I have the following data frame in R:
> str(df)
'data.frame': 545227 obs. of 15 variables:
$ ykod : int 93 93 93 93 93 93 93 93 93 93 ...
$ yad : Factor w/ 42 levels "BAKUGAN","BARBIE",..: 30 30 30 30 30 30 30 30 30 30 ...
$ per : Factor w/ 3 levels "2 AYLIK","3 AYLIK",..: 3 3 3 3 3 3 3 3 3 3 ...
$ donem: int 201101 201101 201101 201101 201101 201101 201101 201101 201101 201101 ...
$ sayi : int 201101 201101 201101 201101 201101 201101 201101 201101 201101 201101 ...
$ mkod : int 4 5 9 11 12 18 20 22 25 26 ...
$ mad : Factor w/ 10464 levels " Defne Market ",..: 405 8075 9710 10145 9297 7973 2542 3892 2759 5769 ...
$ mtip : Factor w/ 29 levels "Abone Bürosu ",..: 2 20 20 2 2 2 2 2 2 2 ...
$ kanal: Factor w/ 2 levels "OB","SS": 2 2 2 2 2 2 2 2 2 2 ...
$ bkod : int 110565 110565 110565 110565 110565 110565 110565 110565 110565 110565 ...
$ bad : Factor w/ 212 levels "4. Levent","500 Evler",..: 167 167 167 167 167 167 167 167 167 167 ...
$ bolge: Factor w/ 12 levels "Adana Sehiriçi",..: 7 7 7 7 7 7 7 7 7 7 ...
$ sevk : int 2 3 3 3 2 2 2 6 2 2 ...
$ iade : int 2 1 0 2 0 2 1 0 0 2 ...
$ satis: int 0 2 3 1 2 0 1 6 2 0 ...
I want to list unique (like SQL's DISTINCT) values for selected multiple variables. For example, unique(yad) gives me the names of each 42 elements, but I need to extract two columns (yad and per together, with all unique combinations):
yad per
--- ---
BARBIE AYLIK
BAKUGAN 2 AYLIK
MICKEY MOUSE 2 AYLIK
TINKERBELL 3 AYLIK
... ...
How can I achieve this?
This dplyr method works nicely when piping.
For selected columns:
library(dplyr)
iris %>%
select(Sepal.Width, Species) %>%
t %>% c %>% unique
[1] "3.5" "setosa" "3.0" "3.2" "3.1"
[6] "3.6" "3.9" "3.4" "2.9" "3.7"
[11] "4.0" "4.4" "3.8" "3.3" "4.1"
[16] "4.2" "2.3" "versicolor" "2.8" "2.4"
[21] "2.7" "2.0" "2.2" "2.5" "2.6"
[26] "virginica"
Or for the whole dataframe:
iris %>% t %>% c %>% unique
[1] "5.1" "3.5" "1.4" "0.2" "setosa" "4.9"
[7] "3.0" "4.7" "3.2" "1.3" "4.6" "3.1"
[13] "1.5" "5.0" "3.6" "5.4" "3.9" "1.7"
[19] "0.4" "3.4" "0.3" "4.4" "2.9" "0.1"
[25] "3.7" "4.8" "1.6" "4.3" "1.1" "5.8"
[31] "4.0" "1.2" "5.7" "3.8" "1.0" "3.3"
[37] "0.5" "1.9" "5.2" "4.1" "5.5" "4.2"
[43] "4.5" "2.3" "0.6" "5.3" "7.0" "versicolor"
[49] "6.4" "6.9" "6.5" "2.8" "6.3" "2.4"
[55] "6.6" "2.7" "2.0" "5.9" "6.0" "2.2"
[61] "6.1" "5.6" "6.7" "6.2" "2.5" "1.8"
[67] "6.8" "2.6" "virginica" "7.1" "2.1" "7.6"
[73] "7.3" "7.2" "7.7" "7.4" "7.9"
This is an addition to Josh's answer.
You can also keep the values of other variables while filtering out duplicated rows in data.table
Example:
library(data.table)
#create data table
dt <- data.table(
V1=LETTERS[c(1,1,1,1,2,3,3,5,7,1)],
V2=LETTERS[c(2,3,4,2,1,4,4,6,7,2)],
V3=c(1),
V4=c(2) )
> dt
# V1 V2 V3 V4
# A B 1 2
# A C 1 2
# A D 1 2
# A B 1 2
# B A 1 2
# C D 1 2
# C D 1 2
# E F 1 2
# G G 1 2
# A B 1 2
# set the key to all columns
setkey(dt)
# Get Unique lines in the data table
unique( dt[list(V1, V2), nomatch = 0] )
# V1 V2 V3 V4
# A B 1 2
# A C 1 2
# A D 1 2
# B A 1 2
# C D 1 2
# E F 1 2
# G G 1 2
Alert: If there are different combinations of values in the other variables, then your result will be
unique combination of V1 and V2
There are a few ways to get all unique combinations of a set of factors.
with(df, interaction(yad, per, drop=TRUE)) # gives labels
with(df, yad:per) # ditto
aggregate(numeric(nrow(df)), df[c("yad", "per")], length) # gives a data frame
Source: Stackoverflow.com