糖尿病數據的時間序列分析

Question

我有一個看起來像這樣的數據集

data=
    
**ID  HbA1cRes Year**
    1    65      2003
    2    125     2008
    3    40      2010
    4    110     2007
    5    125     2006
    6    136     2011
    7    20      2012 
    8    58      2009
    9    12      2006 
    10   123     2008

HbA1cRes > 65 的患者被歸類為“高風險”，以下被歸類為“低風險”。 我正在嘗試使用以下代碼進行時間序列分析（以查看高風險和低風險案例隨時間的上升和下降）和 Year <- data$REport_YrMonth

library(tidyverse)
    data$risk <- factor( ifelse( data$HbA1cRes  > 65 ,"High risk patients", "Low risk patients") )
    ggplot(data, aes(x=Year)) + 
      geom_line(aes(y=risk)) + 
      labs(title="Analysis of diabetes' patients status over time", 
           y="Returns %")

但是，返回的 output 如下：

猜猜我在這里做錯了什么？

Answer 1

計算每年有多少“高風險患者”和“低風險患者”，然后計算Year數據。

library(ggplot2)
library(dplyr)

data %>%
  mutate(risk = factor(ifelse(HbA1cRes  > 65 ,
                       "High risk patients", "Low risk patients"))) %>%
  count(Year, risk) %>%
  ggplot(aes(x=Year, y = n, color = risk)) + 
  geom_line() + 
  labs(title="Analysis of diabetes' patients status over time")

Answer 2

case_when function 可能是數據分類的優雅解決方案。

geom_col 或 geom_density 可能會提供更好的選擇，而不是 geom_line。

df <- tibble(
  id = 1:10,
  hb = c(65,125,40,110,125,136,20,58,12,123),
  year = c(2003,2008,2010,2007,2006,2011,2012,2009,2006,2008)
)

df <- df %>% 
  mutate(
    risk = case_when(
      hb > 65 ~"high risk",
      TRUE ~"low risk"
    )
  ) %>% 
  count(
    year,
    risk
  )

df %>% 
  ggplot(aes(x=year, y = n, group = risk, fill = risk)) + 
  geom_col(position = "dodge") +
  labs(
    title="Analysis of diabetes' patients status over time", 
    y="Returns %",
    fill = "Risk Status")
  

df %>% 
  ggplot(aes(x=year, fill = risk)) + 
  geom_density(position = "fill") + 
  labs(
    title="Analysis of diabetes' patients status over time", 
    y="Returns %",
    fill = "Risk Status")