Joining Data with dplyr in R

目录

• inner_join
  • Joining three tables
• left_join
• right-join
• full_join
• semi- and anti-join
• Stack Overflow questions
• bind_rows
• split

inner_join

按条件取交集dplyr高效处理函数笔记

The inner_join is the key to bring tables together. To use it, you need to provide the two tables that must be joined and the columns on which they should be joined.

> # Use the suffix argument to replace .x and .y suffixes
> parts %>%
  	inner_join(part_categories, by = c("part_cat_id" = "id"), suffix = c("_part", "_category"))
# A tibble: 17,501 x 4
   part_num   name_part                             part_cat_id name_category
   <chr>      <chr>                                       <dbl> <chr>
 1 0901       Baseplate 16 x 30 with Set 080 Yello~           1 Baseplates
 2 0902       Baseplate 16 x 24 with Set 080 Small~           1 Baseplates
 3 0903       Baseplate 16 x 24 with Set 080 Red H~           1 Baseplates
 4 0904       Baseplate 16 x 24 with Set 080 Large~           1 Baseplates
 5 1          Homemaker Bookcase 2 x 4 x 4                    7 Containers
 6 10016414   Sticker Sheet #1 for 41055-1                   58 Stickers
 7 10026stk01 Sticker for Set 10026 - (44942/41841~          58 Stickers
 8 10039      Pullback Motor 8 x 4 x 2/3                     44 Mechanical
 9 10048      Minifig Hair Tousled                           65 Minifig Headwear
10 10049      Minifig Shield Broad with Spiked Bot~          27 Minifig Accesso~
# ... with 17,491 more rows

> # Combine the parts and inventory_parts tables
> inventory_parts %>%
  	inner_join(parts, by = "part_num")
# A tibble: 258,958 x 6
   inventory_id part_num    color_id quantity name                   part_cat_id
          <dbl> <chr>          <dbl>    <dbl> <chr>                        <dbl>
 1           21 3009               7       50 Brick 1 x 6                     11
 2           25 21019c00pa~       15        1 Legs and Hips with Bl~          61
 3           25 24629pr0002       78        1 Minifig Head Special ~          59
 4           25 24634pr0001        5        1 Headwear Accessory Bo~          27
 5           25 24782pr0001        5        1 Minifig Hipwear Skirt~          27
 6           25 88646              0        1 Tile Special 4 x 3 wi~          15
 7           25 973pr3314c~        5        1 Torso with 1 White Bu~          60
 8           26 14226c11           0        3 String with End Studs~          31
 9           26 2340px2           15        1 Tail 4 x 1 x 3 with '~          35
10           26 2340px3           15        1 Tail 4 x 1 x 3 with '~          35
# ... with 258,948 more rows

Joining three tables

sets %>%
	# Add inventories using an inner join
	inner_join(inventories, by = "set_num") %>%
	# Add inventory_parts using an inner join 一般这种情况是因为两个表的列名不同导致的
	inner_join(inventory_parts, by = c("id" = "inventory_id"))

left_join

取第一个参数全部的值以及第二个参数与第一个参数的交集部分

匹配左边的表

return all rows from x, and all columns from x and y. Rows in x with no match in y will have NA values in the new columns. If there are multiple matches between x and y, all combinations of the matches are returned.

# Combine the star_destroyer and millennium_falcon tables
millennium_falcon %>%
  left_join(star_destroyer, by = c("part_num", "color_id"), suffix = c("_falcon", "_star_destroyer"))

> # Aggregate Millennium Falcon for the total quantity in each part
> millennium_falcon_colors <- millennium_falcon %>%
    group_by(color_id) %>%
    summarize(total_quantity = sum(quantity))
>
> # Aggregate Star Destroyer for the total quantity in each part
> star_destroyer_colors <- star_destroyer %>%
    group_by(color_id) %>%
    summarize(total_quantity = sum(quantity))
>
> # Left join the Millennium Falcon colors to the Star Destroyer colors
> millennium_falcon_colors %>%
    left_join(star_destroyer_colors,by="color_id",suffix=c("_falcon", "_star_destroyer"))
# A tibble: 21 x 3
   color_id total_quantity_falcon total_quantity_star_destroyer
      <dbl>                 <dbl>                         <dbl>
 1        0                   201                           336
 2        1                    15                            23
 3        4                    17                            53
 4       14                     3                             4
 5       15                    15                            17
 6       19                    95                            12
 7       28                     3                            16
 8       33                     5                            NA
 9       36                     1                            14
10       41                     6                            15
# ... with 11 more rows

right-join

取第二个参数的全部以及第一个参数与第二个的交集部分

> parts %>%
  	count(part_cat_id) %>%
  	right_join(part_categories, by = c("part_cat_id" = "id")) %>%
  	# Filter for NA
  	filter(is.na(n))
# A tibble: 1 x 3
  part_cat_id     n name
        <dbl> <int> <chr>
1          66    NA Modulex

full_join

有的记录数+a独有的记录数+b独有的记录数，这里要注意顺序

inventory_parts_joined %>%
  # Combine the sets table with inventory_parts_joined
  inner_join(sets, by = "set_num") %>%
  # Combine the themes table with your first join
  inner_join(themes, by = c("theme_id" = "id"), suffix = c("_set", "_theme"))

> batman_parts %>%
    # Combine the star_wars_parts table
    full_join(star_wars_parts, by = c("part_num", "color_id"), suffix = c("_batman", "_star_wars")) %>%
    # Replace NAs with 0s in the n_batman and n_star_wars columns
    replace_na(list(n_batman = 0, n_star_wars = 0))
# A tibble: 3,628 x 4
   part_num color_id n_batman n_star_wars
   <chr>       <dbl>    <dbl>       <dbl>
 1 10113           0       11           0
 2 10113         272        1           0
 3 10113         320        1           0
 4 10183          57        1           0
 5 10190           0        2           0
 6 10201           0        1          21
 7 10201           4        3           0
 8 10201          14        1           0
 9 10201          15        6           0
10 10201          71        4           5
# ... with 3,618 more rows

semi- and anti-join

semi_join连接其实是在inner_join的结果中只取属于第一个参数的字段（也就是列）

而anti_join其实就是第一个参数独有的记录

# Filter the batwing set for parts that are also in the batmobile set
> batwing %>%
    semi_join(batmobile, by = c("part_num"))
# A tibble: 126 x 3
   part_num color_id quantity
   <chr>       <dbl>    <dbl>
 1 3023            0       22
 2 3024            0       22
 3 3623            0       20
 4 2780            0       17
 5 3666            0       16
 6 3710            0       14
 7 6141            4       12
 8 2412b          71       10
 9 6141           72       10
10 6558            1        9
# ... with 116 more rows
>
> # Filter the batwing set for parts that aren't in the batmobile set
> batwing %>%
    anti_join(batmobile, by = c("part_num"))
# A tibble: 183 x 3
   part_num color_id quantity
   <chr>       <dbl>    <dbl>
 1 11477           0       18
 2 99207          71       18
 3 22385           0       14
 4 99563           0       13
 5 10247          72       12
 6 2877           72       12
 7 61409          72       12
 8 11153           0       10
 9 98138          46       10
10 2419           72        9
# ... with 173 more rows

 batman_colors %>%
    full_join(star_wars_colors, by = "color_id", suffix = c("_batman", "_star_wars")) %>%
    replace_na(list(total_batman = 0, total_star_wars = 0)) %>%
    inner_join(colors, by = c("color_id" = "id")) %>%
    # Create the difference and total columns
    mutate(difference = percent_batman - percent_star_wars,
           total = total_batman + total_star_wars) %>%
    # Filter for totals greater than 200
    filter(total >= 200)
# A tibble: 16 x 9
   color_id total_batman percent_batman total_star_wars percent_star_wa~ name
      <dbl>        <dbl>          <dbl>           <dbl>            <dbl> <chr>
 1        0         2807        0.296              3258          0.207   Black
 2        1          243        0.0256              410          0.0261  Blue
 3        4          529        0.0558              434          0.0276  Red
 4       14          426        0.0449              207          0.0132  Yell~
 5       15          404        0.0426             1771          0.113   White
 6       19          142        0.0150             1012          0.0644  Tan
 7       28           98        0.0103              183          0.0116  Dark~
 8       36           86        0.00907             246          0.0156  Tran~
 9       46          200        0.0211               39          0.00248 Tran~
10       70          297        0.0313              373          0.0237  Redd~
11       71         1148        0.121              3264          0.208   Ligh~
12       72         1453        0.153              2433          0.155   Dark~
13       84          278        0.0293               31          0.00197 Medi~
14      179          154        0.0162              232          0.0148  Flat~
15      378           22        0.00232             430          0.0273  Sand~
16        7            0       NA                   209          0.0133  Ligh~
# ... with 3 more variables: rgb <chr>, difference <dbl>, total <dbl>

# Create a bar plot using colors_joined and the name and difference columns
> ggplot(colors_joined, aes(name, difference, fill = name)) +
    geom_col() +
    coord_flip() +
    scale_fill_manual(values = color_palette, guide = FALSE) +
    labs(y = "Difference: Batman - Star Wars")

Stack Overflow questions

# Replace the NAs in the tag_name column
questions %>%
  left_join(question_tags, by = c("id" = "question_id")) %>%
  left_join(tags, by = c("tag_id" = "id")) %>%
replace_na(list(tag_name="only-r"))

bind_rows

按行结合时不需要列名相同，但是bind_cols按行结合时需要列名相同

按行连接参考博客

# Combine the two tables into posts_with_tags
> posts_with_tags <- bind_rows(questions_with_tags %>% mutate(type = "question"),
                                answers_with_tags %>% mutate(type = "answer"))
>
> # Add a year column, then aggregate by type, year, and tag_name
> posts_with_tags %>%
    mutate(year = year(creation_date)) %>%
    count(type, year, tag_name)
# A tibble: 58,299 x 4
   type    year tag_name                      n
   <chr>  <dbl> <chr>                     <int>
 1 answer  2008 bayesian                      1
 2 answer  2008 dataframe                     3
 3 answer  2008 dirichlet                     1
 4 answer  2008 eof                           1
 5 answer  2008 file                          1
 6 answer  2008 file-io                       1
 7 answer  2008 function                      7
 8 answer  2008 global-variables              7
 9 answer  2008 math                          2
10 answer  2008 mathematical-optimization     1
# ... with 58,289 more rows

split

split函数用于裂解数据框，可以根据因子来裂解，裂解后得到的是一个list list就非常适合与lapply,sapply,tapply等结合起来使用了

再补充，缺例子