r - Dplyr函数可计算平均值，n，sd和标准误差

Question

我发现自己一直在编写这段代码，以产生群均值的标准错误(然后用于绘制置信区间)。

不过，最好编写自己的函数来在一行代码中执行此操作。我已经阅读了dplyr中关于非标准评估的nse小插图和this blog post。我有所了解，但我实在是个菜鸟，无法靠自己解决这个问题。有人可以帮忙吗？谢谢。

var1<-sample(c('red', 'green'), size=10, replace=T)
var2<-rnorm(10, mean=5, sd=1)
df<-data.frame(var1, var2)
df %>% 
group_by(var1) %>% 
summarize(avg=mean(var2), n=n(), sd=sd(var2), se=sd/sqrt(n))

Answer 1

您可以使用enquo函数在函数调用中显式命名变量:

my_fun <- function(x, cat_var, num_var){
  cat_var <- enquo(cat_var)
  num_var <- enquo(num_var)

  x %>%
    group_by(!!cat_var) %>%
    summarize(avg = mean(!!num_var), n = n(), 
              sd = sd(!!num_var), se = sd/sqrt(n))
}

这给你:

> my_fun(df, var1, var2)
# A tibble: 2 x 5
    var1      avg     n        sd        se
  <fctr>    <dbl> <int>     <dbl>     <dbl>
1  green 4.873617     7 0.7515280 0.2840509
2    red 5.337151     3 0.1383129 0.0798550

并且与您的示例的输出匹配:

> df %>% 
+   group_by(var1) %>% 
+   summarize(avg=mean(var2), n=n(), sd=sd(var2), se=sd/sqrt(n))
# A tibble: 2 x 5
    var1      avg     n        sd        se
  <fctr>    <dbl> <int>     <dbl>     <dbl>
1  green 4.873617     7 0.7515280 0.2840509
2    red 5.337151     3 0.1383129 0.0798550

编辑:

OP要求从函数中删除 group_by语句，以使group_by具有多个变量。进行此IMO有两种方法。首先，您可以简单地删除 group_by语句，然后将分组的数据帧通过管道传递到函数中。该方法如下所示:

my_fun <- function(x, num_var){
  num_var <- enquo(num_var)

  x %>%
    summarize(avg = mean(!!num_var), n = n(), 
              sd = sd(!!num_var), se = sd/sqrt(n))
}

df %>%
  group_by(var1) %>%
  my_fun(var2)

解决此问题的另一种方法是使用 ...和 quos允许该函数捕获 group_by语句的多个参数。看起来像这样:

#first, build the new dataframe
var1<-sample(c('red', 'green'), size=10, replace=T)
var2<-rnorm(10, mean=5, sd=1)
var3 <- sample(c("A", "B"), size = 10, replace = TRUE)
df<-data.frame(var1, var2, var3)

# using the first version `my_fun`, it would look like this
df %>%
  group_by(var1, var3) %>%
  my_fun(var2)

# A tibble: 4 x 6
# Groups:   var1 [?]
    var1   var3      avg     n        sd        se
  <fctr> <fctr>    <dbl> <int>     <dbl>     <dbl>
1  green      A 5.248095     1       NaN       NaN
2  green      B 5.589881     2 0.7252621 0.5128378
3    red      A 5.364265     2 0.5748759 0.4064986
4    red      B 4.908226     5 1.1437186 0.5114865

# Now doing it with a new function `my_fun2`
my_fun2 <- function(x, num_var, ...){
  group_var <- quos(...)
  num_var <- enquo(num_var)

  x %>%
    group_by(!!!group_var) %>%
    summarize(avg = mean(!!num_var), n = n(), 
              sd = sd(!!num_var), se = sd/sqrt(n))
}

df %>%
  my_fun2(var2, var1, var3)

# A tibble: 4 x 6
# Groups:   var1 [?]
    var1   var3      avg     n        sd        se
  <fctr> <fctr>    <dbl> <int>     <dbl>     <dbl>
1  green      A 5.248095     1       NaN       NaN
2  green      B 5.589881     2 0.7252621 0.5128378
3    red      A 5.364265     2 0.5748759 0.4064986
4    red      B 4.908226     5 1.1437186 0.5114865