# 使用 pivot_longer 将宽格式数据转换为长格式 long_data <- wide_data %>% pivot_longer( cols = starts_with("var"), # 选择以 "var" 开头的列 names_to = "variable", # 新列名,存储原始列名 values_to = "value" # 新列名,存储原始值 ) print(long_data) 输出
上述代码中,我们首先创建了一个示例数据框data,其中包含了ID列和两个内部因子Factor1和Factor2的A、B两个水平。然后,我们使用pivot_longer()函数将数据从宽到长转换。在pivot_longer()函数中,我们通过cols参数指定要转换的列,使用names_to参数指定新列的名称,通过names_sep参数指定列名中的分...
billboard billboard %>% pivot_longer( cols = starts_with("wk"), names_to = "week", names_prefix = "wk", values_to = "rank", values_drop_na = TRUE ) 结果: (3.3) 选择特定列,并转换为长表的多列: # Multiple variables stored in column names who who %>% pivot_longer( cols = ...
x <- x |> pivot_longer(cols = starts_with("kpu_"), names_prefix = "kpu_", names_to = "unit", values_to = "n_unit") |> pivot_longer(cols = starts_with("sf_"), names_prefix = "sf_", names_to = "unit2", values_to = "sf_unit") 还有另一个: x <- x |> pivot_...
pivot_longer()"lengthens"数据,增加行数并减少列数。逆变换为pivot_wider() 在vignette("pivot")中了解更多信息。 用法 pivot_longer( data, cols,..., cols_vary ="fastest", names_to ="name", names_prefix =NULL, names_sep =NULL, names_pattern =NULL, ...
Using pivot_longer(), you can reshape this data into a more manageable format. Code: long_data_product <- pivot_longer(wide_data, cols = starts_with("Sales") | starts_with("Price"), names_to = c(".value", "Year"), names_pattern = "([A-Za-z]+)_(\\d+)") print(long_data...
survey_data_long <- pivot_longer(survey_data, cols = starts_with("Q"), names_to = "Question", values_to = "Response") In this example, the `starts_with` function is used to identify all columns starting with "Q," which are then melted using the pivot_longer function. The resulting...
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字符串中间)：名称模式我们可以从子字符串中创建三列，也可以通过不捕获pivot_longer中的内部数字（基于...
但是代码很混乱，我正在寻找一种更干净的方法来实现它。我有两组列需要并行透视。您可以在pivot_longer...