xuefliang

 library(tidyverse) # 定义节假日和补班日 holidays_2024 <- as.Date(c(   "2024-01-01",  # 元旦   "2024-02-10", "2024-02-11", "2024-02-12", "2024-02-13", "2024-02-14", "2024-02-15", "2024-02-16",  # 春节   "2024-04-04", "2024-04-05", "2024-04-06",  # 清明节   "2024-05-01", "2024-05-02", "2024-05-03",  # 劳动节   "2024-06-08", "2024-06-09", "2024-06-10",  # 端午节   "2024-09-15", "2024-09-16", "2024-09-17",  # 中秋节   "2024-10-01", "2024-10-02", "2024-10-03", "2024-10-04", "2024-10-05", "2024-10-06", "2024-10-07"  # 国庆节 )) workdays_2024 <- as.Date(c(   "2024-02-04", "2024-02-18",  # 春节补班   "2024-04-07",  # 清明补班   "2024-04-28", "2024-05-11",  # 劳动节补班   "2024-06-02",  # 端午补班   "20...

  1 、安装 Positron 2 、 Positron 中安装 kv9898.open-remote-wsl-0.0.9.vsix 扩展 3 、 Press Ctrl+Shift+P (or Cmd+Shift+P on macOS) Run the command:   Register this WSL extension for launching Positron from WSL 4 、打开虚拟网卡模式和 Ubuntu ， Press Ctrl+Shift+P (or Cmd+Shift+P on macOS) Run the command:   Remote-WSL Connect to WSL

 library(INLA) library(sf) library(gstat) library(tidyverse) data(sic97) # 直接在一个流程中准备全部数据 df_rain <- sic_full %>%    st_as_sf() %>%    mutate(     altitude = raster::extract(raster::raster(demstd), as.matrix(st_coordinates(.))),     coords = st_coordinates(.)   ) %>%    mutate(     lat = coords[, 1] / 1000,     lon = coords[, 2] / 1000   ) %>%    dplyr::select(ID, rainfall, altitude, lat, lon) # 检查空间依赖性 df_rain %>%     as_Spatial() %>%   variogram(rainfall ~ 1, data = .) %>%   graphics::plot() # 创建网格 loc <- cbind(df_rain$lat, df_rain$lon) Mesh <- inla.mesh.2d(   loc.domain = loc,   max.edge = c(20, 100),   cutoff = 1 ) ggplot() +   inlabru::gg(Mesh) +   geom_point(data = data.frame(x = loc[,1], y = loc[,2]),              aes(x = x, y = y), color = "red", shape = 1) +   theme_...

 library(readxl) library(purrr) library(dplyr) # 设置文件夹路径 folder_path <- "/mnt/c/Users/xuefl/Downloads/新冠存疑分市州" # 获取文件夹中所有的xlsx文件 xlsx_files <- list.files(path = folder_path, pattern = "\\.xlsx$", full.names = TRUE) # 如果没有找到任何xlsx文件，输出警告 if (length(xlsx_files) == 0) {   warning("未在指定文件夹找到任何xlsx文件") } # 创建一个函数来读取Excel文件，并添加文件名作为标识 read_excel_with_source <- function(file_path) {   file_name <- basename(file_path)   df <- try(read_excel(file_path), silent = TRUE)      if (inherits(df, "try-error")) {     warning(paste("无法读取文件:", file_name))     return(NULL)   }      # 添加文件名作为源标识   df$source_file <- file_name      return(df) } # 使用map函数批量读取所有xlsx文件 all_data_list <- map(xlsx_files, read_excel_with_source) # 移除读取失败的NULL元素 all_data_list <- all_data_list[!sapply(all_data_list, is.null)] # 检查是否所有数据框的列数相同 col_counts <- sapply(all_data_list, ncol) if (length(unique(col_counts)) ...

  import ibis import ibis . selectors as s from ibis import _ ibis . options . interactive = True con = ibis .duckdb.connect( '/mnt/d/xg.db' ) mon3 = con .table( 'mon3' ) mon4 = con .table( 'mon4' ) mon5 = con .table( 'mon5' ) mon6 = con .table( 'mon6' ) mon7 = con .table( 'mon7' ) mon8 = con .table( 'mon8' ) mon9 = con .table( 'mon9' ) mon10 = con .table( 'mon10' ) mon11 = con .table( 'mon11' ) mon12 = con .table( 'mon12' ) jg = con .read_xlsx( '/mnt/d/疫苗批号价格.xlsx' ) jg = (     jg     .distinct( on = [ '疫苗批号' , '价格' ], keep = 'first' )     .select( '疫苗批号' , '价格' ) ) #7026,2021030002G (     mon4     .group_by( 'YM_PH' )     .aggregate( n = _ .GRDA_CODE.count())     .asof_join( jg , on = ( 'YM_PH' , '疫苗批号' ))     .filter( _ .价格 == 90 ) ) #3,NCOV202104002V (     mon5     .group_by( 'YM_PH' )     .aggregate( n = _ ...

 library(BayesFactor) # 创建一个2x2列联表作为例子 data <- matrix(c(15, 25, 35, 25), nrow = 2) colnames(data) <- c("Treatment", "Control") rownames(data) <- c("Success", "Failure") data # 使用传统卡方检验 chisq.test(data) # 使用贝叶斯卡方检验 - 通过计算贝叶斯因子 bf <- contingencyTableBF(data, sampleType = "jointMulti") bf # 解释贝叶斯因子 extractBF(bf) # contingencyTableBF计算了独立假设与非独立假设之间的贝叶斯因子。如果贝叶斯因子大于1表明是非独立的。 # BF = 1：没有证据支持任一假设。 # BF = 1-3：证据“微弱”（anecdotal evidence）。 # BF = 3-10：证据“中等”（moderate evidence）。 # BF > 10：证据“强”（strong evidence）。 library(brms) # 创建二项式数据 successes <- c(15, 35)  # Treatment和Control组的成功次数 trials <- c(15+25, 35+25)  # Treatment和Control组的总试验次数 treatment <- c(1, 0)  # 1=Treatment, 0=Control df2 <- data.frame(successes=successes, trials=trials, treatment=treatment) # 拟合贝叶斯逻辑回归模型 brm_model <- brm(successes | trials(trials) ~ treatment,                   data = df2,             ...

  #随机抽样 tmp = (     result     .filter( _ .A == '兰州' )     .order_by( ibis . random ())   # 随机排序     .limit( 2 )   # 取前 2 行 ) #按比例随机抽样 tmp = (     result     .sample( 0.1 ) )