本文介绍了保存构成列表对象的tidypmc输出，并根据PMCID将其保存到单个文件中的处理方法，对大家解决问题具有一定的参考价值，需要的朋友们下面随着小编来一起学习吧！
问题描述
因此我在进行查询时会返回我PMCID，然后再次使用tidypmc libray进行查询以解析包含来自各种论文的元数据的表，最终将其作为列表返回.某些PMCID将为空，因为它不会有一个适当的表标记等.所以现在我想将每个PMCID保存到单独的文件中，我尝试了但是我遇到了一个错误，如果我得到它，它就不那么简单了.由于在每个PMCID列表下都有多个表，还应该将其保存在该PMCID下.
So im making a query it returns me PMCIDs which is again used to query using tidypmc libray to parse table which contains metadata from various papers, which is finally returned as list.Some of the PMCIDs will be empty since it doesn't have a proper table tag etc. So now I want to save each PMCIDs into individual file, which i tried but i got an error, its not that straightforward if i get it. Since under each list of PMCIDs there are multiple table which should be also need to saved under that PMCIDs.
不确定如何进行操作，但是我认为，如果PMCID包含4个表，然后在该接受PMCID文件夹下包含4个表，则应该将每个PMCID结果写入单独的文件夹中.
Not sure how to proceed but a way i can think is each PMCID result should be written inside individual folder if a PMCID contain 4 table then 4 table under that receptive PMCID folder.
下面是我正在使用的代码
Below is the code I m using
library("europepmc")
library(xml2)
library(tidypmc)
b <-epmc_search(query = 'acute myeloid leukemia drug studies',output = 'parsed',limit = 20)
a <- b %>% select(pmid,pmcid)
a <- a[complete.cases(a),]
c <- a$pmcid
pub_tables <- lapply(c, function(pmc_id) {
message("-- Trying ", pmc_id, "...")
doc <- tryCatch(pmc_xml(pmc_id),
error = function(e) {
message("------ Failed to recover PMCID")
return(NULL)
})
if(!is.null(doc)) {
#-- If succeed, try to get table
tables <- pmc_table(doc)
if(!is.null(tables)) {
#-- If succeed, try to get table name
table_caps <- pmc_caption(doc) %>%
filter(tag == "table")
#names(tables) <- paste(table_caps$label, table_caps$text, sep = " - ")
}
return(tables)
} else {
#-- If fail, return NA
return(NA)
}
Sys.sleep(sample(1:10))
})
names(pub_tables) <- c
for (i in 1:length(pub_tables)) {
write.csv(pub_tables[i], file=paste0("output/", names(pub_tables)[i], ".txt"))
}
我将使用20尝试的示例查询 dput ，以使对象较小
I will dput my sample query i tried with 20 so that the object is small
dput(pub_tables)
list(PMC6968541 = NULL, PMC7170320 = NULL, PMC7269076 = NULL,
PMC7219522 = NULL, PMC7372828 = list(`Table 1` = structure(list(
X1 = c("AML with recurrent genetic abnormalities", "AML with t(8;21)(q22;q22.1);RUNX1-RUNX1T1",
"AML with inv. (16)(p13.1q22) or t(16;16)(p13.1;q22);CBFB-MYH11",
"APL with PML-RARA", "AML with t(9;11)(p21.3;q23.3);MLLT3-KMT2A",
"AML with t(6;9)(p23;q34.1);DEK-NUP214", "AML with inv. (3)(q21.3q26.2) or t(3;3)(q21.3;q26.2); GATA2, MECOM",
"AML (megakaryoblastic) with t(1;22)(p13.3;q13.3);RBM15-MKL1",
"Provisional entity: AML with BCR-ABL1", "AML with mutated NPM1",
"AML with biallelic mutations of CEBPA", "Provisional entity: AML with mutated RUNX1",
"AML with myelodysplasia-related changes", "Therapy-related myeloid neoplasms",
"AML, NOS", "AML with minimal differentiation", "AML without maturation",
"AML with maturation", "Acute myelomonocytic leukemia",
"Acute monoblastic/monocytic leukemia", "Pure erythroid leukemia",
"Acute megakaryoblastic leukemia", "Acute basophilic leukemia",
"Acute panmyelosis with myelofibrosis", "Myeloid sarcoma",
"Myeloid proliferations related to Down syndrome", "Transient abnormal myelopoiesis (TAM)",
"Myeloid leukemia associated with Down syndrome"), X2 = c(NA_character_,
NA_character_, NA_character_, NA_character_, NA_character_,
NA_character_, NA_character_, NA_character_, NA_character_,
NA_character_, NA_character_, NA_character_, NA_character_,
NA_character_, NA_character_, NA_character_, NA_character_,
NA_character_, NA_character_, NA_character_, NA_character_,
NA_character_, NA_character_, NA_character_, NA_character_,
NA_character_, NA_character_, NA_character_)), row.names = c(NA,
-28L), class = c("tbl_df", "tbl", "data.frame"), caption = "The 2016 WHO classification of acute myeloid leukemia (AML) and related neoplasms", footnotes = "APL, acute promyelocytic leukemia; NOS, not otherwise specified"),
`Table 2` = structure(list(`Functional category` = c("Myeloid transcription-factor genes",
"Nucleophosmin (NPM1) gene", "Tumor suppressor genes",
"Signaling genes", "DNA methylation", "Chromatin modifier",
"Cohesin complex", "Splicing factors"), `Gene members` = c("Transcription factor fusions by chromosomal rearrangements, such as t(8;21)(q22;q22); RUNX1-RUNX1T1 and inv(16)(p13.1q22) or t(16;16)(p13.1;q22); CBFB-MYH11GATA2, RUNX1 and CEBPA",
"NPM1", "TP53, WT1, PHF6", "FLT3, KIT, PTPN11, RAS",
"DNMT3A, TET2, IDH1, IDH2", "ASXL1, EZH2 and KMT2A",
"STAG1, STAG2, RAD21, SMC1A, SMC3,", "SRSF2, SF3B1, U2AF1, ZRSR2"
), `Role in AML Leukemogenesis` = c("Transcriptional deregulation and impaired hematopoietic differentiation.",
"Aberrant cytoplasmic localization of NPM1 and its interacting proteins",
"Transcriptional deregulation and impaired degradation via the negative regulator (MDM2 and PTEN oncogenes)",
"Proliferative advantage through the RAS-RAF, JAK-STAT, and PI3K-AKT signaling pathways",
"Deregulation of DNA methylation and oncometabolite production",
"Deregulation of chromatin modification and impairment of methyltransferases function",
"Impairment of accurate chromosome segregation and transcriptional regulation",
"Deregulated RNA processing and aberrant splicing patterns"
)), row.names = c(NA, -8L), class = c("tbl_df", "tbl",
"data.frame"), caption = "Functional categories of genes that are commonly mutated in acute myeloid leukemia (AML)"),
`Table 3` = structure(list(`Risk profiles` = c("Favorable",
"Favorable", "Favorable", "Favorable", "Favorable", "Intermediate",
"Intermediate", "Intermediate", "Intermediate", "Intermediate",
"Adverse", "Adverse", "Adverse", "Adverse", "Adverse",
"Adverse", "Adverse", "Adverse", "Adverse", "Adverse"
), Subgroups = c("t(8;21)(q22;q22.1); RUNX1-RUNX1T1",
"inv (16)(p13.1q22) or t(16;16)(p13.1;q22); CBFB-MYH11",
"Mutated NPM1 without FLT3-ITD", "Mutated NPM1 with FLT3-ITDlow",
"Biallelic mutated CEBPA", "Mutated NPM1 and FLT3-ITDhigh",
"Wild-type NPM1 without FLT3-ITD", "Wild-type NPM1 with FLT3-ITDlow",
"t(9;11)(p21.3;q23.3); MLLT3-KMT2A", "Cytogenetic abnormalities not classified",
"t(6;9)(p23;q34.1); DEK-NUP214", "t(v;11q23.3); KMT2A rearranged",
"t(9;22)(q34.1;q11.2); BCR-ABL1", "inv (3)(q21.3q26.2) or t(3;3)(q21.3;q26.2); GATA2,MECOM(EVI1)",
"Complex karyotype, monosomal karyotype", "-5 or del(5q); −7; −17/abn(17p)",
"Wild-type NPM1 and FLT3-ITDhigh", "Mutated RUNX1", "Mutated ASXL1",
"Mutated TP53")), row.names = c(NA, -20L), class = c("tbl_df",
"tbl", "data.frame"), caption = "Risk stratification of AML according To 2017 ELN recommendations [24]", footnotes = "Low, low allelic ratio (< 0.5); high, high allelic ratio (≥0.5)")),
PMC7374966 = list(`Table 1` = structure(list(`Year of publication, region/country (reference)` = c("1970, West Virginia (USA)[7, 10]",
"1983, Thailand[5]", "1990, Texas (USA)[8]", "1992, Mississippi (USA)[12]",
"1994, Maryland (USA)[13]", "2009, India[11]", "2010, Germany[14]",
"2011, Japan[9]", "2018, Wisconsin (USA)[6]", "2019, Switzerland(present case)"
), `Underlying conditions` = c("1 year-old male, no underlying conditions",
"20 year-old female, no underlying conditions", "29 year-old male, cocaine abuse",
"64 year-old male, kidney transplantation", "32 year-old female, lymphocytic lymphoma with leukemic transformation (neutropenia)",
"10 year-old female, T-cell acute lymphoblastic leukemia",
"78 year-old female, myelodysplastic syndrome", "61 year-old male, mantle cell lymphoma, allogeneic HSCT",
"15 year-old male, B-cell lymphoblastic leukemia (neutropenia)",
"71 year-old, acute myeloid leukemia (neutropenia)"), `Organs affected` = c("Mediastinum, lungs, pericardium",
"Soft tissues (breast), lungs, mediastinum, liver, gastro-intestinal tract",
"Endocardium, blood, skin, heart, lungs, kidneys, brain, muscles",
"Lungs, myocardium, brain, kidney, thyroid", "Lungs, pericardium",
"Sinus, soft tissues (facial)", "Sinus, soft tissues (facial), brain",
"Lungs, heart, spleen, kidney, bladder, thyroid", "Sinus, lungs",
"Lungs"), Species = c("C. incongruus", "C. incongruus", "Conidiobolus spp.",
"C. coronatus", "C. incongruus", "C. coronatus", "C. incongruus",
"C. lamprauges", "C. coronatus", "Conidiobolus spp."), `Treatment (dose), duration and outcome` = c("Deoxycholate amphotericin B (1 mg/kg/day), 10 weeksOutcome: cure",
"Co-trimoxazole (2 g/day), duration NSOutcome: death",
"NoneOutcome: death", "Deoxycholate amphotericin B (50 mg every other day), until deathOutcome: death",
"Deoxycholate amphotericin B (0.5 mg/kg/day, then 1.5 mg/kg/day) and flucytosine (150 mg/kg/day), until deathSurgeryOutcome: death",
"Amphotericin B (NS), until deathSurgeryOutcome: death",
"Liposomal amphotericin B (200 mg/day), until deathSurgeryOutcome: death",
"Micafungin (150 mg/day) and liposomal amphotericin B (2.5 mg/kg/day), then intravenous voriconazole (6 mg/kg/day on day 1, then 4 mg/kg/day) and micafungin (150 mg/day), until deathOutcome: death",
"Liposomal amphotericin B (10 mg/kg/day) and anidulafungin (1.5 mg/kg/day) and oral terbinafine (250 mg twice per day), duration NSSurgery, granulocyte transfusionOutcome: cure",
"Caspofungin (70 mg/day on day 1, then 50 mg/day), then liposomal amphotericin B (5 mg/kg/day), then oral isavuconazole (200 mg three times per day on day 1 and 2, then 200 mg/day), 2 monthsSurgeryOutcome: cure"
)), row.names = c(NA, -10L), class = c("tbl_df", "tbl", "data.frame"
), caption = "Case reports of invasive fungal infections due to Conidiobolus spp.", footnotes = "NS Not specified")))
任何建议或帮助将不胜感激.
Any Suggestion or help would be really appreciated.
推荐答案
您需要通过Open Access过滤搜索(或通过isOpenAccess列过滤结果)
You need to filter the search by Open Access (or the results by the isOpenAccess column)
library(europepmc)
b <-epmc_search(query = 'acute myeloid leukemia drug studies OPEN_ACCESS:Y',limit = 20)
pmcids <- b$pmcid[b$isOpenAccess=="Y"]
然后我将遍历PMC ID，并保存文本和表格
Then I would loop through the PMC ids and save the text and tables
library(tidypmc)
n <- length(pmcids)
txt <- vector("list", n)
tbl <- vector("list", n)
names(txt) <- pmcids
names(tbl) <- pmcids
for(i in 1:n){
id <- pmcids[i]
message("Parsing ", i, ". ", id)
doc <- pmc_xml(id)
txt[[i]] <- pmc_text(doc)
## pmc_table returns NULL if missing, which will delete the element!
x <- pmc_table(doc)
if(!is.null(x)) tbl[[i]] <- x
Sys.sleep(sample(1:3))
}
最后，将表折叠为列名和单元格值对.
Finally, collapse the tables into column names and cell values pairs.
library(tidyverse)
txt2 <- bind_rows(txt, .id="PMCID")
tbl2 <- bind_rows( lapply(tbl, collapse_rows), .id="PMCID")
标题和脚注另存为属性，因此您也可以获取它们(purrr专家可能会设置更好的格式)
The caption and footnotes are saved as attributes, so you can get those too (and a purrr expert could probably format this better)
attributes(tbl[[5]][[1]])
# $caption
# [1] "The 2016 WHO classification of acute myeloid leukemia (AML) and related neoplasms"
# $footnotes
# [1] "APL, acute promyelocytic leukemia; NOS, not otherwise specified"
enframe( unlist( lapply(tbl, sapply, attr, "caption")))
# name value
# <chr> <chr>
# 1 PMC7372828.Table 1 The 2016 WHO classification of acute myeloid leukemia (AML) and related neoplasms
# 2 PMC7372828.Table 2 Functional categories of genes that are commonly mutated in acute myeloid leukemia (AML)
# 3 PMC7372828.Table 3 Risk stratification of AML according To 2017 ELN recommendations [24]
# 4 PMC7374966.Table 1 Case reports of invasive fungal infections due to Conidiobolus spp.
# 5 PMC7362563.Table 1 Best overall response for patients with AML at any time on treatment
这篇关于保存构成列表对象的tidypmc输出，并根据PMCID将其保存到单个文件中的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持！