In this module, we will show you how to:
We will largely focus on the dplyr package which is part of the tidyverse.
Some resources on how to use dplyr:
The dplyr package is one of the most helpful packages for altering your data to get it into a form that is useful for creating visualizations, summarizing, or more deeply analyzing.
So you can imagine using pliers on your data.
See this website for a list of the packages included in the tidyverse: https://www.tidyverse.org/packages/
library(tidyverse) # loads dplyr and other packages!
── Attaching packages ──────────────────────────────────────────────────────────────────────────────────────── tidyverse 1.3.1 ──
✓ ggplot2 3.3.5 ✓ purrr 0.3.4 ✓ tibble 3.1.6 ✓ stringr 1.4.0 ✓ tidyr 1.1.4 ✓ forcats 0.5.1 ✓ readr 2.1.1
── Conflicts ─────────────────────────────────────────────────────────────────────────────────────────── tidyverse_conflicts() ── x dplyr::filter() masks stats::filter() x dplyr::lag() masks stats::lag()
Here we use one of the datasets that comes with base R called mtcars. We will now create a toy data frame named df using this data. This way we can alter df without worrying about changing mtcars.
df <- mtcars # df is a copy of mtcars head(df) # changing df does **not** change mtcars!
mpg cyl disp hp drat wt qsec vs am gear carb Mazda RX4 21.0 6 160 110 3.90 2.620 16.46 0 1 4 4 Mazda RX4 Wag 21.0 6 160 110 3.90 2.875 17.02 0 1 4 4 Datsun 710 22.8 4 108 93 3.85 2.320 18.61 1 1 4 1 Hornet 4 Drive 21.4 6 258 110 3.08 3.215 19.44 1 0 3 1 Hornet Sportabout 18.7 8 360 175 3.15 3.440 17.02 0 0 3 2 Valiant 18.1 6 225 105 2.76 3.460 20.22 1 0 3 1
dim()The dim() , nrow(), and ncol() functions are good options to check the dimensions of your data before moving forward.
dim(df) # rows, columns
[1] 32 11
nrow(df) # number of rows
[1] 32
ncol(df) # number of columns
[1] 11
glimpse()In addition to head() and tail(), the glimpse()function of the dplyr package is another great function to look at your data.
glimpse(df)
Rows: 32 Columns: 11 $ mpg <dbl> 21.0, 21.0, 22.8, 21.4, 18.7, 18.1, 14.3, 24.4, 22.8, 19.2, 17.8,… $ cyl <dbl> 6, 6, 4, 6, 8, 6, 8, 4, 4, 6, 6, 8, 8, 8, 8, 8, 8, 4, 4, 4, 4, 8,… $ disp <dbl> 160.0, 160.0, 108.0, 258.0, 360.0, 225.0, 360.0, 146.7, 140.8, 16… $ hp <dbl> 110, 110, 93, 110, 175, 105, 245, 62, 95, 123, 123, 180, 180, 180… $ drat <dbl> 3.90, 3.90, 3.85, 3.08, 3.15, 2.76, 3.21, 3.69, 3.92, 3.92, 3.92,… $ wt <dbl> 2.620, 2.875, 2.320, 3.215, 3.440, 3.460, 3.570, 3.190, 3.150, 3.… $ qsec <dbl> 16.46, 17.02, 18.61, 19.44, 17.02, 20.22, 15.84, 20.00, 22.90, 18… $ vs <dbl> 0, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0,… $ am <dbl> 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0,… $ gear <dbl> 4, 4, 4, 3, 3, 3, 3, 4, 4, 4, 4, 3, 3, 3, 3, 3, 3, 4, 4, 4, 3, 3,… $ carb <dbl> 4, 4, 1, 1, 2, 1, 4, 2, 2, 4, 4, 3, 3, 3, 4, 4, 4, 1, 2, 1, 1, 2,…
slice_sample()What if you want to see the middle of your data? You can use the slice_sample() function of the dplyr package to see a random set of rows. You can specify the number of rows with the n argument or use a proportion with the prop argument.
slice_sample(df, n = 3)
mpg cyl disp hp drat wt qsec vs am gear carb Datsun 710 22.8 4 108 93 3.85 2.320 18.61 1 1 4 1 Mazda RX4 Wag 21.0 6 160 110 3.90 2.875 17.02 0 1 4 4 Duster 360 14.3 8 360 245 3.21 3.570 15.84 0 0 3 4
slice_sample(df, prop = .2)
mpg cyl disp hp drat wt qsec vs am gear carb Ferrari Dino 19.7 6 145 175 3.62 2.770 15.50 0 1 5 6 Camaro Z28 13.3 8 350 245 3.73 3.840 15.41 0 0 3 4 Ford Pantera L 15.8 8 351 264 4.22 3.170 14.50 0 1 5 4 Chrysler Imperial 14.7 8 440 230 3.23 5.345 17.42 0 0 3 4 Dodge Challenger 15.5 8 318 150 2.76 3.520 16.87 0 0 3 2 Pontiac Firebird 19.2 8 400 175 3.08 3.845 17.05 0 0 3 2
data.frame(df)
mpg cyl disp hp drat wt qsec vs am gear carb Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4 Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4 Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1 Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1 Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2 Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1 Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4 Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2 Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2 Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4 Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4 Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3 Merc 450SL 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3 Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3 Cadillac Fleetwood 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4 Lincoln Continental 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4 Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4 Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 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 1 1 4 1 Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1 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 Camaro Z28 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4 Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2 Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1 Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2 Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2 Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4 Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6 Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8 Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2
Need to assign the output of the function to keep the result!
df_updated <-data.frame(df) # this would overwrite the existing df object df<-data.frame(df)
Or directly when reading in a csv with the read.csv() function (also base R)
# function comes from base R - no package loading required
df_example_readr <- read.csv("documents/data_analysis/data_file.csv")
We can create a fancier version of the previous data frame which can be really helpful.
tibbleIf we would like to create a tibble (“fancy” data frame), we can using the tibble() function.
tbl <- dplyr::tibble(df) tbl
# A tibble: 32 × 11
mpg cyl disp hp drat wt qsec vs am gear carb
<dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 21 6 160 110 3.9 2.62 16.5 0 1 4 4
2 21 6 160 110 3.9 2.88 17.0 0 1 4 4
3 22.8 4 108 93 3.85 2.32 18.6 1 1 4 1
4 21.4 6 258 110 3.08 3.22 19.4 1 0 3 1
5 18.7 8 360 175 3.15 3.44 17.0 0 0 3 2
6 18.1 6 225 105 2.76 3.46 20.2 1 0 3 1
7 14.3 8 360 245 3.21 3.57 15.8 0 0 3 4
8 24.4 4 147. 62 3.69 3.19 20 1 0 4 2
9 22.8 4 141. 95 3.92 3.15 22.9 1 0 4 2
10 19.2 6 168. 123 3.92 3.44 18.3 1 0 4 4
# … with 22 more rows
Note don’t necessarily need to use head()- tibbles conveniently print a portion of the data.
Alternatively we can read data files using the tidyverse with the read_csv() function of the readr package from the tidyverse to make a tibble.
df_example_readr <- read_csv("documents/data_analysis/data_file.csv")
You may start to notice how the tidyverse package work well together!
Base R:
Using read.csv() and data.frame() you can make data frames
Tidyverse (fancier version):
Using read_csv() and tibble() you can make tibbles
We generally recommend using tibbles, but you can do a lot with tibbles too.
In the “tidy” data format, rownames are removed. For example, df has each car name as a row name. Here we use the head() function to see the first 2 rows of each. In this case we would want to make the rownames a new column first before making into a tibble.
head(df, 2)
mpg cyl disp hp drat wt qsec vs am gear carb Mazda RX4 21 6 160 110 3.9 2.620 16.46 0 1 4 4 Mazda RX4 Wag 21 6 160 110 3.9 2.875 17.02 0 1 4 4
head(tibble(df), 2)
# A tibble: 2 × 11
mpg cyl disp hp drat wt qsec vs am gear carb
<dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 21 6 160 110 3.9 2.62 16.5 0 1 4 4
2 21 6 160 110 3.9 2.88 17.0 0 1 4 4
If you run into losing a variable contained in your row names, you can also use rownames_to_column to add it before turning it into a tibble to keep them:
head(rownames_to_column(df, var = "car"), 2)
car mpg cyl disp hp drat wt qsec vs am gear carb 1 Mazda RX4 21 6 160 110 3.9 2.620 16.46 0 1 4 4 2 Mazda RX4 Wag 21 6 160 110 3.9 2.875 17.02 0 1 4 4
head(tibble(rownames_to_column(df, var = "car")), 2)
# A tibble: 2 × 12 car mpg cyl disp hp drat wt qsec vs am gear carb <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> 1 Mazda RX4 21 6 160 110 3.9 2.62 16.5 0 1 4 4 2 Mazda RX4 W… 21 6 160 110 3.9 2.88 17.0 0 1 4 4
To rename columns in dplyr, you can use the rename function.
For example, let’s rename mpg to MPG. Notice the new name is listed first!
# general format! not code!
{data you are creating or changing} <- rename({data you are using},
{New Name} = {Old name})
df <- dplyr::rename(df, MPG = mpg) head(df)
MPG cyl disp hp drat wt qsec vs am gear carb Mazda RX4 21.0 6 160 110 3.90 2.620 16.46 0 1 4 4 Mazda RX4 Wag 21.0 6 160 110 3.90 2.875 17.02 0 1 4 4 Datsun 710 22.8 4 108 93 3.85 2.320 18.61 1 1 4 1 Hornet 4 Drive 21.4 6 258 110 3.08 3.215 19.44 1 0 3 1 Hornet Sportabout 18.7 8 360 175 3.15 3.440 17.02 0 0 3 2 Valiant 18.1 6 225 105 2.76 3.460 20.22 1 0 3 1
To rename all columns you use the rename_all(). In this case we will use toupper() to make all letters upper case. Could also use tolower() function.
df_upper <- dplyr::rename_all(df, toupper) head(df_upper, 3)
MPG CYL DISP HP DRAT WT QSEC VS AM GEAR CARB Mazda RX4 21.0 6 160 110 3.90 2.620 16.46 0 1 4 4 Mazda RX4 Wag 21.0 6 160 110 3.90 2.875 17.02 0 1 4 4 Datsun 710 22.8 4 108 93 3.85 2.320 18.61 1 1 4 1
df <- dplyr::rename_all(df, tolower) head(df, 3)
mpg cyl disp hp drat wt qsec vs am gear carb Mazda RX4 21.0 6 160 110 3.90 2.620 16.46 0 1 4 4 Mazda RX4 Wag 21.0 6 160 110 3.90 2.875 17.02 0 1 4 4 Datsun 710 22.8 4 108 93 3.85 2.320 18.61 1 1 4 1
We can grab the carb column using the $ operator. This is the base R way to do this:
df$carb
[1] 4 4 1 1 2 1 4 2 2 4 4 3 3 3 4 4 4 1 2 1 1 2 2 4 2 1 2 2 4 6 8 2
tidyverse way:To grab the carb column the tidyverse way we can use the pull function:
pull(df, carb)
[1] 4 4 1 1 2 1 4 2 2 4 4 3 3 3 4 4 4 1 2 1 1 2 2 4 2 1 2 2 4 6 8 2
The select command from dplyr allows you to subset (gives a tibble!)
select(df, mpg)
mpg Mazda RX4 21.0 Mazda RX4 Wag 21.0 Datsun 710 22.8 Hornet 4 Drive 21.4 Hornet Sportabout 18.7 Valiant 18.1 Duster 360 14.3 Merc 240D 24.4 Merc 230 22.8 Merc 280 19.2 Merc 280C 17.8 Merc 450SE 16.4 Merc 450SL 17.3 Merc 450SLC 15.2 Cadillac Fleetwood 10.4 Lincoln Continental 10.4 Chrysler Imperial 14.7 Fiat 128 32.4 Honda Civic 30.4 Toyota Corolla 33.9 Toyota Corona 21.5 Dodge Challenger 15.5 AMC Javelin 15.2 Camaro Z28 13.3 Pontiac Firebird 19.2 Fiat X1-9 27.3 Porsche 914-2 26.0 Lotus Europa 30.4 Ford Pantera L 15.8 Ferrari Dino 19.7 Maserati Bora 15.0 Volvo 142E 21.4
Note that if you want a single vector (not a tibble), use pull or $:
pull(df, mpg)
[1] 21.0 21.0 22.8 21.4 18.7 18.1 14.3 24.4 22.8 19.2 17.8 16.4 17.3 15.2 10.4 [16] 10.4 14.7 32.4 30.4 33.9 21.5 15.5 15.2 13.3 19.2 27.3 26.0 30.4 15.8 19.7 [31] 15.0 21.4
# pull with select works too! pull(select(df,mpg))
[1] 21.0 21.0 22.8 21.4 18.7 18.1 14.3 24.4 22.8 19.2 17.8 16.4 17.3 15.2 10.4 [16] 10.4 14.7 32.4 30.4 33.9 21.5 15.5 15.2 13.3 19.2 27.3 26.0 30.4 15.8 19.7 [31] 15.0 21.4
The select command from dplyr allows you to subset columns matching strings:
select(df, mpg, cyl)
mpg cyl Mazda RX4 21.0 6 Mazda RX4 Wag 21.0 6 Datsun 710 22.8 4 Hornet 4 Drive 21.4 6 Hornet Sportabout 18.7 8 Valiant 18.1 6 Duster 360 14.3 8 Merc 240D 24.4 4 Merc 230 22.8 4 Merc 280 19.2 6 Merc 280C 17.8 6 Merc 450SE 16.4 8 Merc 450SL 17.3 8 Merc 450SLC 15.2 8 Cadillac Fleetwood 10.4 8 Lincoln Continental 10.4 8 Chrysler Imperial 14.7 8 Fiat 128 32.4 4 Honda Civic 30.4 4 Toyota Corolla 33.9 4 Toyota Corona 21.5 4 Dodge Challenger 15.5 8 AMC Javelin 15.2 8 Camaro Z28 13.3 8 Pontiac Firebird 19.2 8 Fiat X1-9 27.3 4 Porsche 914-2 26.0 4 Lotus Europa 30.4 4 Ford Pantera L 15.8 8 Ferrari Dino 19.7 6 Maserati Bora 15.0 8 Volvo 142E 21.4 4
select(df, starts_with("c"))
cyl carb Mazda RX4 6 4 Mazda RX4 Wag 6 4 Datsun 710 4 1 Hornet 4 Drive 6 1 Hornet Sportabout 8 2 Valiant 6 1 Duster 360 8 4 Merc 240D 4 2 Merc 230 4 2 Merc 280 6 4 Merc 280C 6 4 Merc 450SE 8 3 Merc 450SL 8 3 Merc 450SLC 8 3 Cadillac Fleetwood 8 4 Lincoln Continental 8 4 Chrysler Imperial 8 4 Fiat 128 4 1 Honda Civic 4 2 Toyota Corolla 4 1 Toyota Corona 4 1 Dodge Challenger 8 2 AMC Javelin 8 2 Camaro Z28 8 4 Pontiac Firebird 8 2 Fiat X1-9 4 1 Porsche 914-2 4 2 Lotus Europa 4 2 Ford Pantera L 8 4 Ferrari Dino 6 6 Maserati Bora 8 8 Volvo 142E 4 2
Here are a few:
one_of() # if they exist last_col() ends_with() contains() # like searching
Type tidyselect:: in the console and see what RStudio suggests:
tidyslect::
The command in dplyr for subsetting rows is filter.
filter(df, mpg > 20)
mpg cyl disp hp drat wt qsec vs am gear carb Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4 Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4 Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1 Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1 Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2 Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2 Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 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 1 1 4 1 Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1 Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1 Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2 Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2 Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2
Note, no $ or subsetting is necessary. R “knows” mpg refers to a column of df.
You can have multiple logical conditions using the following:
== : equals to!=: not equal to (! : not/negation)> / <: greater than / less than>= or <=: greater than or equal to / less than or equal to& : AND| : ORThe %in% operator can be used find values from a pre-made list (using c()):
filter(df, mpg %in% c(20,21,22))
mpg cyl disp hp drat wt qsec vs am gear carb Mazda RX4 21 6 160 110 3.9 2.620 16.46 0 1 4 4 Mazda RX4 Wag 21 6 160 110 3.9 2.875 17.02 0 1 4 4
You can filter by two conditions using & or commas:
filter(df, mpg > 20 & cyl == 4)
mpg cyl disp hp drat wt qsec vs am gear carb Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1 Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2 Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2 Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 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 1 1 4 1 Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1 Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1 Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2 Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2 Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2
filter(df, mpg > 20, cyl == 4)
mpg cyl disp hp drat wt qsec vs am gear carb Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1 Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2 Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2 Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 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 1 1 4 1 Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1 Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1 Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2 Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2 Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2
If you want OR statements (meaning the data can meet either condition does not need to meet both), you need to use the pipe | between conditions:
filter(df, mpg > 20 | cyl == 4)
mpg cyl disp hp drat wt qsec vs am gear carb Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4 Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4 Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1 Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1 Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2 Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2 Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 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 1 1 4 1 Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1 Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1 Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2 Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2 Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2
filter and selectYou can combine filter and select to subset the rows and columns, respectively, of a data frame:
select(filter(df, mpg > 20 & cyl == 4), cyl, hp)
cyl hp Datsun 710 4 93 Merc 240D 4 62 Merc 230 4 95 Fiat 128 4 66 Honda Civic 4 52 Toyota Corolla 4 65 Toyota Corona 4 97 Fiat X1-9 4 66 Porsche 914-2 4 91 Lotus Europa 4 113 Volvo 142E 4 109
In R, the common way to perform multiple operations is to wrap functions around each other in a nested way such as above.
One can also create temporary objects and reassign them:
df2 <- filter(df, mpg > 20 & cyl == 4) df2 <- select(df2, cyl, hp) head(df2,4)
cyl hp Datsun 710 4 93 Merc 240D 4 62 Merc 230 4 95 Fiat 128 4 66
pipe (comes with dplyr):Recently, the pipe %>% makes things such as this much more readable. It reads left side “pipes” into right side. RStudio CMD/Ctrl + Shift + M shortcut. Pipe df into filter, then pipe that into select:
df %>% filter(mpg > 20 & cyl == 4) %>% select(cyl, hp)
cyl hp Datsun 710 4 93 Merc 240D 4 62 Merc 230 4 95 Fiat 128 4 66 Honda Civic 4 52 Toyota Corolla 4 65 Toyota Corona 4 97 Fiat X1-9 4 66 Porsche 914-2 4 91 Lotus Europa 4 113 Volvo 142E 4 109
You can add a new column, called newcol to df, using the $ operator:
df$newcol <- df$wt/2.2 head(df,3)
mpg cyl disp hp drat wt qsec vs am gear carb newcol Mazda RX4 21.0 6 160 110 3.90 2.620 16.46 0 1 4 4 1.190909 Mazda RX4 Wag 21.0 6 160 110 3.90 2.875 17.02 0 1 4 4 1.306818 Datsun 710 22.8 4 108 93 3.85 2.320 18.61 1 1 4 1 1.054545
tidyverse way)The $ method is very common.
The mutate function in dplyr allows you to add or modify columns of a data frame.
# General format - Not the code!
{data object to update} <- mutate({data to use},
{new variable name} = {new variable source})
df <- mutate(df, newcol = wt/2.2)
You can remove a column by assigning to NULL:
df$newcol <- NULL
The NULL method is still very common.
The select function can remove a column with minus (-):
select(df, - newcol)
mpg cyl disp hp drat wt qsec vs am gear carb Mazda RX4 21.0 6 160 110 3.90 2.620 16.46 0 1 4 4 Mazda RX4 Wag 21.0 6 160 110 3.90 2.875 17.02 0 1 4 4 Datsun 710 22.8 4 108 93 3.85 2.320 18.61 1 1 4 1 Hornet 4 Drive 21.4 6 258 110 3.08 3.215 19.44 1 0 3 1 Hornet Sportabout 18.7 8 360 175 3.15 3.440 17.02 0 0 3 2 Valiant 18.1 6 225 105 2.76 3.460 20.22 1 0 3 1
Or, you can simply select the columns you want to keep, ignoring the ones you want to remove.
You can use c() to list the columns to remove.
Remove newcol and drat:
select(df, -c("newcol", "drat"))
mpg cyl disp hp wt qsec vs am gear carb Mazda RX4 21.0 6 160.0 110 2.620 16.46 0 1 4 4 Mazda RX4 Wag 21.0 6 160.0 110 2.875 17.02 0 1 4 4 Datsun 710 22.8 4 108.0 93 2.320 18.61 1 1 4 1 Hornet 4 Drive 21.4 6 258.0 110 3.215 19.44 1 0 3 1 Hornet Sportabout 18.7 8 360.0 175 3.440 17.02 0 0 3 2 Valiant 18.1 6 225.0 105 3.460 20.22 1 0 3 1 Duster 360 14.3 8 360.0 245 3.570 15.84 0 0 3 4 Merc 240D 24.4 4 146.7 62 3.190 20.00 1 0 4 2 Merc 230 22.8 4 140.8 95 3.150 22.90 1 0 4 2 Merc 280 19.2 6 167.6 123 3.440 18.30 1 0 4 4 Merc 280C 17.8 6 167.6 123 3.440 18.90 1 0 4 4 Merc 450SE 16.4 8 275.8 180 4.070 17.40 0 0 3 3 Merc 450SL 17.3 8 275.8 180 3.730 17.60 0 0 3 3 Merc 450SLC 15.2 8 275.8 180 3.780 18.00 0 0 3 3 Cadillac Fleetwood 10.4 8 472.0 205 5.250 17.98 0 0 3 4 Lincoln Continental 10.4 8 460.0 215 5.424 17.82 0 0 3 4 Chrysler Imperial 14.7 8 440.0 230 5.345 17.42 0 0 3 4 Fiat 128 32.4 4 78.7 66 2.200 19.47 1 1 4 1 Honda Civic 30.4 4 75.7 52 1.615 18.52 1 1 4 2 Toyota Corolla 33.9 4 71.1 65 1.835 19.90 1 1 4 1 Toyota Corona 21.5 4 120.1 97 2.465 20.01 1 0 3 1 Dodge Challenger 15.5 8 318.0 150 3.520 16.87 0 0 3 2 AMC Javelin 15.2 8 304.0 150 3.435 17.30 0 0 3 2 Camaro Z28 13.3 8 350.0 245 3.840 15.41 0 0 3 4 Pontiac Firebird 19.2 8 400.0 175 3.845 17.05 0 0 3 2 Fiat X1-9 27.3 4 79.0 66 1.935 18.90 1 1 4 1 Porsche 914-2 26.0 4 120.3 91 2.140 16.70 0 1 5 2 Lotus Europa 30.4 4 95.1 113 1.513 16.90 1 1 5 2 Ford Pantera L 15.8 8 351.0 264 3.170 14.50 0 1 5 4 Ferrari Dino 19.7 6 145.0 175 2.770 15.50 0 1 5 6 Maserati Bora 15.0 8 301.0 335 3.570 14.60 0 1 5 8 Volvo 142E 21.4 4 121.0 109 2.780 18.60 1 1 4 2
The select function can reorder columns.
head(df) select(df, cyl, mpg, wt, car) %>% head()
We can also use the relocate() function of dplyr to rearrange the columns.
For example, let say we just wanted wt to be first.
head(df)
mpg cyl disp hp drat wt qsec vs am gear carb newcol Mazda RX4 21.0 6 160 110 3.90 2.620 16.46 0 1 4 4 1.190909 Mazda RX4 Wag 21.0 6 160 110 3.90 2.875 17.02 0 1 4 4 1.306818 Datsun 710 22.8 4 108 93 3.85 2.320 18.61 1 1 4 1 1.054545 Hornet 4 Drive 21.4 6 258 110 3.08 3.215 19.44 1 0 3 1 1.461364 Hornet Sportabout 18.7 8 360 175 3.15 3.440 17.02 0 0 3 2 1.563636 Valiant 18.1 6 225 105 2.76 3.460 20.22 1 0 3 1 1.572727
df_carb <- relocate(.data = df, wt,
.before = mpg)
df_carb
wt mpg cyl disp hp drat qsec vs am gear carb
Mazda RX4 2.620 21.0 6 160.0 110 3.90 16.46 0 1 4 4
Mazda RX4 Wag 2.875 21.0 6 160.0 110 3.90 17.02 0 1 4 4
Datsun 710 2.320 22.8 4 108.0 93 3.85 18.61 1 1 4 1
Hornet 4 Drive 3.215 21.4 6 258.0 110 3.08 19.44 1 0 3 1
Hornet Sportabout 3.440 18.7 8 360.0 175 3.15 17.02 0 0 3 2
Valiant 3.460 18.1 6 225.0 105 2.76 20.22 1 0 3 1
Duster 360 3.570 14.3 8 360.0 245 3.21 15.84 0 0 3 4
Merc 240D 3.190 24.4 4 146.7 62 3.69 20.00 1 0 4 2
Merc 230 3.150 22.8 4 140.8 95 3.92 22.90 1 0 4 2
Merc 280 3.440 19.2 6 167.6 123 3.92 18.30 1 0 4 4
Merc 280C 3.440 17.8 6 167.6 123 3.92 18.90 1 0 4 4
Merc 450SE 4.070 16.4 8 275.8 180 3.07 17.40 0 0 3 3
Merc 450SL 3.730 17.3 8 275.8 180 3.07 17.60 0 0 3 3
Merc 450SLC 3.780 15.2 8 275.8 180 3.07 18.00 0 0 3 3
Cadillac Fleetwood 5.250 10.4 8 472.0 205 2.93 17.98 0 0 3 4
Lincoln Continental 5.424 10.4 8 460.0 215 3.00 17.82 0 0 3 4
Chrysler Imperial 5.345 14.7 8 440.0 230 3.23 17.42 0 0 3 4
Fiat 128 2.200 32.4 4 78.7 66 4.08 19.47 1 1 4 1
Honda Civic 1.615 30.4 4 75.7 52 4.93 18.52 1 1 4 2
Toyota Corolla 1.835 33.9 4 71.1 65 4.22 19.90 1 1 4 1
Toyota Corona 2.465 21.5 4 120.1 97 3.70 20.01 1 0 3 1
Dodge Challenger 3.520 15.5 8 318.0 150 2.76 16.87 0 0 3 2
AMC Javelin 3.435 15.2 8 304.0 150 3.15 17.30 0 0 3 2
Camaro Z28 3.840 13.3 8 350.0 245 3.73 15.41 0 0 3 4
Pontiac Firebird 3.845 19.2 8 400.0 175 3.08 17.05 0 0 3 2
Fiat X1-9 1.935 27.3 4 79.0 66 4.08 18.90 1 1 4 1
Porsche 914-2 2.140 26.0 4 120.3 91 4.43 16.70 0 1 5 2
Lotus Europa 1.513 30.4 4 95.1 113 3.77 16.90 1 1 5 2
Ford Pantera L 3.170 15.8 8 351.0 264 4.22 14.50 0 1 5 4
Ferrari Dino 2.770 19.7 6 145.0 175 3.62 15.50 0 1 5 6
Maserati Bora 3.570 15.0 8 301.0 335 3.54 14.60 0 1 5 8
Volvo 142E 2.780 21.4 4 121.0 109 4.11 18.60 1 1 4 2
newcol
Mazda RX4 1.1909091
Mazda RX4 Wag 1.3068182
Datsun 710 1.0545455
Hornet 4 Drive 1.4613636
Hornet Sportabout 1.5636364
Valiant 1.5727273
Duster 360 1.6227273
Merc 240D 1.4500000
Merc 230 1.4318182
Merc 280 1.5636364
Merc 280C 1.5636364
Merc 450SE 1.8500000
Merc 450SL 1.6954545
Merc 450SLC 1.7181818
Cadillac Fleetwood 2.3863636
Lincoln Continental 2.4654545
Chrysler Imperial 2.4295455
Fiat 128 1.0000000
Honda Civic 0.7340909
Toyota Corolla 0.8340909
Toyota Corona 1.1204545
Dodge Challenger 1.6000000
AMC Javelin 1.5613636
Camaro Z28 1.7454545
Pontiac Firebird 1.7477273
Fiat X1-9 0.8795455
Porsche 914-2 0.9727273
Lotus Europa 0.6877273
Ford Pantera L 1.4409091
Ferrari Dino 1.2590909
Maserati Bora 1.6227273
Volvo 142E 1.2636364
The arrange function can reorder rows By default, arrange orders in ascending order:
arrange(df, mpg)
mpg cyl disp hp drat wt qsec vs am gear carb
Cadillac Fleetwood 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4
Lincoln Continental 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4
Camaro Z28 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4
Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4
Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4
Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8
Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3
AMC Javelin 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2
Dodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2
Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4
Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3
Merc 450SL 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3
Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4
Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1
Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2
Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4
Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2
Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6
Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4
Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4
Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1
Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2
Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1
Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1
Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2
Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2
Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2
Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1
Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2
Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2
Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1
Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1
newcol
Cadillac Fleetwood 2.3863636
Lincoln Continental 2.4654545
Camaro Z28 1.7454545
Duster 360 1.6227273
Chrysler Imperial 2.4295455
Maserati Bora 1.6227273
Merc 450SLC 1.7181818
AMC Javelin 1.5613636
Dodge Challenger 1.6000000
Ford Pantera L 1.4409091
Merc 450SE 1.8500000
Merc 450SL 1.6954545
Merc 280C 1.5636364
Valiant 1.5727273
Hornet Sportabout 1.5636364
Merc 280 1.5636364
Pontiac Firebird 1.7477273
Ferrari Dino 1.2590909
Mazda RX4 1.1909091
Mazda RX4 Wag 1.3068182
Hornet 4 Drive 1.4613636
Volvo 142E 1.2636364
Toyota Corona 1.1204545
Datsun 710 1.0545455
Merc 230 1.4318182
Merc 240D 1.4500000
Porsche 914-2 0.9727273
Fiat X1-9 0.8795455
Honda Civic 0.7340909
Lotus Europa 0.6877273
Fiat 128 1.0000000
Toyota Corolla 0.8340909
Use the desc to arrange the rows in descending order:
arrange(df, desc(mpg))
mpg cyl disp hp drat wt qsec vs am gear carb
Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1
Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1
Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2
Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2
Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1
Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2
Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2
Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1
Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2
Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1
Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1
Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2
Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4
Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4
Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6
Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4
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
Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1
Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4
Merc 450SL 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3
Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3
Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4
Dodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2
Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3
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 0 1 5 8
Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4
Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4
Camaro Z28 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4
Cadillac Fleetwood 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4
Lincoln Continental 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4
newcol
Toyota Corolla 0.8340909
Fiat 128 1.0000000
Honda Civic 0.7340909
Lotus Europa 0.6877273
Fiat X1-9 0.8795455
Porsche 914-2 0.9727273
Merc 240D 1.4500000
Datsun 710 1.0545455
Merc 230 1.4318182
Toyota Corona 1.1204545
Hornet 4 Drive 1.4613636
Volvo 142E 1.2636364
Mazda RX4 1.1909091
Mazda RX4 Wag 1.3068182
Ferrari Dino 1.2590909
Merc 280 1.5636364
Pontiac Firebird 1.7477273
Hornet Sportabout 1.5636364
Valiant 1.5727273
Merc 280C 1.5636364
Merc 450SL 1.6954545
Merc 450SE 1.8500000
Ford Pantera L 1.4409091
Dodge Challenger 1.6000000
Merc 450SLC 1.7181818
AMC Javelin 1.5613636
Maserati Bora 1.6227273
Chrysler Imperial 2.4295455
Duster 360 1.6227273
Camaro Z28 1.7454545
Cadillac Fleetwood 2.3863636
Lincoln Continental 2.4654545
Increasing and decreasing orderings:
arrange(df, mpg, desc(hp))
mpg cyl disp hp drat wt qsec vs am gear carb
Lincoln Continental 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4
Cadillac Fleetwood 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4
Camaro Z28 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4
Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4
Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4
Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8
Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3
AMC Javelin 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2
Dodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2
Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4
Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3
Merc 450SL 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3
Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4
Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1
Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2
Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2
Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4
Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6
Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4
Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4
Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1
Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2
Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1
Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2
Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1
Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2
Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2
Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1
Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2
Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2
Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1
Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1
newcol
Lincoln Continental 2.4654545
Cadillac Fleetwood 2.3863636
Camaro Z28 1.7454545
Duster 360 1.6227273
Chrysler Imperial 2.4295455
Maserati Bora 1.6227273
Merc 450SLC 1.7181818
AMC Javelin 1.5613636
Dodge Challenger 1.6000000
Ford Pantera L 1.4409091
Merc 450SE 1.8500000
Merc 450SL 1.6954545
Merc 280C 1.5636364
Valiant 1.5727273
Hornet Sportabout 1.5636364
Pontiac Firebird 1.7477273
Merc 280 1.5636364
Ferrari Dino 1.2590909
Mazda RX4 1.1909091
Mazda RX4 Wag 1.3068182
Hornet 4 Drive 1.4613636
Volvo 142E 1.2636364
Toyota Corona 1.1204545
Merc 230 1.4318182
Datsun 710 1.0545455
Merc 240D 1.4500000
Porsche 914-2 0.9727273
Fiat X1-9 0.8795455
Lotus Europa 0.6877273
Honda Civic 0.7340909
Fiat 128 1.0000000
Toyota Corolla 0.8340909
One frequently-used tool is creating variables with conditions.
A general function for creating new variables based on existing variables is the ifelse() function, which “returns a value depending on whether the element of test is TRUE or FALSE.”
ifelse(test, yes, no)
# test: an object which can be coerced
to logical mode.
# yes: return values for true elements of test.
# no: return values for false elements of test.
df$disp
[1] 160.0 160.0 108.0 258.0 360.0 225.0 360.0 146.7 140.8 167.6 167.6 275.8 [13] 275.8 275.8 472.0 460.0 440.0 78.7 75.7 71.1 120.1 318.0 304.0 350.0 [25] 400.0 79.0 120.3 95.1 351.0 145.0 301.0 121.0
Now with ifelse()
#ifelse(test, yes, no) ifelse(df$disp <= 200, "low", "high")
[1] "low" "low" "low" "high" "high" "high" "high" "low" "low" "low" [11] "low" "high" "high" "high" "high" "high" "high" "low" "low" "low" [21] "low" "high" "high" "high" "high" "low" "low" "low" "high" "low" [31] "high" "low"
Combined with ifelse(condition, TRUE, FALSE), it can give you:
df = mutate(df,
disp_cat = ifelse(disp <= 200, "Low","High")
)
head(df,2)
mpg cyl disp hp drat wt qsec vs am gear carb newcol
Mazda RX4 21 6 160 110 3.9 2.620 16.46 0 1 4 4 1.190909
Mazda RX4 Wag 21 6 160 110 3.9 2.875 17.02 0 1 4 4 1.306818
disp_cat
Mazda RX4 Low
Mazda RX4 Wag Low
Alternatively, case_when provides a clean syntax as well:
df = mutate(df,
disp_cat2 = case_when(
disp <= 200 ~ "Low",
disp > 200 ~ "High",
))
head(df$disp_cat2)
[1] "Low" "Low" "Low" "High" "High" "High"
We can use the colnames function to extract and/or directly reassign column names of df:
colnames(df) # just prints
[1] "mpg" "cyl" "disp" "hp" "drat" "wt" [7] "qsec" "vs" "am" "gear" "carb" "newcol" [13] "disp_cat" "disp_cat2"
colnames(df)[1:3] = c("MPG", "CYL", "DISP") # reassigns
head(df)
MPG CYL DISP hp drat wt qsec vs am gear carb newcol
Mazda RX4 21.0 6 160 110 3.90 2.620 16.46 0 1 4 4 1.190909
Mazda RX4 Wag 21.0 6 160 110 3.90 2.875 17.02 0 1 4 4 1.306818
Datsun 710 22.8 4 108 93 3.85 2.320 18.61 1 1 4 1 1.054545
Hornet 4 Drive 21.4 6 258 110 3.08 3.215 19.44 1 0 3 1 1.461364
Hornet Sportabout 18.7 8 360 175 3.15 3.440 17.02 0 0 3 2 1.563636
Valiant 18.1 6 225 105 2.76 3.460 20.22 1 0 3 1 1.572727
disp_cat disp_cat2
Mazda RX4 Low Low
Mazda RX4 Wag Low Low
Datsun 710 Low Low
Hornet 4 Drive High High
Hornet Sportabout High High
Valiant High High
colnames(df)[1:3] = c("mpg", "cyl", "disp") #reset - just to keep consistent
We can assign the column names, change the ones we want, and then re-assign the column names:
cn = colnames(df) cn[ cn == "drat"] = "DRAT" colnames(df) = cn head(df)
mpg cyl disp hp DRAT wt qsec vs am gear carb newcol
Mazda RX4 21.0 6 160 110 3.90 2.620 16.46 0 1 4 4 1.190909
Mazda RX4 Wag 21.0 6 160 110 3.90 2.875 17.02 0 1 4 4 1.306818
Datsun 710 22.8 4 108 93 3.85 2.320 18.61 1 1 4 1 1.054545
Hornet 4 Drive 21.4 6 258 110 3.08 3.215 19.44 1 0 3 1 1.461364
Hornet Sportabout 18.7 8 360 175 3.15 3.440 17.02 0 0 3 2 1.563636
Valiant 18.1 6 225 105 2.76 3.460 20.22 1 0 3 1 1.572727
disp_cat disp_cat2
Mazda RX4 Low Low
Mazda RX4 Wag Low Low
Datsun 710 Low Low
Hornet 4 Drive High High
Hornet Sportabout High High
Valiant High High
colnames(df)[ colnames(df) == "DRAT"] = "drat" #reset
Let’s select rows 1 and 3 from df using brackets:
df[ c(1, 3), ]
mpg cyl disp hp drat wt qsec vs am gear carb newcol disp_cat
Mazda RX4 21.0 6 160 110 3.90 2.62 16.46 0 1 4 4 1.190909 Low
Datsun 710 22.8 4 108 93 3.85 2.32 18.61 1 1 4 1 1.054545 Low
disp_cat2
Mazda RX4 Low
Datsun 710 Low
We can also subset a data frame using the bracket [, ] subsetting.
For data frames and matrices (2-dimensional objects), the brackets are [rows, columns] subsetting. We can grab the x column using the index of the column or the column name (“carb”)
df[, 11]
[1] 4 4 1 1 2 1 4 2 2 4 4 3 3 3 4 4 4 1 2 1 1 2 2 4 2 1 2 2 4 6 8 2
df[, "carb"]
[1] 4 4 1 1 2 1 4 2 2 4 4 3 3 3 4 4 4 1 2 1 1 2 2 4 2 1 2 2 4 6 8 2
tbl and data frame:Mostly, tbl (tibbles) are the same as data frames, except they don’t print all lines. When subsetting only one column using brackets, a data frame will return a vector, but a tbl will return a tbl
df[, 1]
[1] 21.0 21.0 22.8 21.4 18.7 18.1 14.3 24.4 22.8 19.2 17.8 16.4 17.3 15.2 10.4 [16] 10.4 14.7 32.4 30.4 33.9 21.5 15.5 15.2 13.3 19.2 27.3 26.0 30.4 15.8 19.7 [31] 15.0 21.4
tbl[, 1]
# A tibble: 32 × 1
mpg
<dbl>
1 21
2 21
3 22.8
4 21.4
5 18.7
6 18.1
7 14.3
8 24.4
9 22.8
10 19.2
# … with 22 more rows
tbl[, "mpg"]
# A tibble: 32 × 1
mpg
<dbl>
1 21
2 21
3 22.8
4 21.4
5 18.7
6 18.1
7 14.3
8 24.4
9 22.8
10 19.2
# … with 22 more rows
df[, 1, drop = FALSE]
mpg Mazda RX4 21.0 Mazda RX4 Wag 21.0 Datsun 710 22.8 Hornet 4 Drive 21.4 Hornet Sportabout 18.7 Valiant 18.1 Duster 360 14.3 Merc 240D 24.4 Merc 230 22.8 Merc 280 19.2 Merc 280C 17.8 Merc 450SE 16.4 Merc 450SL 17.3 Merc 450SLC 15.2 Cadillac Fleetwood 10.4 Lincoln Continental 10.4 Chrysler Imperial 14.7 Fiat 128 32.4 Honda Civic 30.4 Toyota Corolla 33.9 Toyota Corona 21.5 Dodge Challenger 15.5 AMC Javelin 15.2 Camaro Z28 13.3 Pontiac Firebird 19.2 Fiat X1-9 27.3 Porsche 914-2 26.0 Lotus Europa 30.4 Ford Pantera L 15.8 Ferrari Dino 19.7 Maserati Bora 15.0 Volvo 142E 21.4
We can select multiple columns using multiple column names:
df[, c("mpg", "cyl")]
mpg cyl Mazda RX4 21.0 6 Mazda RX4 Wag 21.0 6 Datsun 710 22.8 4 Hornet 4 Drive 21.4 6 Hornet Sportabout 18.7 8 Valiant 18.1 6 Duster 360 14.3 8 Merc 240D 24.4 4 Merc 230 22.8 4 Merc 280 19.2 6 Merc 280C 17.8 6 Merc 450SE 16.4 8 Merc 450SL 17.3 8 Merc 450SLC 15.2 8 Cadillac Fleetwood 10.4 8 Lincoln Continental 10.4 8 Chrysler Imperial 14.7 8 Fiat 128 32.4 4 Honda Civic 30.4 4 Toyota Corolla 33.9 4 Toyota Corona 21.5 4 Dodge Challenger 15.5 8 AMC Javelin 15.2 8 Camaro Z28 13.3 8 Pontiac Firebird 19.2 8 Fiat X1-9 27.3 4 Porsche 914-2 26.0 4 Lotus Europa 30.4 4 Ford Pantera L 15.8 8 Ferrari Dino 19.7 6 Maserati Bora 15.0 8 Volvo 142E 21.4 4