search1.Rmd

---
title: "resach1"
author: "xiaodi"
date: "2018年10月28日"
output: word_document
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
options(digits = 2)
```

```{r, include=FALSE, warning=FALSE}
library(dplyr)
library(readr)
library(ggplot2)
library(readr)
library(knitr)
library(purrr)
library(tidyr)
paper_search <- read_csv("paper_search.csv", 
                         col_types = cols(id_index = col_character(), 
                                          id_index_1 = col_character()))
# 体重身高有异常值手动修改
Data <- paper_search %>% 
  filter(CysC < 50,
         CRE > 2 ) %>% 
  select(-c(id_index_1)) %>% 
  mutate(bmi = weight / (height ^ 2)) %>% 
  mutate(Y_CLASS = case_when(
    ACR >= 300            ~ "大量白蛋白尿组",
    ACR >= 30 & ACR < 300 ~ "微量白蛋白尿组",
    ACR <  30             ~ "正常白蛋白尿组",
    TRUE                  ~ "未知"
  )) %>% 
  distinct(id_index, be_admission_time, .keep_all = TRUE) %>% 
  filter(Y_CLASS == "大量白蛋白尿组") %>% 
  mutate(ageEffect = if_else(gender == '男', 1, 0.742)) %>% 
  mutate(GFR =  30849 * (CRE^(-1.154)) * (age^(-0.203)) * ageEffect) %>% 
  mutate(
    GFR_CLASS = case_when(
    GFR >= 90           ~ "正常",
    GFR >= 60 & GFR <90 ~ "轻度下降",
    GFR >= 30 & GFR <60 ~ "中度下降",
    GFR >= 15 & GFR <30 ~ "重度下降",
    GFR <15             ~ "肾衰竭",
    TRUE                ~ "未知"
  ),
    GFR_CLASS_ARR = case_when(
    GFR >= 90           ~ 1,
    GFR >= 60 & GFR <90 ~ 2,
    GFR >= 30 & GFR <60 ~ 3,
    GFR >= 15 & GFR <30 ~ 4,
    GFR <15             ~ 5,
    TRUE                ~ 0
  ),
  #   AGE_CLASS = case_when(
  #   age >= 90            ~ '长寿老年人',
  #   age >= 75 & age < 90 ~ '老人',
  #   age >= 60 & age < 75 ~ '老人前期',
  #   age >= 45 & age < 60 ~ '中年',
  #   age <  45            ~ '青年',
  #   TRUE                 ~ '未知'
  # )
      AGE_CLASS = case_when(
    age >= 80            ~ '80以上',
    age >= 70 & age < 80 ~ '70-80',
    age >= 60 & age < 70 ~ '60-70',
    age >= 50 & age < 60 ~ '50-60',
    age >= 40 & age < 50 ~ '40-50',
    # age >= 30 & age < 40 ~ '30-40',
    age >= 20 & age < 40 ~ '20-40',
    age <  20            ~ '20以下',
    TRUE                 ~ '未知'
  ),
  BMI_CLASS = case_when(
    bmi >= 28            ~ "肥胖",
    bmi >= 24 & bmi < 28 ~ "超重BMI",
    bmi >= 18.5 & bmi < 24 ~ "健康体重",
    bmi <  18.5             ~ "轻体重",
    TRUE                  ~ "未知"
  )
  )
print(names(Data))
```


```{r, include=FALSE, warning=FALSE}
#患者总人数
patient_num <- as.character(Data %>% summarise(num = n()))

#患者男女人数
patient_sex_num <- Data %>% group_by(gender) %>% summarise(num = n())

#患者年龄跨度
patient_age_range <- Data %>% summarise(max = max(age), min = min(age))

#患者年龄均值95%置信区间

patient_age_mean <- as.numeric(t.test(Data$age)$estimate)
patient_age_range_conf <- patient_age_mean - as.numeric(t.test(Data$age)$conf.int)[1]


#患者病程跨度
patient_courseofdiabetes_range <- Data %>% summarise(max = max(courseofdiabetes), min = min(courseofdiabetes))

#患者病程均值95%置信区间
patient_courseofdiabetes_mean <- as.numeric(t.test(Data$courseofdiabetes)$estimate)
patient_courseofdiabetes_range_conf <- patient_courseofdiabetes_mean - as.numeric(t.test(Data$courseofdiabetes)$conf.int)[1]

#患者BMI跨度
patient_BMI_range <- Data %>% summarise(max = max(bmi), min = min(bmi))

#患者BMI均值95%置信区间
patient_BMI_mean <- as.numeric(t.test(Data$bmi)$estimate)
patient_BMI_range_conf <- patient_BMI_mean - as.numeric(t.test(Data$bmi)$conf.int)[1]
print(patient_BMI_mean)

# 各组有多少人
Y_CLASS_num <- Data %>% group_by(GFR_CLASS) %>% summarise(num = n())

```

## 1、初步统计资料
1、肾内科住院部收治的2 型糖尿病患者(`r patient_num`)例，其中男 `r patient_sex_num$num[1]`
 例，女 `r patient_sex_num$num[2]` 例，年龄`r patient_age_range$min`-`r patient_age_range$max` 岁，平均 (`r patient_age_mean`± `r patient_age_range_conf`) 岁；病程 `r patient_courseofdiabetes_range$min`-`r patient_courseofdiabetes_range$max` 年，平均 (`r patient_courseofdiabetes_mean`± `r patient_courseofdiabetes_range_conf`)  年;BMI `r patient_BMI_range$min`-`r patient_BMI_range$max`，平均 (`r patient_BMI_mean`± `r patient_BMI_range_conf`)。
按eGFR[ml/(min·1 .73 m2)]分为:≥90,60-89,30-59,15-29,<15 分组,其中：

≥90（`r Y_CLASS_num$num[3]`）例。
60-89（`r Y_CLASS_num$num[1]`）例。
30-59（`r Y_CLASS_num$num[4]`）例,
15-29（`r Y_CLASS_num$num[5]`）例，
<15 （`r Y_CLASS_num$num[2]`）例，


## 2、可比性：性别（男/女）、年龄、病程、BMI


+ 性别


```{r, echo=FALSE}
#性别
genderdata <- Data %>%
  group_by(GFR_CLASS, GFR_CLASS_ARR, gender) %>% 
  summarise(num = n()) %>% 
  ungroup() %>% 
  spread(gender, num) %>% 
  arrange(GFR_CLASS_ARR) %>% 
  select(-GFR_CLASS_ARR)
  # select(gender, '正常', '轻度下降', '中度下降', '重度下降', '肾衰竭')
kable(genderdata, caption = '各组男女人数分布')
```

在不同ACR水平下男女的人数。根据数据看，在不同ACR水平下还是男的人数占大部分


+ 年龄


```{r, echo=FALSE}
#年龄
agedata <- Data %>%
  group_by(GFR_CLASS, GFR_CLASS_ARR) %>% 
  summarise(max = max(age), 
            upper = quantile(age, 0.75),
            median = median(age),
            mean = mean(age),
            lower = quantile(age, 0.25),
            min = min(age)
            ) %>% 
  ungroup() %>% 
  gather(key = type, value = value, max, upper, median, mean, lower, min) %>% 
  spread(type, value) %>% 
  arrange(GFR_CLASS_ARR) %>% 
  select(-GFR_CLASS_ARR)
  # select(type, '正常', '轻度下降', '中度下降', '重度下降', '肾衰竭')

kable(agedata, caption = '年龄分布')
```

min为最小值，lower为下四分位， median为中位数， mean为均值， upper为上四分位， max为最大值

例如：
正常组 年龄最小为31岁，最大值57岁， 平均数47岁，中位数为48岁， 下四分位（等于该样本中所有数值由小到大排列后第25%的数字）为42，
上四分位（等于该样本中所有数值由小到大排列后第75%的数字）为52，

每组中年龄还是有较大差异，不建议做可比性

+ 病程


```{r, echo=FALSE}
#病程
coursedata <- Data %>%
  group_by(GFR_CLASS, GFR_CLASS_ARR) %>% 
  summarise(max = max(courseofdiabetes), 
            upper = quantile(courseofdiabetes, 0.75),
            median = median(courseofdiabetes),
            mean = mean(courseofdiabetes),
            lower = quantile(courseofdiabetes, 0.25),
            min = min(courseofdiabetes)
            ) %>% 
  ungroup() %>% 
  gather(key = type, value = value, max, upper, median, mean, lower, min) %>% 
  spread(type, value) %>% 
  arrange(GFR_CLASS_ARR) %>% 
  select(-GFR_CLASS_ARR)
  # select(type, '正常', '轻度下降', '中度下降', '重度下降', '肾衰竭')

kable(coursedata, caption = '病程分布')
```

+ BMI

```{r, echo=FALSE}
#BMI
BMIdata <- Data %>%
  group_by(GFR_CLASS, GFR_CLASS_ARR) %>% 
  summarise(max = max(bmi), 
            upper = quantile(bmi, 0.75),
            median = median(bmi),
            mean = mean(bmi),
            lower = quantile(bmi, 0.25),
            min = min(bmi)
            ) %>% 
  ungroup() %>% 
  gather(key = type, value = value, max, upper, median, mean, lower, min) %>% 
  spread(type, value) %>% 
  arrange(GFR_CLASS_ARR) %>% 
  select(-GFR_CLASS_ARR)
  # select(type, '正常', '轻度下降', '中度下降', '重度下降', '肾衰竭')

kable(BMIdata, caption = 'BMI分布')
```


## 3、各组以下指标x±s

UREA为BUN CRE为scr 
```{r, echo=FALSE}
T.test <- function(DaTa, Class, Col){
  DATA <- DaTa %>% filter(GFR_CLASS == Class) %>% as.data.frame(.) %>% .[, quo_name(Col)]
  
  data.frame(
             y_class = Class,
             index = Col,
             mean = t.test(DATA)$estimate,
             conf = t.test(DATA)$estimate - as.numeric(t.test(DATA)$conf.int)[1], 
             row.names = NULL)
  
}
Y_CLASS_name <- distinct(Data, GFR_CLASS) %>% mutate(num = 1)
select_name <- tibble(name = c('CysC', 'UREA', 'CRE', 'ACR'), num=1)

index_combie <- Y_CLASS_name %>% left_join(select_name, by = c("num"))
newdata <- data.frame()
for (i in 1:nrow(index_combie)) {
  TTest <- T.test(DaTa = Data, 
                  Class = as.character(index_combie[i, 'GFR_CLASS']), 
                  Col = as.character(index_combie[i, 'name']))
  newdata <- rbind(newdata, TTest)
}
newdata1 <- newdata %>% 
  mutate(rs = sprintf('%.2f±%.2f', mean, conf)) %>% 
  select(index, y_class, rs) %>% 
  spread(y_class, rs) %>% 
  select(index,  '正常', '轻度下降', '中度下降', '重度下降', '肾衰竭')
kable(newdata1, format = "pandoc", caption = '各指标平均值')
```

随着GFR的下降，各项指标都是呈现上升的趋势


## 4、各组指标与ACR相关关系
### 总体

#### CysC与ACR+GFR各组

```{r, echo=FALSE, warning=FALSE, message=FALSE}


total_CysC_ACR_plot <- Data %>% 
  select(CysC, ACR, BMI_CLASS, GFR_CLASS)

ggplot(total_CysC_ACR_plot, aes(CysC, ACR, colour = GFR_CLASS)) + 
  geom_point() + # + geom_smooth()
  ggtitle('总体_CysC与ACR+GFR_CLASS') +
  scale_colour_discrete(limits=c('正常', '轻度下降', '中度下降', '重度下降', '肾衰竭'))
```

从CysC与ACR的散点图可以看出，CysC能有效区分GFR水平，但却与ACR无明显关系，随着CysC增加，能看出GFR下降，但ACR却忽高忽低。

#### CysC与ACR加年龄分段+BMI分段

```{r, echo=FALSE, warning=FALSE, message=FALSE}


total_CysC_ACR_plot <- Data %>% 
  select(CysC, ACR, BMI_CLASS, AGE_CLASS)

ggplot(total_CysC_ACR_plot, aes(CysC, ACR, colour = BMI_CLASS, shape=AGE_CLASS)) + 
  geom_point() + # + geom_smooth()
  ggtitle('总体_CysC与ACR+AGE_CLASS+BMI_CLASS')  
  # scale_colour_discrete(limits=c('正常', '轻度下降', '中度下降', '重度下降', '肾衰竭'))
```

加入年龄分段以及BMI分段，想要识别ACR升高却CysC较低的人群。看图关系也不是很明显


#### UREA与ACR+GFR各组
    

```{r, echo=FALSE, warning=FALSE, message=FALSE}

total_UREA_ACR_plot <- Data %>% 
  select(UREA, ACR, GFR_CLASS)

ggplot(total_UREA_ACR_plot, aes(UREA, ACR, colour = GFR_CLASS)) + 
  geom_point() + 
  ggtitle('总体_UREA与ACR') + 
  scale_colour_discrete(limits=c('正常', '轻度下降', '中度下降', '重度下降', '肾衰竭'))
```

同时在UREA与ACR中，也看得出UREA与GFR的明显关系，随着UREA升高，GFR是下降的。却没有CysC在轻度与中度的区分度好。但在UREA与ACR的关系上，在UREA处于较低水平时，部分ACR已经上升很高。 在UREA处于较高水平时，ACR却是较低的。


#### UREA与ACR加年龄分段+BMI分段

```{r, echo=FALSE, warning=FALSE, message=FALSE}


total_UREA_ACR_plot <- Data %>% 
  select(UREA, ACR, BMI_CLASS, AGE_CLASS)

ggplot(total_UREA_ACR_plot, aes(UREA, ACR, colour = BMI_CLASS, shape=AGE_CLASS)) + 
  geom_point() + # + geom_smooth()
  ggtitle('总体_UREA与ACR+AGE_CLASS+BMI_CLASS')  
  # scale_colour_discrete(limits=c('正常', '轻度下降', '中度下降', '重度下降', '肾衰竭'))
```

尝试用年龄以及BMI去区分在UREA较低和较高时的异常值，却并不能找到导致这种原因的特定人群，或还存在未关注到的分类指标，能很好划分特殊人群。


#### CRE与ACR+GFR各组


```{r, echo=FALSE, warning=FALSE, message=FALSE}

total_CRE_ACR_plot <- Data %>% 
  select(CRE, ACR, GFR_CLASS)

ggplot(total_CRE_ACR_plot, aes(CRE, ACR, colour = GFR_CLASS)) + 
  geom_point() + 
  ggtitle('总体_CRE与ACR') + 
  scale_colour_discrete(limits=c('正常', '轻度下降', '中度下降', '重度下降', '肾衰竭'))
```

CRE在重度以及肾衰竭有着较好区分度，ACR与CRE没有明显关系

#### CRE与ACR加年龄分段+BMI分段

```{r, echo=FALSE, warning=FALSE, message=FALSE}


total_CRE_ACR_plot <- Data %>% 
  select(CRE, ACR, BMI_CLASS, AGE_CLASS)

ggplot(total_CRE_ACR_plot, aes(CRE, ACR, colour = BMI_CLASS, shape=AGE_CLASS)) + 
  geom_point() + # + geom_smooth()
  ggtitle('总体_CRE与ACR+AGE_CLASS+BMI_CLASS')
```



### 正常组

#### CysC与ACR

```{r, echo=FALSE, warning=FALSE, message=FALSE}
nomal_data <- Data %>% 
  filter(GFR_CLASS == '正常')

nomal_CysC_ACR_plot <- nomal_data %>% 
  select(CysC, ACR, BMI_CLASS, AGE_CLASS)

ggplot(nomal_CysC_ACR_plot, aes(CysC, ACR, colour = BMI_CLASS, shape=AGE_CLASS)) + 
  geom_point() + # + geom_smooth()
   ggtitle('正常组_CysC与ACR')
```

在正常组中，ACR异常的均为体重异常的


#### UREA与ACR
    

```{r, echo=FALSE, warning=FALSE, message=FALSE}

nomal_UREA_ACR_plot <- nomal_data %>% 
  select(UREA, ACR, BMI_CLASS, AGE_CLASS)

ggplot(nomal_UREA_ACR_plot, aes(UREA, ACR, colour = BMI_CLASS, shape=AGE_CLASS)) + 
  geom_point() + 
   ggtitle('正常组_UREA与ACR')
```


#### CRE与ACR


```{r, echo=FALSE, warning=FALSE, message=FALSE}

nomal_CRE_ACR_plot <- nomal_data %>% 
  select(CRE, ACR, BMI_CLASS, AGE_CLASS)

ggplot(nomal_CRE_ACR_plot, aes(CRE, ACR, colour = BMI_CLASS, shape=AGE_CLASS)) + 
  geom_point() + 
   ggtitle('正常组_CRE与ACR')
```



### 轻度下降

#### CysC与ACR

```{r, echo=FALSE, warning=FALSE, message=FALSE}
little_data <- Data %>% 
  filter(GFR_CLASS == '轻度下降')

little_CysC_ACR_plot <- little_data %>% 
  select(CysC, ACR, BMI_CLASS, AGE_CLASS)

ggplot(little_CysC_ACR_plot, aes(CysC, ACR, colour = BMI_CLASS, shape=AGE_CLASS)) + 
  geom_point() + 
   ggtitle('轻度下降组_CysC与ACR')
```

#### UREA与ACR

```{r, echo=FALSE, warning=FALSE, message=FALSE}

little_UREA_ACR_plot <- little_data %>% 
  select(UREA, ACR, BMI_CLASS, AGE_CLASS)

ggplot(little_UREA_ACR_plot, aes(UREA, ACR, colour = BMI_CLASS, shape=AGE_CLASS)) + 
  geom_point() + 
   ggtitle('轻度下降组_UREA与ACR')
```

#### CRE与ACR

```{r, echo=FALSE, warning=FALSE, message=FALSE}

little_CRE_ACR_plot <- little_data %>% 
  select(CRE, ACR, BMI_CLASS, AGE_CLASS)

ggplot(little_CRE_ACR_plot, aes(CRE, ACR, colour = BMI_CLASS, shape=AGE_CLASS)) + 
  geom_point()+ 
   ggtitle('轻度下降组_CRE与ACR')
```


### 中度下降

#### CysC与ACR

```{r, echo=FALSE, warning=FALSE, message=FALSE}
median_data <- Data %>% 
  filter(GFR_CLASS == '中度下降')

median_CysC_ACR_plot <- median_data %>% 
  select(CysC, ACR, BMI_CLASS, AGE_CLASS)

ggplot(median_CysC_ACR_plot, aes(CysC, ACR, colour = BMI_CLASS, shape=AGE_CLASS)) + 
  geom_point()+ 
   ggtitle('中度下降组_CysC与ACR')
```


#### UREA与ACR

```{r, echo=FALSE, warning=FALSE, message=FALSE}

median_UREA_ACR_plot <- median_data %>% 
  select(UREA, ACR, BMI_CLASS, AGE_CLASS)

ggplot(median_UREA_ACR_plot, aes(UREA, ACR, colour = BMI_CLASS, shape=AGE_CLASS)) + 
  geom_point()+ 
   ggtitle('中度下降组_UREA与ACR')
```

#### CRE与ACR

```{r, echo=FALSE, warning=FALSE, message=FALSE}

median_CRE_ACR_plot <- median_data %>% 
  select(CRE, ACR, BMI_CLASS, AGE_CLASS)

ggplot(median_CRE_ACR_plot, aes(CRE, ACR, colour = BMI_CLASS, shape=AGE_CLASS)) + 
  geom_point()+ 
   ggtitle('中度下降组_CRE与ACR')
```



### 重度下降

#### CysC与ACR

```{r, echo=FALSE, warning=FALSE, message=FALSE}
heavy_data <- Data %>% 
  filter(GFR_CLASS == '重度下降')

heavy_CysC_ACR_plot <- heavy_data %>% 
  select(CysC, ACR, BMI_CLASS, AGE_CLASS)

ggplot(heavy_CysC_ACR_plot, aes(CysC, ACR, colour = BMI_CLASS, shape=AGE_CLASS)) + 
  geom_point()+ 
   ggtitle('重度下降组_CysC与ACR')
```

#### UREA与ACR

```{r, echo=FALSE, warning=FALSE, message=FALSE}

heavy_UREA_ACR_plot <- heavy_data %>% 
  select(UREA, ACR, BMI_CLASS, AGE_CLASS)

ggplot(heavy_UREA_ACR_plot, aes(UREA, ACR, colour = BMI_CLASS, shape=AGE_CLASS)) + 
  geom_point()+ 
   ggtitle('重度下降组_UREA与ACR')
```

#### CRE与ACR

```{r, echo=FALSE, warning=FALSE, message=FALSE}

heavy_CRE_ACR_plot <- heavy_data %>% 
  select(CRE, ACR, BMI_CLASS, AGE_CLASS)

ggplot(heavy_CRE_ACR_plot, aes(CRE, ACR, colour = BMI_CLASS, shape=AGE_CLASS)) + 
  geom_point()+ 
   ggtitle('重度下降组_CRE与ACR')
```


### 肾衰竭

#### CysC与ACR

```{r, echo=FALSE, warning=FALSE, message=FALSE}
end_data <- Data %>% 
  filter(GFR_CLASS == '肾衰竭')

end_CysC_ACR_plot <- end_data %>% 
  select(CysC, ACR, BMI_CLASS, AGE_CLASS)

ggplot(end_CysC_ACR_plot, aes(CysC, ACR, colour = BMI_CLASS, shape=AGE_CLASS)) + 
  geom_point()  + #+ geom_smooth()
   ggtitle('肾衰竭组_CysC与ACR')
```

#### UREA与ACR

```{r, echo=FALSE, warning=FALSE, message=FALSE}

end_UREA_ACR_plot <- end_data %>% 
  select(UREA, ACR, BMI_CLASS, AGE_CLASS)

ggplot(end_UREA_ACR_plot, aes(UREA, ACR, colour = BMI_CLASS, shape=AGE_CLASS)) + 
  geom_point()+ 
   ggtitle('肾衰竭组_UREA与ACR')
```

#### CRE与ACR

```{r, echo=FALSE, warning=FALSE, message=FALSE}

end_CRE_ACR_plot <- end_data %>% 
  select(CRE, ACR, BMI_CLASS, AGE_CLASS)

ggplot(end_CRE_ACR_plot, aes(CRE, ACR, colour = BMI_CLASS, shape=AGE_CLASS)) + 
  geom_point()+ 
   ggtitle('肾衰竭组_CRE与ACR')
```


个人感觉，CysC、UREA、CRE与GFR有明显关系，但与ACR，能确定的是部分数据有同增关系，增加年龄与BMI试图去区分ACR高，其他指标低或者反之的人群却不明显，估计没有找到合适的指标进行区分。