xuefliang

 library(tidyverse) library(openxlsx) dat <- read.xlsx('./Documents/镇原县注射器库存.xlsx') %>%    select(b) %>%    as_vector() %>%    str_c(collapse = "','")

library(lubridate) calc_age <- function(birthDate, refDate = Sys.Date()) {   require(lubridate)   period <- as.period(interval(birthDate, refDate),unit = "days")   period$day } #满8月龄 ymd('2020-05-31') %m-% months(3*12) ymd('2019-12-31') %m+% months(1:12)

vim  /etc/hosts 0.0.0.0 account.jetbrains.com 0.0.0.0 www.jetbrains.com 127.0.0.1 transact.netsarang.com 127.0.0.1 update.netsarang.com 127.0.0.1 www.netsarang.com 127.0.0.1 www.netsarang.co.kr 127.0.0.1 sales.netsarang.com

mtcars %>%   group_by(cyl) %>%   group_modify(     ~broom::tidy(lm(mpg ~ wt, data = .))   ) mtcars %>%   group_by(cyl) %>%   summarise(     broom::tidy(lm(mpg ~ wt))   ) mtcars %>%   group_by(cyl) %>%   summarise(     broom::tidy(lm(mpg ~ wt, data = cur_data()))   ) mtcars %>%   group_by(cyl) %>%   nest() %>%   mutate(model = purrr::map(data, ~ lm(mpg ~ wt, data = .))) %>%   mutate(result = purrr::map(model, ~ broom::tidy(.))) %>%   unnest(result) mtcars %>%   nest_by(cyl) %>%   mutate(model = list(lm(mpg ~ wt, data = data))) %>%   summarise(broom::tidy(model)) mtcars %>%   nest_by(cyl) %>%   summarise(     broom::tidy(lm(mpg ~ wt, data = data))   )

if (!requireNamespace("BiocManager", quietly = TRUE))   install.packages("BiocManager") BiocManager::install(version = "3.11") if (!requireNamespace("BiocManager", quietly = TRUE))   install.packages("BiocManager") BiocManager::install("LBE") sudo R CMD javareconf JAVAC=/usr/lib/jvm/java-1.8.0-openjdk-amd64/bin/javac JAR=/usr/lib/jvm/java-1.8.0-openjdk-amd64/bin/jar JAVA_HOME=/usr/lib/jvm/java-1.8.0-openjdk-amd64/bin/jar

#R 实现 inoc %>%   filter(str_sub(gldw_bm,1,2)=='62') %>%    mutate(yzyl=floor(day(as.period(interval(csrq,ymd('2021-01-25')),unit = "days"))/30)) %>%       filter(yzyl>=8 & yzyl<18) %>%    group_by(xian) %>%   summarise(deno=n_distinct(grda_et_lsh),num=sum(str_sub(ym_bm,1,2) %in% c('14') & jz_zc %in% c(1),na.rm = TRUE)) %>%   mutate(prec=round(num/deno*100,1)) %>%    left_join(xzqh,by=c('xian'='dzbm')) %>%    arrange(xian) Python实现 (inoc >>head( 1000 ) >>mutate( yzyl =((pd.to_datetime( '2021-01-25' )-_.csrq).dt.days/ 30 ), xian =_.gldw_bm.str[ 0 : 6 ]) >> filter (_.gldw_bm.str[ 0 : 2 ]== '62' ,_.yzyl>= 8 ) >>group_by(_.xian) >>summarize( n =n_distinct(_.grda_code), n3 =(((_.ym_bm.str[ 0 : 2 ]== '12' ) & (_.jz_zc== 1 )).sum( skipna = True ))) >>mutate( pert =np.round(_.n3/_.n* 100 , 2 )) >>arrange(_.xian) )

library(tidyverse) x <- c("A","z","g",NA) x %>% as.factor()  # 默认按照字母排序 x %>% as_factor()  # 默认按照出现顺序排列 gss_cat$relig %>% fct_count() %>% arrange(-n) #fct_anon:用任意数字标识符替换因子级别 gss_cat$relig %>% fct_anon() %>% fct_count() %>% arrange(-n) gss_cat$relig %>% fct_anon("X") %>% fct_count() #fct_c:合并级别，连接因子 fa <- factor("a") fb <- factor("b") fab <- factor(c("a", "b")) c(fa, fb, fab) fct_c(fa, fb, fab) #fct_collapse：因子转换，将多个因子合并 fct_count(gss_cat$partyid) %>% arrange(n) partyid2 <- fct_collapse(gss_cat$partyid,                          missing = c("No answer", "Don't know"),                          other = "Other party",                          rep = c("Strong republican", "Not str republican"),         ...