Question

我需要一些使用R进行数据清理的帮助。我的CSV文件如下所示。

"id","gender","age","category1","category2","category3","category4","category5","category6","category7","category8","category9","category10"
1,"Male",22,"movies","music","travel","cloths","grocery",,,,,
2,"Male",28,"travel","books","movies",,,,,,,
3,"Female",27,"rent","fuel","grocery","cloths",,,,,,
4,"Female",22,"rent","grocery","travel","movies","cloths",,,,,
5,"Female",22,"rent","online-shopping","utiliy",,,,,,,

我需要重新格式化如下。

id gender age category            rank
1 Male    22  movies               1
1 Male    22  music                2
1 Male    22  travel               3
1 Male    22  cloths               4
1 Male    22  grocery              5
1 Male    22  books                NA
1 Male    22  rent                 NA
1 Male    22  fuel                 NA
1 Male    22  utility              NA
1 Male    22  online-shopping      NA
...................................
5 Female    22  movies             NA
5 Female    22  music              NA
5 Female    22  travel             NA
5 Female    22  cloths             NA
5 Female    22  grocery            NA
5 Female    22  books              NA
5 Female    22  rent               1
5 Female    22  fuel               NA
5 Female    22  utility            NA
5 Female    22  online-shopping    2

到目前为止，我的努力如下：

mini <- read.csv("~/MS/coding/mini.csv", header=FALSE)
mini_clean <- mini[-1,]
df_mini <- melt(df_clean, id.vars=c("V1","V2","V3"))
sqldf('select * from df_mini order by  "V1"')

现在我想知道填充所有缺失类别的最佳方法是什么，以及如何根据CSV文件中的位置对类别进行排名。为了更加清晰，请参阅上面的CSV文件和预期的输出。

Answer 1

text1='"id","gender","age","category1","category2","category3","category4","category5","category6","category7","category8","category9","category10"
1,"Male",22,"movies","music","travel","cloths","grocery",,,,,
2,"Male",28,"travel","books","movies",,,,,,,
3,"Female",27,"rent","fuel","grocery","cloths",,,,,,
4,"Female",22,"rent","grocery","travel","movies","cloths",,,,,
5,"Female",22,"rent","online-shopping","utiliy",,,,,,,'

d1 <- read.table(text=text1, sep=",", head=T, as.is=T)

library(reshape2)
d2 <- melt(d1, id.vars=c("id","gender","age"))
names(d2)[5] <- "category"
names(d2)[4] <- "rank"
d2$rank <- gsub("category", "", d2$rank)
head(d2)
#   id gender age rank category
# 1  1   Male  22    1   movies
# 2  2   Male  28    1   travel
# 3  3 Female  27    1     rent
# 4  4 Female  22    1     rent
# 5  5 Female  22    1     rent
# 6  1   Male  22    2    music

Answer 2

我们可以使用gather

中的tidyr

library(tidyr)
d2 <- gather(d1,  rank, category, -(1:3))  %>% 
                extract(rank, into='rank', '.*(\\d+)')
head(d2)
#  id gender age rank category
#1  1   Male  22    1   movies
#2  2   Male  28    1   travel
#3  3 Female  27    1     rent
#4  4 Female  22    1     rent
#5  5 Female  22    1     rent
#6  1   Male  22    2    music

需要帮助使用R进行数据清理

2 个答案: