R ifelse（）命令出现问题，其中看起来相同的两个不同数据帧中的两列未标识为相同_R_If Statement_Encoding_Match

R ifelse（）命令出现问题，其中看起来相同的两个不同数据帧中的两列未标识为相同

r if-statement encoding

R ifelse（）命令出现问题，其中看起来相同的两个不同数据帧中的两列未标识为相同,r,if-statement,encoding,match,R,If Statement,Encoding,Match,ifelse（）命令有一个问题，两个不同数据帧中看起来相同的两列没有被标识为相同。我可以使用任何指导来解决这个问题，这样代码就可以相互比较数据帧，并生成适当的输出，而不必亲自手动输入材料/键入文本这是我的两个起始数据集，df_1和df_2： > df_1 DV_name 1 submission_time_minutes 2 submission_time_minutes 3 WC 4

ifelse（）命令有一个问题，两个不同数据帧中看起来相同的两列没有被标识为相同。我可以使用任何指导来解决这个问题，这样代码就可以相互比较数据帧，并生成适当的输出，而不必亲自手动输入材料/键入文本

这是我的两个起始数据集，

df_1

和

df_2

：

> df_1
                   DV_name
1  submission_time_minutes
2  submission_time_minutes
3                       WC
4                       WC
5         Analytic_z_score
6         Analytic_z_score
7            Clout_z_score
8            Clout_z_score
9        Authentic_z_score
10       Authentic_z_score
11            Tone_z_score
12            Tone_z_score
13 submission_time_minutes
14 submission_time_minutes
15                      WC
16                      WC
17        Analytic_z_score
18        Analytic_z_score
19           Clout_z_score
20           Clout_z_score
21       Authentic_z_score
22       Authentic_z_score
23            Tone_z_score
24            Tone_z_score
25 submission_time_minutes
26 submission_time_minutes
27                      WC
28                      WC
29        Analytic_z_score
30        Analytic_z_score
31           Clout_z_score
32           Clout_z_score
33       Authentic_z_score
34       Authentic_z_score
35            Tone_z_score
36            Tone_z_score
37 submission_time_minutes
38 submission_time_minutes
39                      WC
40                      WC
41        Analytic_z_score
42        Analytic_z_score
43           Clout_z_score
44           Clout_z_score
45       Authentic_z_score
46       Authentic_z_score
47            Tone_z_score
48            Tone_z_score
> df_2
        Variable_analyses             Variable_label
1 submission_time_minutes Submission time in minutes
2                      WC                 Word count
3        Analytic_z_score             Analytic score
4           Clout_z_score                Clout score
5       Authentic_z_score            Authentic score
6            Tone_z_score                 Tone score

我想根据

df\u 1$DV\u name

和

df\u 2$Variable\u analysis

之间的匹配材料，创建一列

df\u 1$Variable\u label

，该列源自

df\u 2$Variable\u analysis


### creates matching variables, which removes some invisible characters from data
# ---- NOTE: for df_1$DV_name, creating df_1$DV_name_for_matching
df_1$DV_name_for_matching <- 
  as.character(str_remove_all(df_1$DV_name, "[^A-z|0-9|[:punct:]|_|\\s]"))
# ---- NOTE: for df_2$Variable_analyses, creating 
df_2$Variable_analyses_for_matching <- 
  as.character(str_remove_all(df_2$Variable_analyses, "[^A-z|0-9|[:punct:]|_|\\s]"))

要做到这一点，还有很长的路要走，这是成功的：

> ## long way > > ### creates Variable_label > # ---- NOTE: does not directly extract Variable_label from df_2 and insert it into df_1 > # ---- NOTE: based on df_1$Variable_label > df_1$Variable_label <- + ifelse(df_1$DV_name == "submission_time_minutes", "Submission time in minutes", + ifelse(df_1$DV_name == "WC", "Word count", + ifelse(df_1$DV_name == "Analytic_z_score", "Analytic score", + ifelse(df_1$DV_name == "Clout_z_score", "Clout score", + ifelse(df_1$DV_name == "Authentic_z_score", "Authentic score", + ifelse(df_1$DV_name == "Tone_z_score", "Tone score", NA + )))))) > > ### displays df > # ---- NOTE: displays df with created variable in desired output form > df_1 DV_name Variable_label 1 submission_time_minutes Submission time in minutes 2 submission_time_minutes Submission time in minutes 3 WC Word count 4 WC Word count 5 Analytic_z_score Analytic score 6 Analytic_z_score Analytic score 7 Clout_z_score Clout score 8 Clout_z_score Clout score 9 Authentic_z_score Authentic score 10 Authentic_z_score Authentic score 11 Tone_z_score Tone score 12 Tone_z_score Tone score 13 submission_time_minutes Submission time in minutes 14 submission_time_minutes Submission time in minutes 15 WC Word count 16 WC Word count 17 Analytic_z_score Analytic score 18 Analytic_z_score Analytic score 19 Clout_z_score Clout score 20 Clout_z_score Clout score 21 Authentic_z_score Authentic score 22 Authentic_z_score Authentic score 23 Tone_z_score Tone score 24 Tone_z_score Tone score 25 submission_time_minutes Submission time in minutes 26 submission_time_minutes Submission time in minutes 27 WC Word count 28 WC Word count 29 Analytic_z_score Analytic score 30 Analytic_z_score Analytic score 31 Clout_z_score Clout score 32 Clout_z_score Clout score 33 Authentic_z_score Authentic score 34 Authentic_z_score Authentic score 35 Tone_z_score Tone score 36 Tone_z_score Tone score 37 submission_time_minutes Submission time in minutes 38 submission_time_minutes Submission time in minutes 39 WC Word count 40 WC Word count 41 Analytic_z_score Analytic score 42 Analytic_z_score Analytic score 43 Clout_z_score Clout score 44 Clout_z_score Clout score 45 Authentic_z_score Authentic score 46 Authentic_z_score Authentic score 47 Tone_z_score Tone score 48 Tone_z_score Tone score
然后，我使用新变量
df\u 1$DV\u name\u进行匹配
和
df\u 2$Variable\u analysiss\u进行匹配
作为ifelse（）命令的基础：
我不知道为什么快速方式不起作用。请告知我如何使用快捷方式工作
仅供参考，我在2013 Intel Macbook Pro上使用RStudio
谢谢

下面是我用来创建帖子的代码

# creates df_1$Variable_label # ---- NOTE: column(s) with values to be transfered - df_2$Variable_label # ---- NOTE: column(s) for matching - df_1$DV_name, df_2$Variable_analyses ## displays data frames df_1 df_2 ## quick way # ---- NOTE: quick way does not work ### creates matching variables, which removes some invisible characters from data # ---- NOTE: for df_1$DV_name, creating df_1$DV_name_for_matching df_1$DV_name_for_matching <- as.character(str_remove_all(df_1$DV_name, "[^A-z|0-9|[:punct:]|_|\\s]")) # ---- NOTE: for df_2$Variable_analyses, creating df_2$Variable_analyses_for_matching <- as.character(str_remove_all(df_2$Variable_analyses, "[^A-z|0-9|[:punct:]|_|\\s]")) ### uses ifelse to complete matching task df_1[["Variable_label"]] <- ifelse(((df_1[["DV_name_for_matching"]]) == (df_2[["Variable_analyses_for_matching"]])), df_2[["Variable_label"]], NA) ### displays df # ---- NOTE: displays df, quick way does not work, not desired output df_1 ## long way ### creates Variable_label # ---- NOTE: does not directly extract Variable_label from df_2 and insert it into df_1 # ---- NOTE: based on df_1$Variable_label df_1$Variable_label <- ifelse(df_1$DV_name == "submission_time_minutes", "Submission time in minutes", ifelse(df_1$DV_name == "WC", "Word count", ifelse(df_1$DV_name == "Analytic_z_score", "Analytic score", ifelse(df_1$DV_name == "Clout_z_score", "Clout score", ifelse(df_1$DV_name == "Authentic_z_score", "Authentic score", ifelse(df_1$DV_name == "Tone_z_score", "Tone score", NA )))))) ### displays df # ---- NOTE: displays df with created variable in desired output form df_1

#创建df_1$Variable_标签 #----注意：带有要传输的值的列-df_2$Variable_标签 #----注：用于匹配的列-df_1$DV_名称，df_2$Variable_分析 ##显示数据帧 df_1 df_2 ##捷径 #----注意：快速方式不起作用 ###创建匹配变量，从而从数据中删除一些不可见的字符 #----注意：对于df_1$DV_name，创建df_1$DV_name_进行匹配 df_1$DV_name_用于匹配我相信您只需执行左join（）我相信你可以做一个left\u join（）要比较的向量是两种不同的长度。您应该合并两个数据帧。如果您这样做，那么Variable\u Label 将重复。合并（df\u 1，df\u 2，by.x=“DV\u name”，by.y=“Variable\u analysis”，all.x=T）在baseR 谢谢。我感谢这两个评论。另一位评论者的dplyr方法运行良好。我要用它，你们比较的向量是两种不同的长度。您应该合并两个数据帧。如果您这样做，那么Variable\u Label 将重复。合并（df\u 1，df\u 2，by.x=“DV\u name”，by.y=“Variable\u analysis”，all.x=T）在baseR谢谢。我感谢这两个评论。另一位评论者的dplyr方法运行良好。我要用这个，这个效果很好。谢谢这很有效。谢谢 ### displays df # ---- NOTE: displays df, quick way does not work, not desired output df_1 # creates df_1$Variable_label # ---- NOTE: column(s) with values to be transfered - df_2$Variable_label # ---- NOTE: column(s) for matching - df_1$DV_name, df_2$Variable_analyses ## displays data frames df_1 df_2 ## quick way # ---- NOTE: quick way does not work ### creates matching variables, which removes some invisible characters from data # ---- NOTE: for df_1$DV_name, creating df_1$DV_name_for_matching df_1$DV_name_for_matching <- as.character(str_remove_all(df_1$DV_name, "[^A-z|0-9|[:punct:]|_|\\s]")) # ---- NOTE: for df_2$Variable_analyses, creating df_2$Variable_analyses_for_matching <- as.character(str_remove_all(df_2$Variable_analyses, "[^A-z|0-9|[:punct:]|_|\\s]")) ### uses ifelse to complete matching task df_1[["Variable_label"]] <- ifelse(((df_1[["DV_name_for_matching"]]) == (df_2[["Variable_analyses_for_matching"]])), df_2[["Variable_label"]], NA) ### displays df # ---- NOTE: displays df, quick way does not work, not desired output df_1 ## long way ### creates Variable_label # ---- NOTE: does not directly extract Variable_label from df_2 and insert it into df_1 # ---- NOTE: based on df_1$Variable_label df_1$Variable_label <- ifelse(df_1$DV_name == "submission_time_minutes", "Submission time in minutes", ifelse(df_1$DV_name == "WC", "Word count", ifelse(df_1$DV_name == "Analytic_z_score", "Analytic score", ifelse(df_1$DV_name == "Clout_z_score", "Clout score", ifelse(df_1$DV_name == "Authentic_z_score", "Authentic score", ifelse(df_1$DV_name == "Tone_z_score", "Tone score", NA )))))) ### displays df # ---- NOTE: displays df with created variable in desired output form df_1 library(tidyverse) left_join(df_1, df_2, by = c("DV_name" = "Variable_analyses"))