我的两个数据框具有相同的字符列。使用dplyr :: full_joint通过此列加入它们很容易。但问题是共同列在拼写上有轻微但明显的差异。相对于定义技能的每个字符串，拼写差异很小：
Skill Grade_Judge_A
pack & ship 1
pack & store 5
sell 3
Design a room 9
Skill Grade_Judge_B
pack and store 3
pack & ship 7
sell 2
Design room 6
如何在下面获得所需的输出：
Skill Grade_Judge_A Grade_Judge_B
pack & ship 1 3
pack & store 5 7
sell 3 2
Design a room 9 6
我在考虑根据“技能”列中字符串之间的距离匹配两个数据帧中的行，例如使用stringdist包。如果两个字符串之间的差异很小，那么这意味着技能是相同的。
我更喜欢dplyr / tidyverse解决方案。
这是数据帧A的实际输入：
> dput(df_A)
structure(list(skill = c(" [Assess abdomen for a floating mass]",
" [Assess Nerve Root Compression]", " [Evaluate breathing effort (rate, patterns, chest expansions)]",
" [Evaluate Plantar Reflex/Babinski sign]", " [Evaluate Speech]",
" [External palpation of a uterus]", " [Heel to Shin test]",
" [Inspect anterior chamber of eye with ophthalmoscope or penlight]",
" [Inspect breast]", " [Inspect Overall Skin Color/Tone]", " [Inspect Skin Lesions]",
" [Inspect Wounds]", " [Mental Status/level of consciousness]",
" [Nose/index finger]", " [Percuss abdomen to determine spleen size]",
" [Percuss costovertebral angle for kidney tenderness]", " [Percuss for diaphragmatic excursion]",
" [Percuss the abdomen for abdominal tones]", " [Percuss the abdomen to determine liver span]"
), `2016-09-17 13:41:08` = c(1, 1, 5, 3, 4, 0, 4, 3, 3, 5, 4,
5, 5, 3, 1, 1, 2, 4, 1)), class = c("tbl_df", "tbl", "data.frame"
), row.names = c(NA, -19L), .Names = c("skill", "2016-09-17 13:41:08"
))
和dataframe B：
> dput(df_B)
structure(list(skill = c(" [Assess abdomen for floating mass]",
" [Assess nerve root compression]", " [Evaluate breathing effort (rate, patterns, chest expansion)]",
" [Evaluate plantar reflex/Babinski sign]", " [Evaluate speech]",
" [External palpation of uterus]", " [Heel to shin test]", " [Inspect anterior chamber of the eye with opthalmoscope or penlight]",
" [Inspect breasts]", " [Inspect overall skin color/tone]", " [Inspect skin lesions]",
" [Inspect wounds]", " [Mental status/level of consciousness]",
" [Nose/Index finger]", " [Percuss costovertebral angle for kidney tenderness]",
" [Percuss for diaphragmatic excursion]", " [Percuss the abdomen for abdominal tones]",
" [Percuss the abdomen to determine liver span]", " [Percuss the abdomen to determine spleen size]"
), `2016-09-21 07:58:43` = c(0, 2, 2, 2, 2, 1, 1, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2)), class = c("tbl_df", "tbl", "data.frame"
), row.names = c(NA, -19L), .Names = c("skill", "2016-09-21 07:58:43"
))
以下是两个数据框的负责人：
> head(df_A)
# A tibble: 6 × 2
skill `2016-09-17 13:41:08`
<chr> <dbl>
1 [Assess abdomen for a floating mass] 1
2 [Assess Nerve Root Compression] 1
3 [Evaluate breathing effort (rate, patterns, chest expansions)] 5
4 [Evaluate Plantar Reflex/Babinski sign] 3
5 [Evaluate Speech] 4
6 [External palpation of a uterus] 0
和第二个：
> head(df_B)
# A tibble: 6 × 2
skill `2016-09-21 07:58:43`
<chr> <dbl>
1 [Assess abdomen for floating mass] 0
2 [Assess nerve root compression] 2
3 [Evaluate breathing effort (rate, patterns, chest expansion)] 2
4 [Evaluate plantar reflex/Babinski sign] 2
5 [Evaluate speech] 2
6 [External palpation of uterus] 1
答案 0 :(得分：0)
如果拼写错误中没有模式，我相信唯一剩下的方法是在加入数据之前确保拼写相同。我们可以用
splitstackshape包
library(splitstackshape)
yourdata$skill<-stri_replace_all(yourdata8$skill,"pack & store" ,fixed = "pack and store")
此代码将pack and store替换为数据集pack & store列中的skill
答案 1 :(得分：0)
这有多接近？
require(tidyverse)
require(stringdist)
df_A %>%
rownames_to_column %>%
mutate(foo=1) %>%
full_join((df_B %>% rownames_to_column %>% mutate(foo=1)), by='foo') %>%
select(-foo) %>%
mutate(dist = stringdist(skill.x, skill.y), norm_dist = dist / length(skill.x)) %>%
arrange(norm_dist) %>%
filter(norm_dist < 0.015)
我在df_A和df_B上进行了真正的（关系代数式）完全加入，如果您拥有的实际数据很大（例如，如果两个数据框都有1000行，连接的结果将是1,000,000行）。这种连接是通过创建一个虚拟列foo完成的，该列对于每一行都是相等的，然后连接到虚拟列。
注释中提到的stringdist包然后将两个字符串的每个可能组合的行A与行B进行比较。对于您的示例数据，标准化字符串距离的截止值为0.015，结果似乎很好。当然，这个任意截止点可能会超出您的示例数据。