英文:
Transform a dataframe with column value as column names in R
问题
我理解你的需求，你想将原始的数据框按照特定的方式进行转换，使其具有类似于示例中的结构。你提到尝试使用pivot_wider，但似乎没有成功。以下是你提供的代码的修改版本：
# 使用pivot_wider重新尝试df_transformed <- df %>%pivot_wider(names_from = date,values_from = c(price, num_floors),names_glue = "{.value}_{date}",values_fill = list(price = NaN, num_floors = NaN))
这将按照你描述的方式转换数据框，每列包含给定日期的价格和楼层数。每列中，前两行是第一个房屋的数据，接下来两行是第二个房屋的数据，并使用NaN填充缺失的条目。
英文:
I have a dataframe like this
date price num_floors house1 2023-01-01 94.30076 3 A2 2023-01-01 95.58771 2 B3 2023-01-02 102.78559 1 C4 2023-01-03 93.29053 3 D
and I want to change it, so that each column contains the prices and num_floors for all houses for a given date. For one column, the first two rows of a column refer to the first house, the next two to the second house. The remaining entries without data are filled with the missing value NaN.
Now, I want to transform the above dataframe, so that it has a similar structure as the following:
2023-01-01 2023-01-02 2023-01-031 94.30076 102.78559 93.290532 3 1 33 95.58771 NA NA4 2 NA NA
Each column contains the prices and num_floors for all houses for a given date. For one column, the first two rows of a column refer to the first house, the next two to the second house. The remaining entries without data are filled with the missing value NaN.
I tried with pivot_wider. It does not work:
# Pivot the dataframedf_transformed <- df %>%pivot_wider(names_from = date,values_from = c(price, num_floors),values_fill = NaN)
答案1
得分: 1
以下是翻译好的内容：
"首先，您可以将数据转换为长格式，然后在添加行号后再转换为宽格式。"
"输出："
| `2023-01-01` | `2023-01-02` | `2023-01-03` || ------------ | ------------ | ------------ || <dbl> | <dbl> | <dbl> || 94.3 | 103. | 93.3 || 3 | 1 | 3 || 95.6 | NA | NA || 2 | NA | NA |
（我要注意的是，这种转换方式似乎不太可能是表示您的数据的最佳方式）。
英文:
You can pivot to long format first, and then go wide after adding a row number.
pivot_wider(pivot_longer(df, -date) %>% mutate(n=row_number(), .by=date),id_cols = n, names_from=date, values_from = value) %>% select(-n)
Output:
`2023-01-01` `2023-01-02` `2023-01-03`<dbl> <dbl> <dbl>1 94.3 103. 93.32 3 1 33 95.6 NA NA4 2 NA NA
(I will note that this transformation seem unlikely to be the optimal way to represent your data).
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