity_sz_df), 
    tooltip = paste0(stadt, "\n", "Multi. ", sampleID_multiplicity, 
      "\n", "Anzahl ", n)), 
    stat = "identity", color = "black", fill = "lightgrey") +
  facet_wrap(~stadt) +
  scale_x_continuous(breaks = 1:max(blutproben_arranged$sampleID_multiplicity)) +
  labs(x = "ID-Multiplizität", y = "Anzahl von Proben") +
  theme(aspect.ratio = 9/16, plot.margin = margin(b = 2, unit = "cm"))

dist_data_multiplicity_sz_df <- blutproben_arranged %>%
  select(-ID, -dateinname) %>%
  unique() %>%
  count(stadt, data_multiplicity)
plot_dist_data_multiplicity_sz <- ggplot(dist_data_multiplicity_sz_df,
  aes(x = data_multiplicity, y = n)) +
  geom_bar_interactive(aes(data_id = 1:nrow(dist_data_multiplicity_sz_df), 
    tooltip = paste0(stadt, "\n", "Multi. ", data_multiplicity, 
      "\n", "Anzahl ", n)),
    stat = "identity", color = "black", fill = "lightgrey") +
  facet_wrap(~stadt) +
  scale_x_continuous(breaks = 1:max(blutproben_arranged$data_multiplicity)) +  
  labs(x = "Daten-Multiplizität", y = "Anzahl von Proben") +
  theme(aspect.ratio = 9/16, plot.margin = margin(b = 2, unit = "cm"))
```

### Versendete Proben nach Zeit und Stadt (SZ)

```{r}
ggiraph(code = {print(plot_ID_format_sz)}, hover_css = "fill:#F8B71C")
```

### Versendete Proben nach Stadt (SZ)

```{r}
ggiraph(code = {print(plot_dist_ID_format_sz)}, hover_css = "fill:#F8B71C")
```

Row
-------------------------------------------------------------------------------

### Wie oft kommen einzelne Proben-IDs vor? (SZ)

```{r}
ggiraph(code = {print(plot_dist_ID_multiplicity_sz)}, hover_css = "fill:#F8B71C")
```

### Wie oft kommen einzelne Datenpunkte vor? (SZ)

```{r}
ggiraph(code = {print(plot_dist_data_multiplicity_sz)}, hover_css = "fill:#F8B71C")
```

QS / Lab
=======================================================================

Row
-------------------------------------------------------------------------------

### Tabelle

```{r}
datatable(
  laborergebnisse_arranged %>% 
    mutate(across(where(is_character), ReplaceNAWithChar)) %>% 
    mutate(across(where(is_character), as_factor)),
  rownames = FALSE, 
  filter = list(position = "top", clear = FALSE),
  class = "cell-border stripe nowrap compact hover",
  extensions = c("Scroller", "Buttons"),
  options = list(
    dom = "Bfrtip",
    buttons = c("copy", "csv", "excel"), 
    scrollY = 400, 
    scroller = TRUE, 
    fixedHeader = TRUE, 
    deferRender = TRUE
  )
)
```

Row {data-height 600}
-------------------------------------------------------------------------------

```{r}
ID_format_lab_df <- laborergebnisse_arranged %>% count(datum, ID_format, stadt)
plot_ID_format_lab <- ggplot(ID_format_lab_df, aes(datum, n, fill = ID_format, color = ID_format)) +
  # geom_bar(stat = "identity", position = "stack", width = 1) +
  geom_bar_interactive(aes(data_id = 1:nrow(ID_format_lab_df), 
    tooltip = paste0(stadt, "\n", ID_format, "\n", datum, "\n", n)), 
    stat = "identity", position = "stack", width = 0.9, size = 0.1) +
  facet_wrap(~stadt) +
  scale_color_manual(values = ID_format_colors) +
  scale_fill_manual(values = ID_format_colors) +
  labs(x = "Tagesdatum", y = "Anzahl von Proben", fill = "Proben-ID-Format") +
  theme(aspect.ratio = 9/16, plot.margin = margin(b = 2, unit = "cm"),
    legend.position = "bottom") +
  guides(color = FALSE)

dist_ID_format_lab_df <- laborergebnisse_arranged %>% count(ID_format, stadt)
plot_dist_ID_format_lab <- ggplot(dist_ID_format_lab_df,
  aes(x = ID_format, y = n, color = ID_format, fill = ID_format)) +
  # geom_bar(stat = "identity") +
  geom_bar_interactive(aes(data_id = 1:nrow(dist_ID_format_lab_df),
    tooltip = paste0(stadt, "\n", ID_format, "\n", n)), 
    stat = "identity") +
  facet_wrap(~stadt) +
  scale_color_manual(values = ID_format_colors) +
  scale_fill_manual(values = ID_format_colors) +
  labs(x = "Proben-ID-Format", y = "Anzahl von Proben", fill = "Proben-ID-Format") +
  theme(aspect.ratio = 9/16, legend.position = "none", 
    plot.margin = margin(b = 2, unit = "cm"))

dist_ID_multiplicity_lab_df <- laborergebnisse_arranged %>%
  select(sampleID, sampleID_multiplicity, stadt) %>%
  unique() %>%
  count(sampleID_multiplicity, stadt)
plot_dist_ID_multiplicity_lab <- ggplot(dist_ID_multiplicity_lab_df,
  aes(x = sampleID_multiplicity, y = n)) +
  geom_bar_interactive(aes(data_id = 1:nrow(dist_ID_multiplicity_lab_df), 
    tooltip = paste0(stadt, "\n", "Multi. ", sampleID_multiplicity, 
      "\n", "Anzahl ", n)), 
    stat = "identity", color = "black", fill = "lightgrey") +
  facet_wrap(~stadt) +
  scale_x_continuous(breaks = 1:max(laborergebnisse_arranged$sampleID_multiplicity)) +
  labs(x = "ID-Multiplizität", y = "Anzahl von Proben") +
  theme(aspect.ratio = 9/16, plot.margin = margin(b = 2, unit = "cm"))

dist_data_multiplicity_lab_df <- laborergebnisse_arranged %>%
  select(-ID, -dateinname) %>%
  unique() %>%
  count(stadt, data_multiplicity)
plot_dist_data_multiplicity_lab <- ggplot(dist_data_multiplicity_lab_df,
  aes(x = data_multiplicity, y = n)) +
  geom_bar_interactive(aes(data_id = 1:nrow(dist_data_multiplicity_lab_df), 
    tooltip = paste0(stadt, "\n", "Multi. ", data_multiplicity, 
      "\n", "Anzahl ", n)),
    stat = "identity", color = "black", fill = "lightgrey") +
  facet_wrap(~stadt) +
  scale_x_continuous(breaks = 1:max(laborergebnisse_arranged$data_multiplicity)) +  
  labs(x = "Daten-Multiplizität", y = "Anzahl von Proben") +
  theme(aspect.ratio = 9/16, plot.margin = margin(b = 2, unit = "cm"))

qual_rate_lab_df <- laborergebnisse_arranged %>%
  add_count(stadt, datum, name = "daily_sample_number") %>%
  count(stadt, datum, daily_sample_number, Ergebnis..qualitativ.,
    name = "daily_qual_results") %>%
  complete(nesting(stadt, datum, daily_sample_number), Ergebnis..qualitativ., 
    fill = list(daily_qual_results = 0)) %>%
  mutate(rate = daily_qual_results / daily_sample_number) %>%
  arrange(stadt, datum)
plot_qual_rate_lab <- ggplot(qual_rate_lab_df, aes(x = datum, y = rate, 
  color = Ergebnis..qualitativ.)) +
  geom_line() +
  geom_point_interactive(aes(data_id = 1:nrow(qual_rate_lab_df),
    tooltip = paste0(stadt, "\n", datum, "\n", Ergebnis..qualitativ., "\n",
      signif(rate, digits = 2))), 
    shape = 1, size = 1) +
  facet_wrap(~stadt) +
  scale_y_log10() +
  scale_color_manual(values = vega_colors) +
  labs(x = "Tagesdatum", y = "Rate", color = "Ergebniss") +
  theme(legend.position = "bottom", aspect.ratio = 9/16, 
    plot.margin = margin(b = 2, unit = "cm"), panel.grid.minor = element_blank())
```

### Analysierte Proben nach Zeit und Stadt (Lab)

```{r}
ggiraph(code = {print(plot_ID_format_lab)}, hover_css = "fill:#F8B71C")
```

### Analysierte Proben nach Stadt (Lab)

```{r}
ggiraph(code = {print(plot_dist_ID_format_lab)}, hover_css = "fill:#F8B71C")
```

Row
-------------------------------------------------------------------------------

### Wie oft kommen einzelne Proben-IDs vor? (Lab)

```{r}
ggiraph(code = {print(plot_dist_ID_multiplicity_lab)}, 
  hover_css = "fill:#F8B71C")
```

### Wie oft kommen einzelne Datenpunkte vor? (Lab)

```{r}
ggiraph(code = {print(plot_dist_data_multiplicity_lab)}, 
  hover_css = "fill:#F8B71C")
```

Row
-------------------------------------------------------------------------------

### Raten von qualitativen Ergebnissen nach Zeit und Stadt (Lab)

```{r}
ggiraph(code = {print(plot_qual_rate_lab)}, hover_css = "fill:#F8B71C")
```

###

QS / SZ+Lab (Proben-IDs)
=======================================================================

```{r}
first_columns_mergedsampleID <- c("sampleID", "ID_format", "matched", 
  "sampleID_multiplicity", "data_multiplicity",
  "sampleID_corrected.sz", "sampleID_corrected.lab", 
  "sampleID_multiplicity.sz", "sampleID_multiplicity.lab", 
  "data_multiplicity.sz", "data_multiplicity.lab", 
  "stadt.sz", "stadt.lab", "datum.sz", "datum.lab", "dataset.sz", "dataset.lab",
  "ID.sz", "ID.lab", "dateinname.sz", "dateinname.lab")
merged_datasets_sampleID <- full_join(
  blutproben_arranged,
  laborergebnisse_arranged,
  by = c("sampleID", "ID_format"),
  suffix = c(".sz", ".lab")
) %>%
  mutate(matched = !is.na(dataset.sz) & !is.na(dataset.lab))
merged_datasets_sampleID <- merged_datasets_sampleID %>%
  add_count_(vars = names(merged_datasets_sampleID)[
    !names(merged_datasets_sampleID) %in% 
      c("dateinname.sz", "dateinname.lab", "ID.sz", "ID.lab")
  ]) %>%
  rename(data_multiplicity = n) %>%
  add_count(sampleID, name = "sampleID_multiplicity") %>%
  select(all_of(c(
    first_columns_mergedsampleID, 
    names(merged_datasets_sampleID)[
      !names(merged_datasets_sampleID) %in% first_columns_mergedsampleID
    ]
  )))
```

Row
-------------------------------------------------------------------------------

### Tabelle

```{r}
datatable(
  merged_datasets_sampleID %>% 
    mutate(across(where(is_character), ReplaceNAWithChar)) %>% 
    mutate(across(where(is_character), as_factor)),
  rownames = FALSE, 
  filter = list(position = "top", clear = FALSE),
  class = "cell-border stripe nowrap compact hover",
  extensions = c("Scroller", "Buttons"),
  options = list(
    dom = "Bfrtip",
    buttons = c("copy", "csv", "excel"), 
    scrollY = 400, 
    scroller = TRUE, 
    fixedHeader = TRUE, 
    deferRender = TRUE
  )
)
```

Row
-------------------------------------------------------------------------------

```{r}
datum_sampleID_df <- merged_datasets_sampleID %>% 
  filter(matched) %>% 
  count(datum.sz, datum.lab, stadt.sz)
scatterplot_datum_sampleID <- ggplot(datum_sampleID_df, aes(datum.sz, datum.lab,
  size = n, fill = stadt.sz)) +
  geom_abline() +
  geom_point_interactive(aes(data_id = 1:nrow(datum_sampleID_df),
    tooltip = paste0(stadt.sz, "\n", "Versandd. ", datum.sz, "\n", 
      "Analysed. ", datum.lab, "\n", "Probenanz. ", n)), shape = 21, 
    color = "black", alpha = 0.5) +
  scale_fill_manual(values = city_colors) +
  labs(x = "Versand-Datum", y = "Analyse-Datum", size = "Proben-Anzahl", 
    fill = "Stadt (SZ)") +
  theme(legend.position = "right",
    plot.margin = margin(b = 2, unit = "cm"))  
```

### Vergleich Versand- und Analyse-Daten für gematchte Proben (über Proben-IDs)

```{r}
ggiraph(code = {print(scatterplot_datum_sampleID)}, hover_css = "fill:#F8B71C")
```

###

QS / SZ+Lab (korr. Proben-IDs)
=======================================================================

```{r}
first_columns_mergedsampleID_corrected <- c("sampleID_corrected", "matched",
  "sampleID_corrected_multiplicity", "data_multiplicity",
  "sampleID.sz", "ID_format.sz", "sampleID.lab", "ID_format.lab", 
  "sampleID_multiplicity.sz", "sampleID_multiplicity.lab", 
  "data_multiplicity.sz", "data_multiplicity.lab", 
  "stadt.sz", "stadt.lab", "datum.sz", "datum.lab", "dataset.sz", "dataset.lab",
  "ID.sz", "ID.lab", "dateinname.sz", "dateinname.lab")
merged_datasets_sampleID_corrected <- full_join(
  blutproben_arranged,
  laborergebnisse_arranged,
  by = c("sampleID_corrected"),
  suffix = c(".sz", ".lab")
) %>%
  mutate(matched = !is.na(dataset.sz) & !is.na(dataset.lab))
merged_datasets_sampleID_corrected <- merged_datasets_sampleID_corrected %>%
  add_count_(vars = names(merged_datasets_sampleID_corrected)[
    !names(merged_datasets_sampleID_corrected) %in% 
      c("dateinname.sz", "dateinname.lab", "ID.sz", "ID.lab")
  ]) %>%
  rename(data_multiplicity = n) %>%
  add_count(sampleID_corrected, name = "sampleID_corrected_multiplicity") %>%
  select(all_of(c(
    first_columns_mergedsampleID_corrected, 
    names(merged_datasets_sampleID_corrected)[
      !names(merged_datasets_sampleID_corrected) %in% 
        first_columns_mergedsampleID_corrected
    ]
  )))
```

Row
-------------------------------------------------------------------------------

### Tabelle

```{r}
datatable(
  merged_datasets_sampleID_corrected %>% 
    mutate(across(where(is_character), ReplaceNAWithChar)) %>% 
    mutate(across(where(is_character), as_factor)),
  rownames = FALSE, 
  filter = list(position = "top", clear = FALSE),
  class = "cell-border stripe nowrap compact hover",
  extensions = c("Scroller", "Buttons"),
  options = list(
    dom = "Bfrtip",
    buttons = c("copy", "csv", "excel"), 
    scrollY = 400, 
    scroller = TRUE, 
    fixedHeader = TRUE, 
    deferRender = TRUE
  )
)
```

Row
-------------------------------------------------------------------------------

```{r}
datum_sampleID_corrected_df <- merged_datasets_sampleID_corrected %>% 
  filter(matched) %>% 
  count(datum.sz, datum.lab, stadt.sz)
scatterplot_datum_sampleID_corrected <- ggplot(datum_sampleID_corrected_df, 
  aes(datum.sz, datum.lab,size = n, fill = stadt.sz)) +
  geom_abline() +
  geom_point_interactive(aes(data_id = 1:nrow(datum_sampleID_corrected_df),
    tooltip = paste0(stadt.sz, "\n", "Versandd. ", datum.sz, "\n", 
      "Analysed. ", datum.lab, "\n", "Probenanz. ", n)), shape = 21, 
    color = "black", alpha = 0.5) +
  scale_fill_manual(values = city_colors) +
  labs(x = "Versand-Datum", y = "Analyse-Datum", size = "Proben-Anzahl", 
    fill = "Stadt (SZ)") +
  theme(legend.position = "right",
    plot.margin = margin(b = 2, unit = "cm"))  
```

### Vergleich Versand- und Analyse-Daten für gematchte Proben (über Proben-IDs)

```{r}
ggiraph(code = {print(scatterplot_datum_sampleID_corrected)}, 
  hover_css = "fill:#F8B71C")
```

###

LoqTaq
=======================================================================

Column {.tabset .tabset-fade}
-----------------------------------------------------------------------

### Freiburg

```{r}
frei_data <- tabellen %>% 
  filter(stadt == "Freiburg") %>% 
  arrange(datum, index) %>% 
  mutate(ind = case_when(nchar(file) < 2 ~ paste0("0", as.character(file)),
    TRUE ~ as.character(file))) %>% 
  mutate(file = paste0("Datei: ", ind))

if (nrow(frei_data) > 0) {
  frei_data %>% 
    ggplot(aes(index, celsius, group = file_id)) +
    geom_line() +
    facet_wrap(~start_date) + 
    my_facet_theme  
}

```

### Reutlingen

```{r}
tabellen %>% 
  filter(stadt == "Reutlingen") %>% 
  arrange(datum, index) %>% 
  mutate(ind = case_when(nchar(file) < 2 ~ paste0("0", as.character(file)),
    TRUE ~ as.character(file))) %>% 
  mutate(file = paste0("Datei: ", ind)) %>% 
  ggplot(aes(index, celsius, group = file_id)) +
  geom_line() +
  facet_wrap(~start_date) +
  my_facet_theme
```


Column {.tabset .tabset-fade}
-----------------------------------------------------------------------

### Freiburg

```{r}
tabellen %>% 
  filter(stadt == "Freiburg") %>%  
  group_by(file_id, start_date) %>% 
  summarise(avg = mean(celsius),
    min = min(celsius),
    max = max(celsius)) %>% 
  knitr::kable("html") %>% 
  kableExtra::kable_styling("striped", full_width = FALSE)
```


### Reutlingen

```{r}
tabellen %>% 
  filter(stadt == "Reutlingen") %>%  
  group_by(file_id, start_date) %>% 
  summarise(avg = mean(celsius),
    min = min(celsius),
    max = max(celsius)) %>% 
  knitr::kable("html") %>% 
  kableExtra::kable_styling("striped", full_width = FALSE)
```



Blutprobenlisten
=======================================================================

Column
-----------------------------------------------------------------------

### Anzahl Blutproben

```{r}
count_bp <- blutproben %>% 
  count(monovette)
valueBox(nrow(count_bp), icon = "fa-pencil")
```

### Anzahl der Duplikate

```{r}
n_dups <- count_bp %>% 
  filter(n > 1) %>% 
  nrow() 
valueBox(n_dups, icon = "fa-pencil", color = "danger")
```


Column {.tabset .tabset-fade}
-----------------------------------------------------------------------

### Freiburg

```{r}
blutproben %>% 
  filter(stadt == "Freiburg") %>% 
  add_count(monovette) %>% 
  filter(n > 1) %>% 
  select(-n) %>% 
  knitr::kable("html") %>% 
  kableExtra::kable_styling("striped", full_width = FALSE)
```


### Reutlingen

```{r}
blutproben %>% 
  filter(stadt == "Reutlingen") %>% 
  add_count(monovette) %>% 
  filter(n > 1) %>% 
  select(-n) %>% 
  knitr::kable("html") %>% 
  kableExtra::kable_styling("striped", full_width = FALSE)
```