R 写入函数…接收变量为'的错误；找不到

我不明白为什么我会出错

Error: object 'grade' not found

在编写函数时

等级显然在数据集中，并包含在函数中。如果我不使用函数，只使用

dat%>%
cohort.fun()%>%
group_by(cohort, variable, timepoint)%>%
    summarize(perf_measure = mean(measure))

一切正常。以下是我正在使用的两个函数：

library(reshape2)
library(tidyverse)

cohort.fun <- function(dat){
  dat%>%
    mutate(grade = as.numeric(grade))%>%
    mutate(cohort = ifelse(grade%in%c(3,4),3,ifelse(
      grade%in%c(5,6), 5, ifelse(
        grade%in%c(7,8), 7, grade))))%>%
    mutate(cohort = as.character(cohort))
}

melt.fun <- function(dat){
   melt(c("pid", "grade", "timepoint"), value.name = "measure")%>%
  cohort.fun()
}

我得到了上面的错误。有什么想法吗？多谢

以下是dput：

structure(list(pid = c("ADMIN-UCSF-bo004", "ADMIN-UCSF-bo005", 
"ADMIN-UCSF-bo008", "ADMIN-UCSF-bo010", "ADMIN-UCSF-bo011", "ADMIN-UCSF-bo012", 
"ADMIN-UCSF-bo013", "ADMIN-UCSF-bo014", "ADMIN-UCSF-bo015", "ADMIN-UCSF-bo016"
), grade = c("3", "3", "3", "3", "3", "3", "3", "3", "3", "3"
), RC1 = c(-1.81295211570392, -1.31252376878321, -1.1701654183369, 
-1.58244557144815, -1.95383829351231, -0.516109923323212, -0.370765686983851, 
-1.93212644807752, -1.6241046548069, -1.34160382084709), RC2 = c(-0.363819589341912, 
0.268206917949323, -2.24123725035034, -0.25274997192688, 0.313608190056975, 
-0.0393486670413662, -0.0623610937831014, 0.803692668734253, 
0.416065992573585, -0.069880541013785), RC3 = c(-2.69157047028032, 
-0.822917456389246, -1.52186068360016, -0.590070546800741, 0.583790188582597, 
-0.253888391947117, 1.22197349838073, -1.63335701437031, 1.24595192142446, 
0.0191275904777839), timepoint = c(1, 1, 1, 1, 1, 1, 1, 1, 1, 
1)), class = c("tbl_df", "tbl", "data.frame"), row.names = c(NA, 
-10L))

---

在你的函数

melt.fun

中，你实际传递给

队列的是什么。fun

是

melt

的结果，而不是

dat

。Ergo

队列。fun

未找到其参数

定义

melt.fun

如下：

melt.fun <- function(dat){
   melt(dat, c("pid", "grade", "timepoint"), value.name = "measure") %>%
  cohort.fun()
}

melt.fun%
3.fun（）
}

我们应该做到这一点。您可以看到magrittr的正向管道操作符的用法

编辑：我在这里包含了整个脚本，以便大家可以看到我到目前为止所做的工作：

#Loading libraries
library(tidyverse)
library(reshape2)

#Loading data
dat <- structure(list(pid = c("ADMIN-UCSF-bo004", "ADMIN-UCSF-bo005", 
                              "ADMIN-UCSF-bo008", "ADMIN-UCSF-bo010", "ADMIN-UCSF-bo011", "ADMIN-UCSF-bo012", "ADMIN-UCSF-bo013", "ADMIN-UCSF-bo014", "ADMIN-UCSF-bo015", "ADMIN-UCSF-bo016"), grade = c("3", "3", "3", "3", "3", "3", "3", "3", "3", "3"), RC1 = c(-1.81295211570392, -1.31252376878321, -1.1701654183369, -1.58244557144815, -1.95383829351231, -0.516109923323212, -0.370765686983851, -1.93212644807752, -1.6241046548069, -1.34160382084709), RC2 = c(-0.363819589341912, 0.268206917949323, -2.24123725035034, -0.25274997192688, 0.313608190056975, -0.0393486670413662, -0.0623610937831014, 0.803692668734253, 0.416065992573585, -0.069880541013785), RC3 = c(-2.69157047028032, -0.822917456389246, -1.52186068360016, -0.590070546800741, 0.583790188582597, -0.253888391947117, 1.22197349838073, -1.63335701437031, 1.24595192142446, 0.0191275904777839), timepoint = c(1, 1, 1, 1, 1, 1, 1, 1, 1, 1)), class = c("tbl_df", "tbl", "data.frame"), row.names = c(NA, -10L))

#Defining functions
#Function1
cohort.fun <- function(dat){
  dat%>%
    mutate(grade = as.numeric(grade))%>%
    mutate(cohort = ifelse(grade%in%c(3,4),3,ifelse(
      grade%in%c(5,6), 5, ifelse(
        grade%in%c(7,8), 7, grade))))%>%
    mutate(cohort = as.character(cohort))
}

#Function2
melt.fun <- function(dat){
  melt(dat, c("pid", "grade", "timepoint"), value.name = "measure") %>%
  cohort.fun()
}

#Executing
dat%>%
  melt.fun()

#Result
> dat%>%
+   melt.fun()
                pid grade timepoint variable     measure cohort
1  ADMIN-UCSF-bo004     3         1      RC1 -1.81295212      3
2  ADMIN-UCSF-bo005     3         1      RC1 -1.31252377      3
3  ADMIN-UCSF-bo008     3         1      RC1 -1.17016542      3
4  ADMIN-UCSF-bo010     3         1      RC1 -1.58244557      3
5  ADMIN-UCSF-bo011     3         1      RC1 -1.95383829      3
6  ADMIN-UCSF-bo012     3         1      RC1 -0.51610992      3
7  ADMIN-UCSF-bo013     3         1      RC1 -0.37076569      3
8  ADMIN-UCSF-bo014     3         1      RC1 -1.93212645      3
9  ADMIN-UCSF-bo015     3         1      RC1 -1.62410465      3
10 ADMIN-UCSF-bo016     3         1      RC1 -1.34160382      3
11 ADMIN-UCSF-bo004     3         1      RC2 -0.36381959      3
12 ADMIN-UCSF-bo005     3         1      RC2  0.26820692      3
13 ADMIN-UCSF-bo008     3         1      RC2 -2.24123725      3
14 ADMIN-UCSF-bo010     3         1      RC2 -0.25274997      3
15 ADMIN-UCSF-bo011     3         1      RC2  0.31360819      3
16 ADMIN-UCSF-bo012     3         1      RC2 -0.03934867      3
17 ADMIN-UCSF-bo013     3         1      RC2 -0.06236109      3
18 ADMIN-UCSF-bo014     3         1      RC2  0.80369267      3
19 ADMIN-UCSF-bo015     3         1      RC2  0.41606599      3
20 ADMIN-UCSF-bo016     3         1      RC2 -0.06988054      3
21 ADMIN-UCSF-bo004     3         1      RC3 -2.69157047      3
22 ADMIN-UCSF-bo005     3         1      RC3 -0.82291746      3
23 ADMIN-UCSF-bo008     3         1      RC3 -1.52186068      3
24 ADMIN-UCSF-bo010     3         1      RC3 -0.59007055      3
25 ADMIN-UCSF-bo011     3         1      RC3  0.58379019      3
26 ADMIN-UCSF-bo012     3         1      RC3 -0.25388839      3
27 ADMIN-UCSF-bo013     3         1      RC3  1.22197350      3
28 ADMIN-UCSF-bo014     3         1      RC3 -1.63335701      3
29 ADMIN-UCSF-bo015     3         1      RC3  1.24595192      3
30 ADMIN-UCSF-bo016     3         1      RC3  0.01912759      3

#加载库
图书馆（tidyverse）
图书馆（E2）
#加载数据
dat%
突变（队列=ifelse）（等级%c（3,4），3，ifelse(
%c（5,6），5，如果其他(
%c（7,8），7，年级）中的年级百分比%>%
变异（队列=作为字符（队列））
}
#功能2
融化。乐趣%
3.fun（）
}
#执行
dat%>%
melt.fun（）
#结果
>dat%>%
+melt.fun（）
pid等级时间点变量测量队列
1 ADMIN-UCSF-bo004 3 1 RC1-1.81295212 3
2 ADMIN-UCSF-bo005 3 RC1-1.31252377 3
3 ADMIN-UCSF-bo008 3 RC1-1.17016542 3
4 ADMIN-UCSF-bo010 3 RC1-1.58244557 3
5 ADMIN-UCSF-bo011 3 RC1-1.953829 3
6 ADMIN-UCSF-bo012 3 RC1-0.51610992 3
7 ADMIN-UCSF-bo013 3 1 RC1-0.37076569 3
8 ADMIN-UCSF-bo014 3 1 RC1-1.93212645 3
9 ADMIN-UCSF-bo015 3 RC1-1.62410465 3
10 ADMIN-UCSF-bo016 3 RC1-1.34160382 3
11 ADMIN-UCSF-bo004 3 1 RC2-0.36381959 3
12 ADMIN-UCSF-bo005 3 1 RC2 0.26820692 3
13 ADMIN-UCSF-bo008 3 1 RC2-2.24123725 3
14 ADMIN-UCSF-bo010 3 RC2-0.25274997 3
15 ADMIN-UCSF-bo011 3 1 RC2 0.31360819 3
16 ADMIN-UCSF-bo012 3 RC2-0.03934867 3
17 ADMIN-UCSF-bo013 3 1 RC2-0.06236109 3
18 ADMIN-UCSF-bo014 3 1 RC2 0.80369267 3
19 ADMIN-UCSF-bo015 3 1 RC2 0.41606599 3
20 ADMIN-UCSF-bo016 3 1 RC2-0.06988054 3
21 ADMIN-UCSF-bo004 3 1 RC3-2.69157047 3
22 ADMIN-UCSF-bo005 3 1 RC3-0.82291746 3
23 ADMIN-UCSF-bo008 3 1 RC3-1.52186068 3
24 ADMIN-UCSF-bo010 3 1 RC3-0.59007055 3
25 ADMIN-UCSF-bo011 3 1 RC3 0.58379019 3
26 ADMIN-UCSF-bo012 3 RC3-0.25388839 3
27 ADMIN-UCSF-bo013 3 1 RC3 1.22197350 3
28 ADMIN-UCSF-bo014 3 RC3-1.63335701 3
29 ADMIN-UCSF-bo015 3 1 RC3 1.24595192 3
30 ADMIN-UCSF-bo016 3 1 RC3 0.01912759 3

在您的函数中

melt.fun

您实际传递给

队列的内容。fun

是

melt

的结果，而不是

dat

。Ergo

队列。fun

未找到其参数

定义

melt.fun

如下：

melt.fun <- function(dat){
   melt(dat, c("pid", "grade", "timepoint"), value.name = "measure") %>%
  cohort.fun()
}

melt.fun%
3.fun（）
}

我们应该做到这一点。您可以看到magrittr的正向管道操作符的用法

编辑：我在这里包含了整个脚本，以便大家可以看到我到目前为止所做的工作：

#Loading libraries
library(tidyverse)
library(reshape2)

#Loading data
dat <- structure(list(pid = c("ADMIN-UCSF-bo004", "ADMIN-UCSF-bo005", 
                              "ADMIN-UCSF-bo008", "ADMIN-UCSF-bo010", "ADMIN-UCSF-bo011", "ADMIN-UCSF-bo012", "ADMIN-UCSF-bo013", "ADMIN-UCSF-bo014", "ADMIN-UCSF-bo015", "ADMIN-UCSF-bo016"), grade = c("3", "3", "3", "3", "3", "3", "3", "3", "3", "3"), RC1 = c(-1.81295211570392, -1.31252376878321, -1.1701654183369, -1.58244557144815, -1.95383829351231, -0.516109923323212, -0.370765686983851, -1.93212644807752, -1.6241046548069, -1.34160382084709), RC2 = c(-0.363819589341912, 0.268206917949323, -2.24123725035034, -0.25274997192688, 0.313608190056975, -0.0393486670413662, -0.0623610937831014, 0.803692668734253, 0.416065992573585, -0.069880541013785), RC3 = c(-2.69157047028032, -0.822917456389246, -1.52186068360016, -0.590070546800741, 0.583790188582597, -0.253888391947117, 1.22197349838073, -1.63335701437031, 1.24595192142446, 0.0191275904777839), timepoint = c(1, 1, 1, 1, 1, 1, 1, 1, 1, 1)), class = c("tbl_df", "tbl", "data.frame"), row.names = c(NA, -10L))

#Defining functions
#Function1
cohort.fun <- function(dat){
  dat%>%
    mutate(grade = as.numeric(grade))%>%
    mutate(cohort = ifelse(grade%in%c(3,4),3,ifelse(
      grade%in%c(5,6), 5, ifelse(
        grade%in%c(7,8), 7, grade))))%>%
    mutate(cohort = as.character(cohort))
}

#Function2
melt.fun <- function(dat){
  melt(dat, c("pid", "grade", "timepoint"), value.name = "measure") %>%
  cohort.fun()
}

#Executing
dat%>%
  melt.fun()

#Result
> dat%>%
+   melt.fun()
                pid grade timepoint variable     measure cohort
1  ADMIN-UCSF-bo004     3         1      RC1 -1.81295212      3
2  ADMIN-UCSF-bo005     3         1      RC1 -1.31252377      3
3  ADMIN-UCSF-bo008     3         1      RC1 -1.17016542      3
4  ADMIN-UCSF-bo010     3         1      RC1 -1.58244557      3
5  ADMIN-UCSF-bo011     3         1      RC1 -1.95383829      3
6  ADMIN-UCSF-bo012     3         1      RC1 -0.51610992      3
7  ADMIN-UCSF-bo013     3         1      RC1 -0.37076569      3
8  ADMIN-UCSF-bo014     3         1      RC1 -1.93212645      3
9  ADMIN-UCSF-bo015     3         1      RC1 -1.62410465      3
10 ADMIN-UCSF-bo016     3         1      RC1 -1.34160382      3
11 ADMIN-UCSF-bo004     3         1      RC2 -0.36381959      3
12 ADMIN-UCSF-bo005     3         1      RC2  0.26820692      3
13 ADMIN-UCSF-bo008     3         1      RC2 -2.24123725      3
14 ADMIN-UCSF-bo010     3         1      RC2 -0.25274997      3
15 ADMIN-UCSF-bo011     3         1      RC2  0.31360819      3
16 ADMIN-UCSF-bo012     3         1      RC2 -0.03934867      3
17 ADMIN-UCSF-bo013     3         1      RC2 -0.06236109      3
18 ADMIN-UCSF-bo014     3         1      RC2  0.80369267      3
19 ADMIN-UCSF-bo015     3         1      RC2  0.41606599      3
20 ADMIN-UCSF-bo016     3         1      RC2 -0.06988054      3
21 ADMIN-UCSF-bo004     3         1      RC3 -2.69157047      3
22 ADMIN-UCSF-bo005     3         1      RC3 -0.82291746      3
23 ADMIN-UCSF-bo008     3         1      RC3 -1.52186068      3
24 ADMIN-UCSF-bo010     3         1      RC3 -0.59007055      3
25 ADMIN-UCSF-bo011     3         1      RC3  0.58379019      3
26 ADMIN-UCSF-bo012     3         1      RC3 -0.25388839      3
27 ADMIN-UCSF-bo013     3         1      RC3  1.22197350      3
28 ADMIN-UCSF-bo014     3         1      RC3 -1.63335701      3
29 ADMIN-UCSF-bo015     3         1      RC3  1.24595192      3
30 ADMIN-UCSF-bo016     3         1      RC3  0.01912759      3

#加载库
图书馆（tidyverse）
图书馆（E2）
#加载数据
dat%
突变（队列=ifelse）（等级%c（3,4），3，ifelse(
%c（5,6），5，如果其他(
%c（7,8），7，年级）中的年级百分比%>%
变异（队列=作为字符（队列））
}
#功能2
融化。乐趣%
3.fun（）
}
#执行
dat%>%
melt.fun（）
#结果
>dat%>%
+melt.fun（）
pid等级时间点变量测量队列
1 ADMIN-UCSF-bo004 3 1 RC1-1.81295212 3
2 ADMIN-UCSF-bo005 3 RC1-1.31252377 3
3 ADMIN-UCSF-bo008 3 RC1-1.17016542 3
4 ADMIN-UCSF-bo010 3 RC1-1.58244557 3
5 ADMIN-UCSF-bo011 3 RC1-1.953829 3
6 ADMIN-UCSF-bo012 3 RC1-0.51610992 3
7 ADMIN-UCSF-bo013 3 1 RC1-0.37076569 3
8 ADMIN-UCSF-bo014 3 1 RC1-1.93212645 3
9 ADMIN-UCSF-bo015 3 RC1-1.62410465 3
10 ADMIN-UCSF-bo016 3 RC1-1.34160382 3
11 ADMIN-UCSF-bo004 3 1 RC2-0.36381959 3
12 ADMIN-UCSF-bo005 3 1 RC2 0.26820692 3
13 ADMIN-UCSF-bo008 3 1 RC2-2.24123725 3
14 ADMIN-UCSF-bo010 3 RC2-0.25274997 3
15 ADMIN-UCSF-bo011 3 1 RC2 0.31360819 3
16 ADMIN-UCSF-bo012 3 RC2-0.03934867 3
17 ADMIN-UCSF-bo013 3 1 RC2-0.06236109 3
18 ADMIN-UCSF-bo014 3 1 RC2 0.80369267 3
19 ADMIN-UCSF-bo015 3 1 RC2 0.41606599 3
20 ADMIN-UCSF-bo016 3 1 RC2-0.06988054 3
21 ADMIN-UCSF-bo004 3 1 RC3-2.69157047 3
22 ADMIN-UCSF-bo005 3 1 RC3-0.82291746 3
23 ADMIN-UCSF-bo008 3 1 RC3-1.52186068 3
24 ADMIN-UCSF-bo010 3 1 RC3-0.59007055 3
25 ADMIN-UCSF-bo011 3 1 RC3 0.58379019 3
26 ADMIN-UCSF-bo012 3 RC3-0.25388839 3
27 ADMIN-UCSF-bo013 3 1 RC3 1.22197350 3
28 ADMIN-UCSF-bo014 3 RC3-1。