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如何将摘要统计作为每个统计的新变量连接回原始数据帧

summary data-manipulation dplyr join r

1

Choc_waffles · 技术社区 · 8 年前

需要帮助来创建一个函数,以便此函数可以生成合并回原始但以列(带有前缀)命名的摘要统计信息(即,每个变量的cyl\u event\u pct、cyl\u distn\u事件以及数据帧中相应变量的值。该函数将需要数据帧和自变量的输入参数。在下面的示例中,自变量将为mtcars$am

df <- mtcars %>% 
  select(cyl,gear,vs,am) 

df %>%
  gather(variable,value, -ncol(df)) %>%
  group_by(variable, value) %>%
  summarise (n = n(),
             n_events = sum(am),
             event_pct = round(mean(am),4)
  ) %>%
  mutate(distn_events = round(n_events/sum(n_events),4))

希望将event\u pct和distn\u事件作为新变量添加到原始df。

   # A tibble: 8 x 6
# Groups:   variable [3]
  variable value     n n_events event_pct distn_events
     <chr> <dbl> <int>    <dbl>     <dbl>        <dbl>
1      cyl     4    11        8    0.7273       0.6154
2      cyl     6     7        3    0.4286       0.2308
3      cyl     8    14        2    0.1429       0.1538
4     gear     3    15        0    0.0000       0.0000
5     gear     4    12        8    0.6667       0.6154
6     gear     5     5        5    1.0000       0.3846
7       vs     0    18        6    0.3333       0.4615
8       vs     1    14        7    0.5000       0.5385

希望在下表中添加额外的列,如cyl\u event\u pct、cyl\u distn\u events、gear\u event\u pct、gear\u distn\u events,以及数据帧中相应变量的值(不包括am的自变量)

df
                    cyl gear vs am
Mazda RX4             6    4  0  1
Mazda RX4 Wag         6    4  0  1
Datsun 710            4    4  1  1
Hornet 4 Drive        6    3  1  0
Hornet Sportabout     8    3  0  0
Valiant               6    3  1  0
Duster 360            8    3  0  0
Merc 240D             4    4  1  0
Merc 230              4    4  1  0
Merc 280              6    4  1  0
Merc 280C             6    4  1  0

感谢你们在先进的宇宙大师! JT公司

2 回复 | 直到 8 年前

1

Prem 8 年前

你可以使用

cols <- c("cyl", "gear", "vs")
df[paste0(cols,"_event_pct")] <- lapply(cols, function(x) df_stat[df_stat$variable==x,"event_pct"][match(df[[x]], df_stat[df_stat$variable==x,"value"])])
df[paste0(cols,"_distn_events")] <- lapply(cols, function(x) df_stat[df_stat$variable==x,"distn_events"][match(df[[x]], df_stat[df_stat$variable==x,"value"])])
df

输出为:

                  cyl gear vs am cyl_event_pct gear_event_pct vs_event_pct cyl_distn_events gear_distn_events vs_distn_events
Mazda RX4           6    4  0  1        0.4286         0.6667       0.3333           0.2308            0.6154          0.4615
Mazda RX4 Wag       6    4  0  1        0.4286         0.6667       0.3333           0.2308            0.6154          0.4615
Datsun 710          4    4  1  1        0.7273         0.6667       0.5000           0.6154            0.6154          0.5385
Hornet 4 Drive      6    3  1  0        0.4286         0.0000       0.5000           0.2308            0.0000          0.5385
Hornet Sportabout   8    3  0  0        0.1429         0.0000       0.3333           0.1538            0.0000          0.4615
Valiant             6    3  1  0        0.4286         0.0000       0.5000           0.2308            0.0000          0.5385
Duster 360          8    3  0  0        0.1429         0.0000       0.3333           0.1538            0.0000          0.4615
Merc 240D           4    4  1  0        0.7273         0.6667       0.5000           0.6154            0.6154          0.5385
Merc 230            4    4  1  0        0.7273         0.6667       0.5000           0.6154            0.6154          0.5385
Merc 280            6    4  1  0        0.4286         0.6667       0.5000           0.2308            0.6154          0.5385
Merc 280C           6    4  1  0        0.4286         0.6667       0.5000           0.2308            0.6154          0.5385

#sample data
> dput(df)
structure(list(cyl = c(6L, 6L, 4L, 6L, 8L, 6L, 8L, 4L, 4L, 6L, 
6L), gear = c(4L, 4L, 4L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L), vs = c(0L, 
0L, 1L, 1L, 0L, 1L, 0L, 1L, 1L, 1L, 1L), am = c(1L, 1L, 1L, 0L, 
0L, 0L, 0L, 0L, 0L, 0L, 0L)), .Names = c("cyl", "gear", "vs", 
"am"), class = "data.frame", row.names = c("Mazda RX4", "Mazda RX4 Wag", 
"Datsun 710", "Hornet 4 Drive", "Hornet Sportabout", "Valiant", 
"Duster 360", "Merc 240D", "Merc 230", "Merc 280", "Merc 280C"
))
> dput(df_stat)
structure(list(variable = structure(c(1L, 1L, 1L, 2L, 2L, 2L, 
3L, 3L), .Label = c("cyl", "gear", "vs"), class = "factor"), 
    value = c(4L, 6L, 8L, 3L, 4L, 5L, 0L, 1L), n = c(11L, 7L, 
    14L, 15L, 12L, 5L, 18L, 14L), n_events = c(8L, 3L, 2L, 0L, 
    8L, 5L, 6L, 7L), event_pct = c(0.7273, 0.4286, 0.1429, 0, 
    0.6667, 1, 0.3333, 0.5), distn_events = c(0.6154, 0.2308, 
    0.1538, 0, 0.6154, 0.3846, 0.4615, 0.5385)), .Names = c("variable", 
"value", "n", "n_events", "event_pct", "distn_events"), class = "data.frame", row.names = c("1", 
"2", "3", "4", "5", "6", "7", "8"))

2

1

Choc_waffles 8 年前

这是我尝试使用Prem的解决方案编写函数。如果有更优雅的方法,请告诉我。

library(dplyr)
library(tidyr)

cols <- c("cyl", "gear", "vs")
y <- 'am'

gen_stat_df <- function(df, char_col, y){

  my_df <- df %>%  
    select(char_col,y)

  excl_y_cols <- setdiff(char_col,y)

  #create summary statistics
  df_stat <- my_df %>%
    gather(variable,value, excl_y_cols) %>%
    group_by(variable, value) %>%
    summarise (n = n(),
               n_events = sum(!! rlang::sym(y)),
               event_pct = round(mean(!! rlang::sym(y)),4)
    ) %>%
    mutate(distn_events = round(n_events/sum(n_events),4)) %>% data.frame()

  df[paste0(char_col,"_distn_events")] <- lapply(char_col, function(x) df_stat[df_stat$variable==x,"distn_events"][match(df[[x]], df_stat[df_stat$variable==x,"value"])])

  return(df)

}
gen_stat_df(mtcars,cols,y)

和输出

> gen_stat_df(mtcars,cols,y)
                     mpg cyl  disp  hp drat    wt  qsec vs am gear carb cyl_distn_events gear_distn_events
Mazda RX4           21.0   6 160.0 110 3.90 2.620 16.46  0  1    4    4           0.2308            0.6154
Mazda RX4 Wag       21.0   6 160.0 110 3.90 2.875 17.02  0  1    4    4           0.2308            0.6154
Datsun 710          22.8   4 108.0  93 3.85 2.320 18.61  1  1    4    1           0.6154            0.6154
Hornet 4 Drive      21.4   6 258.0 110 3.08 3.215 19.44  1  0    3    1           0.2308            0.0000
Hornet Sportabout   18.7   8 360.0 175 3.15 3.440 17.02  0  0    3    2           0.1538            0.0000
Valiant             18.1   6 225.0 105 2.76 3.460 20.22  1  0    3    1           0.2308            0.0000
Duster 360          14.3   8 360.0 245 3.21 3.570 15.84  0  0    3    4           0.1538            0.0000
Merc 240D           24.4   4 146.7  62 3.69 3.190 20.00  1  0    4    2           0.6154            0.6154
Merc 230            22.8   4 140.8  95 3.92 3.150 22.90  1  0    4    2           0.6154            0.6154
Merc 280            19.2   6 167.6 123 3.92 3.440 18.30  1  0    4    4           0.2308            0.6154
Merc 280C           17.8   6 167.6 123 3.92 3.440 18.90  1  0    4    4           0.2308            0.6154
Merc 450SE          16.4   8 275.8 180 3.07 4.070 17.40  0  0    3    3           0.1538            0.0000
Merc 450SL          17.3   8 275.8 180 3.07 3.730 17.60  0  0    3    3           0.1538            0.0000