有什么方法可以让我的代码函数工作在多个变量上，而不是我现在的一个变量？

Question

我有一个数据集，它看起来如下（有大约200个人）。

NAME AGE2012 SEX   SurveyDate12 WAZ12 BAZ12 HAZ12 HB12 SurveyDate14 WAZ14 BAZ14 HAZ14 HB14
1      22    Male   2012-11-26  -1.2 -0.54 -0.01 11.9  2014-11-26  -1.5  -0.52 -0.43 12.2
2      26    Female 2012-11-26  -1.5 -0.36 -0.04 11.2  2014-11-26  -1.7  -0.84 -0.32 11.4

我目前正在用他们做一个斜图 所以我必须对这个数据集做一些事情 比如使用pivot长。我正在练习使用因子，所以试图将我所做的一切都保持在一个函数内。我的代码可以只用HB来做图（见下文）。但我想做的是让这段代码适用于我在数据集中的所有4个变量，只是使用函数。谁能帮我解决这个问题？

slopegraph_prep <- function(health_longer, HB){health %>%
  select(NAME:SEX, starts_with("HB")) %>%
  pivot_longer(cols = starts_with("HB"),
               names_to = "Year",
               names_prefix = "HB",
               values_to = "HB") %>%
  mutate(
    HB = case_when(
      HB < 0        ~ "NA",
      TRUE ~ as.character(HB) 
    )
  )  %>%
  na_if("NA") %>% 
  mutate(HB = as.numeric(HB)) %>%

  mutate(
    Year = case_when(
      Year=="12" ~ "2012",
      Year=="14" ~ "2014",
      Year=="19" ~ "2019")
  )
}



slopegraph_by_sex <- function(health, HB, SEX){ Subsetdata <- subset(health, SEX == SEX)
newggslopegraph(Subsetdata , Year , HB, NAME,
                Title = "Haemoglobin", 
                SubTitle = SEX, 
                Caption = NULL,
                RemoveMissing = FALSE)
 }

df_healthmeas <- slopegraph_prep(health, "HB")


df_healthmeas_female <- slopegraph_by_sex(df_healthmeas, "Haemoglobin", "female")

df_healthmeas_male <- slopegraph_by_sex(df_healthmeas, "Haemoglobin", "male")

我真正想做的是能够运行这个例子，让它运行，但我觉得我需要让我的变量更通用？

   df_healthmeas <- slopegraph_prep(health, "WAZ")
    df_healthmeas_female <- slopegraph_by_sex(df_healthmeas, "Weight to Age WAZ", "female") 
df_healthmeas_male <- slopegraph_by_sex(df_healthmeas, "Weight to Age WAZ", "male")

任何帮助都将是巨大的感激。

Answer 1

嘿，你的代码已经可以做到了，你只需要指定函数参数。

df_healthmeas <- slopegraph_prep(health_longer = health, HB =WAZ)

你在函数代码中指定的变量只是一个 "虚 "字 所以你可以把它重新写成这样，以提高可读性。

slopegraph_prep <- function(data, var){data %>%
  select(NAME:SEX, starts_with("var")) %>%
  pivot_longer(cols = starts_with("var"),
               names_to = "Year",
               names_prefix = "var",
               values_to = "var") %>%
  mutate(
    var = case_when(
      var < 0        ~ "NA",
      TRUE ~ as.character(var) 
    )
  )  %>%
  na_if("NA") %>% 
  mutate(var = as.numeric(var)) %>%

  mutate(
    Year = case_when(
      Year=="12" ~ "2012",
      Year=="14" ~ "2014",
      Year=="19" ~ "2019")
  )
}