恶性肿瘤与医疗人员数据集
包含恶性肿瘤新病例、发病率、医疗人员薪资和人口数据的多表格数据集。
基本信息
资源简介
该数据集包含多个文件:恶性肿瘤新病例数据(按部位、性别、年龄组分类)、恶性肿瘤年龄特异发病率数据(按部位、性别分类)、医疗保健人员平均月薪及额外支付数据(按职业、年龄组分类)、年度平均人口数据(按性别、年份、年龄组分类),以及一个待处理的问题数据集。数据格式包括JSON、CSV和TSV。
下载信息
注册下载
Tips: 该数据集需要在对应的数据源网站注册通过后,才能进行数据下载,注册有对应要求,或者需要收费。
暂未开放公开下载
Tips: 该数据集属于公开下载,应该可以免费公开下载。
免登录有偿下载
Tips: 该数据集 Qianfanghub 可以协助提供有偿下载服务,注意,服务不针对数据相关产权,只是技术服务费。
提供高速下载与技术交付服务(收技术服务费,非数据销售)
暂未开放千方医数集,医疗数据集部分,是为社区服务的公开医疗数据集搜索引擎,并不存储或者下载原始的任何数据。 如果您有其他医疗数据需求,可以和客服联系,或者下工单。我们有强大的三甲医疗机构帮助您提供个性化的医疗数据定制、采集、标注服务。
使用方式
数据集获取
git clone https://github.com/rstats-tartu/datasets.git
curl -L -o repo.zip https://github.com/rstats-tartu/datasets/archive/refs/heads/master.zip
unzip repo.zip
源站 README 摘录(使用方式)
Downloading files from datasets repository
We had problem with reading quiz.csv file directly from its GitHub url:
library(readr)
url <- "https://raw.githubusercontent.com/rstats-tartu/datasets/master/quiz.csv"
quiz <– read_csv(url)
For some people using read_csv() directly with url gave an error.
Here’'s simple workaround: first download this quiz.csv file and then import to R, see below.
Get file url.
Click on the file name you want to download.
You need to see raw contents of the file.
Find button “Raw” and click on it, you will see raw text.
Copy url from browser.
Download file.
First, we create folder called “data” to host all necessary data files.
dir.create("data")
Function will issue a warning when dir already exists.
First download file into “data” directory:
download.file(url, "data/quiz.csv")
Read file:
library(readr)
library(dplyr)
quiz <- read_csv("data/quiz.csv")
quiz
This can be used with all other files too.
Just change url and file name accordingly.
Imported quiz data needs some munging:
colnames(quiz) <- c("time","education", "supervision", "stats_course",
"analysed_data", "proficiency", "junk", "plan_before",
"decide_after","publication_bias","publish_all",
"collect_more","different_method")
## 数据加载示例(表格/文本类)
```python
import pandas as pd, glob, os
files = (glob.glob(os.path.join(path, "**", "*.csv"), recursive=True)
+ glob.glob(os.path.join(path, "**", "*.tsv"), recursive=True)
+ glob.glob(os.path.join(path, "**", "*.xlsx"), recursive=True))
print("数据文件:", files)
df = pd.read_csv(files[0])
print(df.shape); print(df.columns.tolist()); print(df.head(3))
精度瓶颈?数据缺失?
当前公开数据无法满足您的算法精度?千方提供针对 癌症(总论) 的高质量、多模态真实临床数据定制解决方案。




