right_join वर्ब

dplyr के साथ डेटा जॉइन करना

Chris Cardillo

Data Scientist

Right join

batmobile %>%
  left_join(batwing, by = c("part_num", "color_id"), suffix = c("_batmobile", "_batwing"))
# A tibble: 173 x 4
   part_num color_id quantity_batmobile quantity_batwing
   <chr>       <dbl>              <dbl>            <dbl>
 1 3023           72                 62               NA
 2 2780            0                 28               17
 3 50950           0                 28                2
 4 3004           71                 26                2
 5 43093           1                 25                6
 6 3004            0                 23                4
 7 3010            0                 21               NA
 8 30363           0                 21               NA
 9 32123b         14                 19               NA
10 3622            0                 18                2
# … with 163 more rows
dplyr के साथ डेटा जॉइन करना

Left और Right join

Left join left join

Right join right join

dplyr के साथ डेटा जॉइन करना

आईने जैसी जोड़ियाँ

batmobile %>%
  right_join(batwing, by = c("part_num", "color_id"), suffix = c("_batmobile", "_batwing"))
# A tibble: 312 x 4
   part_num color_id quantity_batmobile quantity_batwing
   <chr>       <dbl>              <dbl>            <dbl>
 1 3023            0                 NA               22
 2 3024            0                  2               22
 3 3623            0                 10               20
 4 11477           0                 NA               18
 5 99207          71                 NA               18
 6 2780            0                 28               17
 7 2780            0                  1               17
 8 3666            0                 NA               16
 9 22385           0                 NA               14
10 3710            0                 NA               14
# … with 302 more rows
dplyr के साथ डेटा जॉइन करना

Count और sort

sets %>%
  count(theme_id, sort = TRUE)
# A tibble: 569 x 2
   theme_id     n
      <dbl> <int>
 1      501   122
 2      494   111
 3      435    94
 4      505    94
 5      632    93
 6      371    89
 7      497    86
 8      503    82
 9      516    78
10      220    72
# … with 559 more rows
dplyr के साथ डेटा जॉइन करना

Inner join

sets %>%
  count(theme_id, sort = TRUE) %>%
  inner_join(themes, by = c("theme_id" = "id"))
# A tibble: 569 x 4
   theme_id     n name              parent_id
      <dbl> <int> <chr>                 <dbl>
 1      501   122 Gear                     NA
 2      494   111 Friends                  NA
 3      435    94 Ninjago                  NA
 4      505    94 Basic Set               504
 5      632    93 Town                    504
 6      371    89 Supplemental            365
 7      497    86 Books                    NA
 8      503    82 Key Chain               501
 9      516    78 Duplo and Explore       507
10      220    72 City                    217
# … with 559 more rows
dplyr के साथ डेटा जॉइन करना

Right join

sets %>%
  count(theme_id, sort = TRUE) %>%
  right_join(themes, by = c("theme_id" = "id"))
# A tibble: 665 x 4
   theme_id     n name           parent_id
      <dbl> <int> <chr>              <dbl>
 1        1    58 Technic               NA
 2        2     1 Arctic Technic         1
 3        3     4 Competition            1
 4        4    13 Expert Builder         1
 5        5     6 Model                  1
 6        6     7 Airport                5
 7        7    20 Construction           5
 8        8    NA Farm                   5
 9        9     2 Fire                   5
10       10     3 Harbor                 5
# … with 655 more rows
dplyr के साथ डेटा जॉइन करना

NA को बदलें

library(tidyr)

sets %>%
  count(theme_id, sort = TRUE) %>%
  right_join(themes, by = c("theme_id" = "id")) %>%
  replace_na(list(n = 0))
# A tibble: 665 x 4
   theme_id     n name           parent_id
      <dbl> <dbl> <chr>              <dbl>
 1        1    58 Technic               NA
 2        2     1 Arctic Technic         1
 3        3     4 Competition            1
 4        4    13 Expert Builder         1
 5        5     6 Model                  1
 6        6     7 Airport                5
 7        7    20 Construction           5
 8        8     0 Farm                   5
 9        9     2 Fire                   5
10       10     3 Harbor                 5
# … with 655 more rows
dplyr के साथ डेटा जॉइन करना

अभ्यास करते हैं!

dplyr के साथ डेटा जॉइन करना

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