试图将我的头围绕索引到一个小标题列表中答案

【问题标题】：trying to wrap my head around indexing into a tibble of lists of tibbles试图将我的头围绕索引到一个小标题列表中
【发布时间】：2019-06-19 14:54:09
【问题描述】：

我想从由一些常规列和一些小标题列表组成的 tbl 中索引值。基本结构是这样的：

# A tibble: 1,200 x 13
   city    country       month data               model    modelgam  glance_lm           rsq glance_gam       aic_gam tidy_lm          augment_lm         res        
   <chr>   <chr>         <dbl> <list>             <list>   <list>    <list>            <dbl> <list>             <dbl> <list>           <list>             <list>     
 1 Abidjan Côte D'Ivoire     1 <tibble [114 × 6]> <S3: lm> <S3: Gam> <tibble [1 × 11]> 0.233 <tibble [1 × 6]>   154.  <tibble [2 × 5]> <tibble [114 × 9]> <dbl [114]>
 2 Abidjan Côte D'Ivoire     2 <tibble [114 × 6]> <S3: lm> <S3: Gam> <tibble [1 × 11]> 0.342 <tibble [1 × 6]>   164.  <tibble [2 × 5]> <tibble [114 × 9]> <dbl [114]>
 3 Abidjan Côte D'Ivoire     3 <tibble [114 × 6]> <S3: lm> <S3: Gam> <tibble [1 × 11]> 0.274 <tibble [1 × 6]>   152.  <tibble [2 × 5]> <tibble [114 × 9]> <dbl [114]>
 4 Abidjan Côte D'Ivoire     4 <tibble [114 × 6]> <S3: lm> <S3: Gam> <tibble [1 × 11]> 0.376 <tibble [1 × 6]>   145.  <tibble [2 × 5]> <tibble [114 × 9]> <dbl [114]>
 5 Abidjan Côte D'Ivoire     5 <tibble [114 × 6]> <S3: lm> <S3: Gam> <tibble [1 × 11]> 0.451 <tibble [1 × 6]>   112.  <tibble [2 × 5]> <tibble [114 × 9]> <dbl [114]>
 6 Abidjan Côte D'Ivoire     6 <tibble [114 × 6]> <S3: lm> <S3: Gam> <tibble [1 × 11]> 0.419 <tibble [1 × 6]>    87.4 <tibble [2 × 5]> <tibble [114 × 9]> <dbl [114]>
 7 Abidjan Côte D'Ivoire     7 <tibble [114 × 6]> <S3: lm> <S3: Gam> <tibble [1 × 11]> 0.249 <tibble [1 × 6]>   129.  <tibble [2 × 5]> <tibble [114 × 9]> <dbl [114]>
 8 Abidjan Côte D'Ivoire     8 <tibble [114 × 6]> <S3: lm> <S3: Gam> <tibble [1 × 11]> 0.228 <tibble [1 × 6]>   153.  <tibble [2 × 5]> <tibble [114 × 9]> <dbl [114]>
 9 Abidjan Côte D'Ivoire     9 <tibble [114 × 6]> <S3: lm> <S3: Gam> <tibble [1 × 11]> 0.230 <tibble [1 × 6]>   124.  <tibble [2 × 5]> <tibble [113 × 9]> <dbl [113]>
10 Abidjan Côte D'Ivoire    10 <tibble [113 × 6]> <S3: lm> <S3: Gam> <tibble [1 × 11]> 0.292 <tibble [1 × 6]>    98.7 <tibble [2 × 5]> <tibble [113 × 9]> <dbl [113]>
# ... with 1,190 more rows

如果我想将 p.value 从glance_lm 中取出第一条记录（例如），我可以使用双括号（因为它是一个列表），如下所示：

> cmodels_details$glance_lm[[1]]$p.value
[1] 5.430896e-08

但是，如果我想要所有 p.values，这将不起作用：

> cmodels_details$glance_lm[[]]$p.value
NULL

在我的例子中，我特别想将这两个模型的估计值从它们的列表中提取出来，并在每条记录的图形上绘制回归线。所以能够像这样抓取截距和斜率（一次一条记录）：

 cmodels_details$tidy_lm[[1]]$estimate
[1] 26.379383982  0.007833912

或者只是拦截：

> cmodels_details$tidy_lm[[1]]$estimate[1]
[1] 26.37938

不是很有帮助。最终，我想在数据结构中添加一些 ggplot 代码，为每条记录绘制一个图，但是为了为两个模型（lm 和 gam）制作回归线，我需要从每条记录的 tibble 中获取估计值（或小标题列表）

这是我正在使用的数据的片段，以及进入正确形状的代码。

> dput(small_tb)
structure(list(dt = structure(c(14245, 14276, 14304, 14335, 14365, 
14396, 14426, 14457, 14488, 14518, 14549, 14579, 14610, 14641, 
14669, 14700, 14730, 14761, 14791, 14822, 14853, 14883, 14914, 
14944, 14975, 15006, 15034, 15065, 15095, 15126, 15156, 15187, 
15218, 15248, 15279, 15309, 15340, 15371, 15400, 15431, 15461, 
15492, 15522, 15553, 15584, 15614, 15645, 15675, 15706, 15737, 
15765, 15796, 15826, 15857, 15887, 15918, 15949, 14245, 14276, 
14304, 14335, 14365, 14396, 14426, 14457, 14488, 14518, 14549, 
14579, 14610, 14641, 14669, 14700, 14730, 14761, 14791, 14822, 
14853, 14883, 14914, 14944, 14975, 15006, 15034, 15065, 15095, 
15126, 15156, 15187, 15218, 15248, 15279, 15309, 15340, 15371, 
15400, 15431, 15461, 15492, 15522, 15553, 15584, 15614, 15645, 
15675, 15706, 15737, 15765, 15796, 15826, 15857, 15887, 15918, 
15949), class = "Date"), averagetemperature = c(-4.299, 1.454, 
4.808, 7.623, 12.627, 17.305, 19.792, 21.724, 19.502, 11.22, 
10.261, 1.563, -0.595, 0.771, 6.489, 10.935, 13.803, 19.055, 
24.106, 24.948, 19.229, 14.582, 8.539, -0.071, -1.582, 0.276, 
3.474, 7.383, 12.133, 18.011, 24.412, 23.414, 18.331, 13.837, 
9.555, 5.327, 2.67, 3.698, 12.145, 8.383, 14.956, 19.532, 25.909, 
22.778, 18.693, 12.229, 7.27, 5.592, 1.056, -0.509, 1.323, 6.644, 
13.734, 17.913, 21.914, 22.23, 19.977, -5.36, -0.372, 3.579, 
10.478, 15.447, 19.058, 21.103, 22.769, 17.043, 10.364, 8.217, 
-0.624, -2.359, -1.456, 6.715, 12.076, 17.119, 21.943, 24.789, 
22.67, 19.172, 11.911, 5.876, -2.165, -4.463, -1.244, 3.474, 
10.555, 16.917, 21.032, 24.564, 22.13, 19.301, 12.001, 8.013, 
2.987, -0.0410000000000004, 2.185, 8.734, 10.324, 17.779, 20.165, 
24.479, 22.731, 18.177, 12.436, 4.103, 2.586, -0.968, -1.365, 
2.518, 9.723, 15.544, 20.892, 24.722, 21.001, 17.408), averagetemperatureuncertainty = c(0.336, 
0.328, 0.247, 0.348, 0.396, 0.554, 0.481, 0.315, 0.225, 0.162, 
0.372, 0.348, 0.348, 0.364, 0.357, 0.538, 0.892, 0.33, 0.325, 
0.36, 0.322, 0.241, 0.307, 0.326, 0.522, 0.446, 0.279, 0.265, 
0.733, 0.773, 0.255, 0.404, 0.173, 0.154, 0.334, 0.483, 0.727, 
0.567, 0.369, 0.347, 0.835, 0.519, 0.516, 0.42, 0.329, 0.333, 
0.263, 0.537, 0.528, 0.473, 0.275, 0.462, 0.863, 0.669, 0.322, 
0.373, 1.033, 0.288, 0.214, 0.14, 0.259, 0.267, 0.452, 0.348, 
0.277, 0.22, 0.153, 0.181, 0.228, 0.314, 0.319, 0.235, 0.135, 
0.2, 0.387, 0.28, 0.257, 0.165, 0.154, 0.174, 0.436, 0.355, 0.33, 
0.167, 0.222, 0.312, 0.42, 0.438, 0.163, 0.16, 0.23, 0.298, 0.466, 
0.493, 0.253, 0.276, 0.258, 0.301, 0.39, 0.403, 0.224, 0.269, 
0.344, 0.298, 0.257, 0.29, 0.241, 0.255, 0.355, 0.281, 0.273, 
0.279, 0.323, 1.048), city = c("Chicago", "Chicago", "Chicago", 
"Chicago", "Chicago", "Chicago", "Chicago", "Chicago", "Chicago", 
"Chicago", "Chicago", "Chicago", "Chicago", "Chicago", "Chicago", 
"Chicago", "Chicago", "Chicago", "Chicago", "Chicago", "Chicago", 
"Chicago", "Chicago", "Chicago", "Chicago", "Chicago", "Chicago", 
"Chicago", "Chicago", "Chicago", "Chicago", "Chicago", "Chicago", 
"Chicago", "Chicago", "Chicago", "Chicago", "Chicago", "Chicago", 
"Chicago", "Chicago", "Chicago", "Chicago", "Chicago", "Chicago", 
"Chicago", "Chicago", "Chicago", "Chicago", "Chicago", "Chicago", 
"Chicago", "Chicago", "Chicago", "Chicago", "Chicago", "Chicago", 
"New York", "New York", "New York", "New York", "New York", "New York", 
"New York", "New York", "New York", "New York", "New York", "New York", 
"New York", "New York", "New York", "New York", "New York", "New York", 
"New York", "New York", "New York", "New York", "New York", "New York", 
"New York", "New York", "New York", "New York", "New York", "New York", 
"New York", "New York", "New York", "New York", "New York", "New York", 
"New York", "New York", "New York", "New York", "New York", "New York", 
"New York", "New York", "New York", "New York", "New York", "New York", 
"New York", "New York", "New York", "New York", "New York", "New York", 
"New York", "New York", "New York"), country = c("United States", 
"United States", "United States", "United States", "United States", 
"United States", "United States", "United States", "United States", 
"United States", "United States", "United States", "United States", 
"United States", "United States", "United States", "United States", 
"United States", "United States", "United States", "United States", 
"United States", "United States", "United States", "United States", 
"United States", "United States", "United States", "United States", 
"United States", "United States", "United States", "United States", 
"United States", "United States", "United States", "United States", 
"United States", "United States", "United States", "United States", 
"United States", "United States", "United States", "United States", 
"United States", "United States", "United States", "United States", 
"United States", "United States", "United States", "United States", 
"United States", "United States", "United States", "United States", 
"United States", "United States", "United States", "United States", 
"United States", "United States", "United States", "United States", 
"United States", "United States", "United States", "United States", 
"United States", "United States", "United States", "United States", 
"United States", "United States", "United States", "United States", 
"United States", "United States", "United States", "United States", 
"United States", "United States", "United States", "United States", 
"United States", "United States", "United States", "United States", 
"United States", "United States", "United States", "United States", 
"United States", "United States", "United States", "United States", 
"United States", "United States", "United States", "United States", 
"United States", "United States", "United States", "United States", 
"United States", "United States", "United States", "United States", 
"United States", "United States", "United States", "United States", 
"United States"), latitude = c("42.59N", "42.59N", "42.59N", 
"42.59N", "42.59N", "42.59N", "42.59N", "42.59N", "42.59N", "42.59N", 
"42.59N", "42.59N", "42.59N", "42.59N", "42.59N", "42.59N", "42.59N", 
"42.59N", "42.59N", "42.59N", "42.59N", "42.59N", "42.59N", "42.59N", 
"42.59N", "42.59N", "42.59N", "42.59N", "42.59N", "42.59N", "42.59N", 
"42.59N", "42.59N", "42.59N", "42.59N", "42.59N", "42.59N", "42.59N", 
"42.59N", "42.59N", "42.59N", "42.59N", "42.59N", "42.59N", "42.59N", 
"42.59N", "42.59N", "42.59N", "42.59N", "42.59N", "42.59N", "42.59N", 
"42.59N", "42.59N", "42.59N", "42.59N", "42.59N", "40.99N", "40.99N", 
"40.99N", "40.99N", "40.99N", "40.99N", "40.99N", "40.99N", "40.99N", 
"40.99N", "40.99N", "40.99N", "40.99N", "40.99N", "40.99N", "40.99N", 
"40.99N", "40.99N", "40.99N", "40.99N", "40.99N", "40.99N", "40.99N", 
"40.99N", "40.99N", "40.99N", "40.99N", "40.99N", "40.99N", "40.99N", 
"40.99N", "40.99N", "40.99N", "40.99N", "40.99N", "40.99N", "40.99N", 
"40.99N", "40.99N", "40.99N", "40.99N", "40.99N", "40.99N", "40.99N", 
"40.99N", "40.99N", "40.99N", "40.99N", "40.99N", "40.99N", "40.99N", 
"40.99N", "40.99N", "40.99N", "40.99N", "40.99N", "40.99N"), 
    longitude = c("87.27W", "87.27W", "87.27W", "87.27W", "87.27W", 
    "87.27W", "87.27W", "87.27W", "87.27W", "87.27W", "87.27W", 
    "87.27W", "87.27W", "87.27W", "87.27W", "87.27W", "87.27W", 
    "87.27W", "87.27W", "87.27W", "87.27W", "87.27W", "87.27W", 
    "87.27W", "87.27W", "87.27W", "87.27W", "87.27W", "87.27W", 
    "87.27W", "87.27W", "87.27W", "87.27W", "87.27W", "87.27W", 
    "87.27W", "87.27W", "87.27W", "87.27W", "87.27W", "87.27W", 
    "87.27W", "87.27W", "87.27W", "87.27W", "87.27W", "87.27W", 
    "87.27W", "87.27W", "87.27W", "87.27W", "87.27W", "87.27W", 
    "87.27W", "87.27W", "87.27W", "87.27W", "74.56W", "74.56W", 
    "74.56W", "74.56W", "74.56W", "74.56W", "74.56W", "74.56W", 
    "74.56W", "74.56W", "74.56W", "74.56W", "74.56W", "74.56W", 
    "74.56W", "74.56W", "74.56W", "74.56W", "74.56W", "74.56W", 
    "74.56W", "74.56W", "74.56W", "74.56W", "74.56W", "74.56W", 
    "74.56W", "74.56W", "74.56W", "74.56W", "74.56W", "74.56W", 
    "74.56W", "74.56W", "74.56W", "74.56W", "74.56W", "74.56W", 
    "74.56W", "74.56W", "74.56W", "74.56W", "74.56W", "74.56W", 
    "74.56W", "74.56W", "74.56W", "74.56W", "74.56W", "74.56W", 
    "74.56W", "74.56W", "74.56W", "74.56W", "74.56W", "74.56W", 
    "74.56W")), row.names = c(NA, -114L), class = c("tbl_df", 
"tbl", "data.frame"), spec = structure(list(cols = list(dt = structure(list(
    format = ""), class = c("collector_date", "collector")), 
    AverageTemperature = structure(list(), class = c("collector_double", 
    "collector")), AverageTemperatureUncertainty = structure(list(), class = c("collector_double", 
    "collector")), City = structure(list(), class = c("collector_character", 
    "collector")), Country = structure(list(), class = c("collector_character", 
    "collector")), Latitude = structure(list(), class = c("collector_character", 
    "collector")), Longitude = structure(list(), class = c("collector_character", 
    "collector"))), default = structure(list(), class = c("collector_guess", 
"collector"))), class = "col_spec"))

还有一些代码：

# load libraries
library(tidyverse)
library(lubridate)
library(gam)
# library(broom)
# library(purrr)

# Load the data from dput() and take a look
summary(gltd)
str(gltd)
names(gltd)

# make lowercasae
gltd <- rename_all(gltd, tolower)
names(gltd)

# nest data, 100 major cities
by_city_month <- gltd %>% 
  filter(year(dt) >= 1900) %>%
  mutate(month = month(dt)) %>%
  mutate(yr1900 = year(dt) - 1900) %>%
  group_by(city, country, month) %>%
  nest()

by_city_month

# define function for linear model
city_model_lm <- function(df) {
  lm(averagetemperature ~ yr1900, data = df)
}

# define function for GAM
city_model_gam <- function(df) {
  gam(averagetemperature ~ s(yr1900), data = df)
}

# create columns for the models
cmodels <- by_city_month %>%
  mutate(model = map(data, city_model_lm), 
         modelgam = map(data, city_model_gam)
  )

cmodels_details <- cmodels %>%
  mutate(
    glance_lm = model %>% map(glance),  #model summary: rsquared...
    rsq = glance_lm %>% map_dbl("r.squared"),  #extract rsquared

    glance_gam = modelgam %>% map(broom::glance), #GAM model summary
    aic_gam = glance_gam %>% map_dbl("AIC"), #extract AIC

    tidy_lm = model %>% map(tidy), #model estimate: coeff...

    augment_lm = model %>% map(augment), #observation stats: resid,hat...
    res = augment_lm %>% map(".resid") #extract resid
  )

任何建议将不胜感激。

【问题讨论】：

sapply(cmodels_details$glance_lm, '[[', '.pvalue')?
适用于上一个术语更正为 p.value。'[[' 做什么？
它提取列表成员之后的名称，在本例中为'p.value'。（抱歉打错字了，顺便说一句。）

标签： r list ggplot2 nested tidyr

【解决方案1】：

这是一个tidyverse 解决方案，应该可以满足您的需求：

library(tidyverse)

cmodels_details %>%
  transmute(
    city = city,
    month = month,
    p_val = map_dbl(glance_lm, "p.value"),
    coefs = map(tidy_lm, "estimate")
  ) %>% 
  unnest(coefs) %>% 
  mutate(x = rep(c("intercept", "x1"), nrow(.) / 2))) %>% 
  spread(x, coefs)
# A tibble: 24 x 5
#   city    month  p_val intercept      x1
#   <chr>   <dbl>  <dbl>     <dbl>   <dbl>
# 1 Chicago     1 0.0790  -156.     1.40  
# 2 Chicago     2 0.875     12.2   -0.0999
# 3 Chicago     3 0.935     20.2   -0.131 
# 4 Chicago     4 0.468     58.3   -0.451 
# 5 Chicago     5 0.411    -23.9    0.337 
# 6 Chicago     6 0.630     -0.429  0.169 
# 7 Chicago     7 0.505    -43.9    0.605 
# 8 Chicago     8 0.814     35.9   -0.116 
# 9 Chicago     9 0.872     14.6    0.0414
#10 Chicago    10 0.807    -12.2    0.228 
# ... with 14 more rows

【讨论】：

【解决方案2】：

针对以下内容：“最终我想在数据结构中添加一些 ggplot 代码，为每条记录绘制一个图形，但是为了为两个模型（lm 和 gam）制作回归线，我需要从每条记录的 tibble（或 tibble 列表）中获取估计值”，试试geom_smooth()。您不需要拟合所有模型来绘制 LM 和 GAM 模型！希望这对你有帮助！

by_city_month %>% unnest() %>% # your data from above
  ggplot(aes(x = yr1900, y = averagetemperature, group = interaction(city,month))) + 
  geom_smooth(method = "lm", se = F, color = "black", alpha = .7) + 
  geom_smooth(method = "gam", se = F, color = "pink", alpha = .7, size = .5, linetype = 2)

【讨论】：

更晦涩的方法怎么样？我可以让这些工作正常，但不是通过方法=生存::survreg，或者通过指定一个公式（这看起来很有希望。类似的东西；方法='lm'，公式=平均温度〜yr1900 +月。
在这种情况下，试试@DiceboyT 的建议。 map_dbl() 函数绝对是要走的路。