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Let's write a reproducible example. I will use data(mtcars).

This dataset has row names in each row:

row.names(mtcars)
 [1] "Mazda RX4"           "Mazda RX4 Wag"       "Datsun 710"          "Hornet 4 Drive"     
 [5] "Hornet Sportabout"   "Valiant"             "Duster 360"          "Merc 240D"          
 [9] "Merc 230"            "Merc 280"            "Merc 280C"           "Merc 450SE"         
[13] "Merc 450SL"          "Merc 450SLC"         "Cadillac Fleetwood"  "Lincoln Continental"
[17] "Chrysler Imperial"   "Fiat 128"            "Honda Civic"         "Toyota Corolla"     
[21] "Toyota Corona"       "Dodge Challenger"    "AMC Javelin"         "Camaro Z28"         
[25] "Pontiac Firebird"    "Fiat X1-9"           "Porsche 914-2"       "Lotus Europa"       
[29] "Ford Pantera L"      "Ferrari Dino"        "Maserati Bora"       "Volvo 142E"

Now I have another dataframe:

df2 <- structure(list(Cluster = c("Group 1", "Group 1", "Group 1", "Group 1", 
"Group 1", "Group 1", "Group 1", "Group 1", "Group 2", "Group 2", 
"Group 2", "Group 2", "Group 2", "Group 2", "Group 2")), row.names = c("Mazda RX4", 
"Mazda RX4 Wag", "Datsun 710", "Hornet 4 Drive", "Hornet Sportabout", 
"Valiant", "Duster 360", "Merc 240D", "Merc 230", "Merc 280", 
"Merc 280C", "Merc 450SE", "Merc 450SL", "Merc 450SLC", "Cadillac Fleetwood"
), class = "data.frame")

df2
                   Cluster
Mazda RX4          Group 1
Mazda RX4 Wag      Group 1
Datsun 710         Group 1
Hornet 4 Drive     Group 1
Hornet Sportabout  Group 1
Valiant            Group 1
Duster 360         Group 1
Merc 240D          Group 1
Merc 230           Group 2
Merc 280           Group 2
Merc 280C          Group 2
Merc 450SE         Group 2
Merc 450SL         Group 2
Merc 450SLC        Group 2
Cadillac Fleetwood Group 2

What I would like to do is to create a new column in the original mtcars dataset (mtcars$Cluster) with the information of the column df2$Cluster, by following these rules:

  1. Search that the row name in df2 is also present in the row name of mtcars.
  2. If they are (same name in both datasets), introduce in mtcars$Cluster the value present in df2$Cluster.
  3. If they are not, skip that row and go to the next.

Be aware that this is an example, but my original dataframes:

  1. Some row names in df2 might not be in mtcars.
  2. It will not happen that they are ordered between mtcars and df2.

Any idea?

antecessor
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2 Answers2

1

What you're describing is a join.

E.g. implemented as a left_join in dplyr. However, to perform such join on the row names, you'll first want to move these to a column (here using rownames_to_column from tibble). If you want to go the other way afterwards use column_to_rownames().

library(dplyr)

mtcars |>
  rownames_to_column() |>
  left_join(rownames_to_column(df2))

Output:

               rowname  mpg cyl  disp  hp drat    wt  qsec vs am gear carb Cluster
1            Mazda RX4 21.0   6 160.0 110 3.90 2.620 16.46  0  1    4    4 Group 1
2        Mazda RX4 Wag 21.0   6 160.0 110 3.90 2.875 17.02  0  1    4    4 Group 1
3           Datsun 710 22.8   4 108.0  93 3.85 2.320 18.61  1  1    4    1 Group 1
4       Hornet 4 Drive 21.4   6 258.0 110 3.08 3.215 19.44  1  0    3    1 Group 1
5    Hornet Sportabout 18.7   8 360.0 175 3.15 3.440 17.02  0  0    3    2 Group 1
6              Valiant 18.1   6 225.0 105 2.76 3.460 20.22  1  0    3    1 Group 1
7           Duster 360 14.3   8 360.0 245 3.21 3.570 15.84  0  0    3    4 Group 1
8            Merc 240D 24.4   4 146.7  62 3.69 3.190 20.00  1  0    4    2 Group 1
9             Merc 230 22.8   4 140.8  95 3.92 3.150 22.90  1  0    4    2 Group 2
10            Merc 280 19.2   6 167.6 123 3.92 3.440 18.30  1  0    4    4 Group 2
11           Merc 280C 17.8   6 167.6 123 3.92 3.440 18.90  1  0    4    4 Group 2
12          Merc 450SE 16.4   8 275.8 180 3.07 4.070 17.40  0  0    3    3 Group 2
13          Merc 450SL 17.3   8 275.8 180 3.07 3.730 17.60  0  0    3    3 Group 2
14         Merc 450SLC 15.2   8 275.8 180 3.07 3.780 18.00  0  0    3    3 Group 2
15  Cadillac Fleetwood 10.4   8 472.0 205 2.93 5.250 17.98  0  0    3    4 Group 2
16 Lincoln Continental 10.4   8 460.0 215 3.00 5.424 17.82  0  0    3    4    <NA>
17   Chrysler Imperial 14.7   8 440.0 230 3.23 5.345 17.42  0  0    3    4    <NA>
18            Fiat 128 32.4   4  78.7  66 4.08 2.200 19.47  1  1    4    1    <NA>
19         Honda Civic 30.4   4  75.7  52 4.93 1.615 18.52  1  1    4    2    <NA>
20      Toyota Corolla 33.9   4  71.1  65 4.22 1.835 19.90  1  1    4    1    <NA>
21       Toyota Corona 21.5   4 120.1  97 3.70 2.465 20.01  1  0    3    1    <NA>
22    Dodge Challenger 15.5   8 318.0 150 2.76 3.520 16.87  0  0    3    2    <NA>
23         AMC Javelin 15.2   8 304.0 150 3.15 3.435 17.30  0  0    3    2    <NA>
24          Camaro Z28 13.3   8 350.0 245 3.73 3.840 15.41  0  0    3    4    <NA>
25    Pontiac Firebird 19.2   8 400.0 175 3.08 3.845 17.05  0  0    3    2    <NA>
26           Fiat X1-9 27.3   4  79.0  66 4.08 1.935 18.90  1  1    4    1    <NA>
27       Porsche 914-2 26.0   4 120.3  91 4.43 2.140 16.70  0  1    5    2    <NA>
28        Lotus Europa 30.4   4  95.1 113 3.77 1.513 16.90  1  1    5    2    <NA>
29      Ford Pantera L 15.8   8 351.0 264 4.22 3.170 14.50  0  1    5    4    <NA>
30        Ferrari Dino 19.7   6 145.0 175 3.62 2.770 15.50  0  1    5    6    <NA>
31       Maserati Bora 15.0   8 301.0 335 3.54 3.570 14.60  0  1    5    8    <NA>
32          Volvo 142E 21.4   4 121.0 109 4.11 2.780 18.60  1  1    4    2    <NA>
harre
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1

In base R:

df <- mtcars
df$Cluster <- df2[rownames(df), 'Cluster']

This will also keep the row names:

df
#                      mpg cyl  disp  hp drat    wt  qsec vs am gear carb Cluster
# Mazda RX4           21.0   6 160.0 110 3.90 2.620 16.46  0  1    4    4 Group 1
# Mazda RX4 Wag       21.0   6 160.0 110 3.90 2.875 17.02  0  1    4    4 Group 1
# Datsun 710          22.8   4 108.0  93 3.85 2.320 18.61  1  1    4    1 Group 1
# Hornet 4 Drive      21.4   6 258.0 110 3.08 3.215 19.44  1  0    3    1 Group 1
# Hornet Sportabout   18.7   8 360.0 175 3.15 3.440 17.02  0  0    3    2 Group 1
# Valiant             18.1   6 225.0 105 2.76 3.460 20.22  1  0    3    1 Group 1
# Duster 360          14.3   8 360.0 245 3.21 3.570 15.84  0  0    3    4 Group 1
# Merc 240D           24.4   4 146.7  62 3.69 3.190 20.00  1  0    4    2 Group 1
# Merc 230            22.8   4 140.8  95 3.92 3.150 22.90  1  0    4    2 Group 2
# Merc 280            19.2   6 167.6 123 3.92 3.440 18.30  1  0    4    4 Group 2
# Merc 280C           17.8   6 167.6 123 3.92 3.440 18.90  1  0    4    4 Group 2
# Merc 450SE          16.4   8 275.8 180 3.07 4.070 17.40  0  0    3    3 Group 2
# Merc 450SL          17.3   8 275.8 180 3.07 3.730 17.60  0  0    3    3 Group 2
# Merc 450SLC         15.2   8 275.8 180 3.07 3.780 18.00  0  0    3    3 Group 2
# Cadillac Fleetwood  10.4   8 472.0 205 2.93 5.250 17.98  0  0    3    4 Group 2
# Lincoln Continental 10.4   8 460.0 215 3.00 5.424 17.82  0  0    3    4    <NA>
# Chrysler Imperial   14.7   8 440.0 230 3.23 5.345 17.42  0  0    3    4    <NA>
# Fiat 128            32.4   4  78.7  66 4.08 2.200 19.47  1  1    4    1    <NA>
# Honda Civic         30.4   4  75.7  52 4.93 1.615 18.52  1  1    4    2    <NA>
# Toyota Corolla      33.9   4  71.1  65 4.22 1.835 19.90  1  1    4    1    <NA>
# Toyota Corona       21.5   4 120.1  97 3.70 2.465 20.01  1  0    3    1    <NA>
# Dodge Challenger    15.5   8 318.0 150 2.76 3.520 16.87  0  0    3    2    <NA>
# AMC Javelin         15.2   8 304.0 150 3.15 3.435 17.30  0  0    3    2    <NA>
# Camaro Z28          13.3   8 350.0 245 3.73 3.840 15.41  0  0    3    4    <NA>
# Pontiac Firebird    19.2   8 400.0 175 3.08 3.845 17.05  0  0    3    2    <NA>
# Fiat X1-9           27.3   4  79.0  66 4.08 1.935 18.90  1  1    4    1    <NA>
# Porsche 914-2       26.0   4 120.3  91 4.43 2.140 16.70  0  1    5    2    <NA>
# Lotus Europa        30.4   4  95.1 113 3.77 1.513 16.90  1  1    5    2    <NA>
# Ford Pantera L      15.8   8 351.0 264 4.22 3.170 14.50  0  1    5    4    <NA>
# Ferrari Dino        19.7   6 145.0 175 3.62 2.770 15.50  0  1    5    6    <NA>
# Maserati Bora       15.0   8 301.0 335 3.54 3.570 14.60  0  1    5    8    <NA>
# Volvo 142E          21.4   4 121.0 109 4.11 2.780 18.60  1  1    4    2    <NA>
Robert Hacken
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