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<!DOCTYPE html>
<html lang="" xml:lang="">
<head>
<title>Trình bày kết quả nghiên cứu với R - Bài 2 - SSC</title>
<meta charset="utf-8" />
<meta name="author" content="Tien N.T.Nguyen" />
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<textarea id="source">
class: center, middle
<div>
<style type="text/css">.xaringan-extra-logo {
width: 110px;
height: 100px;
z-index: 0;
background-image: url(ssc_logo.png);
background-size: contain;
background-repeat: no-repeat;
position: absolute;
top:0.05em;right:0.56em;
}
</style>
<script>(function () {
let tries = 0
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.title[.bold[Trình bày kết quả nghiên cứu với R]]
.font120[
**Bài 3: Vẽ một số biểu đồ thường gặp với R**
]
<style>
.title {
font-size: 56px;
font-family: 'Roboto', Arial, sans-serif;
color: #008080;
}
</style>
<img src="R_Rstudio.png" width="200px"/>
</br>
Tien N.T.Nguyen | Trung tâm .orange[DI] & .blue[ADR] Quốc gia, HUP | 20 May. 2022
---
### .ssc[Đọc các file sử dụng cho buổi hôm nay]
```r
df1 = read.csv("E:/SSC_R_visu/data_ggplot2_layer.csv")
df_bar = read_xlsx("E:/SSC_R_visu/bar_error_bar.xlsx", sheet = "No_Error_bar")
df_bar_error = read_xlsx("E:/SSC_R_visu/bar_error_bar.xlsx", sheet = "With_error_bar")
df_evo_1 = read_xlsx("E:/SSC_R_visu/Anbio_asump.xlsx", sheet = "Tidy")
names(df_evo_1) = c("Time", "Year", "Month", "Drug", "DDD")
df_evo_2 = read_xlsx("E:/SSC_R_visu/PK_data.xlsx")
df_table = read_xlsx("E:/SSC_R_visu/data_table.xlsx")
```
**Lưu ý: Đây đều là dữ liệu có sẵn/tự tạo -> sử dụng mà không cần sự cho phép**
---
### .ssc[Biểu diễn phân bố với biểu đồ hộp (boxplot)]
--
<br>
.center[
<img src="box_normal.png" width="60%" height="60%" />
]
---
### .ssc[`df1` dataset]
<div id="htmlwidget-57ae5fcc1387d898f6a5" style="width:100%;height:auto;" class="datatables html-widget"></div>
<script type="application/json" data-for="htmlwidget-57ae5fcc1387d898f6a5">{"x":{"filter":"none","vertical":false,"extensions":["Buttons"],"data":[[11001,11002,11003,11005,11006,11007,11008,11009,11010,11011,11012,11013,11014,11015,11016,11017,11018,11019,11020,11004,21001,21002,21003,21004,21005,21006,21007,21008,21009,21010,21011,21012,21013,21014,21015,21016,21017,21018,21019,21020,101,104,105,106,107,108,109,110,111,112,113,114,115,116,201,202,203,204,205,206,207,208,209,210,211,212,213,214,215,301,302,303,304,305,306,307,308,309,310,311,312,313,314,315,401,402,403,404,405,406,407,408,409,410,411,412,413,414,415,416],[25,23,23,49,29,28,44,45,44,40,52,25,33,37,45,22,26,29,46,47,43,47,24,26,39,46,25,45,44,31,36,26,20,39,27,27,23,21,25,21,39,27,30,41,24,38,39,29,26,45,30,38,36,38,41,37,37,37,25,35,43,35,28,45,44,25,39,45,44,66,66,67,74,66,68,72,66,72,71,70,68,76,67,65,32,26,20,21,24,32,26,21,29,32,20,20,19,21,30,21],[90.7,53.8,80.8,62.9,74.3,63.2,62.1,83.2,88,54.5,68.9,92.4,87.7,87.2,65.9,73.5,71.5,64.5,77.4,67.2,76,91,89.5,94,72,75,76,89,65,69,70,70,58,77,68,99,66,75,80,78,74.7,101,84.5,81,87.5,71,72.4,59.8,95,75,75.9,61.8,94.5,65,67,72,96,93,64,77,81,89,81,81,82,76,81,73,91,64,86,76,90,68,80,71,72,76,74,90,80,89,89,77,80.6,50.2,65.8,58.9,55.4,67.2,69.9,80.4,67.7,97.7,68.4,64.7,83.4,72.3,84.6,84.6],[178.9,168.4,181.9,160.5,179,168,168.5,180.1,176.5,166.5,162.8,178.3,180.4,180,172.5,174,179,161.5,177.5,173.1,166,177,176,180,166,165,167,185,159,165,171,174,155,171,162,182,178,180,175,170,178,187,171.5,172.5,176.5,172,166,151,175,176,180.5,166.5,176,181,180,184,178,181,170,186,175,183,177,178,184,191,180,180,192,166,178,174,184,183,169,161,167,182,173,188,171,172,183,176,180.5,161,170.5,161.5,161.5,169,169,179,160,186,161.7,169,177.5,175,163.5,185.5],["Male","Male","Male","Female","Male","Male","Female","Male","Male","Female","Female","Male","Male","Male","Female","Male","Male","Female","Male","Male","Female","Male","Male","Male","Female","Female","Female","Male","Female","Female","Male","Male","Female","Male","Female","Male","Male","Female","Male","Female","Male","Male","Male","Male","Male","Male","Female","Female","Male","Male","Female","Female","Male","Male","Male","Male","Male","Male","Male","Male","Male","Male","Male","Male","Male","Male","Male","Male","Male","Male","Male","Male","Male","Male","Male","Male","Male","Male","Male","Male","Male","Male","Male","Male","Male","Female","Male","Female","Female","Male","Male","Male","Female","Male","Female","Female","Male","Male","Female","Male"],[3,3,1,1,3,3,1,1,1,3,1,2,1,1,1,3,1,1,1,3,1,1,2,2,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,3,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,4,1,1,3,3,1,1,1,1,1,3,3,1,1],[181.1,97.1,131.3,96.5,127.3,98.3,117.3,122,106.7,91.9,119.3,123,144.8,178.2,105.6,133.8,125.8,120.7,126.3,124,145.1,98,144.2,135.3,107.3,118.9,147.4,130.5,81.9,111,91.9,110.8,117.4,120,151.2,172.6,134.1,117.1,142,109.6,104.8,144.1,99.3,101.3,128.2,91.4,107.9,130.6,115.7,110,89.6,93,105,102.3,92.1,114.4,196.2,166.3,127.8,112.3,90.9,162.2,126,106.9,136.7,134.9,126.3,107,134.8,94,98.2,96.3,91.7,99.8,80,83.8,82.2,79.8,78.8,97.2,80,87.9,112.8,80.2,109.9,96.5,137.1,118.2,108.4,112,138.3,147.6,147.9,122.1,121.1,101.8,140.2,119.5,183.1,155.4],["Yes","Yes","Yes","Yes","No","Yes","No","Yes","No","Yes","No","No","No","Yes","No","Yes","Yes","Yes","No","No","Yes","No","Yes","Yes","No","Yes","No","No","No","Yes","Yes","Yes","Yes","No","No","Yes","Yes","Yes","Yes","Yes","No","No","Yes","Yes","No","Yes","Yes","Yes","No","No","No","Yes","Yes","No","Yes","Yes","No","No","No","Yes","Yes","No","Yes","No","Yes","Yes","No","No","Yes","No","No","Yes","No","Yes","Yes","Yes","Yes","No","Yes","Yes","Yes","No","Yes","No","No","No","Yes","No","Yes","Yes","No","No","No","Yes","Yes","Yes","No","No","No","No"]],"container":"<table class=\"compact\">\n <thead>\n <tr>\n <th>ID<\/th>\n <th>AGE<\/th>\n <th>WEIGHT<\/th>\n <th>HEIGHT<\/th>\n <th>GENDER<\/th>\n <th>RACE<\/th>\n <th>CRCL<\/th>\n <th>Dai_thao_duong<\/th>\n <\/tr>\n <\/thead>\n<\/table>","options":{"dom":"tBp","buttons":["copy","excel"],"pageLength":10,"columnDefs":[{"className":"dt-right","targets":[0,1,2,3,5,6]}],"order":[],"autoWidth":false,"orderClasses":false}},"evals":[],"jsHooks":[]}</script>
---
### .ssc[Distribution plot - boxplot]
.pull-left[
```r
ggplot(data = df1, aes(x = GENDER, y = AGE)) +
* geom_boxplot() +
theme_minimal()
```
]
--
.pull-right[
<img src="SSC_R_visu_Bai3_some_graph_files/figure-html/boxplot1-1.png" width="672" />
]
---
### .ssc[Distribution plot - boxplot]
.pull-left[
`Fill` màu box bằng biến `GENDER`
```r
ggplot(data = df1, aes(x = GENDER, y = AGE)) +
geom_boxplot(
* aes(fill = GENDER)
) +
theme_minimal()
```
]
--
.pull-right[
<img src="SSC_R_visu_Bai3_some_graph_files/figure-html/boxplot2-1.png" width="672" />
]
---
### .ssc[Distribution plot - boxplot - một số tùy chỉnh cơ bản]
<u>**Lưu ý:**</u> Các `đôi số` (ví dụ `alpha`, `width`, `fill`, `color`,...) sử dụng tương tự cho các `geom_` khác. Sử dụng trong lệnh `aes()` nếu muốn `mapping`. Trong trường hợp này đang `map` màu của box (`fill`) cho `GENDER`
.pull-left[
```r
ggplot(data = df1, aes(x = GENDER, y = AGE)) +
geom_boxplot(
aes(fill = GENDER),
# Thay đổi size của outliers points
outlier.size = 0.5,
# Thay đổi độ rộng của box
width = 0.52,
# Thay đổi độ mờ của box
alpha = 0.48,
# Thay đổi màu và độ dày đường viền ngoài
color = "black",
size = 1
) +
theme_bw()
```
]
--
.pull-right[
<img src="SSC_R_visu_Bai3_some_graph_files/figure-html/boxplot3-1.png" width="672" />
]
---
### .ssc[Distribution plot - boxplot - một số tùy chỉnh cơ bản]
Ví dụ nếu muốn màu viền `map` theo `GENDER` thì để đối số `color` trong hàm `aes()`
.pull-left[
```r
ggplot(data = df1, aes(x = GENDER, y = AGE)) +
geom_boxplot(
aes(fill = GENDER,
* color = GENDER),
# Thay đổi size của outliers points
outlier.size = 0.5,
# Thay đổi độ rộng của box
width = 0.52,
# Thay đổi độ mờ của box
alpha = 0.48,
# Thay đổi màu và độ dày đường viền ngoài
size = 1
) +
theme_minimal()
```
]
--
.pull-right[
<img src="SSC_R_visu_Bai3_some_graph_files/figure-html/boxplot_color-1.png" width="672" />
]
---
### .ssc[Distribution plot - boxplot]
.pull-left[
Boxplot with Jitter plot - **Kết hợp layer**
```r
ggplot(data = df1, aes(x = GENDER, y = AGE)) +
geom_boxplot(
aes(fill = GENDER), outlier.size = 0.8, width = 0.42,
alpha = 0.48, color = "black", size = 1
) +
* geom_jitter(
aes(color = GENDER),
alpha = 0.38, size = 2
) +
theme_bw()
```
]
--
.pull-right[
<img src="SSC_R_visu_Bai3_some_graph_files/figure-html/boxplot4-1.png" width="672" />
]
---
class: center, middle
# .ssc[Biểu diễn tương quan - Correlation plot]
---
### .ssc[Correlation - scatter plot]
.pull-left[
Tương quan tuổi và chức năng thận
```r
ggplot(data = df1, aes(x = AGE, y = CRCL)) +
* geom_point(
aes(
color = GENDER, # Chỉnh màu theo GENDER
shape = GENDER # Chỉnh shape theo GENDER
)
) +
theme_minimal()
```
]
--
.pull-right[
<img src="SSC_R_visu_Bai3_some_graph_files/figure-html/scat-1.png" width="672" />
]
---
### .ssc[Correlation - Correlogram]
Tương quan giữa các biến liên tục
.pull-left[
```r
*ggcorr(
data = df1,
label = TRUE, # hiển thị hệ số r,
label_size = 3, # cỡ chữ hệ số r
size = 4 # cỡ chữ tên biến (AGE, CRCL, ...)
) +
theme_bw() +
labs(title = "Correlation Matrix",
subtitle = "Correlation Coefficients for continuous variables")
```
]
--
.pull-right[
<img src="SSC_R_visu_Bai3_some_graph_files/figure-html/Correlogram1-1.png" width="4667" />
]
---
### .ssc[Correlation - Correlogram]
Tương quan giữa các biến liên tục
.pull-left[
Chỉnh break cho hệ số r
```r
ggcorr(
data = df1,
label = TRUE,
label_size = 3,
size = 4,
* nbreaks = 5
) +
theme_bw() +
labs(title = "Correlation Matrix",
subtitle = "Correlation Coefficients for continuous variables")
```
]
--
.pull-right[
<img src="SSC_R_visu_Bai3_some_graph_files/figure-html/Correlogram2-1.png" width="4667" />
]
---
### .ssc[Correlation - Correlogram]
Biến liên tục
.pull-left[
```r
*ggpairs(
data = df1,
# Lưu ý tên biến lúc này phải biểu diễn như character
columns = c("AGE", "HEIGHT", "WEIGHT", "CRCL"),
) +
theme_bw() +
labs(title = "Correlogram graph")
```
]
--
.pull-right[
<img src="SSC_R_visu_Bai3_some_graph_files/figure-html/Correlogram3-1.png" width="672" />
]
---
### .ssc[Correlation - Correlogram]
Biến liên tục -> chia màu theo biến phân loại (`factor`)
.pull-left[
```r
ggpairs(
data = df1,
# Lưu ý tên biến lúc này phải biểu diễn như character
columns = c("AGE", "HEIGHT", "WEIGHT", "CRCL"),
* aes(color = df1$GENDER,
alpha = 0.5)
) +
theme_bw() +
labs(title = "Correlogram graph")
```
]
--
.pull-right[
<img src="SSC_R_visu_Bai3_some_graph_files/figure-html/Correlogram4-1.png" width="672" />
]
---
### .ssc[Correlation - Correlogram]
Có thêm biến phân loại
.pull-left[
```r
ggpairs(
data = df1,
# Lưu ý tên biến lúc này phải biểu diễn như character
columns = c("AGE", "HEIGHT", "WEIGHT", "CRCL", "GENDER"),
) +
theme_bw() +
labs(title = "Correlogram graph")
```
]
--
.pull-right[
<img src="SSC_R_visu_Bai3_some_graph_files/figure-html/Correlogram6-1.png" width="672" />
]
---
class: center, middle
# .ssc[Ranking - biểu đồ cột (bar graph)]
---
### .ssc[`df_bar` dataset]
<div id="htmlwidget-c2c978ceeb0a3583dc0f" style="width:100%;height:auto;" class="datatables html-widget"></div>
<script type="application/json" data-for="htmlwidget-c2c978ceeb0a3583dc0f">{"x":{"filter":"none","vertical":false,"extensions":["Buttons"],"data":[["1A","1B","1C","1D","Drug1","Drug2","1A","1B","1C","1D","Drug1","Drug2"],["Cell_1","Cell_1","Cell_1","Cell_1","Cell_1","Cell_1","Cell_2","Cell_2","Cell_2","Cell_2","Cell_2","Cell_2"],[5.1,1.98,1.55,2.88,9.98,1.02,9.123,3.88,2.63,3.12,18.12,2.11]],"container":"<table class=\"compact\">\n <thead>\n <tr>\n <th>Group<\/th>\n <th>Cell<\/th>\n <th>IC50<\/th>\n <\/tr>\n <\/thead>\n<\/table>","options":{"dom":"tBp","buttons":["copy","excel"],"pageLength":10,"columnDefs":[{"className":"dt-right","targets":2}],"order":[],"autoWidth":false,"orderClasses":false}},"evals":[],"jsHooks":[]}</script>
---
### .ssc[Ranking - bar plot]
.pull-left[
```r
ggplot(data = df_bar,
aes(x = Group, y = IC50)) +
* geom_col(
aes(fill = Cell), # để màu theo Cell
width = 0.5, alpha = 0.68, color = "black", size = 0.72
) +
theme_bw()
```
]
--
.pull-right[
<img src="SSC_R_visu_Bai3_some_graph_files/figure-html/bar1-1.png" width="672" />
]
---
### .ssc[Ranking - bar plot]
`position = "fill"`
.pull-left[
```r
ggplot(data = df_bar,
aes(x = Group, y = IC50)) +
geom_col(
aes(fill = Cell), # để màu theo Cell
width = 0.5, alpha = 0.68, color = "black", size = 0.72,
* position = "fill"
) +
theme_bw()
```
]
--
.pull-right[
<img src="SSC_R_visu_Bai3_some_graph_files/figure-html/bar2-1.png" width="672" />
]
---
### .ssc[Ranking - bar plot]
`position = "dodge"`
.pull-left[
```r
ggplot(data = df_bar,
aes(x = Group, y = IC50)) +
geom_col(
aes(fill = Cell), # để màu theo Cell
width = 0.6, alpha = 0.68, color = "black", size = 0.72,
* position = "dodge"
) +
theme_bw() +
labs( x = "", # Để trục x ko có j nếu muốn xóa
y = "IC50 (mg/L)") +
theme(
axis.text.x = element_text(face = "bold", size = 12)
)
```
]
--
.pull-right[
<img src="SSC_R_visu_Bai3_some_graph_files/figure-html/bar3-1.png" width="672" />
]
---
class: center, middle
# .ssc[Evolution, Time-series]
---
### .ssc[`df_evo_2` dataset]
<div id="htmlwidget-a58fee50ebb1c9daa5ae" style="width:100%;height:auto;" class="datatables html-widget"></div>
<script type="application/json" data-for="htmlwidget-a58fee50ebb1c9daa5ae">{"x":{"filter":"none","vertical":false,"extensions":["Buttons"],"data":[[1,1,1,1,1,1,2,2,2,2,2,2,3,3,3,3,3,3,4,4,4,4,4,4],[80,80,80,80,80,80,64.6,64.6,64.6,64.6,64.6,64.6,70.5,70.5,70.5,70.5,70.5,70.5,65,65,65,65,65,65],[4,4,4,4,4,4,4.95,4.95,4.95,4.95,4.95,4.95,4.53,4.53,4.53,4.53,4.53,4.53,4.92,4.92,4.92,4.92,4.92,4.92],[0,0.27,2.03,3.57,5,23.85,0,0.25,3.48,5,6.98,24.22,0,0.25,2.02,3.53,5.05,24.12,0,0.98,1.98,3.6,12.12,24.08],[0,1.29,6.32,5.53,4.94,0.92,0.15,0.85,7.09,6.66,5.25,1.15,0,3.05,7.56,6.59,5.88,1.25,0,8,6.81,5.87,2.69,0.86]],"container":"<table class=\"compact\">\n <thead>\n <tr>\n <th>Subject<\/th>\n <th>Wt<\/th>\n <th>Dose<\/th>\n <th>Time<\/th>\n <th>conc<\/th>\n <\/tr>\n <\/thead>\n<\/table>","options":{"dom":"tBp","buttons":["copy","excel"],"pageLength":10,"columnDefs":[{"className":"dt-right","targets":[0,1,2,3,4]}],"order":[],"autoWidth":false,"orderClasses":false}},"evals":[],"jsHooks":[]}</script>
---
### .ssc[Evolution - line plot]
Đối số `group`
.pull-left[
```r
ggplot(df_evo_2, aes(x = Time, y = conc)) +
geom_line(
* aes(group = Subject),
color = "purple",
size = 0.88
) +
theme_bw()
```
]
--
.pull-right[
<img src="SSC_R_visu_Bai3_some_graph_files/figure-html/line2-1.png" width="3733" />
]
---
### .ssc[Evolution - line plot]
Nhóm `màu` theo bệnh nhân. **Cần** chuyển biến `Subject` về dạng `factor` (hiện tại đang là biến liên tục)
.pull-left[
`group` aesthetics
```r
ggplot(df_evo_2, aes(x = Time, y = conc)) +
geom_line(
aes(group = Subject,
* color = as.factor(Subject)),
size = 0.88
) +
geom_point(aes(color = as.factor(Subject))) +
theme_bw() +
labs(
x = "Th\u1EDDi gian (gi\u1EDD)",
y = "N\u1ED3ng \u0111\u1ED9 (mg/L)",
color = "B\u1EC7nh nh\u00E2n"
)
```
]
--
.pull-right[
<img src="SSC_R_visu_Bai3_some_graph_files/figure-html/line3-1.png" width="3733" />
]
---
### .ssc[Evolution - line plot]
Nếu các `geom_` cùng `mapping` -> có thể để `map` luôn trong hàm `ggplot()` (Giống như `map` `x` và `y`)
.pull-left[
`group` aesthetics
```r
ggplot(df_evo_2, aes(x = Time, y = conc,
* color = as.factor(Subject))) +
geom_line(
# Map group chỉ dùng cho geom_line()
* aes(group = Subject),
size = 0.88
) +
geom_point() +
theme_bw() +
labs(
x = "Th\u1EDDi gian (gi\u1EDD)",
y = "N\u1ED3ng \u0111\u1ED9 (mg/L)",
color = "B\u1EC7nh nh\u00E2n"
)
```
]
--
.pull-right[
<img src="SSC_R_visu_Bai3_some_graph_files/figure-html/same_mapping-1.png" width="3733" />
]
---
### .ssc[Evolution - line plot]
Tương tự áp dụng cho các trường hợp khác như: kết quả thử hòa tan từ các công thức theo thời gian, kết quả thay đổi pH theo thể tích đệm pH 6.8 (thể tích đêm xem như biến time),...
---
### .ssc[Evolution - Stacked area]
Ví dụ về dữ liệu `date_time` với `df_evo_1` dataset
|Time | Year| Month|Drug | DDD|
|:----------|----:|-----:|:--------------|---------:|
|2016-01-01 | 2016| 1|CiprofloxacinO | 4.2817538|
|2016-01-01 | 2016| 1|CiprofloxacinP | 2.7954615|
|2016-01-01 | 2016| 1|LevofloxacinO | 0.0000000|
|2016-01-01 | 2016| 1|LevofloxacinP | 0.1540199|
|2016-01-01 | 2016| 1|MoxifloxacinP | 0.0000000|
|2016-02-01 | 2016| 2|CiprofloxacinO | 5.1242550|
|2016-02-01 | 2016| 2|CiprofloxacinP | 2.1594483|
|2016-02-01 | 2016| 2|LevofloxacinO | 0.0000000|
|2016-02-01 | 2016| 2|LevofloxacinP | 0.4355190|
|2016-02-01 | 2016| 2|MoxifloxacinP | 0.0000000|
|2016-03-01 | 2016| 3|CiprofloxacinO | 6.8278741|
|2016-03-01 | 2016| 3|CiprofloxacinP | 1.8124719|
|2016-03-01 | 2016| 3|LevofloxacinO | 0.1270302|
|2016-03-01 | 2016| 3|LevofloxacinP | 0.1224934|
|2016-03-01 | 2016| 3|MoxifloxacinP | 1.2231450|
|2016-04-01 | 2016| 4|CiprofloxacinO | 9.1242152|
|2016-04-01 | 2016| 4|CiprofloxacinP | 2.8687351|
|2016-04-01 | 2016| 4|LevofloxacinO | 0.0000000|
|2016-04-01 | 2016| 4|LevofloxacinP | 0.6825776|
|2016-04-01 | 2016| 4|MoxifloxacinP | 1.1111511|
|2016-05-01 | 2016| 5|CiprofloxacinO | 4.1241563|
|2016-05-01 | 2016| 5|CiprofloxacinP | 2.4610298|
|2016-05-01 | 2016| 5|LevofloxacinO | 1.6249410|
|2016-05-01 | 2016| 5|LevofloxacinP | 0.3448276|
|2016-05-01 | 2016| 5|MoxifloxacinP | 0.0000000|
|2016-06-01 | 2016| 6|CiprofloxacinO | 3.1452514|
|2016-06-01 | 2016| 6|CiprofloxacinP | 2.0233580|
|2016-06-01 | 2016| 6|LevofloxacinO | 3.0167213|
|2016-06-01 | 2016| 6|LevofloxacinP | 0.9050164|
|2016-06-01 | 2016| 6|MoxifloxacinP | 0.8321321|
|2016-07-01 | 2016| 7|CiprofloxacinO | 5.9198925|
|2016-07-01 | 2016| 7|CiprofloxacinP | 1.8011588|
|2016-07-01 | 2016| 7|LevofloxacinO | 4.7359140|
|2016-07-01 | 2016| 7|LevofloxacinP | 0.5038206|
|2016-07-01 | 2016| 7|MoxifloxacinP | 0.0000000|
|2016-08-01 | 2016| 8|CiprofloxacinO | 5.8141496|
|2016-08-01 | 2016| 8|CiprofloxacinP | 2.1523737|
|2016-08-01 | 2016| 8|LevofloxacinO | 0.8263766|
|2016-08-01 | 2016| 8|LevofloxacinP | 0.9359980|
|2016-08-01 | 2016| 8|MoxifloxacinP | 0.0000000|
|2016-09-01 | 2016| 9|CiprofloxacinO | 7.4607572|
|2016-09-01 | 2016| 9|CiprofloxacinP | 2.3337950|
|2016-09-01 | 2016| 9|LevofloxacinO | 0.2770083|
|2016-09-01 | 2016| 9|LevofloxacinP | 1.2234534|
|2016-09-01 | 2016| 9|MoxifloxacinP | 0.0000000|
|2016-10-01 | 2016| 10|CiprofloxacinO | 5.2241554|
|2016-10-01 | 2016| 10|CiprofloxacinP | 1.8019884|
|2016-10-01 | 2016| 10|LevofloxacinO | 0.3498113|
|2016-10-01 | 2016| 10|LevofloxacinP | 1.0770505|
|2016-10-01 | 2016| 10|MoxifloxacinP | 0.0000000|
|2016-11-01 | 2016| 11|CiprofloxacinO | 7.7082730|
|2016-11-01 | 2016| 11|CiprofloxacinP | 1.8245004|
|2016-11-01 | 2016| 11|LevofloxacinO | 1.7569263|
|2016-11-01 | 2016| 11|LevofloxacinP | 1.5348972|
|2016-11-01 | 2016| 11|MoxifloxacinP | 0.1214115|
|2016-12-01 | 2016| 12|CiprofloxacinO | 8.2894984|
|2016-12-01 | 2016| 12|CiprofloxacinP | 2.1698290|
|2016-12-01 | 2016| 12|LevofloxacinO | 0.8078151|
|2016-12-01 | 2016| 12|LevofloxacinP | 2.7193312|
|2016-12-01 | 2016| 12|MoxifloxacinP | 0.0000000|
|2017-01-01 | 2017| 1|CiprofloxacinO | 6.8928443|
|2017-01-01 | 2017| 1|CiprofloxacinP | 3.3837057|
|2017-01-01 | 2017| 1|LevofloxacinO | 0.4061641|
|2017-01-01 | 2017| 1|LevofloxacinP | 1.0572214|
|2017-01-01 | 2017| 1|MoxifloxacinP | 0.1111455|
|2017-02-01 | 2017| 2|CiprofloxacinO | 7.3185012|
|2017-02-01 | 2017| 2|CiprofloxacinP | 3.4516713|
|2017-02-01 | 2017| 2|LevofloxacinO | 0.0000000|
|2017-02-01 | 2017| 2|LevofloxacinP | 0.5109644|
|2017-02-01 | 2017| 2|MoxifloxacinP | 0.0000000|
|2017-03-01 | 2017| 3|CiprofloxacinO | 5.0133929|
|2017-03-01 | 2017| 3|CiprofloxacinP | 2.8370536|
|2017-03-01 | 2017| 3|LevofloxacinO | 2.2678571|
|2017-03-01 | 2017| 3|LevofloxacinP | 2.5312500|
|2017-03-01 | 2017| 3|MoxifloxacinP | 0.1339286|
|2017-04-01 | 2017| 4|CiprofloxacinO | 5.0716546|
|2017-04-01 | 2017| 4|CiprofloxacinP | 2.7870836|
|2017-04-01 | 2017| 4|LevofloxacinO | 3.3221664|
|2017-04-01 | 2017| 4|LevofloxacinP | 2.7079844|
|2017-04-01 | 2017| 4|MoxifloxacinP | 0.1023637|
|2017-05-01 | 2017| 5|CiprofloxacinO | 5.7296725|
|2017-05-01 | 2017| 5|CiprofloxacinP | 3.0303699|
|2017-05-01 | 2017| 5|LevofloxacinO | 1.7921780|
|2017-05-01 | 2017| 5|LevofloxacinP | 2.7809658|
|2017-05-01 | 2017| 5|MoxifloxacinP | 0.0794562|
|2017-06-01 | 2017| 6|CiprofloxacinO | 6.1060525|
|2017-06-01 | 2017| 6|CiprofloxacinP | 3.7270130|
|2017-06-01 | 2017| 6|LevofloxacinO | 3.5797179|
|2017-06-01 | 2017| 6|LevofloxacinP | 1.5577575|
|2017-06-01 | 2017| 6|MoxifloxacinP | 0.0000000|
|2017-07-01 | 2017| 7|CiprofloxacinO | 5.3735836|
|2017-07-01 | 2017| 7|CiprofloxacinP | 3.5543555|
|2017-07-01 | 2017| 7|LevofloxacinO | 1.2393768|
|2017-07-01 | 2017| 7|LevofloxacinP | 1.3854462|
|2017-07-01 | 2017| 7|MoxifloxacinP | 0.0000000|
|2017-08-01 | 2017| 8|CiprofloxacinO | 6.5793029|
|2017-08-01 | 2017| 8|CiprofloxacinP | 2.5983968|
|2017-08-01 | 2017| 8|LevofloxacinO | 0.7115194|
|2017-08-01 | 2017| 8|LevofloxacinP | 1.1843646|
|2017-08-01 | 2017| 8|MoxifloxacinP | 0.0090066|
|2017-09-01 | 2017| 9|CiprofloxacinO | 7.2821558|
|2017-09-01 | 2017| 9|CiprofloxacinP | 3.1180746|
|2017-09-01 | 2017| 9|LevofloxacinO | 3.4925982|
|2017-09-01 | 2017| 9|LevofloxacinP | 1.3695594|
|2017-09-01 | 2017| 9|MoxifloxacinP | 0.0443223|
|2017-10-01 | 2017| 10|CiprofloxacinO | 5.3396311|
|2017-10-01 | 2017| 10|CiprofloxacinP | 3.1781377|
|2017-10-01 | 2017| 10|LevofloxacinO | 2.5551057|
|2017-10-01 | 2017| 10|LevofloxacinP | 1.8353576|
|2017-10-01 | 2017| 10|MoxifloxacinP | 0.0000000|
|2017-11-01 | 2017| 11|CiprofloxacinO | 5.6254200|
|2017-11-01 | 2017| 11|CiprofloxacinP | 3.8014784|
|2017-11-01 | 2017| 11|LevofloxacinO | 2.7167131|
|2017-11-01 | 2017| 11|LevofloxacinP | 2.6159163|
|2017-11-01 | 2017| 11|MoxifloxacinP | 0.0767975|
|2017-12-01 | 2017| 12|CiprofloxacinO | 9.0191097|
|2017-12-01 | 2017| 12|CiprofloxacinP | 5.3048713|
|2017-12-01 | 2017| 12|LevofloxacinO | 0.8615928|
|2017-12-01 | 2017| 12|LevofloxacinP | 2.3362421|
|2017-12-01 | 2017| 12|MoxifloxacinP | 0.1215067|
|2018-01-01 | 2018| 1|CiprofloxacinO | 5.6572541|
|2018-01-01 | 2018| 1|CiprofloxacinP | 3.2229796|
|2018-01-01 | 2018| 1|LevofloxacinO | 0.0000000|
|2018-01-01 | 2018| 1|LevofloxacinP | 1.8451801|
|2018-01-01 | 2018| 1|MoxifloxacinP | 0.1265823|
|2018-02-01 | 2018| 2|CiprofloxacinO | 6.8367079|
|2018-02-01 | 2018| 2|CiprofloxacinP | 3.9705496|
|2018-02-01 | 2018| 2|LevofloxacinO | 0.0000000|
|2018-02-01 | 2018| 2|LevofloxacinP | 2.4848804|
|2018-02-01 | 2018| 2|MoxifloxacinP | 0.0262950|
|2018-03-01 | 2018| 3|CiprofloxacinO | 7.9569420|
|2018-03-01 | 2018| 3|CiprofloxacinP | 4.0839192|
|2018-03-01 | 2018| 3|LevofloxacinO | 0.2425353|
|2018-03-01 | 2018| 3|LevofloxacinP | 1.6212654|
|2018-03-01 | 2018| 3|MoxifloxacinP | 0.3075571|
|2018-04-01 | 2018| 4|CiprofloxacinO | 8.0070834|
|2018-04-01 | 2018| 4|CiprofloxacinP | 3.7235570|
|2018-04-01 | 2018| 4|LevofloxacinO | 0.1242526|
|2018-04-01 | 2018| 4|LevofloxacinP | 2.2111611|
|2018-04-01 | 2018| 4|MoxifloxacinP | 0.1148655|
|2018-05-01 | 2018| 5|CiprofloxacinO | 7.4538317|
|2018-05-01 | 2018| 5|CiprofloxacinP | 4.0347043|
|2018-05-01 | 2018| 5|LevofloxacinO | 0.0000000|
|2018-05-01 | 2018| 5|LevofloxacinP | 2.5827460|
|2018-05-01 | 2018| 5|MoxifloxacinP | 0.0892140|
|2018-06-01 | 2018| 6|CiprofloxacinO | 8.0508475|
|2018-06-01 | 2018| 6|CiprofloxacinP | 4.5506709|
|2018-06-01 | 2018| 6|LevofloxacinO | 0.0000000|
|2018-06-01 | 2018| 6|LevofloxacinP | 1.4477401|
|2018-06-01 | 2018| 6|MoxifloxacinP | 0.0617938|
|2018-07-01 | 2018| 7|CiprofloxacinO | 7.7631891|
|2018-07-01 | 2018| 7|CiprofloxacinP | 5.2618050|
|2018-07-01 | 2018| 7|LevofloxacinO | 0.0000000|
|2018-07-01 | 2018| 7|LevofloxacinP | 0.0000000|
|2018-07-01 | 2018| 7|MoxifloxacinP | 0.1265522|
|2018-08-01 | 2018| 8|CiprofloxacinO | 8.3110025|
|2018-08-01 | 2018| 8|CiprofloxacinP | 4.1357976|
|2018-08-01 | 2018| 8|LevofloxacinO | 0.0000000|
|2018-08-01 | 2018| 8|LevofloxacinP | 0.0000000|
|2018-08-01 | 2018| 8|MoxifloxacinP | 0.2127995|
|2018-09-01 | 2018| 9|CiprofloxacinO | 7.6823565|
|2018-09-01 | 2018| 9|CiprofloxacinP | 4.3287889|
|2018-09-01 | 2018| 9|LevofloxacinO | 1.2415256|
|2018-09-01 | 2018| 9|LevofloxacinP | 1.1141450|
|2018-09-01 | 2018| 9|MoxifloxacinP | 0.2686835|
|2018-10-01 | 2018| 10|CiprofloxacinO | 6.3628358|
|2018-10-01 | 2018| 10|CiprofloxacinP | 5.1871422|
|2018-10-01 | 2018| 10|LevofloxacinO | 1.7893523|
|2018-10-01 | 2018| 10|LevofloxacinP | 0.0000000|
|2018-10-01 | 2018| 10|MoxifloxacinP | 0.0440335|
|2018-11-01 | 2018| 11|CiprofloxacinO | 9.0790162|
|2018-11-01 | 2018| 11|CiprofloxacinP | 4.8665620|
|2018-11-01 | 2018| 11|LevofloxacinO | 1.9214721|
|2018-11-01 | 2018| 11|LevofloxacinP | 1.4148015|
|2018-11-01 | 2018| 11|MoxifloxacinP | 0.0087214|
|2018-12-01 | 2018| 12|CiprofloxacinO | 8.8887857|
|2018-12-01 | 2018| 12|CiprofloxacinP | 5.9274044|
|2018-12-01 | 2018| 12|LevofloxacinO | 0.0000000|
|2018-12-01 | 2018| 12|LevofloxacinP | 0.0000000|
|2018-12-01 | 2018| 12|MoxifloxacinP | 0.0000000|
---
### .ssc[Evolution - Stacked area]
Ví dụ về dữ liệu `date_time`
- Lưu ý cần lưu trữ kiểu biến ở dạng `date` trên `excel`
- Cần biến đổi biến về dạng `Date` trên R thông qua lệnh:
```r
df_evo_1$Time = as.Date(df_evo_1$Time)
```
---
### .ssc[Evolution - Stacked area]
Ví dụ biểu diễn dưới dạng `line` chồng lấp khó theo dõi
.pull-left[
```r
ggplot(df_evo_1, aes(x = Time, y = DDD)) +
geom_line(aes(color = Drug), size = 0.88) +
labs(
x = "", y = "DDD/100 ng\u00E0y n\u1EB1m vi\u1EC7n",
color = "Thu\u1ED1c_\u0110\u01B0\u1EDDng d\u00F9ng"
) +
theme_bw()
```
]
--
.pull-right[
<img src="SSC_R_visu_Bai3_some_graph_files/figure-html/line4-1.png" width="3733" />
]
---
### .ssc[Evolution - Stacked area]
.pull-left[
Chuyển quan dạng `Stacked area`
```r
ggplot(df_evo_1, aes(x = Time, y = DDD)) +
* geom_area(aes(fill = Drug)) +
labs(
x = "", y = "DDD/100 ng\u00E0y n\u1EB1m vi\u1EC7n",
color = "Thu\u1ED1c_\u0110\u01B0\u1EDDng d\u00F9ng"
) +
theme_bw()
```
]
--
.pull-right[
<img src="SSC_R_visu_Bai3_some_graph_files/figure-html/line5-1.png" width="3733" />
]
---
### .ssc[Evolution - Stacked area]
.pull-left[
Tùy chình thêm
```r
ggplot(df_evo_1, aes(x = Time, y = DDD)) +
geom_area(aes(fill = Drug),
* alpha = 0.68, color = "white", size = 0.6) +
labs(
x = "", y = "DDD/100 ng\u00E0y n\u1EB1m vi\u1EC7n",
color = "Thu\u1ED1c_\u0110\u01B0\u1EDDng d\u00F9ng",
fill = "Thu\u1ED1c"
) +
theme_bw()
```
]
--
.pull-right[
<img src="SSC_R_visu_Bai3_some_graph_files/figure-html/line6-1.png" width="3733" />
]
---
### .ssc[Evolution - Stacked area]
.pull-left[
Chỉnh hiển thị `ngày tháng`
```r
ggplot(df_evo_1, aes(x = Time, y = DDD)) +
geom_area(aes(fill = Drug),
alpha = 0.68, color = "white", size = 0.6) +
* scale_x_date(
# Hiển thị tháng và năm
date_labels = "%m/%Y",
# Chia khoảng 4 năm
date_breaks = "4 months"
) +
labs(
x = "", y = "DDD/100 ng\u00E0y n\u1EB1m vi\u1EC7n",
color = "Thu\u1ED1c_\u0110\u01B0\u1EDDng d\u00F9ng",
fill = "Thu\u1ED1c"
) +
theme_bw()
```
]
--
.pull-right[
<img src="SSC_R_visu_Bai3_some_graph_files/figure-html/line7-1.png" width="3733" />
]
---
class: center, middle
# .ssc[Một số biểu đồ có *statistic*]
---
### .ssc[Đường chuẩn - Tự tạo dataset]
```r
df_cal_curve = data.frame(
Nong_do = 1:10,
S_peak = c(1.04, 1.95, 3.06, 4.04, 4.66, 5.58, 6.91, 8.74, 9.78, 10.14)
)
```
| Nong_do| S_peak|
|-------:|------:|
| 1| 1.04|
| 2| 1.95|
| 3| 3.06|
| 4| 4.04|
| 5| 4.66|
| 6| 5.58|
| 7| 6.91|
| 8| 8.74|
| 9| 9.78|
| 10| 10.14|
---
### .ssc[Đường chuẩn]
.pull-left[
```r
ggplot(df_cal_curve, aes(x = Nong_do, y = S_peak)) +
geom_point() +
stat_smooth(
method = "lm", se = FALSE,
color = "black", size = 0.6
) +
theme_minimal()
```
]
--
.pull-right[
<img src="SSC_R_visu_Bai3_some_graph_files/figure-html/cal_curve-1.png" width="3733" />
]
---
### .ssc[Đường chuẩn]
Vẽ thêm đường `SE`
.pull-left[
```r
ggplot(df_cal_curve, aes(x = Nong_do, y = S_peak)) +
geom_point() +
stat_smooth(
method = "lm", se = TRUE,
color = "black", size = 0.6
) +
theme_minimal()
```
]
--
.pull-right[
<img src="SSC_R_visu_Bai3_some_graph_files/figure-html/cal_curve2-1.png" width="3733" />
]
---
### .ssc[Đường chuẩn]
Thêm phương trình và hệ số xác định `R^2`
.pull-left[
```r
ggplot(df_cal_curve, aes(x = Nong_do, y = S_peak)) +
geom_point() +
theme_minimal() +
stat_smooth(method = "lm", se = TRUE, color = "black", size = 0.6) +
* stat_poly_eq(
* formula = y ~ x,
* eq.with.lhs = "italic(hat(y))~`=`~",
* aes(label = paste(..eq.label.., ..rr.label.., sep = "~~~")),
* parse = TRUE
)
```
]
--
.pull-right[
<img src="SSC_R_visu_Bai3_some_graph_files/figure-html/cal_curve1-1.png" width="3733" />
]
---
class: center, middle
# .ssc[Biểu đồ chứa sai số (error bar)]
---
### .ssc[`df_bar_error` dataset]
<div id="htmlwidget-86467c068c0959f53693" style="width:100%;height:auto;" class="datatables html-widget"></div>
<script type="application/json" data-for="htmlwidget-86467c068c0959f53693">{"x":{"filter":"none","vertical":false,"extensions":["Buttons"],"data":[["1A","1B","1C","1D","Drug1","Drug2","1A","1B","1C","1D","Drug1","Drug2"],["Cell_1","Cell_1","Cell_1","Cell_1","Cell_1","Cell_1","Cell_2","Cell_2","Cell_2","Cell_2","Cell_2","Cell_2"],[5.1,1.98,1.55,2.88,9.98,1.02,9.123,3.88,2.63,3.12,18.12,2.11],[0.52,0.32,0.18,0.12,1.88,0.55,1.35,0.22,0.1,0.06,0.86,1.88]],"container":"<table class=\"compact\">\n <thead>\n <tr>\n <th>Group<\/th>\n <th>Cell<\/th>\n <th>IC50_mean<\/th>\n <th>SD<\/th>\n <\/tr>\n <\/thead>\n<\/table>","options":{"dom":"tBp","buttons":["copy","excel"],"pageLength":10,"columnDefs":[{"className":"dt-right","targets":[2,3]}],"order":[],"autoWidth":false,"orderClasses":false}},"evals":[],"jsHooks":[]}</script>
---
### .ssc[Barplot with error bar]
Ví dụ mean `\(\pm\)` SD. Tương tự vẽ cho khoảng tin cậy 95% (`95CI`)
.pull-left[
```r
ggplot(data = df_bar_error,
# Cần để aes(fill) luôn từ đầu
aes(x = Group, y = IC50_mean,
* fill = Cell)) +
geom_col(
width = 0.6, alpha = 0.68, color = "black", size = 0.72,
position = "dodge"
) +
geom_errorbar(
aes(ymin = IC50_mean - SD, ymax = IC50_mean + SD),
width = .2, size = 0.22,
# Chỉnh vị trí của thanh error