如何在R中left_join并重复将值连接到多个变量？

科里·彭伯顿

我在这里有些困惑，这不是正确的方法，或者我缺少了left_join的一部分：

我希望按国家和年份加入“ gdp”列，并在所有三个“性别”类别中重复该值，以使同一年的所有三个性别都具有相同的关联gdp。

这是我现在拥有的：

library(tidyverse)

table_1 <- tribble(~"Region",~"Country",~"Year", ~"Gender", ~"median_rate",
 "Central and Southern Asia", "Afghanistan",  2011, "female",       0.186,
"Central and Southern Asia","Afghanistan",  2011, "male",         0.454,
 "Central and Southern Asia", "Afghanistan",  2011, "total",        0.274,
 "Central and Southern Asia", "Afghanistan",  2018, "female",       0.221,
 "Central and Southern Asia", "Afghanistan" , 2018, "male",         0.504,
 "Central and Southern Asia", "Afghanistan",  2018, "total",        0.367)

table_2 <- tribble(~"Country",    ~"gdp", ~"Year",
 "Afghanistan",  551.,  2010,
 "Afghanistan", 599.,2011,
 "Afghanistan",  649.,  2012,
 "Afghanistan",  648.,  2013,
 "Afghanistan",  625.,  2014,
 "Afghanistan",  590.,  2015,
 "Afghanistan",  550.,  2016,
 "Afghanistan",  550.,  2017)

table_1 %>% left_join(table_2, by = "Country")

# A tibble: 48 x 7
   Region                    Country     Year.x Gender median_rate   gdp Year.y
   <chr>                     <chr>        <dbl> <chr>        <dbl> <dbl>  <dbl>
 1 Central and Southern Asia Afghanistan   2011 female       0.186   551   2010
 2 Central and Southern Asia Afghanistan   2011 female       0.186   599   2011
 3 Central and Southern Asia Afghanistan   2011 female       0.186   649   2012
 4 Central and Southern Asia Afghanistan   2011 female       0.186   648   2013
 5 Central and Southern Asia Afghanistan   2011 female       0.186   625   2014
 6 Central and Southern Asia Afghanistan   2011 female       0.186   590   2015
 7 Central and Southern Asia Afghanistan   2011 female       0.186   550   2016
 8 Central and Southern Asia Afghanistan   2011 female       0.186   550   2017
 9 Central and Southern Asia Afghanistan   2011 male         0.454   551   2010
10 Central and Southern Asia Afghanistan   2011 male         0.454   599   2011
# ... with 38 more rows

期望的输出将是这样的，表2中的gdp列已加入，但仅适用于每个匹配的年份（例如，表1中仅提供了2011年和2018年的数据，因此应仅匹配这些年份）

tribble(~"Region",~"Country",~"Year", ~"Gender", ~"median_rate",~"gdp",
        "Central and Southern Asia", "Afghanistan",  2011, "female",0.186, 550,
        "Central and Southern Asia","Afghanistan",  2011, "male",0.454,550,
        "Central and Southern Asia", "Afghanistan",  2011, "total",0.274,550,
        "Central and Southern Asia", "Afghanistan",  2018, "female", 0.221,590,
        "Central and Southern Asia", "Afghanistan" , 2018, "male",         0.504, 590,
        "Central and Southern Asia", "Afghanistan",  2018, "total",        0.367, 590)

谢谢你的帮助，

r2evans

dplyr'join verbs'by=参数可以接受多个列：

table_1 <- tribble(~"Region",~"Country",~"Year", ~"Gender", ~"median_rate",
 "Central and Southern Asia", "Afghanistan",  2011, "female",       0.186,
 "Central and Southern Asia","Afghanistan",  2011, "male",         0.454,
 "Central and Southern Asia", "Afghanistan",  2011, "total",        0.274,
 "Central and Southern Asia", "Afghanistan",  2018, "female",       0.221,
 "Central and Southern Asia", "Afghanistan" , 2018, "male",         0.504,
 "Central and Southern Asia", "Afghanistan",  2018, "total",        0.367)

table_2 <- tribble(~"Country",    ~"gdp", ~"Year",
 "Afghanistan",  551.,  2010,
 "Afghanistan", 599.,2011,
 "Afghanistan",  649.,  2012,
 "Afghanistan",  648.,  2013,
 "Afghanistan",  625.,  2014,
 "Afghanistan",  590.,  2015,
 "Afghanistan",  550.,  2016,
 "Afghanistan",  550.,  2017)

table_1 %>% left_join(table_2, by = c("Country", "Year"))
# # A tibble: 6 x 6
#   Region                    Country      Year Gender median_rate   gdp
#   <chr>                     <chr>       <dbl> <chr>        <dbl> <dbl>
# 1 Central and Southern Asia Afghanistan  2011 female       0.186   599
# 2 Central and Southern Asia Afghanistan  2011 male         0.454   599
# 3 Central and Southern Asia Afghanistan  2011 total        0.274   599
# 4 Central and Southern Asia Afghanistan  2018 female       0.221    NA
# 5 Central and Southern Asia Afghanistan  2018 male         0.504    NA
# 6 Central and Southern Asia Afghanistan  2018 total        0.367    NA

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