R: “保护” 代码免受 “参数暗示不同行数” 的影响

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英文:

R: "Protecting" Code Against " arguments imply differing number of rows"

问题

I am working with the R programming language.

Suppose I have the following dataset:

  1. library(dplyr)
  3. set.seed(123)
  5. Patient_ID = 1:5000
  6. gender <- c("Male","Female")
  7. gender <- sample(gender, 5000, replace=TRUE, prob=c(0.45, 0.55))
  8. Gender <- as.factor(gender)
  10. status <- c("Immigrant","Citizen")
  11. status <- sample(status, 5000, replace=TRUE, prob=c(0.3, 0.7))
  12. Status <- as.factor(status)
  14. Height = rnorm(5000, 150, 10)
  15. Weight = rnorm(5000, 90, 10)
  16. Hospital_Visits = sample.int(20,  5000, replace = TRUE)
  18. ################
  20. disease <- c("Yes","No")
  21. disease <- sample(disease, 5000, replace=TRUE, prob=c(0.4, 0.6))
  22. Disease <- as.factor(disease)
  24. ###################
  25. my_data = data.frame(Patient_ID, Gender, Status, Height, Weight, Hospital_Visits, Disease)

I am trying to calculate the min/max ranges for the height, weight, and hospital_visit variables based on 5 ntiles. I did this with the following code:

  1. table_data <- data.frame(
  2.  Groups = paste0("Group ", 1:5),
  3.  Min_Height = tapply(my_data$Height, ntile(my_data$Height, 5), min),
  4.  Max_Height = tapply(my_data$Height, ntile(my_data$Height, 5), max),
  5.  Min_Weight = tapply(my_data$Weight, ntile(my_data$Weight, 5), min),
  6.  Max_Weight = tapply(my_data$Weight, ntile(my_data$Weight, 5), max),
  7.  Min_Visits = tapply(my_data$Hospital_Visits, ntile(my_data$Hospital_Visits, 5), min),
  8.  Max_Visits = tapply(my_data$Hospital_Visits, ntile(my_data$Hospital_Visits, 5), max)
  9. )

My Question: Suppose now I want to repeat the above code - but for some of the variables have 5 ntiles and for some of the variables have 4 ntiles:

  1. table_data <- data.frame(
  2.  Groups = paste0("Group ", 1:5),
  3.  Min_Height = tapply(my_data$Height, ntile(my_data$Height, 5), min),
  4.  Max_Height = tapply(my_data$Height, ntile(my_data$Height, 5), max),
  5.  Min_Weight = tapply(my_data$Weight, ntile(my_data$Weight, 5), min),
  6.  Max_Weight = tapply(my_data$Weight, ntile(my_data$Weight, 5), max),
  7.  Min_Visits = tapply(my_data$Hospital_Visits, ntile(my_data$Hospital_Visits, 4), min),
  8.  Max_Visits = tapply(my_data$Hospital_Visits, ntile(my_data$Hospital_Visits, 4), max)
  9. )

I then get the following error:

  1. Error in data.frame(Groups = paste0("Group ", 1:5), Min_Height = tapply(my_data$Height,  : 
  2.  arguments imply differing number of rows: 5, 4

In general, is there something I can do to "protect" my R code from such errors? That is, in situations where I have a differing number of ntiles being calculated - can something be done to automatically assign NA values to groups which are not "relevant" for a specific variable?

  1.    Groups Min_Height Max_Height Min_Weight Max_Weight Min_Visits Max_Visits
  2. 1 Group 1   111.5468   141.4839   56.53098   81.83402          1          5
  3. 2 Group 2   141.4965   147.4422   81.85064   87.45406          5         10
  4. 3 Group 3   147.4487   152.3924   87.45935   92.72041         10         15
  5. 4 Group 4   152.4016   158.5178   92.72941   98.54624         16         20
  6. 5 Group 5   158.5187   188.4777   98.55533  121.02420         NA         NA

Thanks!

Note: Currently I am doing this manually (i.e. create a separate table for ntile = 4 and ntile = 5) and then merging the results - but ideally I would like to perform all ntile calculations within the same code.

英文:

I am working with the R programming language.

Suppose I have the following dataset:

  1. library(dplyr)
  3. set.seed(123)
  4. library(dplyr)
  6. Patient_ID = 1:5000
  7. gender &lt;- c(&quot;Male&quot;,&quot;Female&quot;)
  8. gender &lt;- sample(gender, 5000, replace=TRUE, prob=c(0.45, 0.55))
  9. Gender &lt;- as.factor(gender)
  12. status &lt;- c(&quot;Immigrant&quot;,&quot;Citizen&quot;)
  13. status &lt;- sample(status, 5000, replace=TRUE, prob=c(0.3, 0.7))
  14. Status  &lt;- as.factor(status )
  16. Height = rnorm(5000, 150, 10)
  17. Weight = rnorm(5000, 90, 10)
  18. Hospital_Visits = sample.int(20,  5000, replace = TRUE)
  20. ################
  22. disease &lt;- c(&quot;Yes&quot;,&quot;No&quot;)
  23. disease &lt;- sample(disease, 5000, replace=TRUE, prob=c(0.4, 0.6))
  24. Disease &lt;- as.factor(disease)
  26. ###################
  27. my_data = data.frame(Patient_ID, Gender, Status, Height, Weight, Hospital_Visits, Disease)

I am trying to calculate the min/max ranges for the height, weight and hospital_visit variables based on 5 ntiles. I did this with the following code:

  1. table_data &lt;- data.frame(
  2.  Groups = paste0(&quot;Group &quot;, 1:5),
  3.   Min_Height = tapply(my_data$Height, ntile(my_data$Height, 5), min),
  4.   Max_Height = tapply(my_data$Height, ntile(my_data$Height, 5), max),
  5.   Min_Weight = tapply(my_data$Weight, ntile(my_data$Weight, 5), min),
  6.   Max_Weight = tapply(my_data$Weight, ntile(my_data$Weight, 5), max),
  7.  Min_Visits = tapply(my_data$Hospital_Visits, ntile(my_data$Hospital_Visits, 5), min),
  8. Max_Visits = tapply(my_data$Hospital_Visits, ntile(my_data$Hospital_Visits, 5), max)
  9. )

My Question: Suppose now I want to repeat the above code - but for some of the variables have 5 ntiles and for some of the variables have 4 ntiles:

  1.   table_data &lt;- data.frame(
  2.      Groups = paste0(&quot;Group &quot;, 1:5),
  3.       Min_Height = tapply(my_data$Height, ntile(my_data$Height, 5), min),
  4.       Max_Height = tapply(my_data$Height, ntile(my_data$Height, 5), max),
  5.       Min_Weight = tapply(my_data$Weight, ntile(my_data$Weight, 5), min),
  6.       Max_Weight = tapply(my_data$Weight, ntile(my_data$Weight, 5), max),
  7.      Min_Visits = tapply(my_data$Hospital_Visits, ntile(my_data$Hospital_Visits, 4), min),
  8.     Max_Visits = tapply(my_data$Hospital_Visits, ntile(my_data$Hospital_Visits, 4), max)
  9.     )

I then get the following error:

  1. Error in data.frame(Groups = paste0(&quot;Group &quot;, 1:5), Min_Height = tapply(my_data$Height,  : 
  2.   arguments imply differing number of rows: 5, 4

I would have thought that a value of NA would have been inserted on the "Group 5" row for variables where ntile < 5 ... but instead, the entire code does not run now.

In general, is there something I can do to "protect" my R code from such errors? That is, in situations where I have a differing number of ntiles being calculated - can something be done to automatically assign NA values to groups which are not "relevant" for a specific variable?

  1.    Groups Min_Height Max_Height Min_Weight Max_Weight Min_Visits Max_Visits
  2. 1 Group 1   111.5468   141.4839   56.53098   81.83402          1          5
  3. 2 Group 2   141.4965   147.4422   81.85064   87.45406          5          10
  4. 3 Group 3   147.4487   152.3924   87.45935   92.72041         10          15
  5. 4 Group 4   152.4016   158.5178   92.72941   98.54624         16         20
  6. 5 Group 5   158.5187   188.4777   98.55533  121.02420         NA         NA

Thanks!

Note: Currently I am doing this manually (i.e. create a separate table for ntile = 4 and ntile = 5) and then merging the results - but ideally I would like to perform all ntile calculations within the same code.

答案1

得分: 2

I think you're asking yourself "How can I get the answer I want from the data I have?". I think a better question is "How do I construct my data to get the answer I want easily and robustly?".

The answer to the second question is by pivoting your input data. For example:

  1. my_data %>%
  2.   pivot_longer(
  3.     c(Height, Weight, Hospital_Visits), 
  4.     names_to = "Column", 
  5.     values_to = "Value"
  6.   ) 
  7. # A tibble: 15,000 × 6
  8.    Patient_ID Gender Status    Disease Column          Value
  9.         <int> <fct>  <fct>     <fct>   <chr>           <dbl>
  10.  1          1 Female Citizen   No      Height          145. 
  11.  2          1 Female Citizen   No      Weight          114. 
  12.  3          1 Female Citizen   No      Hospital_Visits   1  
  13.  4          2 Male   Immigrant No      Height          161. 
  14.  5          2 Male   Immigrant No      Weight           88.3
  15.  6          2 Male   Immigrant No      Hospital_Visits  18  
  16.  7          3 Female Immigrant Yes     Height          139. 
  17.  8          3 Female Immigrant Yes     Weight           99.3
  18.  9          3 Female Immigrant Yes     Hospital_Visits   6  
  19. 10          4 Male   Citizen   No      Height          165. 
  20. # … with 14,990 more rows
  21. # ℹ Use `print(n = ...)` to see more rows

Now we can easily calculate a "by-column" ntile by using group_map. (This function applies the function defined by its argument to each of the current groups of a data frame.)

Conventionally, the function takes two arguments, .x, which contains the data in the current group, and .y which is a single-row tibble that defines the current group.

Setting .keep to TRUE ensures that the group columns remain in .x. By default, they don't. group_map returns a list, so I use bind_rows to combine the results into a single data frame.

Note that I define the desired number of groups for each column in the original data frame in a vector.

  1. nGroups <- c("Height" = 5, "Weight" = 5, "Hospital_Visits" = 4)
  3. my_data %>%
  4.   pivot_longer(
  5.     c(Height, Weight, Hospital_Visits), 
  6.     names_to = "Column", 
  7.     values_to = "Value"
  8.   ) %>%
  9.   group_by(Column) %>%
  10.   group_map(
  11.     function(.x, .y) {
  12.       .x %>% mutate(Group = paste0("Group_", ntile(Value, nGroups[.y$Column])))
  13.     },
  14.     .keep = TRUE
  15.   ) %>%
  16.   bind_rows() 
  17. # A tibble: 15,000 × 7
  18.    Patient_ID Gender Status    Disease Column Value Group  
  19.         <int> <fct>  <fct>     <fct>   <chr>  <dbl> <chr>  
  20.  1          1 Female Citizen   No      Height  145. Group_2
  21.  2          2 Male   Immigrant No      Height  161. Group_5
  22.  3          3 Female Immigrant Yes     Height  139. Group_1
  23.  4          4 Male   Citizen   No      Height  165. Group_5
  24.  5          5 Male   Citizen   Yes     Height  159. Group_5
  25.  6          6 Female Citizen   Yes     Height  153. Group_4
  26.  7          7 Female Citizen   No      Height  156. Group_4
  27.  8          8 Male   Citizen   Yes     Height  152. Group_3
  28.  9          9 Male   Immigrant Yes     Height  146. Group_2
  29. 10         10 Female Citizen   No      Height  147. Group_2
  30. # … with 14,990 more rows
  31. # ℹ Use `print(n = ...)` to see more rows

Now I can calculate the summaries you want.

  1. my_data %>%
  2.   pivot_longer(
  3.     c(Height, Weight, Hospital_Visits), 
  4.     names_to = "Column", 
  5.     values_to = "Value"
  6.   ) %>%
  7.   group_by(Column) %>%
  8.   group_map(
  9.     function(.x, .y) {
  10.       .x %>% mutate(Group = paste0("Group_", ntile(Value, nGroups[.y$Column])))
  11.     },
  12.     .keep = TRUE
  13.   ) %>%
  14.   bind_rows() %>%
  15.   group_by(Column, Group) %>%
  16.   summarise(
  17.     Min = min(Value),
  18.     Max = max(Value),
  19.     .groups = "drop"
  20.   )
  21. # A tibble: 14 × 4
  22.    Column          Group     Min   Max
  23.    <chr>           <chr>   <dbl> <dbl>
  24.  1 Height          Group_1 112.  141. 
  25.  2 Height          Group_2 141.  147. 
  26.  3 Height          Group_3 147.  152. 
  27.  4 Height          Group_4 152.  159. 
  28.  5 Height          Group_5 159.  188. 
  29.  6 Hospital_Visits Group_1   1     5  
  30.  7 Hospital_Visits Group_2   5    10  
  31.  8 Hospital_Visits Group_3  10    15  
  32.  9 Hospital_Visits Group_4  16    20  
  33. 10 Weight          Group_1  56.5  81.8
  34. 11 Weight          Group_2  81.9  87.5
  35. 12 Weight          Group_3  87.5  92.7
  36. 13 Weight          Group_4  92.7  98.5
  37. 14 Weight          Group_5  98.6 121.

Personally, I'd keep the results in this format for further processing - because it's tidy. But often presentation works better in wider rather than long format. So when ready to present, you can:

  1. my_data %>%
  2.   pivot_longer(
  3.     c(Height, Weight, Hospital_Visits), 
  4.     names_to = "Column", 
  5.     values_to = "Value"
  6.   ) %>%
  7.   group_by(Column) %>%
  8.   group_map(
  9.     function(.x, .y) {
  10.       .x %>% mutate(Group = paste0("Group_", ntile(Value, nGroups[.y$Column])))
  11.     },
  12.     .keep = TRUE
  13.   ) %>%
  14.   bind_rows() %>%
  15.   group_by(Column, Group) %>%
  16.   summarise(
  17.     Min = min(Value),
  18.     Max = max(Value),
  19.     .groups = "drop"
  20.   ) %>%
  21.   pivot_wider(
  22.     id_cols = Group, 
  23.     values_from = c(Min, Max),
  24.     names_from = Column,
  27. <details>
  28. <summary>英文:</summary>
  30. I think you&#39;re asking yourself &quot;How can I get the answer I want from the data I have?&quot;.  I think a better question is &quot;How do I construct my data to get the answer I want easily and robustly?&quot;.
  32. The answer to the second question is by pivoting your input data.  For example:

my_data %>%
pivot_longer(
c(Height, Weight, Hospital_Visits),
names_to = "Column",
values_to = "Value"
)

A tibble: 15,000 × 6

Patient_ID Gender Status Disease Column Value
<int> <fct> <fct> <fct> <chr> <dbl>
1 1 Female Citizen No Height 145.
2 1 Female Citizen No Weight 114.
3 1 Female Citizen No Hospital_Visits 1
4 2 Male Immigrant No Height 161.
5 2 Male Immigrant No Weight 88.3
6 2 Male Immigrant No Hospital_Visits 18
7 3 Female Immigrant Yes Height 139.
8 3 Female Immigrant Yes Weight 99.3
9 3 Female Immigrant Yes Hospital_Visits 6
10 4 Male Citizen No Height 165.

… with 14,990 more rows

ℹ Use print(n = ...) to see more rows

  2. Now we can easily calculate a &quot;by-column&quot; ntile by using `group_map`.  (This function applies the function defined by its argument to each of the current groups of a data frame.)  
  4. Conventionally, the function takes two arguments, `.x`, which contains the data in the current group, and `.y` which is a single-row tibble that _defines_ the current group.
  6. Setting `.keep` to `TRUE` ensures that the group columns remain in `.x`.  By default, they don&#39;t. `group_map` returns a list, so I use `bind_rows` to combine the results into a single data frame.
  8. Note that I define the desired number of groups for each column in the original data frame in a vector.

nGroups <- c("Height" = 5, "Weight" = 5, "Hospital_Visits" = 4)

my_data %>%
pivot_longer(
c(Height, Weight, Hospital_Visits),
names_to = "Column",
values_to = "Value"
) %>%
group_by(Column) %>%
group_map(
function(.x, .y) {
.x %>% mutate(Group = paste0("Group_", ntile(Value, nGroups[.y$Column])))
},
.keep = TRUE
) %>%
bind_rows()

A tibble: 15,000 × 7

Patient_ID Gender Status Disease Column Value Group
<int> <fct> <fct> <fct> <chr> <dbl> <chr>
1 1 Female Citizen No Height 145. Group_2
2 2 Male Immigrant No Height 161. Group_5
3 3 Female Immigrant Yes Height 139. Group_1
4 4 Male Citizen No Height 165. Group_5
5 5 Male Citizen Yes Height 159. Group_5
6 6 Female Citizen Yes Height 153. Group_4
7 7 Female Citizen No Height 156. Group_4
8 8 Male Citizen Yes Height 152. Group_3
9 9 Male Immigrant Yes Height 146. Group_2
10 10 Female Citizen No Height 147. Group_2

… with 14,990 more rows

ℹ Use print(n = ...) to see more rows

  2. Now I can calculate the summaries you want.

my_data %>%
pivot_longer(
c(Height, Weight, Hospital_Visits),
names_to = "Column",
values_to = "Value"
) %>%
group_by(Column) %>%
group_map(
function(.x, .y) {
.x %>% mutate(Group = paste0("Group_", ntile(Value, nGroups[.y$Column])))
},
.keep = TRUE
) %>%
bind_rows() %>%
group_by(Column, Group) %>%
summarise(
Min = min(Value),
Max = max(Value),
.groups = "drop"
)

A tibble: 14 × 4

Column Group Min Max
<chr> <chr> <dbl> <dbl>
1 Height Group_1 112. 141.
2 Height Group_2 141. 147.
3 Height Group_3 147. 152.
4 Height Group_4 152. 159.
5 Height Group_5 159. 188.
6 Hospital_Visits Group_1 1 5
7 Hospital_Visits Group_2 5 10
8 Hospital_Visits Group_3 10 15
9 Hospital_Visits Group_4 16 20
10 Weight Group_1 56.5 81.8
11 Weight Group_2 81.9 87.5
12 Weight Group_3 87.5 92.7
13 Weight Group_4 92.7 98.5
14 Weight Group_5 98.6 121.

  2. Personally, I&#39;d keep the results in this format for further processing - because it&#39;s tidy.  But often _presentation_ works better in wider rather than long format.  So when ready to present, you can:

my_data %>%
pivot_longer(
c(Height, Weight, Hospital_Visits),
names_to = "Column",
values_to = "Value"
) %>%
group_by(Column) %>%
group_map(
function(.x, .y) {
.x %>% mutate(Group = paste0("Group_", ntile(Value, nGroups[.y$Column])))
},
.keep = TRUE
) %>%
bind_rows() %>%
group_by(Column, Group) %>%
summarise(
Min = min(Value),
Max = max(Value),
.groups = "drop"
) %>%
pivot_wider(
id_cols = Group,
values_from = c(Min, Max),
names_from = Column,
names_glue = '{.value}_{Column}'
)

A tibble: 5 × 7

Group Min_Height Min_Hospital_Visits Min_Weight Max_Height Max_Hospital_Visits Max_Weight
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 Group_1 112. 1 56.5 141. 5 81.8
2 Group_2 141. 5 81.9 147. 10 87.5
3 Group_3 147. 10 87.5 152. 15 92.7
4 Group_4 152. 16 92.7 159. 20 98.5
5 Group_5 159. NA 98.6 188. NA 121.

  2. This approach is robust against changes in column names and the desired number of groups.  Hence, it &quot;protects&quot; your code as you request.  The only thing you need to check if you have different variables in the future is that you change the value of `nGroups` accordingly.  if you have extra columns in `nGroups`, that&#39;s fine.  If you have a column that you want to summarise that doesn&#39;t have an entry in `nGroups`, then you&#39;ll get an error.  But it&#39;s easy to protect yourself against that.  To *really* protect yourself, define `nGroups` at the start of your code and change the initial `pivot_longer` to

my_data %>%
pivot_longer(
names(nGroups),
names_to = "Column",
values_to = "Value"
) ...

  2. </details>
  6. # 答案2
  7. **得分**: 1
  9. 以下是翻译好的部分：
  11. "pivot_longer()" 函数将数据首先进行长格式的变换，通过调用 `ntile()` 计算分组变量，然后根据变量名称和分位数组进行分组。首先，我们可以生成数据。
  13. ```r
  14. library(dplyr)
  15. library(tidyr)
  16. set.seed(123)
  18. Patient_ID = 1:5000
  19. gender <- c("Male","Female")
  20. gender <- sample(gender, 5000, replace=TRUE, prob=c(0.45, 0.55))
  21. Gender <- as.factor(gender)
  23. status <- c("Immigrant","Citizen")
  24. status <- sample(status, 5000, replace=TRUE, prob=c(0.3, 0.7))
  25. Status  <- as.factor(status )
  27. Height = rnorm(5000, 150, 10)
  28. Weight = rnorm(5000, 90, 10)
  29. Hospital_Visits = sample.int(20,  5000, replace = TRUE)
  31. ################
  33. disease <- c("Yes","No")
  34. disease <- sample(disease, 5000, replace=TRUE, prob=c(0.4, 0.6))
  35. Disease <- as.factor(disease)
  37. ###################
  38. my_data = data.frame(Patient_ID, Gender, Status, Height, Weight, Hospital_Visits, Disease)

接下来，我们实际进行汇总。

  1. my_data %>%
  2.   pivot_longer(Height:Hospital_Visits, names_to="vbl", values_to="vals") %>%
  3.   group_by(vbl) %>%
  4.   mutate(group = case_when(
  5.     vbl %in% c("Height", "Weight") ~ ntile(vals, 5), 
  6.     vbl %in% c("Hospital_Visits") ~ ntile(vals, 4)), 
  7.     vbl = gsub("Hospital_", "", vbl)) %>%
  8.   group_by(vbl, group) %>%
  9.   reframe(min = min(vals), 
  10.           max=max(vals)) %>%
  11.   pivot_wider(names_from = "vbl", 
  12.               names_glue = "{vbl}_{.value}",
  13.               values_from = c("min", "max"))
  14. #> # A tibble: 5 × 7
  15. #>   group Height_min Visits_min Weight_min Height_max Visits_max Weight_max
  16. #>   <int>      <dbl>      <dbl>      <dbl>      <dbl>      <dbl>      <dbl>
  17. #> 1     1       112.          1       56.5       141.          5       81.8
  18. #> 2     2       141.          5       81.9       147.         10       87.5
  19. #> 3     3       147.         10       87.5       152.         15       92.7
  20. #> 4     4       152.         16       92.7       159.         20       98.5
  21. #> 5     5       159.         NA       98.6       188.         NA      121.

创建于2023年06月25日，使用 reprex v2.0.2

英文:

It may be easier to pivot the data longer first, calculate the group variable by a call to ntile() and then group_by() on the variable name and quantile group. First, we can make the data.

  1. library(dplyr)
  2. library(tidyr)
  3. set.seed(123)
  5. Patient_ID = 1:5000
  6. gender &lt;- c(&quot;Male&quot;,&quot;Female&quot;)
  7. gender &lt;- sample(gender, 5000, replace=TRUE, prob=c(0.45, 0.55))
  8. Gender &lt;- as.factor(gender)
  11. status &lt;- c(&quot;Immigrant&quot;,&quot;Citizen&quot;)
  12. status &lt;- sample(status, 5000, replace=TRUE, prob=c(0.3, 0.7))
  13. Status  &lt;- as.factor(status )
  15. Height = rnorm(5000, 150, 10)
  16. Weight = rnorm(5000, 90, 10)
  17. Hospital_Visits = sample.int(20,  5000, replace = TRUE)
  19. ################
  21. disease &lt;- c(&quot;Yes&quot;,&quot;No&quot;)
  22. disease &lt;- sample(disease, 5000, replace=TRUE, prob=c(0.4, 0.6))
  23. Disease &lt;- as.factor(disease)
  25. ###################
  26. my_data = data.frame(Patient_ID, Gender, Status, Height, Weight, Hospital_Visits, Disease)

Below, we actually do the summarizing.

  1. my_data %&gt;% 
  2.   pivot_longer(Height:Hospital_Visits, names_to=&quot;vbl&quot;, values_to=&quot;vals&quot;) %&gt;% 
  3.   group_by(vbl) %&gt;% 
  4.   mutate(group = case_when(
  5.     vbl %in% c(&quot;Height&quot;, &quot;Weight&quot;) ~ ntile(vals, 5), 
  6.     vbl %in% c(&quot;Hospital_Visits&quot;) ~ ntile(vals, 4)), 
  7.     vbl = gsub(&quot;Hospital_&quot;, &quot;&quot;, vbl)) %&gt;% 
  8.   group_by(vbl, group) %&gt;% 
  9.   reframe(min = min(vals), 
  10.           max=max(vals)) %&gt;%
  11.   pivot_wider(names_from = &quot;vbl&quot;, 
  12.               names_glue = &quot;{vbl}_{.value}&quot;,
  13.               values_from = c(&quot;min&quot;, &quot;max&quot;))
  14. #&gt; # A tibble: 5 &#215; 7
  15. #&gt;   group Height_min Visits_min Weight_min Height_max Visits_max Weight_max
  16. #&gt;   &lt;int&gt;      &lt;dbl&gt;      &lt;dbl&gt;      &lt;dbl&gt;      &lt;dbl&gt;      &lt;dbl&gt;      &lt;dbl&gt;
  17. #&gt; 1     1       112.          1       56.5       141.          5       81.8
  18. #&gt; 2     2       141.          5       81.9       147.         10       87.5
  19. #&gt; 3     3       147.         10       87.5       152.         15       92.7
  20. #&gt; 4     4       152.         16       92.7       159.         20       98.5
  21. #&gt; 5     5       159.         NA       98.6       188.         NA      121.

<sup>Created on 2023-06-25 with reprex v2.0.2</sup>

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  • 本文由 发表于 2023年6月26日 00:11:56
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