如何整理包含多个信息的列的数据集-样本数据放置？

Question

请帮我整理一下数据。谢谢。总观测值是 394，有 26 列。数据从 ms excel 导出。数据样本如下。在这个示例中实际上应该只有三个 observations/rows。在向量 d1..d2..no 和 Farmer.Name 中，对应于 v1 的 NA 的观测值应该被清除并添加到前一行值中。 d1..d2..no 对应于三个观察值（两个日期观察值一个唯一标识号），Farmer.Name 向量也是如此。样本是

d1..d2..no<-c("27/01/2020", "43832", "KE004421", "43832", "43832", 
              "KE003443", "31/12/2019", "43832", "KE0001512")

Farmer.Name<-c("S Jacob Gender:male","farmer type :marginal","farmer category :general", 
               "J Isac Gender :Female","farmer type: large","farmer category :general",
               "P Kumar Gender :Male","farmer type:small","farmer category :general")

adress<-c("k11",NA,NA,"k12",NA,NA,"k13",NA,NA)

amount<-c(25,NA,NA,25,NA,NA,32,NA,NA)

mydata<-data.frame(v1=v1, d1..d2..no=d1..d2..no, Farmer.Name=Farmer.Name, 
               adress=adress, amount=amount)

在向量 d1..d2..no 和 Farmer.Name 中，应清除对应于 v1 的 NA 的观测值并将其添加到前一行值中。 d1..d2..no 对应三个观察值（两个日期观察值一个唯一标识号） Farmer.Name 向量也是如此。也就是说，我的预期结果就像这段代码

v1<-c(1,2,3)

d1<-c("27/01/2020","43832","31/12/2019")
d2<-c("43832","43832","43832")
no<-c("KE004421","KE003443","KE0001512")
Farmer.Name1<-c("S Jacob","J Isac","P Kumar")
Gender<-c("male","female","male")
farmer_type <-c("marginal","large","small")
farmer_category <-c("general", "general", "general")
adress<-c("k11","k12","k13")
amount<-c(25,25,32)

myfinaldata<-data.frame(v1=v1,d1=d1,d2=d2,no=no,
                    Farmer.Name1=Farmer.Name1,
                    farmer_type=farmer_type,
                    farmer_category=farmer_category,
                    adress=adress,amount=amount)

结果应该是

  v1         d1    d2        no Farmer.Name1 farmer_type farmer_category adress amount
1  1 27/01/2020 43832  KE004421      S Jacob    marginal         general    k11     25
2  2      43832 43832  KE003443       J Isac       large         general    k12     25
3  3 31/12/2019 43832 KE0001512      P Kumar       small         general    k13     32

我是编程和r的新手，通过网上资源学习。也是我在这个平台上的第一个 post。有错误请见谅

我在整洁的 vesre 的传播、分离等方面做了很多乱七八糟的事情。但是不知道如何进行。

Answer 1

您的数据集中的日期不是日期格式。考虑在此之后格式化它们。

library(reshape)

df.new <- cbind(mydata[seq(1, nrow(mydata), 3), ], mydata[seq(2, nrow(mydata), 3), ][2:3], mydata[seq(3, nrow(mydata), 3), ][2:3])
colnames(df.new) <- c("v1", "d1", "Farmer.Name1", "adress", "amount", "d2", "farmer_type", "no", "farmer_category")
df.new <- df.new[c(1,2,6, 8,3, 7,9, 4,5)]


library(stringr)
df.new$Farmer.Name1 <- word(df.new$Farmer.Name1,1,sep = "\ Gender")
df.new$farmer_type <- word(df.new$farmer_type,2,sep = "\:")
df.new$farmer_category <- word(df.new$farmer_category,2,sep = "\:")

决赛 table:

> df.new
  v1         d1    d2        no Farmer.Name1 farmer_type farmer_category adress amount
1  1 27/01/2020 43832  KE004421      S Jacob    marginal         general    k11     25
4  2      43832 43832  KE003443       J Isac       large         general    k12     25
7  3 31/12/2019 43832 KE0001512      P Kumar       small         general    k13     32

P.S.: 我没有重命名行号。

Answer 2

不整洁的数据可能是一个挑战。这是一个 tidyverse 方法。

首先，为 d1、d2 和 no 添加了建议的列名称。假定行按此顺序排列。

列 Farmer.Name separated 分为两列，由 :。

Name 本身在单词 Gender 之前被分隔开。

fill允许为同一个人填写共同的值（例如v1、adress、amount和Name） .

pivot_wider 是为了广泛传播数据，首先是 d1、d2 和 no，然后是其他列，包括 [=20] =]、farmer_type 和 farmer_category.

library(tidyverse)

df1 <- mydata %>%
  mutate(d_var = rep(c("d1", "d2", "no"), times = 3)) %>%
  separate(Farmer.Name, into = c("Var", "Val"), sep = ":") %>%
  separate(Var, into = c("Name", "Var"), sep = "(?=Gender)", fill = "left") %>%
  mutate_at(c("Name", "Var"), trimws) %>%
  fill(v1, adress, amount, Name, .direction = "down") %>%
  mutate(Var = gsub(" ", "_", Var)) 

df1 %>%
  pivot_wider(id_cols = c(v1, Name, adress, amount), names_from = d_var, values_from = d1..d2..no) %>%
  left_join(pivot_wider(df1, id_cols = c(v1, Name, adress, amount), names_from = Var, values_from = Val))

输出

# A tibble: 3 x 10
     v1 Name    adress amount d1         d2    no        Gender farmer_type farmer_category
  <dbl> <chr>   <chr>   <dbl> <chr>      <chr> <chr>     <chr>  <chr>       <chr>          
1     1 S Jacob k11        25 27/01/2020 43832 KE004421  male   "marginal"  general        
2     2 J Isac  k12        25 43832      43832 KE003443  Female " large"    general        
3     3 P Kumar k13        32 31/12/2019 43832 KE0001512 Male   "small"     general

如何整理包含多个信息的列的数据集-样本数据放置？

How to tidy the data set with column containing multiple information-Sample data put?

r

tidy