Question

我有以下列表：

library(rjson)
j <- fromJSON(file='https://esgf-data.dkrz.de/esg-search/search/?offset=0&limit=1000&type=Dataset&replica=false&latest=true&project=CORDEX&domain=EUR-11&experiment=rcp85&time_frequency=day&facets=rcm_name%2Cproject%2Cproduct%2Cdomain%2Cinstitute%2Cdriving_model%2Cexperiment%2Cexperiment_family%2Censemble%2Crcm_version%2Ctime_frequency%2Cvariable%2Cvariable_long_name%2Ccf_standard_name%2Cdata_node&format=application%2Fsolr%2Bjson')

我有兴趣从这个组件中提取数据：j$response$docs，这是一个列表列表。 “内部”列表都应该具有相同的名称。

我想将输出保存为data.frame()或tibble()。

以下内容适用于少数选定的变量，并提供所需的输出：

nmod <- length(j$response$docs)
for (i in 1:nmod) {
    #select one list at a time
    j1 <- j$response$docs[[i]]
    tmp <- data.frame(variable=j1$variable,
                        variable_long_name=j1$variable_long_name,
                        rcm_name=j1$rcm_name,
                        driving_model=j1$driving_model,
                        cf_standard_name=j1$cf_standard_name
                        )
    #join them
    if (i==1) {
        d <- tmp
    } else {
        d <- rbind(d, tmp)
    }
}

但是，我想知道是否有更优雅和有效的方式，可能使用tidyr，dplyr或purrr，这也可以让我选择所有¨列，而不是那里选择的少数。

Answer 1

您可以在包 purrr 的帮助下完成此操作。我认为at_depth可能在这里工作，但我最终使用嵌套的map_df。

library(purrr)

您的变量长度不同，因此首先要确保每个变量的长度为1.这可以通过将内部列表的每个元素与paste折叠来完成。我用逗号分隔符。通过map_df执行此操作会返回1行tibble。

以下是第一个内部列表的示例。

map_df(j$response$docs[[1]], paste, collapse = ",")

现在我们可以遍历外部列表，为每个列表创建一行tibble。我们使用map_df将这些中的每一个绑定在一起。输出是832行tibble，每列一行。我使用.id参数将一个分组变量添加到结果中，这可能不需要。

d1 = map_df(j$response$docs, ~map_df(.x, paste, collapse = ","))
d1

# A tibble: 832 × 45
   group                                                                                                   id  version
   <chr>                                                                                                <chr>    <chr>
1      1   cordex.output.EUR-11.DMI.ICHEC-EC-EARTH.rcp85.r3i1p1.HIRHAM5.v1.day.clh.v20131119|cordexesg.dmi.dk 20131119
2      2 cordex.output.EUR-11.DMI.ICHEC-EC-EARTH.rcp85.r3i1p1.HIRHAM5.v1.day.clivi.v20131119|cordexesg.dmi.dk 20131119
3      3  cordex.output.EUR-11.DMI.ICHEC-EC-EARTH.rcp85.r3i1p1.HIRHAM5.v1.day.rsds.v20131119|cordexesg.dmi.dk 20131119
4      4  cordex.output.EUR-11.DMI.ICHEC-EC-EARTH.rcp85.r3i1p1.HIRHAM5.v1.day.rlds.v20131119|cordexesg.dmi.dk 20131119
5      5  cordex.output.EUR-11.DMI.ICHEC-EC-EARTH.rcp85.r3i1p1.HIRHAM5.v1.day.rsus.v20131119|cordexesg.dmi.dk 20131119
6      6  cordex.output.EUR-11.DMI.ICHEC-EC-EARTH.rcp85.r3i1p1.HIRHAM5.v1.day.rlus.v20131119|cordexesg.dmi.dk 20131119
7      7  cordex.output.EUR-11.DMI.ICHEC-EC-EARTH.rcp85.r3i1p1.HIRHAM5.v1.day.rsdt.v20131119|cordexesg.dmi.dk 20131119
8      8  cordex.output.EUR-11.DMI.ICHEC-EC-EARTH.rcp85.r3i1p1.HIRHAM5.v1.day.rsut.v20131119|cordexesg.dmi.dk 20131119
9      9  cordex.output.EUR-11.DMI.ICHEC-EC-EARTH.rcp85.r3i1p1.HIRHAM5.v1.day.rlut.v20131119|cordexesg.dmi.dk 20131119
10    10   cordex.output.EUR-11.DMI.ICHEC-EC-EARTH.rcp85.r3i1p1.HIRHAM5.v1.day.psl.v20131119|cordexesg.dmi.dk 20131119
# ... with 822 more rows, and 42 more variables:

如果要为大于长度1的变量获取多行，例如access和experiment_family，则可以使用tidyr::separate_rows将数据分成多行。

tidyr::separate_rows(d1, experiment_family)

Answer 2

代替rjson使用此内容：

library(jsonlite)
j <- jsonlite::fromJSON('https://esgf-data.dkrz.de/esg-search/search/?offset=0&limit=1000&type=Dataset&replica=false&latest=true&project=CORDEX&domain=EUR-11&experiment=rcp85&time_frequency=day&facets=rcm_name%2Cproject%2Cproduct%2Cdomain%2Cinstitute%2Cdriving_model%2Cexperiment%2Cexperiment_family%2Censemble%2Crcm_version%2Ctime_frequency%2Cvariable%2Cvariable_long_name%2Ccf_standard_name%2Cdata_node&format=application%2Fsolr%2Bjson')

# The names you wan to find in the nested returned data
look_for <- c('variable','variable_long_name' ,
              'rcm_name','driving_model',
              'cf_standard_name')


new_df <- as.data.frame(sapply(look_for, function(i){
  unlist(j$response$docs[[i]])
}))

str(new_df)
'data.frame':   832 obs. of  5 variables:
$ variable          : chr  "clh" "clivi" "rsds" "rlds" ...
$ variable_long_name: chr  "High Level Cloud Fraction" "Ice Water Path" "Surface Downwelling Shortwave Radiation" "Surface Downwelling Longwave Radiation" ...
$ rcm_name          : chr  "HIRHAM5" "HIRHAM5" "HIRHAM5" "HIRHAM5" ...
$ driving_model     : chr  "ICHEC-EC-EARTH" "ICHEC-EC-EARTH" "ICHEC-EC-EARTH" "ICHEC-EC-EARTH" ...
$ cf_standard_name  : chr  "cloud_area_fraction_in_atmosphere_layer" "atmosphere_cloud_ice_content" "surface_downwelling_shortwave_flux_in_air" "surface_downwelling_longwave_flux_in_air" ...

从列表列表中提取多个列，并保存在data.frame中

2 个答案: