dplyr mutate() displaying NA values when matched from dataframe

user_13592325 0
Viewed 83

I am trying to replace values found in one column of a dataframe based upon finding a match in another dataframe using mutate(). Here is an example:

rename_ds <- data.frame(
  car_name = c("Camaro Z28","AMC Javelin"),
  replace_with = c("Camaro","Javelin"),
  stringsAsFactors = FALSE)
mt_cars <- mtcars %>%
  tibble::rownames_to_column() %>%
  dplyr::rename("car_name" = rowname) %>%
  dplyr::mutate(car_name = ifelse(car_name %in% rename_ds$car_name,
                                  rename_ds[which(rename_ds$car_name == car_name),2],
                                  car_name)

When I run this, instead of the car names being replaced by their respective replacements in rename_ds$replace_with, they are NA.

21 Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1
22 Dodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2
23 <NA> 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2
24 <NA> 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4
25 Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2
26 Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1
27 Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2

Any suggestions? Thanks in advance.

dplyr r
3 Answers

We could make it simpler with a named vector and coalesce:

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

In base R, we could do

pmax(row.names(mtcars), setNames(rename_ds$replace_with, 
        rename_ds$car_name)[row.names(mtcars)], na.rm = TRUE)
user_37322710

To me this looks more like a join operation:

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

(Rows 23-24 are updated.)

user_33582720

You are on the right track, you can use str_replace_all

mtcars %>%
  tibble::rownames_to_column() %>%
  dplyr::rename("car_name" = rowname) %>%
  dplyr::mutate(car_name = str_replace_all(car_name,
                                   exec(str_c,collapse="|",rename_ds$car_name),
                                   exec(setNames,!!!unname(rev(rename_ds)))))
                car_name  mpg cyl  disp  hp drat    wt  qsec vs am gear carb
1            Mazda RX4 21.0   6 160.0 110 3.90 2.620 16.46  0  1    4    4
2        Mazda RX4 Wag 21.0   6 160.0 110 3.90 2.875 17.02  0  1    4    4
3           Datsun 710 22.8   4 108.0  93 3.85 2.320 18.61  1  1    4    1
4       Hornet 4 Drive 21.4   6 258.0 110 3.08 3.215 19.44  1  0    3    1
5    Hornet Sportabout 18.7   8 360.0 175 3.15 3.440 17.02  0  0    3    2
6              Valiant 18.1   6 225.0 105 2.76 3.460 20.22  1  0    3    1
7           Duster 360 14.3   8 360.0 245 3.21 3.570 15.84  0  0    3    4
8            Merc 240D 24.4   4 146.7  62 3.69 3.190 20.00  1  0    4    2
9             Merc 230 22.8   4 140.8  95 3.92 3.150 22.90  1  0    4    2
10            Merc 280 19.2   6 167.6 123 3.92 3.440 18.30  1  0    4    4
11           Merc 280C 17.8   6 167.6 123 3.92 3.440 18.90  1  0    4    4
12          Merc 450SE 16.4   8 275.8 180 3.07 4.070 17.40  0  0    3    3
13          Merc 450SL 17.3   8 275.8 180 3.07 3.730 17.60  0  0    3    3
14         Merc 450SLC 15.2   8 275.8 180 3.07 3.780 18.00  0  0    3    3
15  Cadillac Fleetwood 10.4   8 472.0 205 2.93 5.250 17.98  0  0    3    4
16 Lincoln Continental 10.4   8 460.0 215 3.00 5.424 17.82  0  0    3    4
17   Chrysler Imperial 14.7   8 440.0 230 3.23 5.345 17.42  0  0    3    4
18            Fiat 128 32.4   4  78.7  66 4.08 2.200 19.47  1  1    4    1
19         Honda Civic 30.4   4  75.7  52 4.93 1.615 18.52  1  1    4    2
20      Toyota Corolla 33.9   4  71.1  65 4.22 1.835 19.90  1  1    4    1
21       Toyota Corona 21.5   4 120.1  97 3.70 2.465 20.01  1  0    3    1
22    Dodge Challenger 15.5   8 318.0 150 2.76 3.520 16.87  0  0    3    2
23              Camaro 15.2   8 304.0 150 3.15 3.435 17.30  0  0    3    2 # Changed
24             Javelin 13.3   8 350.0 245 3.73 3.840 15.41  0  0    3    4 # Changed
25    Pontiac Firebird 19.2   8 400.0 175 3.08 3.845 17.05  0  0    3    2
26           Fiat X1-9 27.3   4  79.0  66 4.08 1.935 18.90  1  1    4    1
27       Porsche 914-2 26.0   4 120.3  91 4.43 2.140 16.70  0  1    5    2
28        Lotus Europa 30.4   4  95.1 113 3.77 1.513 16.90  1  1    5    2
29      Ford Pantera L 15.8   8 351.0 264 4.22 3.170 14.50  0  1    5    4
30        Ferrari Dino 19.7   6 145.0 175 3.62 2.770 15.50  0  1    5    6
31       Maserati Bora 15.0   8 301.0 335 3.54 3.570 14.60  0  1    5    8
32          Volvo 142E 21.4   4 121.0 109 4.11 2.780 18.60  1  1    4    2
user_83802720
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