Question

我有一个数据帧（将其称为“ df”），其中包含相当数量的变量（数字，逻辑和字符），这些变量代表一项实验，其中不同类型的细胞从一种特定的培养基移至另一种，并且在特定时间对细胞进行定量。第一列和第二列分别保存“源”媒体的名称和单元格要移动到的媒体的名称；第三列描述了活动的量化时间，第四列是细胞类型，第五列是测量的活动，这很有趣。

我有两个主要问题，第一个是要知道是否有一种'R-esque'的方式来完成我获得第六列的工作，该列包含了值的增加/减少（以百分比为单位） “活动”相对于上一行中存在的活动，但以分组方式（每个分组由Cell.Type，Pre.Medium和Time组成）组成，因此这就是为什么每次Time的值为零时其值为NA

假设这是我的数据框（为了使我的问题更清楚，我对其进行了简化）：

df <- structure(list(Pre.Medium = c("Medium1", "Medium1", "Medium1", 
"Medium2", "Medium2", "Medium2", "Medium1", "Medium1", "Medium1", 
"Medium2", "Medium2", "Medium2"), Pos.Medium = c("Medium2", "Medium2", 
"Medium2", "Medium1", "Medium1", "Medium1", "Medium2", "Medium2", 
"Medium2", "Medium1", "Medium1", "Medium1"), Time = c(0, 2, 4, 
0, 2, 4, 0, 2, 4, 0, 2, 4), Cell.Type = c("Cell_A", "Cell_A", 
"Cell_A", "Cell_A", "Cell_A", "Cell_A", "Cell_B", "Cell_B", "Cell_B", 
"Cell_B", "Cell_B", "Cell_B"), Activity = c(0.5, 1, 2, 2, 1, 
0.5, 0.2, 0.8, 0.2, 0.2, 0.2, 0.4), Percent.Increase = c(NA, 
100, 100, NA, -50, -50, NA, 300, -75, NA, 0, 100), Primary.Increase = c(NA, 
TRUE, FALSE, NA, TRUE, FALSE, NA, TRUE, FALSE, NA, FALSE, FALSE
), Secondary.Increase = c(NA, FALSE, FALSE, NA, FALSE, FALSE, 
NA, FALSE, FALSE, NA, FALSE, TRUE)), class = c("spec_tbl_df", 
"tbl_df", "tbl", "data.frame"), row.names = c(NA, -12L), problems = structure(list(
    row = 1L, col = NA_character_, expected = "8 columns", actual = "9 columns", 
    file = "'new 2'"), row.names = c(NA, -1L), class = c("tbl_df", 
"tbl", "data.frame")), spec = structure(list(cols = list(Pre.Medium = structure(list(), class = c("collector_character", 
"collector")), Pos.Medium = structure(list(), class = c("collector_character", 
"collector")), Time = structure(list(), class = c("collector_double", 
"collector")), Cell.Type = structure(list(), class = c("collector_character", 
"collector")), Activity = structure(list(), class = c("collector_double", 
"collector")), Percent.Increase = structure(list(), class = c("collector_double", 
"collector")), Primary.Increase = structure(list(), class = c("collector_logical", 
"collector")), Secondary.Increase = structure(list(), class = c("collector_logical", 
"collector"))), default = structure(list(), class = c("collector_guess", 
"collector")), skip = 1), class = "col_spec"))

### Pre.Med Pos.Med Time  Cell.Type Activity  Percent.Increase  Primary.Increase Secondary.Increase
### Medium1 Medium2   0    Cell_A    0.5           NA           NA                NA 
### Medium1 Medium2   2    Cell_A    1             100          TRUE              FALSE
### Medium1 Medium2   4    Cell_A    2             100          FALSE             FALSE
### Medium2 Medium1   0    Cell_A    2             NA           NA                NA
### Medium2 Medium1   2    Cell_A    1            -50           TRUE              FALSE
### Medium2 Medium1   4    Cell_A    0.5          -50           FALSE             FALSE
### Medium1 Medium2   0    Cell_B    0.2           NA           NA                NA
### Medium1 Medium2   2    Cell_B    0.8           300          TRUE              FALSE
### Medium1 Medium2   4    Cell_B    0.2          -75           FALSE             FALSE
### Medium2 Medium1   0    Cell_B    0.2           NA           NA                NA
### Medium2 Medium1   2    Cell_B    0.2           0            FALSE             FALSE
### Medium2 Medium1   4    Cell_B    0.4           100          FALSE             TRUE

我使用了group_by和mutate函数，然后使用lag函数来计算上一行和上一行的增加/减少，是否有更好的方法呢？对于我的特定情况，滞后就足够了，但是如果我在每个“组”中进行了三次以上的时间测量并且需要落后于时间来进行计算，该怎么办？用我的方法，在某些时候我将不得不使用lag（lag（lag（lag（lag（lag（（Activity / lag（Activity））-1）* 100））））等。

另一件事是我无法以任何方式弄清楚的事情，它是通过将列“ Primary.Increase”和“ Secondary.Increase”变成“ long”数据集进入名为“ Increase.Type”的列，其中每个组（Cell.Type，Pre.Med和Time的组合）的值将由列名（Primary.Response或Secondary.Response）组成，其中其成员之一的值是TRUE。它应该看起来像这样：

df <- structure(list(Pre.Med = c("Medium1", "Medium1", "Medium1", "Medium2", 
"Medium2", "Medium2", "Medium1", "Medium1", "Medium1", "Medium2", 
"Medium2", "Medium2"), Pos.Med = c("Medium2", "Medium2", "Medium2", 
"Medium1", "Medium1", "Medium1", "Medium2", "Medium2", "Medium2", 
"Medium1", "Medium1", "Medium1"), Time = c(0, 2, 4, 0, 2, 4, 
0, 2, 4, 0, 2, 4), Cell.Type = c("Cell_A", "Cell_A", "Cell_A", 
"Cell_A", "Cell_A", "Cell_A", "Cell_B", "Cell_B", "Cell_B", "Cell_B", 
"Cell_B", "Cell_B"), Activity = c(0.5, 1, 2, 2, 1, 0.5, 0.2, 
0.8, 0.2, 0.2, 0.2, 0.4), Percent.Inc = c(NA, 100, 100, NA, -50, 
-50, NA, 300, -75, NA, 0, 100), Increase.Type = c("Primary.Increase", 
"Primary.Increase", "Primary.Increase", "Primary.Increase", "Primary.Increase", 
"Primary.Increase", "Primary.Increase", "Primary.Increase", "Primary.Increase", 
"Secondary.Increase", "Secondary.Increase", "Secondary.Increase"
)), class = c("spec_tbl_df", "tbl_df", "tbl", "data.frame"), row.names = c(NA, 
-12L), spec = structure(list(cols = list(Pre.Med = structure(list(), class = c("collector_character", 
"collector")), Pos.Med = structure(list(), class = c("collector_character", 
"collector")), Time = structure(list(), class = c("collector_double", 
"collector")), Cell.Type = structure(list(), class = c("collector_character", 
"collector")), Activity = structure(list(), class = c("collector_double", 
"collector")), Percent.Inc = structure(list(), class = c("collector_double", 
"collector")), Increase.Type = structure(list(), class = c("collector_character", 
"collector"))), default = structure(list(), class = c("collector_guess", 
"collector")), skip = 1), class = "col_spec"))

### Pre.Med Pos.Med Time  Cell.Type Activity    Percent.Inc Increase.Type 
### Medium1 Medium2   0    Cell_A    0.5           NA         Primary.Increase
### Medium1 Medium2   2    Cell_A    1             100        Primary.Increase
### Medium1 Medium2   4    Cell_A    2             100        Primary.Increase
### Medium2 Medium1   0    Cell_A    2             NA         Primary.Increase
### Medium2 Medium1   2    Cell_A    1            -50         Primary.Increase
### Medium2 Medium1   4    Cell_A    0.5          -50         Primary.Increase
### Medium1 Medium2   0    Cell_B    0.2           NA         Primary.Increase
### Medium1 Medium2   2    Cell_B    0.8           300        Primary.Increase
### Medium1 Medium2   4    Cell_B    0.2          -75         Primary.Increase
### Medium2 Medium1   0    Cell_B    0.2           NA         Secondary.Increase
### Medium2 Medium1   2    Cell_B    0.2           0          Secondary.Increase     
### Medium2 Medium1   4    Cell_B    0.4           100        Secondary.Increase

首先有没有办法做到这一点？我以为是这样，但是到目前为止我还没有做到：/ 我是R的一门新兴生物学专业的本科生，我很喜欢您可以用它做些什么，但是距离要擅长它还有很长的路要走。

非常感谢您的帮助。

Answer 1

我不确定我是否理解第一个问题。如果您执行以下操作：

library(dplyr)

df %>%
  group_by(Cell.Type, Pre.Medium, Pos.Medium) %>%
  arrange(Time, .by_group = TRUE) %>% # remove if Time is always ascending
  mutate(Percent.Increase = ((Activity / lag(Activity)) - 1) * 100)

Percent.Increase的计算被矢量化，因此Activity多久都没关系（另请参见下面的最后解释）。

对于第二个问题，如果我理解正确，您可以这样做：

df %>%
  group_by(Cell.Type, Pre.Medium, Pos.Medium) %>%
  mutate(Increase.Type = if (any(Secondary.Increase, na.rm = TRUE)) "Secondary.Increase" else "Primary.Increase") %>%
  select(-(Primary.Increase:Secondary.Increase))
# A tibble: 12 x 7
# Groups:   Cell.Type, Pre.Medium, Pos.Medium [4]
   Pre.Medium Pos.Medium  Time Cell.Type Activity Percent.Increase Increase.Type     
   <chr>      <chr>      <dbl> <chr>        <dbl>            <dbl> <chr>             
 1 Medium1    Medium2        0 Cell_A         0.5               NA Primary.Increase  
 2 Medium1    Medium2        2 Cell_A         1                100 Primary.Increase  
 3 Medium1    Medium2        4 Cell_A         2                100 Primary.Increase  
 4 Medium2    Medium1        0 Cell_A         2                 NA Primary.Increase  
 5 Medium2    Medium1        2 Cell_A         1                -50 Primary.Increase  
 6 Medium2    Medium1        4 Cell_A         0.5              -50 Primary.Increase  
 7 Medium1    Medium2        0 Cell_B         0.2               NA Primary.Increase  
 8 Medium1    Medium2        2 Cell_B         0.8              300 Primary.Increase  
 9 Medium1    Medium2        4 Cell_B         0.2              -75 Primary.Increase  
10 Medium2    Medium1        0 Cell_B         0.2               NA Secondary.Increase
11 Medium2    Medium1        2 Cell_B         0.2                0 Secondary.Increase
12 Medium2    Medium1        4 Cell_B         0.4              100 Secondary.Increase

mutate内部的转换会从组中看到所有个值，因此any(Secondary.Increase, na.rm = TRUE)一次接收所有元素，如果我们只返回1个值，它将被复制以适合组的大小。

有没有一种方法可以“合并”两列，其中新列的值是具有特定值的原始列的名称（按组）？

1 个答案: