Introduction to R for Applied Epidemiology

class: center, middle, inverse, title-slide

# Введение в R для прикладной эпидемиологии

### Группировка данных

contact@appliedepi.org

---

#  Группировка данных

---
# Цели и расписание

**В этом модуле мы стремимся помочь вам:**  
* Практика использования {janitor} для быстрого создания простого подсчета  
* Попрактиковаться в использовании {dplyr} для универсального обобщения данных  
* Попрактиковаться в использовании {gtsummary} для создания готовых к публикации таблиц  
* Создавать таблицы пригодные для печати и экспортировать их в формате PNG, Word и т.д.

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class="cl-67be2f56">45 минут</td><td class="cl-67be2f60">Сессионные залы</td><td class="cl-67be2f61">Демонстрация и упражнение: janitor</td></tr><tr style="overflow-wrap:break-word;"><td class="cl-67be2f56">45 минут</td><td class="cl-67be2f60">Сессионные залы</td><td class="cl-67be2f61">Демонстрация и упражнение: dplyr</td></tr><tr style="overflow-wrap:break-word;"><td class="cl-67be2f56">45 минут</td><td class="cl-67be2f60">Сессионные залы</td><td class="cl-67be2f61">Демонстрация и упражнение: gtsummary</td></tr><tr style="overflow-wrap:break-word;"><td class="cl-67be2f62">20 минут</td><td class="cl-67be2f6a">Главная</td><td class="cl-67be2f74">Общее обсуждение и демонстрация flextable (если есть время)</td></tr></tbody><tfoot><tr style="overflow-wrap:break-word;"><td colspan="3"class="cl-67be2f7e">Перерывы включены выше, но не показаны как строки</td></tr></tfoot></table></div></template>
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???
Note stretch breaks throughout.

---
class: inverse, center, middle

## {janitor} - скорость

## {tidyverse} - гибкость

## {gtsummary} - красота

---
class: inverse, center, middle

## {janitor} - скорость

---

# `tabyl()`

.pull-left[

```r
surv %>% 
* tabyl(district)
```

]

.pull-right[

```
## district n percent valid_percent
## Central I 31 0.057513915 0.05772812
## Central II 15 0.027829314 0.02793296
## East I 25 0.046382189 0.04655493
## East II 65 0.120593692 0.12104283
## East III 10 0.018552876 0.01862197
## Mountain Rural 180 0.333951763 0.33519553
## West I 14 0.025974026 0.02607076
## West II 104 0.192949907 0.19366853
## West III 93 0.172541744 0.17318436
## <NA> 2 0.003710575 NA
```

]

---

# Функции `adorn_()`

.pull-left[

```r
surv %>% 
  tabyl(district) %>% 
* adorn_totals()
```

]

.pull-right[

]

---

# Функции `adorn_()`

.pull-left[

```r
surv %>% 
  tabyl(district) %>% 
  adorn_totals() %>% 
* adorn_pct_formatting()
```

]

.pull-right[

```
## district n percent valid_percent
## Central I 31 5.8% 5.8%
## Central II 15 2.8% 2.8%
## East I 25 4.6% 4.7%
## East II 65 12.1% 12.1%
## East III 10 1.9% 1.9%
## Mountain Rural 180 33.4% 33.5%
## West I 14 2.6% 2.6%
## West II 104 19.3% 19.4%
## West III 93 17.3% 17.3%
## <NA> 2 0.4% -
## Total 539 100.0% 100.0%
```

]

---

# `tabyl()` кросс-табуляция

.pull-left[

```r
surv %>% 
* tabyl(district, gender)
```

]

.pull-right[

```
## district female male NA_
## Central I 15 15 1
## Central II 10 4 1
## East I 11 12 2
## East II 31 33 1
## East III 3 6 1
## Mountain Rural 99 76 5
## West I 8 5 1
## West II 53 50 1
## West III 44 44 5
## <NA> 1 1 0
```

]

---

# `tabyl()` кросс-табуляция

.pull-left[

```r
surv %>% 
* tabyl(district, gender, show_na = FALSE)
```

Удалите отсутствующие значения из таблицы.

]

.pull-right[

```
##        district female male
##       Central I     15   15
##      Central II     10    4
##          East I     11   12
##         East II     31   33
##        East III      3    6
##  Mountain Rural     99   76
##          West I      8    5
##         West II     53   50
##        West III     44   44
```

]

---

# `adorn_()` кросс-табуляция

.pull-left[

```r
surv %>% 
  tabyl(district, gender, show_na = FALSE) %>% 
* adorn_totals(where = "both")
```

Добавьте итоговые значения как для строк, так и для столбцов.

]

.pull-right[

```
##        district female male Total
##       Central I     15   15    30
##      Central II     10    4    14
##          East I     11   12    23
##         East II     31   33    64
##        East III      3    6     9
##  Mountain Rural     99   76   175
##          West I      8    5    13
##         West II     53   50   103
##        West III     44   44    88
##           Total    274  245   519
```

]

---

# `adorn_()` кросс-табуляция

.pull-left[

```r
surv %>% 
  tabyl(district, gender, show_na = FALSE) %>% 
  adorn_totals(where = "both") %>% 
* adorn_percentages() %>%
* adorn_pct_formatting()
```

Добавьте форматированные проценты с помощью этих двух команд.

]

.pull-right[

```
##        district female  male  Total
##       Central I  50.0% 50.0% 100.0%
##      Central II  71.4% 28.6% 100.0%
##          East I  47.8% 52.2% 100.0%
##         East II  48.4% 51.6% 100.0%
##        East III  33.3% 66.7% 100.0%
##  Mountain Rural  56.6% 43.4% 100.0%
##          West I  61.5% 38.5% 100.0%
##         West II  51.5% 48.5% 100.0%
##        West III  50.0% 50.0% 100.0%
##           Total  52.8% 47.2% 100.0%
```

]

---

# `adorn_()` кросс-табуляция

.pull-left[

```r
surv %>% 
  tabyl(district, gender, show_na = FALSE) %>% 
  adorn_totals(where = "both") %>% 
  adorn_percentages() %>%     
* adorn_pct_formatting(digits = 0)
```

Установите количество десятичных знаков.

]

.pull-right[

```
##        district female male Total
##       Central I    50%  50%  100%
##      Central II    71%  29%  100%
##          East I    48%  52%  100%
##         East II    48%  52%  100%
##        East III    33%  67%  100%
##  Mountain Rural    57%  43%  100%
##          West I    62%  38%  100%
##         West II    51%  49%  100%
##        West III    50%  50%  100%
##           Total    53%  47%  100%
```

]

???
This may look ugly, but you can make this into pretty image very easily

---

# {flextable}

Легко передайте таблицу в канал `qflextable()` для красивой печати в виде html.

.pull-left[

```r
surv %>% 
  tabyl(district, gender, show_na = FALSE) %>% 
  adorn_totals(where = "both") %>% 
  adorn_percentages() %>%     
  adorn_pct_formatting(digits = 0) %>% 
* qflextable()
```
]

.pull-right[
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1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-68937094{width:0.676in;background-color:transparent;vertical-align: middle;border-bottom: 2pt solid rgba(102, 102, 102, 1.00);border-top: 0 solid rgba(0, 0, 0, 1.00);border-left: 0 solid rgba(0, 0, 0, 1.00);border-right: 0 solid rgba(0, 0, 0, 1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}</style><table class='cl-689a99fa'><thead><tr style="overflow-wrap:break-word;"><td class="cl-68937044">district</td><td class="cl-6893704e">female</td><td class="cl-68937058">male</td><td class="cl-68937059">Total</td></tr></thead><tbody><tr style="overflow-wrap:break-word;"><td class="cl-68937062">Central I</td><td class="cl-6893706c">50%</td><td class="cl-6893706d">50%</td><td class="cl-68937076">100%</td></tr><tr style="overflow-wrap:break-word;"><td class="cl-68937062">Central II</td><td class="cl-6893706c">71%</td><td class="cl-6893706d">29%</td><td class="cl-68937076">100%</td></tr><tr style="overflow-wrap:break-word;"><td class="cl-68937062">East I</td><td class="cl-6893706c">48%</td><td class="cl-6893706d">52%</td><td class="cl-68937076">100%</td></tr><tr style="overflow-wrap:break-word;"><td class="cl-68937062">East II</td><td class="cl-6893706c">48%</td><td class="cl-6893706d">52%</td><td class="cl-68937076">100%</td></tr><tr style="overflow-wrap:break-word;"><td class="cl-68937062">East III</td><td class="cl-6893706c">33%</td><td class="cl-6893706d">67%</td><td class="cl-68937076">100%</td></tr><tr style="overflow-wrap:break-word;"><td class="cl-68937062">Mountain Rural</td><td class="cl-6893706c">57%</td><td class="cl-6893706d">43%</td><td class="cl-68937076">100%</td></tr><tr style="overflow-wrap:break-word;"><td class="cl-68937062">West I</td><td class="cl-6893706c">62%</td><td class="cl-6893706d">38%</td><td class="cl-68937076">100%</td></tr><tr style="overflow-wrap:break-word;"><td class="cl-68937062">West II</td><td class="cl-6893706c">51%</td><td class="cl-6893706d">49%</td><td class="cl-68937076">100%</td></tr><tr style="overflow-wrap:break-word;"><td class="cl-68937062">West III</td><td class="cl-6893706c">50%</td><td class="cl-6893706d">50%</td><td class="cl-68937076">100%</td></tr><tr style="overflow-wrap:break-word;"><td class="cl-68937077">Total</td><td class="cl-68937078">53%</td><td class="cl-6893708a">47%</td><td class="cl-68937094">100%</td></tr></tbody></table></div></template>
<div class="flextable-shadow-host" id="4021747d-b717-45ad-9e63-24d678ac667f"></div>
<script>
var dest = document.getElementById("4021747d-b717-45ad-9e63-24d678ac667f");
var template = document.getElementById("3252010b-95a3-4926-b2c4-5e8f3eb5c977");
var caption = template.content.querySelector("caption");
var fantome = dest.attachShadow({mode: 'open'});
var templateContent = template.content;
fantome.appendChild(templateContent);
</script>

]

.footnote[`qflextable()` означает "быстрая гибкая таблица".]

---
class: inverse, center, middle

## {tidyverse} - гибкость

---

# Функции

Общие функции группировки/сводки, все из {tidyverse}:

* `count()`  
* `group_by()`  
* `summarise()`  
* `arrange()`

---
# `count()`

Передайте данные в канал `count()` и укажите имя столбца.

Это возвращает количество строк на одно уникальное значение (на группу)

```r
surv %>% 
* count(hospital)
```

```
## hospital n
## 1 Central Hospital 40
## 2 Military Hospital 78
## 3 Other 85
## 4 Port Hospital 174
## 5 SMMH 29
## 6 <NA> 133
```

???
Very quick and fast

---
# `count()`

Вы можете включить несколько столбцов группировки

```r
surv %>% 
* count(hospital, gender)
```

```
## hospital gender n
## 1 Central Hospital female 16
## 2 Central Hospital male 23
## 3 Central Hospital <NA> 1
## 4 Military Hospital female 34
## 5 Military Hospital male 41
## 6 Military Hospital <NA> 3
## 7 Other female 44
## 8 Other male 37
## 9 Other <NA> 4
## 10 Port Hospital female 95
## 11 Port Hospital male 73
## 12 Port Hospital <NA> 6
## 13 SMMH female 17
## 14 SMMH male 11
## 15 SMMH <NA> 1
## 16 <NA> female 69
## 17 <NA> male 61
## 18 <NA> <NA> 3
```

???
Very quick and fast

---
# group_by() и summarise()

Но что, если вам нужны не только подсчеты?

"Каков средний возраст в каждой группе?".

"Какова самая поздняя дата появления симптомов в каждой группе?".

"Каково число смертей среди мужчин в каждой группе?"

`group_by()` и `summarise()` вместе позволяют гибко создавать **новый, сводный датафрейм** со **статистикой по группам**.

.footnote[`summarise()` и `summarize()` эквивалентны]

---
# group_by() и summarise()

```r
surv %>% 
  group_by(hospital)
```

Никаких видимых изменений в наборе данных *пока* не будет применена другая функция {dplyr}.

При печати `Groups:` перечисляются над данными.

```
## # A tibble: 539 × 29
## # Groups: hospital [6]
## case_id date_onset date_report diff gender age age_u…¹ age_y…² age_cat hospi…³ distr…⁴ distr…⁵ distr…⁶ moved wt_kg ht_cm fever chills
## <chr> <date> <date> <drtn> <chr> <dbl> <chr> <dbl> <fct> <chr> <chr> <chr> <chr> <lgl> <int> <int> <chr> <chr> 
## 1 86340d 2014-10-30 2014-10-31 1 days female 1 years 1 0-9 Port H… Mounta… Mounta… Mounta… FALSE 18 29 yes no 
## 2 92d002 2014-08-16 2014-08-20 4 days female 16 years 16 10-19 <NA> Mounta… Mounta… Mounta… FALSE 59 133 yes no 
## 3 544bd1 2014-08-29 2014-08-30 1 days female 10 years 10 10-19 <NA> East II East II East II FALSE 39 106 yes no 
## 4 6056ba 2014-10-20 2014-10-21 1 days female 0 years 0 0-9 <NA> West I… West I… West I… FALSE NA 24 yes yes 
## 5 e64e04 2014-10-06 2014-10-10 4 days female 7 years 7 0-9 <NA> Mounta… Mounta… Mounta… FALSE 32 91 no no 
## 6 5a65bb 2014-09-21 2014-09-22 1 days male 4 years 4 0-9 Port H… Mounta… Mounta… Mounta… FALSE 41 74 yes yes 
## 7 2ae019 2014-05-06 2014-05-11 5 days male 37 years 37 30-39 Other West I… West I… West I… FALSE 81 167 yes no 
## 8 7ca4c0 2014-09-29 2014-09-30 1 days male 11 years 11 10-19 Port H… West I… West I… West I… FALSE 56 163 yes no 
## 9 30af4f NA 2014-11-10 NA days female 6 years 6 0-9 Port H… West I West I West I FALSE 30 75 no no 
## 10 a06123 2014-09-10 2014-09-15 5 days male 10 years 10 10-19 <NA> Mounta… Mounta… Mounta… FALSE 55 125 yes no 
## # … with 529 more rows, 11 more variables: cough <chr>, aches <chr>, vomit <chr>, temp <dbl>, bmi <dbl>, admin3pcod <chr>,
## # lab_confirmed <lgl>, lat <dbl>, lon <dbl>, epilink <chr>, case_def <chr>, and abbreviated variable names ¹age_unit, ²age_years,
## # ³hospital, ⁴district, ⁵district_res, ⁶district_det
```

???
Note the `groups: hospital`, at the top of the tibble

---
# group_by() и summarise()

В `summarise()`, *создайте столбцы в **новом**, обобщенном наборе данных.*

<h4>summarise(имя нового столбца = функция для применения к каждой группе строк)</h4>

<code class ='r hljs remark-code'>surv %>% &nbsp;&nbsp;group_by(hospital) %>% &nbsp;&nbsp;summarise()</code>

```

## # A tibble: 6 × 1
## hospital 
## <chr> 
## 1 Central Hospital 
## 2 Military Hospital
## 3 Other 
## 4 Port Hospital 
## 5 SMMH 
## 6 <NA>

```

---
# group_by() и summarise()

Within the parentheses, *list new columns for the **new**, summary dataset.*

<code class ='r hljs remark-code'>surv %>% &nbsp;&nbsp;group_by(hospital) %>% &nbsp;&nbsp;summarise(n_rows = n()) &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;# n() подсчитывает количество строк в группе</code>

```

## # A tibble: 6 × 2
## hospital n_rows
## <chr> <int>
## 1 Central Hospital 40
## 2 Military Hospital 78
## 3 Other 85
## 4 Port Hospital 174
## 5 SMMH 29
## 6 <NA> 133

```

---
# group_by() и summarise()

В круглых скобках *перечислите новые столбцы для **нового**, сводного набора данных*.

```

## # A tibble: 6 × 3
## hospital n_rows age_avg
## <chr> <int> <dbl>
## 1 Central Hospital 40 14.9
## 2 Military Hospital 78 15.0
## 3 Other 85 16.1
## 4 Port Hospital 174 15.1
## 5 SMMH 29 15.9
## 6 <NA> 133 17.6

```

---
# group_by() и summarise()

В круглых скобках *создайте столбцы в **новом**, сводном наборе данных*.

<code class ='r hljs remark-code'>surv %>% &nbsp;&nbsp;group_by(hospital) %>% &nbsp;&nbsp;summarise( &nbsp;&nbsp;&nbsp;&nbsp;n_rows = n(), &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;# n() подсчитывает количество строк в группе &nbsp;&nbsp;&nbsp;&nbsp;age_avg = mean(age_years, na.rm = T), &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;# средний возраст в группе &nbsp;&nbsp;&nbsp;&nbsp;max_onset = max(date_onset, na.rm=T)) &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;# последняя дата начала заболевания</code>

```

## # A tibble: 6 × 4
## hospital n_rows age_avg max_onset 
## <chr> <int> <dbl> <date> 
## 1 Central Hospital 40 14.9 2014-11-24
## 2 Military Hospital 78 15.0 2014-11-25
## 3 Other 85 16.1 2014-11-26
## 4 Port Hospital 174 15.1 2014-11-28
## 5 SMMH 29 15.9 2014-11-28
## 6 <NA> 133 17.6 2014-11-27

```

---
# group_by() и summarise()

В круглых скобках *создайте столбцы в **новом**, сводном наборе данных*.

<code class ='r hljs remark-code'>surv %>% &nbsp;&nbsp;group_by(hospital) %>% &nbsp;&nbsp;summarise( &nbsp;&nbsp;&nbsp;&nbsp;n_rows &nbsp;= n(), &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;# n() подсчитывает количество строк в группе &nbsp;&nbsp;&nbsp;&nbsp;age_avg = mean(age_years, na.rm = T), &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;# средний возраст в группе &nbsp;&nbsp;&nbsp;&nbsp;max_onset = max(date_onset, na.rm=T), &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;# последняя дата начала заболевания &nbsp;&nbsp;&nbsp; n_female = sum(gender == 'female', na.rm=T)) ## количество женщин в группе</code>

```

## # A tibble: 6 × 5
## hospital n_rows age_avg max_onset n_female
## <chr> <int> <dbl> <date> <int>
## 1 Central Hospital 40 14.9 2014-11-24 16
## 2 Military Hospital 78 15.0 2014-11-25 34
## 3 Other 85 16.1 2014-11-26 44
## 4 Port Hospital 174 15.1 2014-11-28 95
## 5 SMMH 29 15.9 2014-11-28 17
## 6 <NA> 133 17.6 2014-11-27 69

```

---
# group_by() и summarise()

В круглых скобках *создайте столбцы в **новом**, сводном наборе данных*.

<code class ='r hljs remark-code'>surv %>% &nbsp;&nbsp;group_by(hospital) %>% &nbsp;&nbsp;summarise( &nbsp;&nbsp;&nbsp; n_rows = n(), &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;# n() подсчитывает количество строк в группе &nbsp;&nbsp;&nbsp;&nbsp;age_avg = mean(age_years, na.rm = T), &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;# средний возраст в группе &nbsp;&nbsp;&nbsp;&nbsp;max_onset = max(date_onset, na.rm=T), &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;# последняя дата начала заболевания &nbsp;&nbsp;&nbsp; n_female = sum(gender == 'female', na.rm=T), ## количество женщин в группе &nbsp;&nbsp;&nbsp;&nbsp;pct_female = percent(n_female / n_rows)) &nbsp;&nbsp;&nbsp;&nbsp;# процент женщин в группе</code>

```

## # A tibble: 6 × 6
## hospital n_rows age_avg max_onset n_female pct_female
## <chr> <int> <dbl> <date> <int> <chr> 
## 1 Central Hospital 40 14.9 2014-11-24 16 40% 
## 2 Military Hospital 78 15.0 2014-11-25 34 44% 
## 3 Other 85 16.1 2014-11-26 44 52% 
## 4 Port Hospital 174 15.1 2014-11-28 95 55% 
## 5 SMMH 29 15.9 2014-11-28 17 59% 
## 6 <NA> 133 17.6 2014-11-27 69 52%

```

---

# `arrange()`

Отсортируйте таблицу, поместив столбец (столбцы) для сортировки в `arrange()`.

```r
surv %>% 
  group_by(hospital) %>% 
  summarise(
    n_rows  = n(),                               # n() подсчитывает количество строк в группе
    age_avg = mean(age_years, na.rm = T),        # средний возраст в группе
    max_onset = max(date_onset, na.rm=T),        # последняя дата начала заболевания
    n_female = sum(gender == 'female', na.rm=T), # количество женщин в группе
    pct_female = percent(n_female / n_rows)) %>% # процент женщин в группе
  arrange(n_rows)
```

```
## # A tibble: 6 × 6
## hospital n_rows age_avg max_onset n_female pct_female
## <chr> <int> <dbl> <date> <int> <chr> 
## 1 SMMH 29 15.9 2014-11-28 17 59% 
## 2 Central Hospital 40 14.9 2014-11-24 16 40% 
## 3 Military Hospital 78 15.0 2014-11-25 34 44% 
## 4 Other 85 16.1 2014-11-26 44 52% 
## 5 <NA> 133 17.6 2014-11-27 69 52% 
## 6 Port Hospital 174 15.1 2014-11-28 95 55%
```

---

# `arrange()`

Оберните столбец(ы) в `desc()` для обратной сортировки

```r
surv %>% 
  group_by(hospital) %>% 
  summarise(
    n_rows  = n(),                               # n() подсчитывает количество строк в группе
    age_avg = mean(age_years, na.rm = T),        # средний возраст в группе
    max_onset = max(date_onset, na.rm=T),        # последняя дата начала заболевания
    n_female = sum(gender == 'female', na.rm=T), # количество женщин в группе
    pct_female = percent(n_female / n_rows))  %>% # процент женщин в группе
  arrange(-n_rows)                                  
```

```
## # A tibble: 6 × 6
## hospital n_rows age_avg max_onset n_female pct_female
## <chr> <int> <dbl> <date> <int> <chr> 
## 1 Port Hospital 174 15.1 2014-11-28 95 55% 
## 2 <NA> 133 17.6 2014-11-27 69 52% 
## 3 Other 85 16.1 2014-11-26 44 52% 
## 4 Military Hospital 78 15.0 2014-11-25 34 44% 
## 5 Central Hospital 40 14.9 2014-11-24 16 40% 
## 6 SMMH 29 15.9 2014-11-28 17 59%
```

---

# flextable

Как и в случае с {janitor}, передайте `qflextable()` по каналу для быстрого создания HTML-таблицы.

<template id="8801253f-22d5-4566-88ff-b39c5c3079f9"><style>
.tabwid table{
 border-spacing:0px !important;
 border-collapse:collapse;
 line-height:1;
 margin-left:auto;
 margin-right:auto;
 border-width: 0;
 display: table;
 border-color: transparent;
 caption-side: top;
}
.tabwid-caption-bottom table{
 caption-side: bottom;
}
.tabwid_left table{
 margin-left:0;
}
.tabwid_right table{
 margin-right:0;
}
.tabwid td {
 padding: 0;
}
.tabwid a {
 text-decoration: none;
}
.tabwid thead {
 background-color: transparent;
}
.tabwid tfoot {
 background-color: transparent;
}
.tabwid table tr {
background-color: transparent;
}
.katex-display {
 margin: 0 0 !important;
}
</style><div class="tabwid"><style>.cl-6bb65278{}.cl-6ba98584{font-family:'Arial';font-size:11pt;font-weight:normal;font-style:normal;text-decoration:none;color:rgba(0, 0, 0, 1.00);background-color:transparent;}.cl-6bae98ee{margin:0;text-align:left;border-bottom: 0 solid rgba(0, 0, 0, 1.00);border-top: 0 solid rgba(0, 0, 0, 1.00);border-left: 0 solid rgba(0, 0, 0, 1.00);border-right: 0 solid rgba(0, 0, 0, 1.00);padding-bottom:5pt;padding-top:5pt;padding-left:5pt;padding-right:5pt;line-height: 1;background-color:transparent;}.cl-6bae9902{margin:0;text-align:right;border-bottom: 0 solid rgba(0, 0, 0, 1.00);border-top: 0 solid rgba(0, 0, 0, 1.00);border-left: 0 solid rgba(0, 0, 0, 1.00);border-right: 0 solid rgba(0, 0, 0, 1.00);padding-bottom:5pt;padding-top:5pt;padding-left:5pt;padding-right:5pt;line-height: 1;background-color:transparent;}.cl-6baeb450{width:1.372in;background-color:transparent;vertical-align: middle;border-bottom: 2pt solid rgba(102, 102, 102, 1.00);border-top: 2pt 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---
class: inverse, center, middle

## {gtsummary} - красота

---

# `tbl_summary()`

* Вывод по умолчанию должен быть *готовым к публикации*.

.pull-left[

```r
surv %>% 
* select(hospital, age_years)
```

Выберите столбцы для сводки.  
Для демонстрации:  
* Столбец из одного символа (`больница`)  
* Один числовой столбец (`age_years`)

]

---

# `tbl_summary()`

* Вывод по умолчанию должен быть *готовым к публикации*.

.pull-left[

```r
surv %>% 
  select(hospital, age_years) %>% 
* tbl_summary()
```

]

.pull-right[

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 text-indent: 25px;
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</style>
<table class="gt_table">
 
 <thead class="gt_col_headings">
 <tr>
 <th class="gt_col_heading gt_columns_bottom_border gt_left" rowspan="1" colspan="1" scope="col">Characteristic</th>
 <th class="gt_col_heading gt_columns_bottom_border gt_center" rowspan="1" colspan="1" scope="col">N = 5391</th>
 </tr>
 </thead>
 <tbody class="gt_table_body">
 <tr><td class="gt_row gt_left">hospital</td>
<td class="gt_row gt_center"></td></tr>
 <tr><td class="gt_row gt_left" style="text-align: left; text-indent: 10px;">Central Hospital</td>
<td class="gt_row gt_center">40 (9.9%)</td></tr>
 <tr><td class="gt_row gt_left" style="text-align: left; text-indent: 10px;">Military Hospital</td>
<td class="gt_row gt_center">78 (19%)</td></tr>
 <tr><td class="gt_row gt_left" style="text-align: left; text-indent: 10px;">Other</td>
<td class="gt_row gt_center">85 (21%)</td></tr>
 <tr><td class="gt_row gt_left" style="text-align: left; text-indent: 10px;">Port Hospital</td>
<td class="gt_row gt_center">174 (43%)</td></tr>
 <tr><td class="gt_row gt_left" style="text-align: left; text-indent: 10px;">SMMH</td>
<td class="gt_row gt_center">29 (7.1%)</td></tr>
 <tr><td class="gt_row gt_left" style="text-align: left; text-indent: 10px;">Unknown</td>
<td class="gt_row gt_center">133</td></tr>
 <tr><td class="gt_row gt_left">age_years</td>
<td class="gt_row gt_center">13 (6, 23)</td></tr>
 <tr><td class="gt_row gt_left" style="text-align: left; text-indent: 10px;">Unknown</td>
<td class="gt_row gt_center">6</td></tr>
 </tbody>
 
 <tfoot class="gt_footnotes">
 <tr>
 <td class="gt_footnote" colspan="2">1 n (%); Median (IQR)</td>
 </tr>
 </tfoot>
</table>
</div>

]

---

# `tbl_summary()`

* Стратификация по другой переменной

.pull-left[

```r
surv %>% 
  select(hospital, age_years, gender) %>% 
* tbl_summary(by = gender)
```

См. советы по настройке в главе [Epi R Handbook, посвященной описательным таблицам](https://epirhandbook.com/en/descriptive-tables.html#tbl_gt).)
]

.pull-right[

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</style>
<table class="gt_table">
 
 <thead class="gt_col_headings">
 <tr>
 <th class="gt_col_heading gt_columns_bottom_border gt_left" rowspan="1" colspan="1" scope="col">Characteristic</th>
 <th class="gt_col_heading gt_columns_bottom_border gt_center" rowspan="1" colspan="1" scope="col">female, N = 2751</th>
 <th class="gt_col_heading gt_columns_bottom_border gt_center" rowspan="1" colspan="1" scope="col">male, N = 2461</th>
 </tr>
 </thead>
 <tbody class="gt_table_body">
 <tr><td class="gt_row gt_left">age_years</td>
<td class="gt_row gt_center">11 (5, 19)</td>
<td class="gt_row gt_center">17 (7, 28)</td></tr>
 <tr><td class="gt_row gt_left">hospital</td>
<td class="gt_row gt_center"></td>
<td class="gt_row gt_center"></td></tr>
 <tr><td class="gt_row gt_left" style="text-align: left; text-indent: 10px;">Central Hospital</td>
<td class="gt_row gt_center">16 (7.8%)</td>
<td class="gt_row gt_center">23 (12%)</td></tr>
 <tr><td class="gt_row gt_left" style="text-align: left; text-indent: 10px;">Military Hospital</td>
<td class="gt_row gt_center">34 (17%)</td>
<td class="gt_row gt_center">41 (22%)</td></tr>
 <tr><td class="gt_row gt_left" style="text-align: left; text-indent: 10px;">Other</td>
<td class="gt_row gt_center">44 (21%)</td>
<td class="gt_row gt_center">37 (20%)</td></tr>
 <tr><td class="gt_row gt_left" style="text-align: left; text-indent: 10px;">Port Hospital</td>
<td class="gt_row gt_center">95 (46%)</td>
<td class="gt_row gt_center">73 (39%)</td></tr>
 <tr><td class="gt_row gt_left" style="text-align: left; text-indent: 10px;">SMMH</td>
<td class="gt_row gt_center">17 (8.3%)</td>
<td class="gt_row gt_center">11 (5.9%)</td></tr>
 <tr><td class="gt_row gt_left" style="text-align: left; text-indent: 10px;">Unknown</td>
<td class="gt_row gt_center">69</td>
<td class="gt_row gt_center">61</td></tr>
 </tbody>
 
 <tfoot class="gt_footnotes">
 <tr>
 <td class="gt_footnote" colspan="3">1 Median (IQR); n (%)</td>
 </tr>
 </tfoot>
</table>
</div>

]

---

# `tbl_summary()`

* Настройте подписи

.pull-left[

```r
surv %>% 
  select(hospital, age_years, gender) %>% 
  tbl_summary(
       by = gender,
       label = list(
         age_years = "Age (years)", 
         hospital = "Hospital"))     
```

.pull-right[

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}
</style>
<table class="gt_table">
 
 <thead class="gt_col_headings">
 <tr>
 <th class="gt_col_heading gt_columns_bottom_border gt_left" rowspan="1" colspan="1" scope="col">Characteristic</th>
 <th class="gt_col_heading gt_columns_bottom_border gt_center" rowspan="1" colspan="1" scope="col">female, N = 2751</th>
 <th class="gt_col_heading gt_columns_bottom_border gt_center" rowspan="1" colspan="1" scope="col">male, N = 2461</th>
 </tr>
 </thead>
 <tbody class="gt_table_body">
 <tr><td class="gt_row gt_left">Hospital</td>
<td class="gt_row gt_center"></td>
<td class="gt_row gt_center"></td></tr>
 <tr><td class="gt_row gt_left" style="text-align: left; text-indent: 10px;">Central Hospital</td>
<td class="gt_row gt_center">16 (7.8%)</td>
<td class="gt_row gt_center">23 (12%)</td></tr>
 <tr><td class="gt_row gt_left" style="text-align: left; text-indent: 10px;">Military Hospital</td>
<td class="gt_row gt_center">34 (17%)</td>
<td class="gt_row gt_center">41 (22%)</td></tr>
 <tr><td class="gt_row gt_left" style="text-align: left; text-indent: 10px;">Other</td>
<td class="gt_row gt_center">44 (21%)</td>
<td class="gt_row gt_center">37 (20%)</td></tr>
 <tr><td class="gt_row gt_left" style="text-align: left; text-indent: 10px;">Port Hospital</td>
<td class="gt_row gt_center">95 (46%)</td>
<td class="gt_row gt_center">73 (39%)</td></tr>
 <tr><td class="gt_row gt_left" style="text-align: left; text-indent: 10px;">SMMH</td>
<td class="gt_row gt_center">17 (8.3%)</td>
<td class="gt_row gt_center">11 (5.9%)</td></tr>
 <tr><td class="gt_row gt_left" style="text-align: left; text-indent: 10px;">Unknown</td>
<td class="gt_row gt_center">69</td>
<td class="gt_row gt_center">61</td></tr>
 <tr><td class="gt_row gt_left">Age (years)</td>
<td class="gt_row gt_center">11 (5, 19)</td>
<td class="gt_row gt_center">17 (7, 28)</td></tr>
 </tbody>
 
 <tfoot class="gt_footnotes">
 <tr>
 <td class="gt_footnote" colspan="3">1 n (%); Median (IQR)</td>
 </tr>
 </tfoot>
</table>
</div>

]

---
class: inverse, center, middle

## Что использовать?

В R у вас есть много вариантов.  
Закрепите знание инструмента, который вам подходит.