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This article introduces the knowledge of "how to merge dplyr in R language". In the operation of actual cases, many people will encounter such a dilemma, so let the editor lead you to learn how to deal with these situations. I hope you can read it carefully and be able to achieve something!

1. Conversion and merger

Left_join (a, b, by = "x1"): adds matching dataset b records (rows) to dataset a.

Right_join (a, b, by = "x1"): adds a matching dataset a record to dataset b.

Inner_join (a, b, by = "x1"): merge data. Only matching records are retained.

Full_join (a, b, by = "x1"): merge data. Keep all records, all lines. Those that are not available are filled with NA

Library (dplyr) a=data.frame (x1roomc ("A", "B", "C"), x2folc (1:3)) b=data.frame (x1folc ("A", "B", "D"), x3folc ("T", "F", "T")) a Matching dataset b records are added to dataset a by adding matching dataset b records to dataset a # # x1 x2 x3 records # 1 A 1 records # 2 B 2 fills # 3 C 3 right_join (a, b, by = "x1") # add a matching dataset a record to dataset b. # # x1 x2 x3 NA Tinner_join # 1 A 1 threading # 2 B 2 fusing # 3 D NA Tinner_join (a, b, by = "x1") # merge data. Only matching records are retained. # # x1 x2 x3 Ffull_join # 1 A 1 tweak # 2 B 2 Ffull_join (a, b, by = "x1") # merge data. Keep all records, all rows, none filled with NA # # x1 x2 x3 lines # 1 A 1 thread # 2 B 2 records # 3 C 3 lines # 4 D NA T2, filter and merge

Semi_join (a, b, by = "x1"): the record in dataset a that matches dataset b.

Anti_join (a, b, by = "x1"): records in dataset a that do not match dataset b

3. Set operation

Intersect (x, y): records that both appear in datasets x and y. Xy intersection

Union (x, y): a record that appears in the dataset x or y. Xy union

Setdiff (x, y): records that appear only in dataset x but not in y. The difference between x and y

Bind_rows (x, y): add dataset y to x as a new deleted row.

Bind_cols (x, y): add dataset y to x as a new column.

Note: the data matches according to the location.

Unlike rbind () and cbind (), rbind () / cbind () has strict requirements for data merging.

X=data.frame ("a", "b", "c"), Bracc ("t", "u", "v"), Centrc (1:3)) y=data.frame ("a", "b", "d"), Benec ("t", "u", "v"), Drunc (2:4) x The columns are all displayed, and the missing ones are filled with NA. Select the parameter .id = "name", and add one more column. Column name name## A B C data # 1 a t 1 NA## 2 b u 2 NA## 3 c v 3 NA## 4 a t NA 2 colors # 5 b u NA 3 steps # 6 d v NA 4bind_rows (x, yma.id = "name") # in order to distinguish that the data is from two other databases You can use id to distinguish # # name A B C dashes # 1 a t 1 NA## 2 1 b u 2 NA## 3 c v 3 NA## 4 2 a t NA 2 columns # 52 b u NA 3 columns # 6 d v NA 4bind_cols (x, y) # # A.. .1 C A.. 4 B.. 5 Duan # 1a t 2a t 2b u 2b u 3cm # 3c v 3d v 44, data sort

Arrange (.data, … , .by _ group = FALSE): sort by variable + default ascending order, add desc () before variable becomes descending order + .by _ group = FALSE ignore ascending sort of group by default, set to TRUE to sort the group in ascending order and then sort by comparison variable

Arrange (mtcars, cyl Desc (disp)) # sort by variable # # mpg cyl disp hp drat wt qsec vs am gear carb## Merc 240D 24.4 146.7 62 3.69 3.190 20.00 1 0 42 # # Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 42 # # Volvo 142E 21.4 4 121 .0109 4.11 2.780 18.60 11 4 2 # Porsche 914-226.0 4 120.3 91 4.43 2.140 16.70 01 5 2 Toyota Corona 21.54 120.1 97 3.70 2.465 20.01 10 3 1 Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 11 4 1 Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 11 52 # # Fiat X1-9 27.3 27.3 79.0 66 4.08 1.935 18.90 11 4 1 Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 11 4 1 Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2## Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 11 4 1## Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 10 3 1## Valiant 18.1 6 225.0 105 2.76 3.460 20.22 10 3 1## Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 10 44 # # Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 10 44 # # Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 44 # # Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 44 # # Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 15 6## Cadillac Fleetwood 10.4 8 472.0 205 2.93 5.250 17.98 00 34 # # Lincoln Continental 10.4 8 460.0 215 3.00 5.424 17.82 00 34 # # Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 00 34 # # Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2## Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2## Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4## Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 15 4## Camaro Z28 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4## Dodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2## AMC Javelin 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2## Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 01 5 8## Merc 450SE 16 .4 8 275.8 275.8 3.07 4.070 17.40 00 3 3 Merc 450SL 17.3 8 275.8 180 3.07 3.730 17.60 00 3 3 days # Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 3 3by_cyl% group_by (cyl) by_cyl% >% arrange (desc (wt) .by _ group = TRUE) # ignores the ascending sort of groups by default. If set to TRUE, the groups will be sorted in ascending order and then sorted by comparison variables # A tibble: 32 x 11 ordered # # Groups: cyl [3] # # mpg cyl disp hp drat wt qsec vs am gear carb## 1 24.4 4147. 62 3.69 3.19 20 1 0 4 2## 2 22.8 4 141. 95 3.92 3.15 22.9 10 4 2## 3 21.4 4 121 109 4.11 2.78 18.6 11 4 2## 4 21.5 4 120. 97 3.7 2.46 20.0 10 3 1## 5 22.8 4 108 93 3.85 2.32 18.6 1 1 4 1## 6 32.4 4 78.7 66 4.08 2.2 19.5 1 1 4 1## 7 26 4 120. 91 4.43 2.14 16.7 0 1 52 # # 8 27.3 4 79 66 4.08 1.94 18.9 1 14 1## 9 33.9 4 71.1 65 4.22 1.84 19.9 1 14 1## 10 30.4 4 75.7 52 4.93 1.62 18.5 1 1 4 2 # #... This is the end of with 22 more rows how to merge dplyr in R language. Thank you for reading. If you want to know more about the industry, you can follow the website, the editor will output more high-quality practical articles for you!

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