Package provides pipe-style interface for data.table package. It preserves all data.table features without significant impact on performance. let and take functions are simplified interfaces for most common data manipulation tasks.
take_if(mtcars, am==0)take(mtcars, am, vs, mpg)take(mtcars, mean_mpg = mean(mpg), by = am)take(mtcars, fun = mean, by = am)take(mtcars, mpg, hp, fun = mean, by = am)by argument: take(mtcars, fun = mean)%>% to chain several operations: mtcars %>%
let(mpg_hp = mpg/hp) %>%
take(mean(mpg_hp), by = am) mtcars %>%
let(new_var = 42,
new_var2 = new_var*hp) %>%
head()let(mtcars, am = NULL) %>% head():=: new_var = "my_var"
old_var = "mpg"
mtcars %>%
let((new_var) := get(old_var)*2) %>%
head()
# or,
expr = quote(mean(cyl))
mtcars %>%
let((new_var) := eval(expr)) %>%
head()
# the same with `take`
by_var = "vs,am"
take(mtcars, (new_var) := eval(expr), by = by_var)query_if function translates its arguments one-to-one to [.data.table method. Additionally there are some conveniences such as automatic data.frame conversion to data.table.
We will use for demonstartion well-known mtcars dataset and some examples from dplyr package.
library(maditr)##
## To select columns from data: take(mtcars, am, vs, mpg)data(mtcars)
# Newly created variables are available immediately
mtcars %>%
let(
cyl2 = cyl * 2,
cyl4 = cyl2 * 2
) %>% head()## mpg cyl disp hp drat wt qsec vs am gear carb cyl2 cyl4
## 1: 21.0 6 160 110 3.90 2.620 16.46 0 1 4 4 12 24
## 2: 21.0 6 160 110 3.90 2.875 17.02 0 1 4 4 12 24
## 3: 22.8 4 108 93 3.85 2.320 18.61 1 1 4 1 8 16
## 4: 21.4 6 258 110 3.08 3.215 19.44 1 0 3 1 12 24
## 5: 18.7 8 360 175 3.15 3.440 17.02 0 0 3 2 16 32
## 6: 18.1 6 225 105 2.76 3.460 20.22 1 0 3 1 12 24# You can also use let() to remove variables and
# modify existing variables
mtcars %>%
let(
mpg = NULL,
disp = disp * 0.0163871 # convert to litres
) %>% head()## cyl disp hp drat wt qsec vs am gear carb
## 1: 6 2.621936 110 3.90 2.620 16.46 0 1 4 4
## 2: 6 2.621936 110 3.90 2.875 17.02 0 1 4 4
## 3: 4 1.769807 93 3.85 2.320 18.61 1 1 4 1
## 4: 6 4.227872 110 3.08 3.215 19.44 1 0 3 1
## 5: 8 5.899356 175 3.15 3.440 17.02 0 0 3 2
## 6: 6 3.687098 105 2.76 3.460 20.22 1 0 3 1# window functions are useful for grouped computations
mtcars %>%
let(rank = rank(-mpg, ties.method = "min"),
by = cyl) %>%
head()## mpg cyl disp hp drat wt qsec vs am gear carb rank
## 1: 21.0 6 160 110 3.90 2.620 16.46 0 1 4 4 2
## 2: 21.0 6 160 110 3.90 2.875 17.02 0 1 4 4 2
## 3: 22.8 4 108 93 3.85 2.320 18.61 1 1 4 1 8
## 4: 21.4 6 258 110 3.08 3.215 19.44 1 0 3 1 1
## 5: 18.7 8 360 175 3.15 3.440 17.02 0 0 3 2 2
## 6: 18.1 6 225 105 2.76 3.460 20.22 1 0 3 1 6# You can drop variables by setting them to NULL
mtcars %>%
let(cyl = NULL) %>%
head()## mpg disp hp drat wt qsec vs am gear carb
## 1: 21.0 160 110 3.90 2.620 16.46 0 1 4 4
## 2: 21.0 160 110 3.90 2.875 17.02 0 1 4 4
## 3: 22.8 108 93 3.85 2.320 18.61 1 1 4 1
## 4: 21.4 258 110 3.08 3.215 19.44 1 0 3 1
## 5: 18.7 360 175 3.15 3.440 17.02 0 0 3 2
## 6: 18.1 225 105 2.76 3.460 20.22 1 0 3 1# keeps all existing variables
mtcars %>%
let(displ_l = disp / 61.0237) %>%
head()## mpg cyl disp hp drat wt qsec vs am gear carb displ_l
## 1: 21.0 6 160 110 3.90 2.620 16.46 0 1 4 4 2.621932
## 2: 21.0 6 160 110 3.90 2.875 17.02 0 1 4 4 2.621932
## 3: 22.8 4 108 93 3.85 2.320 18.61 1 1 4 1 1.769804
## 4: 21.4 6 258 110 3.08 3.215 19.44 1 0 3 1 4.227866
## 5: 18.7 8 360 175 3.15 3.440 17.02 0 0 3 2 5.899347
## 6: 18.1 6 225 105 2.76 3.460 20.22 1 0 3 1 3.687092# keeps only the variables you create
mtcars %>%
take(displ_l = disp / 61.0237) %>%
head()## displ_l
## 1: 2.621932
## 2: 2.621932
## 3: 1.769804
## 4: 4.227866
## 5: 5.899347
## 6: 3.687092# can refer to both contextual variables and variable names:
var = 100
mtcars %>%
let(cyl = cyl * var) %>%
head()## mpg cyl disp hp drat wt qsec vs am gear carb
## 1: 21.0 600 160 110 3.90 2.620 16.46 0 1 4 4
## 2: 21.0 600 160 110 3.90 2.875 17.02 0 1 4 4
## 3: 22.8 400 108 93 3.85 2.320 18.61 1 1 4 1
## 4: 21.4 600 258 110 3.08 3.215 19.44 1 0 3 1
## 5: 18.7 800 360 175 3.15 3.440 17.02 0 0 3 2
## 6: 18.1 600 225 105 2.76 3.460 20.22 1 0 3 1# filter by condition
mtcars %>%
take_if(am==0) %>%
head()## mpg cyl disp hp drat wt qsec vs am gear carb
## 1: 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1
## 2: 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2
## 3: 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1
## 4: 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4
## 5: 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2
## 6: 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2# filter by compound condition
mtcars %>%
take_if(am==0 & mpg>mean(mpg))## mpg cyl disp hp drat wt qsec vs am gear carb
## 1: 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1
## 2: 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2
## 3: 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2
## 4: 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1# A 'take' with summary functions applied without 'by' argument returns an aggregated data
mtcars %>%
take(mean = mean(disp), n = .N)## mean n
## 1: 230.7219 32# Usually, you'll want to group first
mtcars %>%
take(mean = mean(disp), n = .N, by = am)## am mean n
## 1: 1 143.5308 13
## 2: 0 290.3789 19# grouping by multiple variables
mtcars %>%
take(mean = mean(disp), n = .N, by = list(am, vs))## am vs mean n
## 1: 1 0 206.2167 6
## 2: 1 1 89.8000 7
## 3: 0 1 175.1143 7
## 4: 0 0 357.6167 12# You can group by expressions:
mtcars %>%
take(
fun = mean,
by = list(vsam = vs + am)
)## vsam mpg cyl disp hp drat wt qsec
## 1: 1 20.28462 5.692308 189.4692 138.46154 3.738462 3.038846 18.04231
## 2: 2 28.37143 4.000000 89.8000 80.57143 4.148571 2.028286 18.70000
## 3: 0 15.05000 8.000000 357.6167 194.16667 3.120833 4.104083 17.14250
## gear carb
## 1: 4.076923 3.307692
## 2: 4.142857 1.428571
## 3: 3.000000 3.083333# parametric evaluation:
var = quote(mean(cyl))
mtcars %>%
let(mean_cyl = eval(var)) %>%
head()## mpg cyl disp hp drat wt qsec vs am gear carb mean_cyl
## 1: 21.0 6 160 110 3.90 2.620 16.46 0 1 4 4 6.1875
## 2: 21.0 6 160 110 3.90 2.875 17.02 0 1 4 4 6.1875
## 3: 22.8 4 108 93 3.85 2.320 18.61 1 1 4 1 6.1875
## 4: 21.4 6 258 110 3.08 3.215 19.44 1 0 3 1 6.1875
## 5: 18.7 8 360 175 3.15 3.440 17.02 0 0 3 2 6.1875
## 6: 18.1 6 225 105 2.76 3.460 20.22 1 0 3 1 6.1875take(mtcars, eval(var))## eval(var)
## 1: 6.1875# all together
new_var = "mean_cyl"
mtcars %>%
let((new_var) := eval(var)) %>%
head()## mpg cyl disp hp drat wt qsec vs am gear carb mean_cyl
## 1: 21.0 6 160 110 3.90 2.620 16.46 0 1 4 4 6.1875
## 2: 21.0 6 160 110 3.90 2.875 17.02 0 1 4 4 6.1875
## 3: 22.8 4 108 93 3.85 2.320 18.61 1 1 4 1 6.1875
## 4: 21.4 6 258 110 3.08 3.215 19.44 1 0 3 1 6.1875
## 5: 18.7 8 360 175 3.15 3.440 17.02 0 0 3 2 6.1875
## 6: 18.1 6 225 105 2.76 3.460 20.22 1 0 3 1 6.1875take(mtcars, (new_var) := eval(var))## mean_cyl
## 1: 6.1875Let’s make datasets for joining:
workers = fread("
name company
Nick Acme
John Ajax
Daniela Ajax
")
positions = fread("
name position
John designer
Daniela engineer
Cathie manager
")
workers## name company
## 1: Nick Acme
## 2: John Ajax
## 3: Daniela Ajaxpositions## name position
## 1: John designer
## 2: Daniela engineer
## 3: Cathie managerDifferent kinds of joins:
workers %>% dt_inner_join(positions)## dt_inner_join: joining, by = "name"## name company position
## 1: John Ajax designer
## 2: Daniela Ajax engineerworkers %>% dt_left_join(positions)## dt_left_join: joining, by = "name"## name company position
## 1: Nick Acme <NA>
## 2: John Ajax designer
## 3: Daniela Ajax engineerworkers %>% dt_right_join(positions)## dt_right_join: joining, by = "name"## name company position
## 1: John Ajax designer
## 2: Daniela Ajax engineer
## 3: Cathie <NA> managerworkers %>% dt_full_join(positions)## dt_full_join: joining, by = "name"## name company position
## 1: Nick Acme <NA>
## 2: John Ajax designer
## 3: Daniela Ajax engineer
## 4: Cathie <NA> manager# filtering joins
workers %>% dt_anti_join(positions)## dt_anti_join: joining, by = "name"## name company
## 1: Nick Acmeworkers %>% dt_semi_join(positions)## dt_semi_join: joining, by = "name"## name company
## 1: John Ajax
## 2: Daniela AjaxTo suppress the message, supply by argument:
workers %>% dt_left_join(positions, by = "name")Use a named by if the join variables have different names:
positions2 = setNames(positions, c("worker", "position")) # rename first column in 'positions'
workers %>% dt_inner_join(positions2, by = c("name" = "worker"))There are a small subset of ‘dplyr’ verbs to work with data.table. Note that there is no group_by verb - use by or keyby argument when needed.
dt_mutate adds new variables or modify existing variables. If data is data.table then it modifies in-place.dt_summarize computes summary statistics. Splits the data into subsets, computes summary statistics for each, and returns the result in the “data.table” form.dt_summarize_all the same as dt_summarize but work over all non-grouping variables.dt_filter Selects rows/cases where conditions are true. Rows where the condition evaluates to NA are dropped.dt_select Selects column/variables from the data.set.dt_arrange sorts dataset by variable(-s). Use ‘-’ to sort in desending order. If data is data.table then it modifies in-place.The same examples with ‘dplyr’-verbs:
# examples from 'dplyr'
# newly created variables are available immediately
mtcars %>%
dt_mutate(
cyl2 = cyl * 2,
cyl4 = cyl2 * 2
) %>%
head()## mpg cyl disp hp drat wt qsec vs am gear carb cyl2 cyl4
## 1: 21.0 6 160 110 3.90 2.620 16.46 0 1 4 4 12 24
## 2: 21.0 6 160 110 3.90 2.875 17.02 0 1 4 4 12 24
## 3: 22.8 4 108 93 3.85 2.320 18.61 1 1 4 1 8 16
## 4: 21.4 6 258 110 3.08 3.215 19.44 1 0 3 1 12 24
## 5: 18.7 8 360 175 3.15 3.440 17.02 0 0 3 2 16 32
## 6: 18.1 6 225 105 2.76 3.460 20.22 1 0 3 1 12 24# you can also use dt_mutate() to remove variables and
# modify existing variables
mtcars %>%
dt_mutate(
mpg = NULL,
disp = disp * 0.0163871 # convert to litres
) %>%
head()## cyl disp hp drat wt qsec vs am gear carb
## 1: 6 2.621936 110 3.90 2.620 16.46 0 1 4 4
## 2: 6 2.621936 110 3.90 2.875 17.02 0 1 4 4
## 3: 4 1.769807 93 3.85 2.320 18.61 1 1 4 1
## 4: 6 4.227872 110 3.08 3.215 19.44 1 0 3 1
## 5: 8 5.899356 175 3.15 3.440 17.02 0 0 3 2
## 6: 6 3.687098 105 2.76 3.460 20.22 1 0 3 1# window functions are useful for grouped mutates
mtcars %>%
dt_mutate(
rank = rank(-mpg, ties.method = "min"),
keyby = cyl) %>%
print()## mpg cyl disp hp drat wt qsec vs am gear carb rank
## 1: 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1 8
## 2: 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2 7
## 3: 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2 8
## 4: 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1 2
## 5: 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2 3
## 6: 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1 1
## 7: 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1 10
## 8: 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1 5
## 9: 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2 6
## 10: 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2 3
## 11: 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2 11
## 12: 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4 2
## 13: 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4 2
## 14: 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1 1
## 15: 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1 6
## 16: 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4 5
## 17: 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4 7
## 18: 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6 4
## 19: 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2 2
## 20: 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4 11
## 21: 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3 4
## 22: 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3 3
## 23: 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3 7
## 24: 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4 13
## 25: 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4 13
## 26: 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4 10
## 27: 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2 6
## 28: 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2 7
## 29: 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4 12
## 30: 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2 1
## 31: 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4 5
## 32: 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8 9
## mpg cyl disp hp drat wt qsec vs am gear carb rank# You can drop variables by setting them to NULL
mtcars %>% dt_mutate(cyl = NULL) %>% head()## mpg disp hp drat wt qsec vs am gear carb
## 1: 21.0 160 110 3.90 2.620 16.46 0 1 4 4
## 2: 21.0 160 110 3.90 2.875 17.02 0 1 4 4
## 3: 22.8 108 93 3.85 2.320 18.61 1 1 4 1
## 4: 21.4 258 110 3.08 3.215 19.44 1 0 3 1
## 5: 18.7 360 175 3.15 3.440 17.02 0 0 3 2
## 6: 18.1 225 105 2.76 3.460 20.22 1 0 3 1# A summary applied without by returns a single row
mtcars %>%
dt_summarise(mean = mean(disp), n = .N)## mean n
## 1: 230.7219 32# Usually, you'll want to group first
mtcars %>%
dt_summarise(mean = mean(disp), n = .N, by = cyl)## cyl mean n
## 1: 6 183.3143 7
## 2: 4 105.1364 11
## 3: 8 353.1000 14# Multiple 'by' - variables
mtcars %>%
dt_summarise(cyl_n = .N, by = list(cyl, vs))## cyl vs cyl_n
## 1: 6 0 3
## 2: 4 1 10
## 3: 6 1 4
## 4: 8 0 14
## 5: 4 0 1# Newly created summaries immediately
# doesn't overwrite existing variables
mtcars %>%
dt_summarise(disp = mean(disp),
sd = sd(disp),
by = cyl)## cyl disp sd
## 1: 6 183.3143 41.56246
## 2: 4 105.1364 26.87159
## 3: 8 353.1000 67.77132# You can group by expressions:
mtcars %>%
dt_summarise_all(mean, by = list(vsam = vs + am))## vsam mpg cyl disp hp drat wt qsec
## 1: 1 20.28462 5.692308 189.4692 138.46154 3.738462 3.038846 18.04231
## 2: 2 28.37143 4.000000 89.8000 80.57143 4.148571 2.028286 18.70000
## 3: 0 15.05000 8.000000 357.6167 194.16667 3.120833 4.104083 17.14250
## gear carb
## 1: 4.076923 3.307692
## 2: 4.142857 1.428571
## 3: 3.000000 3.083333# filter by condition
mtcars %>%
dt_filter(am==0)## mpg cyl disp hp drat wt qsec vs am gear carb
## 1: 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1
## 2: 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2
## 3: 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1
## 4: 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4
## 5: 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2
## 6: 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2
## 7: 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4
## 8: 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4
## 9: 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3
## 10: 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3
## 11: 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3
## 12: 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4
## 13: 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4
## 14: 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4
## 15: 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1
## 16: 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2
## 17: 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2
## 18: 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4
## 19: 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2# filter by compound condition
mtcars %>%
dt_filter(am==0, mpg>mean(mpg))## mpg cyl disp hp drat wt qsec vs am gear carb
## 1: 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1
## 2: 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2
## 3: 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2
## 4: 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1# select
mtcars %>% dt_select(vs:carb, cyl)## vs am gear carb cyl
## 1: 0 1 4 4 6
## 2: 0 1 4 4 6
## 3: 1 1 4 1 4
## 4: 1 0 3 1 6
## 5: 0 0 3 2 8
## 6: 1 0 3 1 6
## 7: 0 0 3 4 8
## 8: 1 0 4 2 4
## 9: 1 0 4 2 4
## 10: 1 0 4 4 6
## 11: 1 0 4 4 6
## 12: 0 0 3 3 8
## 13: 0 0 3 3 8
## 14: 0 0 3 3 8
## 15: 0 0 3 4 8
## 16: 0 0 3 4 8
## 17: 0 0 3 4 8
## 18: 1 1 4 1 4
## 19: 1 1 4 2 4
## 20: 1 1 4 1 4
## 21: 1 0 3 1 4
## 22: 0 0 3 2 8
## 23: 0 0 3 2 8
## 24: 0 0 3 4 8
## 25: 0 0 3 2 8
## 26: 1 1 4 1 4
## 27: 0 1 5 2 4
## 28: 1 1 5 2 4
## 29: 0 1 5 4 8
## 30: 0 1 5 6 6
## 31: 0 1 5 8 8
## 32: 1 1 4 2 4
## vs am gear carb cylmtcars %>% dt_select(-am, -cyl)## mpg disp hp drat wt qsec vs gear carb
## 1: 21.0 160.0 110 3.90 2.620 16.46 0 4 4
## 2: 21.0 160.0 110 3.90 2.875 17.02 0 4 4
## 3: 22.8 108.0 93 3.85 2.320 18.61 1 4 1
## 4: 21.4 258.0 110 3.08 3.215 19.44 1 3 1
## 5: 18.7 360.0 175 3.15 3.440 17.02 0 3 2
## 6: 18.1 225.0 105 2.76 3.460 20.22 1 3 1
## 7: 14.3 360.0 245 3.21 3.570 15.84 0 3 4
## 8: 24.4 146.7 62 3.69 3.190 20.00 1 4 2
## 9: 22.8 140.8 95 3.92 3.150 22.90 1 4 2
## 10: 19.2 167.6 123 3.92 3.440 18.30 1 4 4
## 11: 17.8 167.6 123 3.92 3.440 18.90 1 4 4
## 12: 16.4 275.8 180 3.07 4.070 17.40 0 3 3
## 13: 17.3 275.8 180 3.07 3.730 17.60 0 3 3
## 14: 15.2 275.8 180 3.07 3.780 18.00 0 3 3
## 15: 10.4 472.0 205 2.93 5.250 17.98 0 3 4
## 16: 10.4 460.0 215 3.00 5.424 17.82 0 3 4
## 17: 14.7 440.0 230 3.23 5.345 17.42 0 3 4
## 18: 32.4 78.7 66 4.08 2.200 19.47 1 4 1
## 19: 30.4 75.7 52 4.93 1.615 18.52 1 4 2
## 20: 33.9 71.1 65 4.22 1.835 19.90 1 4 1
## 21: 21.5 120.1 97 3.70 2.465 20.01 1 3 1
## 22: 15.5 318.0 150 2.76 3.520 16.87 0 3 2
## 23: 15.2 304.0 150 3.15 3.435 17.30 0 3 2
## 24: 13.3 350.0 245 3.73 3.840 15.41 0 3 4
## 25: 19.2 400.0 175 3.08 3.845 17.05 0 3 2
## 26: 27.3 79.0 66 4.08 1.935 18.90 1 4 1
## 27: 26.0 120.3 91 4.43 2.140 16.70 0 5 2
## 28: 30.4 95.1 113 3.77 1.513 16.90 1 5 2
## 29: 15.8 351.0 264 4.22 3.170 14.50 0 5 4
## 30: 19.7 145.0 175 3.62 2.770 15.50 0 5 6
## 31: 15.0 301.0 335 3.54 3.570 14.60 0 5 8
## 32: 21.4 121.0 109 4.11 2.780 18.60 1 4 2
## mpg disp hp drat wt qsec vs gear carb# sorting
dt_arrange(mtcars, cyl, disp)## mpg cyl disp hp drat wt qsec vs am gear carb
## 1: 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1
## 2: 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2
## 3: 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1
## 4: 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1
## 5: 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2
## 6: 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1
## 7: 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1
## 8: 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2
## 9: 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2
## 10: 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2
## 11: 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2
## 12: 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6
## 13: 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4
## 14: 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4
## 15: 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4
## 16: 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4
## 17: 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1
## 18: 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1
## 19: 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3
## 20: 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3
## 21: 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3
## 22: 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8
## 23: 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2
## 24: 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2
## 25: 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4
## 26: 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4
## 27: 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2
## 28: 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4
## 29: 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2
## 30: 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4
## 31: 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4
## 32: 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4
## mpg cyl disp hp drat wt qsec vs am gear carbdt_arrange(mtcars, -disp)## mpg cyl disp hp drat wt qsec vs am gear carb
## 1: 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4
## 2: 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4
## 3: 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4
## 4: 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2
## 5: 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2
## 6: 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4
## 7: 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4
## 8: 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4
## 9: 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2
## 10: 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2
## 11: 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8
## 12: 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3
## 13: 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3
## 14: 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3
## 15: 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1
## 16: 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1
## 17: 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4
## 18: 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4
## 19: 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4
## 20: 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4
## 21: 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2
## 22: 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6
## 23: 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2
## 24: 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2
## 25: 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2
## 26: 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1
## 27: 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1
## 28: 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2
## 29: 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1
## 30: 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1
## 31: 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2
## 32: 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1
## mpg cyl disp hp drat wt qsec vs am gear carb