问题描述
df <- data.frame (
party=c("A","B","C","A","D","E","F","G","H","I","J","K","L","L"),edate = c(1991,1991,1995,1998,2000,2005,2010,1999,2001,2006,2006),RRP = c(1,1,0),absseats = c(0,20,30,5,25,10,28,12,100,50,120,75,15,90,60,70,40,10),country=c(11,11,43,43))
我尝试了其他类似的事情：
treat <- df%>%
filter(RRP == "1")%>%
mutate(treat = ifelse(lag(absseat)>= 1,0))%>%
ungroup()
但是我从来没有完全得到我想要的东西。感谢所有帮助！
谢谢！
我要达到的目的是创建一个新列= 1 IF，并且仅当每个国家的先前日期为1）一项RRP == 1的观察值和2）观察值为1时或以上的弃权票。像这样：
treat <- c(NA,NA,0)
ideal.df<- cbind(df,treat)
以下是前100个观测值的结果：
dput(df2[1:100,])
structure(list(country = c(11,11),RRP = c(0,party = structure(c(2L,3L,4L,6L,8L,2L,5L,1L,9L,1L),.Label = c("11110","11220","11320","11420","11520","11620","11710","11810","11951","12110","12220","12221","12230","12320","12410","12420","12520","12620","12810","12951","13001","13110","13210","13220","13221","13229","13230","13320","13330","13410","13420","13421","13422","13520","13620","13720","13951","13952","13953","14110","14221","14222","14223","14310","14320","14420","14430","14440","14520","14620","14810","14820","14901","15111","15220","15320","15321","15322","15323","15328","15420","15430","15440","15450","15620","15621","15630","15710","15810","15951","15952","15953","21111","21112","21221","21230","21320","21321","21322","21330","21420","21421","21422","21423","21424","21425","21426","21430","21520","21521","21522","21911","21912","21913","21914","21915","21916","21917","22110","22210","22220","22230","22310","22320","22321","22330","22420","22430","22521","22522","22523","22524","22525","22526","22527","22528","22710","22711","22720","22722","22730","22951","22952","22953","22954","22955","23111","23112","23113","23220","23230","23320","23420","23520","23951","31021","31110","31111","31220","31230","31240","31320","31421","31425","31430","31521","31522","31523","31529","31621","31622","31623","31624","31625","31626","31630","31631","31720","32021","32022","32031","32051","32055","32061","32090","32110","32111","32210","32211","32212","32213","32220","32221","32230","32310","32320","32321","32322","32329","32330","32410","32420","32421","32440","32450","32451","32460","32520","32521","32522","32528","32529","32530","32610","32611","32629","32630","32640","32710","32720","32901","32902","32903","32904","32906","32951","32952","32953","32955","32956","33020","33025","33091","33092","33093","33095","33096","33097","33098","33099","33210","33220","33320","33420","33430","33438","33439","33440","33512","33610","33611","33612","33710","33901","33902","33903","33904","33905","33906","33907","33908","33909","33910","33911","33912","33913","33914","34020","34210","34211","34212","34213","34214","34313","34314","34340","34410","34510","34511","34512","34610","34710","34720","34730","35060","35110","35120","35210","35211","35220","35229","35310","35311","35312","35313","35315","35316","35317","35520","35710","35951","41111","41112","41113","41220","41221","41222","41223","41320","41420","41521","41522","41620","41711","41712","41911","41912","41951","41952","41953","42110","42120","42220","42320","42420","42421","42430","42520","42710","42951","43020","43110","43120","43220","43320","43321","43420","43520","43530","43531","43540","43710","43711","43810","43811","43901","43902","43951","51110","51210","51320","51330","51340","51420","51421","51430","51620","51621","51901","51902","51903","51951","52320","52620","52710","53021","53110","53220","53221","53230","53231","53240","53250","53320","53321","53420","53520","53620","53714","53810","53951","53981","54320","54620","55110","55321","55322","55340","55421","55422","55423","55430","55610","55711","55720","61320","61620","61621","61622","61911","62110","62320","62420","62620","62621","62622","62623","62901","62951","63110","63320","63321","63330","63410","63620","63621","63622","63710","63810","63901","64110","64320","64321","64420","64421","64422","64620","64621","64901","64902","64951","71110","71111","71220","71320","71321","71322","71410","71420","71430","71530","71620","71621","71622","71623","71624","71625","71626","71630","71640","71650","71660","71710","71951","71952","71953","72091","72092","72220","72221","72222","72223","72224","72225","72310","72311","72312","72320","72322","72323","72324","72325","72326","72327","72328","72410","72411","72412","72413","72414","72415","72420","72425","72427","72428","72430","72431","72440","72450","72530","72531","72532","72533","72534","72535","72610","72611","72612","72613","72614","72615","72620","72621","72622","72623","72624","72625","72701","72901","72903","72951","73310","73330","74210","74321","74322","74323","74324","74325","74326","74621","74622","74623","74624","74625","74626","74627","74628","74640","74710","74711","74712","74714","74715","74716","74717","75220","75320","75421","75621","75622","75623","75624","75721","75722","75810","75951","76041","76222","76321","76322","76420","76421","76430","76521","76610","76620","76711","76712","76901","76902","76903","76904","77220","77221","77420","77430","77710","77951","78211","78212","78213","78214","78410","78430","78710","78901","79030","79090","79221","79222","79223","79321","79322","79421","79422","79423","79440","79610","79721","79722","79723","79730","79901","79951","79953","79954","79955","79956","79957","80051","80061","80062","80071","80110","80220","80221","80228","80229","80320","80330","80410","80411","80510","80610","80620","80630","80640","80710","80810","80811","80812","80813","80901","80902","80951","80952","81021","81022","81032","81041","81042","81043","81061","81091","81111","81112","81220","81221","81222","81229","81230","81410","81411","81420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= "factor"),edate = structure(c(-9237,-9237,-7774,-6311,-4845,-4232,-3392,-1929,-473,262,1354,2453,3545,4644,5736,6835,7927,9026,10490,11945),class = "Date"),absseats = c(15,115,26,39,35,8,112,57,23,110,58,31,6,106,42,19,111,38,45,32,114,34,113,33,3,125,29,37,17,163,41,71,156,51,152,55,86,154,73,64,166,21,56,159,76,44,66,16,138,80,18,22,161,27,131,82,17)),row.names = c(NA,-100L),class = c("tbl_df","tbl","data.frame"))
解决方法
您可以通过以下方式执行此操作：首先为每个country和edate计算下一年是否应获得治疗=1。然后，将其拖到年份变化时，然后使用zoo::na.locf()在列中填充此数据。
library(dplyr)
library(zoo)
df2 <- df %>%
group_by(country,edate) %>%
mutate(
treat_next = any(RRP == 1 & absseats > 0,na.rm = T)
) %>%
group_by(country) %>%
arrange(edate) %>%
mutate(
treat = na.locf(case_when(
lag(edate) < edate ~ as.integer(lag(treat_next)),T ~ NA_integer_
),na.rm = F)
) %>%
ungroup() %>%
arrange(country,edate) %>%
select(-treat_next)
我已经按国家和日期排列了df2，所以行的顺序是不同的。尽管treat中的数据符合预期：）