Question

我有一个数据框，我已经进行了子集化。

以下是glimpse(oc_LV)

    $ city       <chr> "Las Vegas", "Las Vegas", "Las Vegas", "Las Vegas", "Las Vegas", "Las Ve...
    $ is_open    <chr> "1", "0", "1", "1", "0", "0"
    $ categories <chr> "Chinese", "Chinese", "Thai", "Japanese", "Japanese", "Thai"
    $ n          <int> 196, 86, 54, 51, 38, 34´

这是dput(oc_LV)

structure(list(city = c("Las Vegas", "Las Vegas", "Las Vegas", 
    "Las Vegas", "Las Vegas", "Las Vegas"), is_open = c("1", "0", 
    "1", "1", "0", "0"), categories = c("Chinese", "Chinese", "Thai", 
    "Japanese", "Japanese", "Thai"), n = c(196L, 86L, 54L, 51L, 38L, 
    34L)), row.names = c(NA, -6L), class = c("grouped_df", "tbl_df", 
    "tbl", "data.frame"), vars = c("city", "is_open"), drop = TRUE, .Names = c("city", 
    "is_open", "categories", "n"), indices = list(c(1L, 4L, 5L), 
        c(0L, 2L, 3L)), group_sizes = c(3L, 3L), biggest_group_size = 3L, labels = structure(list(
        city = c("Las Vegas", "Las Vegas"), is_open = c("0", "1")), row.names = c(NA, 
    -2L), class = "data.frame", vars = c("city", "is_open"), drop = TRUE, .Names = c("city", 
    "is_open")))

我想为变量table()的频率，平均值和加权平均值is_open

我是这样做的

    cuisine <- c("Chinese", "Thai", "Japanese")
open_fr <- c(196, 54, 51)
closed_fr <- c(86, 34, 38)
open_avg <- c(196/(196+86), 54/(54+36), 51/(51+38))
closed_avg <- c(86/(196+86), 90/(282+90+89), 38/(51+38))
open_wavg <- c(282/(282+90+89) * 196/(196+86), 90/(282+90+89) * 54/(54+36), 89/(282+90+89) * 51/(51+38)) 
closed_wavg <-c(282/(282+90+89) * 86/(196+86), 90/(282+90+89) * 36/(54+36), 89/(282+90+89) * 38/(51+38))


open_closed_LV <- data.frame(cuisine, open_fr, closed_fr, open_avg, closed_avg, open_wavg, closed_wavg) 
open_closed_LV$open_avg <-round(open_closed_LV$open_avg, digits = 2)
open_closed_LV$closed_avg <-round(open_closed_LV$closed_avg, digits = 2)
open_closed_LV$open_wavg <-round(open_closed_LV$open_wavg, digits = 2)
open_closed_LV$closed_wavg <-round(open_closed_LV$closed_wavg, digits = 2)

结果很好，但它很复杂。

Variables: 7
$ cuisine     <fctr> Chinese, Thai, Japanese
$ open_fr     <dbl> 196, 54, 51
$ closed_fr   <dbl> 86, 34, 38
$ open_avg    <dbl> 0.70, 0.60, 0.57
$ closed_avg  <dbl> 0.30, 0.20, 0.43
$ open_wavg   <dbl> 0.43, 0.12, 0.11
$ closed_wavg <dbl> 0.19, 0.08, 0.08

当我尝试通过这样的函数计算数字时：

oc_LV %>% select(n) %>% mean()

我收到这样的错误：

Warning message: In mean.default(.) : argument is not numeric or logical: returning NA

是否有更聪明的方法来实现结果，就像我使用上述方法一样？在这种情况下，由于少数情况，这不是问题。但是如果案例数量增加，这种方法就不起作用了。

Answer 1

你的意思是这样吗？

library(dplyr)
library(tidyr)

df <- oc_LV %>%
  spread(is_open, n) %>%
  `colnames<-` (c('city', 'cuisine', 'closed_fr','open_fr')) %>%
  mutate(open_avg = open_fr/ rowSums(.[,-c(1,2)]),
         closed_avg = closed_fr/ rowSums(.[,-c(1,2)]),
         open_wavg = rowSums(.[,-c(1,2)])/sum(.[,-c(1,2)]) * open_avg,
         closed_wavg = rowSums(.[,-c(1,2)])/sum(.[,-c(1,2)]) * closed_avg)
df

输出是：

# A tibble: 3 x 8
# Groups:   city [1]
       city  cuisine closed_fr open_fr open_avg closed_avg open_wavg closed_wavg
      <chr>    <chr>     <int>   <int>    <dbl>      <dbl>     <dbl>       <dbl>
1 Las Vegas  Chinese        86     196     0.70       0.30      0.43       0.187
2 Las Vegas Japanese        38      51     0.57       0.43      0.11       0.083
3 Las Vegas     Thai        34      54     0.61       0.39      0.12       0.074

#sample data
> dput(oc_LV)
structure(list(city = c("Las Vegas", "Las Vegas", "Las Vegas", 
"Las Vegas", "Las Vegas", "Las Vegas"), is_open = c("1", "0", 
"1", "1", "0", "0"), categories = c("Chinese", "Chinese", "Thai", 
"Japanese", "Japanese", "Thai"), n = c(196L, 86L, 54L, 51L, 38L, 
34L)), .Names = c("city", "is_open", "categories", "n"), row.names = c(NA, 
-6L), class = c("grouped_df", "tbl_df", "tbl", "data.frame"), vars = c("city", 
"is_open"), drop = TRUE, indices = list(c(1L, 4L, 5L), c(0L, 
2L, 3L)), group_sizes = c(3L, 3L), biggest_group_size = 3L, labels = structure(list(
    city = c("Las Vegas", "Las Vegas"), is_open = c("0", "1")), .Names = c("city", 
"is_open"), row.names = c(NA, -2L), class = "data.frame", vars = c("city", 
"is_open"), drop = TRUE))

Answer 2

我认为你想要的东西可以通过dplyr和tidyr的组合来实现。

像

这样的东西

with_cols <- df %>%
  group_by(city, categories, is_open) %>%
  summarise(n=sum(n)) %>%
  spread(is_open, n)

> with_cols
# A tibble: 3 x 4
# Groups:   city, categories [3]
       city categories   `0`   `1`
*     <chr>      <chr> <int> <int>
1 Las Vegas    Chinese    86   196
2 Las Vegas   Japanese    38    51
3 Las Vegas       Thai    34    54

我们可以重命名列以使其更漂亮

with_cols <- with_cols %>% rename(open=`1`, closed=`0`)

从这里可以使用简单的命令完成数据操作：

with_cols %>% mutate(open_avg = open / (open + closed))

从子集化数据框计算frequ，avg，wavg的更智能方法？

2 个答案: