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:
- Search that the row name in
df2is also present in the row name ofmtcars. - If they are (same name in both datasets), introduce in
mtcars$Clusterthe value present indf2$Cluster. - If they are not, skip that row and go to the next.
Be aware that this is an example, but my original dataframes:
- Some row names in
df2might not be inmtcars. - It will not happen that they are ordered between
mtcarsanddf2.
Any idea?
CodePudding user response：
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>
CodePudding user response：
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>