实际上，我已经尝试了数百种这种代码的排列，以期获得能够满足我想要的功能的函数，最后我放弃了。感觉绝对应该可行，而且我非常亲密！
我尝试通过下面的reprex回到这里的小问题。
基本上，我有一个单行数据帧，其中的 一列包含 一个字符串列表（“概念”）。我想为每个字符串创建一个附加列mutate，最好使用，使该列从字符串中获取名称，然后用函数调用的结果填充该列（对于哪个函数，这并不重要现在吗？-我只需要功能的基础结构即可。）
像往常一样，我觉得我必须丢失一些明显的东西……也许只是语法错误。我也想知道是否需要使用purrr::map，也许更简单的矢量化映射会正常工作。
我觉得新列是命名的，..1而不是概念名称，这是关于什么地方出错的线索。
我可以通过手动调用每个概念来创建所需的数据框架（请参阅reprex的结尾），但是由于不同数据框架的概念列表不同，因此我想使用管道和tidyverse技术对其进行功能化，而不是手动进行。
我已阅读以下问题以寻求帮助：
但是这些都没有帮助我解决我遇到的问题。[编辑：在最后一个q中添加了该列表，这可能是我需要的技术]。
<!-- language-all: lang-r -->
# load packages -----------------------------------------------------------
library(rlang)
library(dplyr)
library(tidyr)
library(magrittr)
library(purrr)
library(nomisr)
# set up initial list of tibbles ------------------------------------------
df <- list(
district_population = tibble(
dataset_title = "Population estimates - local authority based by single year",
dataset_id = "NM_2002_1"
),
jsa_claimants = tibble(
dataset_title = "Jobseeker\'s Allowance with rates and proportions",
dataset_id = "NM_1_1"
)
)
# just use the first tibble for now, for testing --------------------------
# ideally I want to map across dfs through a list -------------------------
df <- df[[1]]
# nitty gritty functions --------------------------------------------------
get_concept_list <- function(df) {
dataset_id <- pluck(df, "dataset_id")
nomis_overview(id = dataset_id,
select = c("dimensions", "codes")) %>%
pluck("value", 1, "dimension") %>%
filter(!concept == "geography") %>%
pull("concept")
}
# get_concept_list() returns the strings I need:
get_concept_list(df)
#> [1] "time" "gender" "c_age" "measures"
# Here is a list of examples of types of map* that do various things,
# none of which is what I need it to do
# I'm using toupper() here for simplicity - ultimately I will use
# get_concept_info() to populate the new columns
# this creates four new tibbles
get_concept_list(df) %>%
map(~ mutate(df, {{.x}} := toupper(.x)))
#> [[1]]
#> # A tibble: 1 x 3
#> dataset_title dataset_id ..1
#> <chr> <chr> <chr>
#> 1 Population estimates - local authority based by single year NM_2002_1 TIME
#>
#> [[2]]
#> # A tibble: 1 x 3
#> dataset_title dataset_id ..1
#> <chr> <chr> <chr>
#> 1 Population estimates - local authority based by single year NM_2002_1 GENDER
#>
#> [[3]]
#> # A tibble: 1 x 3
#> dataset_title dataset_id ..1
#> <chr> <chr> <chr>
#> 1 Population estimates - local authority based by single year NM_2002_1 C_AGE
#>
#> [[4]]
#> # A tibble: 1 x 3
#> dataset_title dataset_id ..1
#> <chr> <chr> <chr>
#> 1 Population estimates - local authority based by single year NM_2002_1 MEASUR~
# this throws an error
get_concept_list(df) %>%
map_chr(~ mutate(df, {{.x}} := toupper(.x)))
#> Error: Result 1 must be a single string, not a vector of class `tbl_df/tbl/data.frame` and of length 3
# this creates three extra rows in the tibble
get_concept_list(df) %>%
map_df(~ mutate(df, {{.x}} := toupper(.x)))
#> # A tibble: 4 x 3
#> dataset_title dataset_id ..1
#> <chr> <chr> <chr>
#> 1 Population estimates - local authority based by single year NM_2002_1 TIME
#> 2 Population estimates - local authority based by single year NM_2002_1 GENDER
#> 3 Population estimates - local authority based by single year NM_2002_1 C_AGE
#> 4 Population estimates - local authority based by single year NM_2002_1 MEASUR~
# this does the same as map_df
get_concept_list(df) %>%
map_dfr(~ mutate(df, {{.x}} := toupper(.x)))
#> # A tibble: 4 x 3
#> dataset_title dataset_id ..1
#> <chr> <chr> <chr>
#> 1 Population estimates - local authority based by single year NM_2002_1 TIME
#> 2 Population estimates - local authority based by single year NM_2002_1 GENDER
#> 3 Population estimates - local authority based by single year NM_2002_1 C_AGE
#> 4 Population estimates - local authority based by single year NM_2002_1 MEASUR~
# this creates a single tibble 12 columns wide
get_concept_list(df) %>%
map_dfc(~ mutate(df, {{.x}} := toupper(.x)))
#> # A tibble: 1 x 12
#> dataset_title dataset_id ..1 dataset_title1 dataset_id1 ..11 dataset_title2
#> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
#> 1 Population e~ NM_2002_1 TIME Population es~ NM_2002_1 GEND~ Population es~
#> # ... with 5 more variables: dataset_id2 <chr>, ..12 <chr>,
#> # dataset_title3 <chr>, dataset_id3 <chr>, ..13 <chr>
# function to get info on each concept (except geography) -----------------
# this is the function I want to use eventually to populate my new columns
get_concept_info <- function(df, concept_name) {
dataset_id <- pluck(df, "dataset_id")
nomis_overview(id = dataset_id) %>%
filter(name == "dimensions") %>%
pluck("value", 1, "dimension") %>%
filter(concept == concept_name) %>%
pluck("codes.code", 1) %>%
select(name, value) %>%
nest(data = everything()) %>%
as.list() %>%
pluck("data")
}
# individual mutate works, for comparison ---------------------------------
# I can create the kind of table I want manually using a line like the one below
# df %>% map(~ mutate(., measures = get_concept_info(., concept_name = "measures")))
df %>% mutate(., measures = get_concept_info(df, "measures"))
#> # A tibble: 1 x 3
#> dataset_title dataset_id measures
#> <chr> <chr> <list>
#> 1 Population estimates - local authority based by sin~ NM_2002_1 <tibble [2 x ~
<sup>Created on 2020-02-10 by the [reprex package](https://reprex.tidyverse.org) (v0.3.0)</sup>
使用!!和:=使您可以动态命名列。然后，我们可以减少map()with的列表输出reduce()，即使用数据集标题和id列left_joins（）列表中的所有数据框。
df_2 <-
map(get_concept_list(df),
~ mutate(df,
!!.x := get_concept_info(df, .x))) %>%
reduce(left_join, by = c("dataset_title", "dataset_id"))
df_2
# A tibble: 1 x 6
dataset_title dataset_id time gender c_age measures
<chr> <chr> <list<df[,2]>> <list<df[,2]>> <list<df[,2]>> <list<df[,2]>>
1 Population estimates - local authority based by single year NM_2002_1 [28 x 2] [3 x 2] [121 x 2] [2 x 2]
本文收集自互联网，转载请注明来源。
如有侵权，请联系[email protected] 删除。
我来说两句