# library(installr) # 在RGui中输入这两行命令，更新R
# updateR()
rm(list = ls())
Sys.setenv(LANGUAGE = "en_US.UTF-8")
library(rio)
df = import("flights14.csv")
export(df, "df.csv")
df_csv = import("df.csv")
export(df, "df.txt")
df_txt = import("df.txt")
export(df, "df.rds")
df_rds = import("df.rds")
export(df, "df.feather")
## Loading required namespace: feather
df_feather = import("df.feather")
export(df, "df.dta")
df_dta = import("df.dta")
rm(list = ls())
library(rio)
df = import("df.rds")
class(df)
## [1] "data.frame"
library(tidyverse)
## -- Attaching packages --------------------------------------- tidyverse 1.3.1 --
## v ggplot2 3.3.5 v purrr 0.3.4
## v tibble 3.1.6 v dplyr 1.0.8
## v tidyr 1.2.0 v stringr 1.4.0
## v readr 2.1.2 v forcats 0.5.1
## -- Conflicts ------------------------------------------ tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()
tb = as_tibble(df)
class(tb)
## [1] "tbl_df" "tbl" "data.frame"
library(data.table)
##
## Attaching package: 'data.table'
## The following objects are masked from 'package:dplyr':
##
## between, first, last
## The following object is masked from 'package:purrr':
##
## transpose
dt = as.data.table(df)
class(dt)
## [1] "data.table" "data.frame"
dt_csv = fread("df.csv")
dt_txt = fread("df.txt")
library(dtplyr)
dt2tidy = lazy_dt(dt)
class(dt2tidy)
## [1] "dtplyr_step_first" "dtplyr_step"
dt2tidy
## Source: local data table [253,316 x 11]
## Call: `_DT1`
##
## year month day dep_delay arr_delay carrier origin dest air_time distance
## <int> <int> <int> <int> <int> <chr> <chr> <chr> <int> <int>
## 1 2014 1 1 14 13 AA JFK LAX 359 2475
## 2 2014 1 1 -3 13 AA JFK LAX 363 2475
## 3 2014 1 1 2 9 AA JFK LAX 351 2475
## 4 2014 1 1 -8 -26 AA LGA PBI 157 1035
## 5 2014 1 1 2 1 AA JFK LAX 350 2475
## 6 2014 1 1 4 0 AA EWR LAX 339 2454
## # ... with 253,310 more rows, and 1 more variable: hour <int>
##
## # Use as.data.table()/as.data.frame()/as_tibble() to access results
rm(list = ls())
library(data.table)
# Econometric Analysis, 7th and 8th Edtion, Data Sets
# https://pages.stern.nyu.edu/~wgreene/Text/Edition7/tablelist8new.htm
# http://people.stern.nyu.edu/wgreene/Text/Edition7/
url1 = "http://people.stern.nyu.edu/wgreene/Text/Edition7/TableF4-1.txt"
dt_url1 = fread(url1)
url2 = "http://people.stern.nyu.edu/wgreene/Text/Edition7/TableF5-2.txt"
dt_url2 = fread(url2)
url3 = "http://people.stern.nyu.edu/wgreene/Text/Edition7/TableF6-1.txt"
dt_url3 = fread(url3)
url4 = "http://www.wiley.com/legacy/wileychi/baltagi/supp/Gasoline.dat"
dt_url4 = fread(url4)
url5 = "http://www.wiley.com/legacy/wileychi/baltagi/supp/PRODUC.prn"
dt_url5 = fread(url5)
## Warning in fread(url5): Stopped early on line 819. Expected 11 fields but
## found 1. Consider fill=TRUE and comment.char=. First discarded non-empty line:
## <<PRODUC.PRN>>
url6 = "http://people.stern.nyu.edu/wgreene/Text/Edition7/TableF10-4.txt"
dt_url6 = fread(url6)
## Warning in fread(url6): Detected 1 column names but the data has 5 columns (i.e.
## invalid file). Added 4 extra default column names at the end.
url7 = "http://people.stern.nyu.edu/wgreene/Text/Edition7/TableF19-1.txt"
dt_url7 = fread(url7)
url8 = "http://people.stern.nyu.edu/wgreene/Text/Edition7/TableF21-1.txt"
dt_url8 = fread(url8)
rm(list = ls())
df = mtcars %>% rownames_to_column(var = "car")
library(tidyverse)
tb = as_tibble(df)
library(data.table)
dt = as.data.table(df)
library(dtplyr)
dt2tb = lazy_dt(df) # 使用dtplyr包时，将目标数据集惰性处理
# lazy_data可以通过as.data.frame();as.data.table();as_tibble()函数转换成相应形式的数据框
library(tidyverse)
dt2tb %>%
filter(vs == 1, wt > 3) # 筛选出vs = 1，同时wt > 3的数据
## Source: local data table [6 x 12]
## Call: `_DT2`[vs == 1 & wt > 3]
##
## 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 Hornet 4 Dr~ 21.4 6 258 110 3.08 3.22 19.4 1 0 3 1
## 2 Valiant 18.1 6 225 105 2.76 3.46 20.2 1 0 3 1
## 3 Merc 240D 24.4 4 147. 62 3.69 3.19 20 1 0 4 2
## 4 Merc 230 22.8 4 141. 95 3.92 3.15 22.9 1 0 4 2
## 5 Merc 280 19.2 6 168. 123 3.92 3.44 18.3 1 0 4 4
## 6 Merc 280C 17.8 6 168. 123 3.92 3.44 18.9 1 0 4 4
##
## # Use as.data.table()/as.data.frame()/as_tibble() to access results
dt2tb %>%
filter(row_number() == 1L:16L) # 筛选data中前16行
## Source: local data table [16 x 12]
## Call: `_DT2`[seq_len(.N) == 1L:16L]
##
## 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
## 3 Datsun 710 22.8 4 108 93 3.85 2.32 18.6 1 1 4 1
## 4 Hornet 4 Dr~ 21.4 6 258 110 3.08 3.22 19.4 1 0 3 1
## 5 Hornet Spor~ 18.7 8 360 175 3.15 3.44 17.0 0 0 3 2
## 6 Valiant 18.1 6 225 105 2.76 3.46 20.2 1 0 3 1
## # ... with 10 more rows
##
## # Use as.data.table()/as.data.frame()/as_tibble() to access results
dt2tb %>%
arrange(hp, desc(drat)) # 以hp升序、drat降序排序
## Source: local data table [32 x 12]
## Call: `_DT2`[order(hp, -drat)]
##
## 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 Honda Civic 30.4 4 75.7 52 4.93 1.62 18.5 1 1 4 2
## 2 Merc 240D 24.4 4 147. 62 3.69 3.19 20 1 0 4 2
## 3 Toyota Coro~ 33.9 4 71.1 65 4.22 1.84 19.9 1 1 4 1
## 4 Fiat 128 32.4 4 78.7 66 4.08 2.2 19.5 1 1 4 1
## 5 Fiat X1-9 27.3 4 79 66 4.08 1.94 18.9 1 1 4 1
## 6 Porsche 914~ 26 4 120. 91 4.43 2.14 16.7 0 1 5 2
## # ... with 26 more rows
##
## # Use as.data.table()/as.data.frame()/as_tibble() to access results
dt2tb %>%
select(cyl, disp) # 提取cyl和disp列
## Source: local data table [32 x 2]
## Call: `_DT2`[, .(cyl, disp)]
##
## cyl disp
## <dbl> <dbl>
## 1 6 160
## 2 6 160
## 3 4 108
## 4 6 258
## 5 8 360
## 6 6 225
## # ... with 26 more rows
##
## # Use as.data.table()/as.data.frame()/as_tibble() to access results
dt2tb %>%
select(-mpg) # 删除mpg列
## Source: local data table [32 x 11]
## Call: `_DT2`[, .(car, cyl, disp, hp, drat, wt, qsec, vs, am, gear,
## carb)]
##
## car cyl disp hp drat wt qsec vs am gear carb
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 Mazda RX4 6 160 110 3.9 2.62 16.5 0 1 4 4
## 2 Mazda RX4 Wag 6 160 110 3.9 2.88 17.0 0 1 4 4
## 3 Datsun 710 4 108 93 3.85 2.32 18.6 1 1 4 1
## 4 Hornet 4 Drive 6 258 110 3.08 3.22 19.4 1 0 3 1
## 5 Hornet Sportabout 8 360 175 3.15 3.44 17.0 0 0 3 2
## 6 Valiant 6 225 105 2.76 3.46 20.2 1 0 3 1
## # ... with 26 more rows
##
## # Use as.data.table()/as.data.frame()/as_tibble() to access results
dt2tb %>%
select(vs, everything()) # 调整vs列至第一列
## Source: local data table [32 x 12]
## Call: `_DT2`[, .(vs, car, mpg, cyl, disp, hp, drat, wt, qsec, am, gear,
## carb)]
##
## vs car mpg cyl disp hp drat wt qsec am gear carb
## <dbl> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 0 Mazda RX4 21 6 160 110 3.9 2.62 16.5 1 4 4
## 2 0 Mazda RX4 W~ 21 6 160 110 3.9 2.88 17.0 1 4 4
## 3 1 Datsun 710 22.8 4 108 93 3.85 2.32 18.6 1 4 1
## 4 1 Hornet 4 Dr~ 21.4 6 258 110 3.08 3.22 19.4 0 3 1
## 5 0 Hornet Spor~ 18.7 8 360 175 3.15 3.44 17.0 0 3 2
## 6 1 Valiant 18.1 6 225 105 2.76 3.46 20.2 0 3 1
## # ... with 26 more rows
##
## # Use as.data.table()/as.data.frame()/as_tibble() to access results
dt2tb %>%
mutate(wt_drat = wt * drat) %>% # 计算wt与drat的积添加到列wt_drat
mutate(type = "old") # 增加新列type赋值"old"
## Source: local data table [32 x 14]
## Call: copy(`_DT2`)[, `:=`(wt_drat = wt * drat)][, `:=`(type = "old")]
##
## 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
## 3 Datsun 710 22.8 4 108 93 3.85 2.32 18.6 1 1 4 1
## 4 Hornet 4 Dr~ 21.4 6 258 110 3.08 3.22 19.4 1 0 3 1
## 5 Hornet Spor~ 18.7 8 360 175 3.15 3.44 17.0 0 0 3 2
## 6 Valiant 18.1 6 225 105 2.76 3.46 20.2 1 0 3 1
## # ... with 26 more rows, and 2 more variables: wt_drat <dbl>, type <chr>
##
## # Use as.data.table()/as.data.frame()/as_tibble() to access results
dt2tb %>%
distinct(cyl, .keep_all = T) # 以变量cyl去重，保留所有变量
## Source: local data table [3 x 12]
## Call: unique(`_DT2`, by = "cyl")
##
## 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 Datsun 710 22.8 4 108 93 3.85 2.32 18.6 1 1 4 1
## 3 Hornet Spor~ 18.7 8 360 175 3.15 3.44 17.0 0 0 3 2
##
## # Use as.data.table()/as.data.frame()/as_tibble() to access results
dt2tb %>%
group_by(cyl) %>%
summarize(disp_mean = mean(disp)) # 根据cyl分组，计算disp均值
## Source: local data table [3 x 2]
## Call: `_DT2`[, .(disp_mean = mean(disp)), keyby = .(cyl)]
##
## cyl disp_mean
## <dbl> <dbl>
## 1 4 105.
## 2 6 183.
## 3 8 353.
##
## # Use as.data.table()/as.data.frame()/as_tibble() to access results
# 给data增加标签列id按序排号，调整id列值至第一列，将数据集赋值为data1
dt2tb1 = dt2tb %>%
mutate(id = 1:nrow(dt2tb)) %>%
select(id, everything())
# 提取data1中17到32行数据赋值为data2
dt2tb2 = dt2tb1 %>%
filter(row_number() == 17L:32L)
# 合并数据仅保留匹配的记录 *data1 ∩ data2
inner_join(dt2tb2, dt2tb1, by = "id")
## Source: local data table [16 x 25]
## Call: setnames(copy(`_DT2`)[, `:=`(id = 1:nrow(..dt2tb))][, .(id, car,
## mpg, cyl, disp, hp, drat, wt, qsec, vs, am, gear, carb)][seq_len(.N) ==
## 17L:32L][copy(`_DT2`)[, `:=`(id = 1:nrow(..dt2tb))][, .(id,
## car, mpg, cyl, disp, hp, drat, wt, qsec, vs, am, gear, carb)],
## on = .(id), nomatch = NULL, allow.cartesian = TRUE], c("car",
## "mpg", "cyl", "disp", "hp", "drat", "wt", "qsec", "vs", "am",
## "gear", "carb", "i.car", "i.mpg", "i.cyl", "i.disp", "i.hp",
## "i.drat", "i.wt", "i.qsec", "i.vs", "i.am", "i.gear", "i.carb"
## ), c("car.x", "mpg.x", "cyl.x", "disp.x", "hp.x", "drat.x", "wt.x",
## "qsec.x", "vs.x", "am.x", "gear.x", "carb.x", "car.y", "mpg.y",
## "cyl.y", "disp.y", "hp.y", "drat.y", "wt.y", "qsec.y", "vs.y",
## "am.y", "gear.y", "carb.y"))
##
## id car.x mpg.x cyl.x disp.x hp.x drat.x wt.x qsec.x vs.x am.x gear.x
## <int> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 17 Chrysle~ 14.7 8 440 230 3.23 5.34 17.4 0 0 3
## 2 18 Fiat 128 32.4 4 78.7 66 4.08 2.2 19.5 1 1 4
## 3 19 Honda C~ 30.4 4 75.7 52 4.93 1.62 18.5 1 1 4
## 4 20 Toyota ~ 33.9 4 71.1 65 4.22 1.84 19.9 1 1 4
## 5 21 Toyota ~ 21.5 4 120. 97 3.7 2.46 20.0 1 0 3
## 6 22 Dodge C~ 15.5 8 318 150 2.76 3.52 16.9 0 0 3
## # ... with 10 more rows, and 13 more variables: carb.x <dbl>, car.y <chr>,
## # mpg.y <dbl>, cyl.y <dbl>, disp.y <dbl>, hp.y <dbl>, drat.y <dbl>,
## # wt.y <dbl>, qsec.y <dbl>, vs.y <dbl>, am.y <dbl>, gear.y <dbl>,
## # carb.y <dbl>
##
## # Use as.data.table()/as.data.frame()/as_tibble() to access results
# 将data1，data2以data2中id列为基准进行匹配连接
left_join(dt2tb2, dt2tb1, by = "id")
## Source: local data table [16 x 25]
## Call: setnames(setcolorder(copy(`_DT2`)[, `:=`(id = 1:nrow(..dt2tb))][,
## .(id, car, mpg, cyl, disp, hp, drat, wt, qsec, vs, am, gear,
## carb)][copy(`_DT2`)[, `:=`(id = 1:nrow(..dt2tb))][, .(id,
## car, mpg, cyl, disp, hp, drat, wt, qsec, vs, am, gear, carb)][seq_len(.N) ==
## 17L:32L], on = .(id), allow.cartesian = TRUE], c(1L, 14L,
## 15L, 16L, 17L, 18L, 19L, 20L, 21L, 22L, 23L, 24L, 25L, 2L, 3L,
## 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L)), c("i.car", "i.mpg",
## "i.cyl", "i.disp", "i.hp", "i.drat", "i.wt", "i.qsec", "i.vs",
## "i.am", "i.gear", "i.carb", "car", "mpg", "cyl", "disp", "hp",
## "drat", "wt", "qsec", "vs", "am", "gear", "carb"), c("car.x",
## "mpg.x", "cyl.x", "disp.x", "hp.x", "drat.x", "wt.x", "qsec.x",
## "vs.x", "am.x", "gear.x", "carb.x", "car.y", "mpg.y", "cyl.y",
## "disp.y", "hp.y", "drat.y", "wt.y", "qsec.y", "vs.y", "am.y",
## "gear.y", "carb.y"))
##
## id car.x mpg.x cyl.x disp.x hp.x drat.x wt.x qsec.x vs.x am.x gear.x
## <int> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 17 Chrysle~ 14.7 8 440 230 3.23 5.34 17.4 0 0 3
## 2 18 Fiat 128 32.4 4 78.7 66 4.08 2.2 19.5 1 1 4
## 3 19 Honda C~ 30.4 4 75.7 52 4.93 1.62 18.5 1 1 4
## 4 20 Toyota ~ 33.9 4 71.1 65 4.22 1.84 19.9 1 1 4
## 5 21 Toyota ~ 21.5 4 120. 97 3.7 2.46 20.0 1 0 3
## 6 22 Dodge C~ 15.5 8 318 150 2.76 3.52 16.9 0 0 3
## # ... with 10 more rows, and 13 more variables: carb.x <dbl>, car.y <chr>,
## # mpg.y <dbl>, cyl.y <dbl>, disp.y <dbl>, hp.y <dbl>, drat.y <dbl>,
## # wt.y <dbl>, qsec.y <dbl>, vs.y <dbl>, am.y <dbl>, gear.y <dbl>,
## # carb.y <dbl>
##
## # Use as.data.table()/as.data.frame()/as_tibble() to access results
# 合并数据保留所有记录 *data1 ∪ data2
full_join(dt2tb1, dt2tb2, by = "id")
## Source: local data table [32 x 25]
## Call: merge(copy(`_DT2`)[, `:=`(id = 1:nrow(..dt2tb))][, .(id, car,
## mpg, cyl, disp, hp, drat, wt, qsec, vs, am, gear, carb)],
## copy(`_DT2`)[, `:=`(id = 1:nrow(..dt2tb))][, .(id, car, mpg,
## cyl, disp, hp, drat, wt, qsec, vs, am, gear, carb)][seq_len(.N) ==
## 17L:32L], all = TRUE, by.x = "id", by.y = "id", allow.cartesian = TRUE)
##
## id car.x mpg.x cyl.x disp.x hp.x drat.x wt.x qsec.x vs.x am.x gear.x
## <int> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 1 Mazda R~ 21 6 160 110 3.9 2.62 16.5 0 1 4
## 2 2 Mazda R~ 21 6 160 110 3.9 2.88 17.0 0 1 4
## 3 3 Datsun ~ 22.8 4 108 93 3.85 2.32 18.6 1 1 4
## 4 4 Hornet ~ 21.4 6 258 110 3.08 3.22 19.4 1 0 3
## 5 5 Hornet ~ 18.7 8 360 175 3.15 3.44 17.0 0 0 3
## 6 6 Valiant 18.1 6 225 105 2.76 3.46 20.2 1 0 3
## # ... with 26 more rows, and 13 more variables: carb.x <dbl>, car.y <chr>,
## # mpg.y <dbl>, cyl.y <dbl>, disp.y <dbl>, hp.y <dbl>, drat.y <dbl>,
## # wt.y <dbl>, qsec.y <dbl>, vs.y <dbl>, am.y <dbl>, gear.y <dbl>,
## # carb.y <dbl>
##
## # Use as.data.table()/as.data.frame()/as_tibble() to access results
# 返回无法与data2匹配的data1的所有记录 *data1 - (data1 ∩ data2)
anti_join(dt2tb1, dt2tb2, by = "id")
## Source: local data table [16 x 13]
## Call: copy(`_DT2`)[, `:=`(id = 1:nrow(..dt2tb))][, .(id, car, mpg,
## cyl, disp, hp, drat, wt, qsec, vs, am, gear, carb)][!copy(`_DT2`)[,
## `:=`(id = 1:nrow(..dt2tb))][, .(id, car, mpg, cyl, disp,
## hp, drat, wt, qsec, vs, am, gear, carb)][seq_len(.N) == 17L:32L],
## on = .(id)]
##
## id car mpg cyl disp hp drat wt qsec vs am gear carb
## <int> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 1 Mazda~ 21 6 160 110 3.9 2.62 16.5 0 1 4 4
## 2 2 Mazda~ 21 6 160 110 3.9 2.88 17.0 0 1 4 4
## 3 3 Datsu~ 22.8 4 108 93 3.85 2.32 18.6 1 1 4 1
## 4 4 Horne~ 21.4 6 258 110 3.08 3.22 19.4 1 0 3 1
## 5 5 Horne~ 18.7 8 360 175 3.15 3.44 17.0 0 0 3 2
## 6 6 Valia~ 18.1 6 225 105 2.76 3.46 20.2 1 0 3 1
## # ... with 10 more rows
##
## # Use as.data.table()/as.data.frame()/as_tibble() to access results
# 返回能够与data2匹配的data1的所有记录
semi_join(dt2tb1, dt2tb2, by = "id")
## Source: local data table [16 x 13]
## Call: copy(`_DT2`)[, `:=`(id = 1:nrow(..dt2tb))][, .(id, car, mpg,
## cyl, disp, hp, drat, wt, qsec, vs, am, gear, carb)][unique(copy(`_DT2`)[,
## `:=`(id = 1:nrow(..dt2tb))][, .(id, car, mpg, cyl, disp,
## hp, drat, wt, qsec, vs, am, gear, carb)][copy(`_DT2`)[, `:=`(id = 1:nrow(..dt2tb))][,
## .(id, car, mpg, cyl, disp, hp, drat, wt, qsec, vs, am, gear,
## carb)][seq_len(.N) == 17L:32L], which = TRUE, nomatch = NULL,
## on = .(id)])]
##
## id car mpg cyl disp hp drat wt qsec vs am gear carb
## <int> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 17 Chrys~ 14.7 8 440 230 3.23 5.34 17.4 0 0 3 4
## 2 18 Fiat ~ 32.4 4 78.7 66 4.08 2.2 19.5 1 1 4 1
## 3 19 Honda~ 30.4 4 75.7 52 4.93 1.62 18.5 1 1 4 2
## 4 20 Toyot~ 33.9 4 71.1 65 4.22 1.84 19.9 1 1 4 1
## 5 21 Toyot~ 21.5 4 120. 97 3.7 2.46 20.0 1 0 3 1
## 6 22 Dodge~ 15.5 8 318 150 2.76 3.52 16.9 0 0 3 2
## # ... with 10 more rows
##
## # Use as.data.table()/as.data.frame()/as_tibble() to access results
# Selecting column(s)
dt2tb %>% select(mpg, hp)
## Source: local data table [32 x 2]
## Call: `_DT2`[, .(mpg, hp)]
##
## mpg hp
## <dbl> <dbl>
## 1 21 110
## 2 21 110
## 3 22.8 93
## 4 21.4 110
## 5 18.7 175
## 6 18.1 105
## # ... with 26 more rows
##
## # Use as.data.table()/as.data.frame()/as_tibble() to access results
dt[, .(mpg, hp)]
## mpg hp
## 1: 21.0 110
## 2: 21.0 110
## 3: 22.8 93
## 4: 21.4 110
## 5: 18.7 175
## 6: 18.1 105
## 7: 14.3 245
## 8: 24.4 62
## 9: 22.8 95
## 10: 19.2 123
## 11: 17.8 123
## 12: 16.4 180
## 13: 17.3 180
## 14: 15.2 180
## 15: 10.4 205
## 16: 10.4 215
## 17: 14.7 230
## 18: 32.4 66
## 19: 30.4 52
## 20: 33.9 65
## 21: 21.5 97
## 22: 15.5 150
## 23: 15.2 150
## 24: 13.3 245
## 25: 19.2 175
## 26: 27.3 66
## 27: 26.0 91
## 28: 30.4 113
## 29: 15.8 264
## 30: 19.7 175
## 31: 15.0 335
## 32: 21.4 109
## mpg hp
# Subsetting by row number
dt2tb %>% slice(1:3)
## Source: local data table [3 x 12]
## Call: `_DT2`[(1:3)[between(1:3, -.N, .N)]]
##
## 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
## 3 Datsun 710 22.8 4 108 93 3.85 2.32 18.6 1 1 4 1
##
## # Use as.data.table()/as.data.frame()/as_tibble() to access results
dt[1:3, , ]
## car mpg cyl disp hp drat wt qsec vs am gear carb
## 1: Mazda RX4 21.0 6 160 110 3.90 2.620 16.46 0 1 4 4
## 2: Mazda RX4 Wag 21.0 6 160 110 3.90 2.875 17.02 0 1 4 4
## 3: Datsun 710 22.8 4 108 93 3.85 2.320 18.61 1 1 4 1
# Subsetting by variable values
dt2tb %>% filter(mpg > 20 )
## Source: local data table [14 x 12]
## Call: `_DT2`[mpg > 20]
##
## 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
## 3 Datsun 710 22.8 4 108 93 3.85 2.32 18.6 1 1 4 1
## 4 Hornet 4 Dr~ 21.4 6 258 110 3.08 3.22 19.4 1 0 3 1
## 5 Merc 240D 24.4 4 147. 62 3.69 3.19 20 1 0 4 2
## 6 Merc 230 22.8 4 141. 95 3.92 3.15 22.9 1 0 4 2
## # ... with 8 more rows
##
## # Use as.data.table()/as.data.frame()/as_tibble() to access results
dt[mpg > 20, ]
## car 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: Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2
## 6: Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2
## 7: Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1
## 8: Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2
## 9: Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1
## 10: Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1
## 11: Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1
## 12: Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2
## 13: Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2
## 14: Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2
# Sorting a table (in ascending order)
dt2tb %>% arrange(mpg)
## Source: local data table [32 x 12]
## Call: `_DT2`[order(mpg)]
##
## 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 Cadillac Fl~ 10.4 8 472 205 2.93 5.25 18.0 0 0 3 4
## 2 Lincoln Con~ 10.4 8 460 215 3 5.42 17.8 0 0 3 4
## 3 Camaro Z28 13.3 8 350 245 3.73 3.84 15.4 0 0 3 4
## 4 Duster 360 14.3 8 360 245 3.21 3.57 15.8 0 0 3 4
## 5 Chrysler Im~ 14.7 8 440 230 3.23 5.34 17.4 0 0 3 4
## 6 Maserati Bo~ 15 8 301 335 3.54 3.57 14.6 0 1 5 8
## # ... with 26 more rows
##
## # Use as.data.table()/as.data.frame()/as_tibble() to access results
dt[order(mpg), ]
## car mpg cyl disp hp drat wt qsec vs am gear carb
## 1: Cadillac Fleetwood 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4
## 2: Lincoln Continental 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4
## 3: Camaro Z28 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4
## 4: Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4
## 5: Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4
## 6: Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8
## 7: Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3
## 8: AMC Javelin 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2
## 9: Dodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2
## 10: Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4
## 11: Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3
## 12: Merc 450SL 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3
## 13: Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4
## 14: Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1
## 15: Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2
## 16: Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4
## 17: Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2
## 18: Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6
## 19: Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4
## 20: Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4
## 21: Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1
## 22: Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2
## 23: Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1
## 24: Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1
## 25: Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2
## 26: Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2
## 27: Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2
## 28: Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1
## 29: Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2
## 30: Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2
## 31: Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1
## 32: Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1
## car mpg cyl disp hp drat wt qsec vs am gear carb
# Sorting a table (in descending order)
dt2tb %>% arrange(- mpg)
## Source: local data table [32 x 12]
## Call: `_DT2`[order(-mpg)]
##
## 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 Toyota Coro~ 33.9 4 71.1 65 4.22 1.84 19.9 1 1 4 1
## 2 Fiat 128 32.4 4 78.7 66 4.08 2.2 19.5 1 1 4 1
## 3 Honda Civic 30.4 4 75.7 52 4.93 1.62 18.5 1 1 4 2
## 4 Lotus Europa 30.4 4 95.1 113 3.77 1.51 16.9 1 1 5 2
## 5 Fiat X1-9 27.3 4 79 66 4.08 1.94 18.9 1 1 4 1
## 6 Porsche 914~ 26 4 120. 91 4.43 2.14 16.7 0 1 5 2
## # ... with 26 more rows
##
## # Use as.data.table()/as.data.frame()/as_tibble() to access results
dt[order(- mpg), ]
## car mpg cyl disp hp drat wt qsec vs am gear carb
## 1: Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1
## 2: Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1
## 3: Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2
## 4: Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2
## 5: Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1
## 6: Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2
## 7: Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2
## 8: Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1
## 9: Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2
## 10: Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1
## 11: Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1
## 12: Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2
## 13: Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4
## 14: Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4
## 15: Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6
## 16: Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4
## 17: Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2
## 18: Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2
## 19: Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1
## 20: Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4
## 21: Merc 450SL 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3
## 22: Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3
## 23: Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4
## 24: Dodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2
## 25: Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3
## 26: AMC Javelin 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2
## 27: Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8
## 28: Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4
## 29: Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4
## 30: Camaro Z28 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4
## 31: Cadillac Fleetwood 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4
## 32: Lincoln Continental 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4
## car mpg cyl disp hp drat wt qsec vs am gear carb
# Sorting by multiple columns
dt2tb %>% arrange(cyl, mpg)
## Source: local data table [32 x 12]
## Call: `_DT2`[order(cyl, mpg)]
##
## 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 Volvo 142E 21.4 4 121 109 4.11 2.78 18.6 1 1 4 2
## 2 Toyota Coro~ 21.5 4 120. 97 3.7 2.46 20.0 1 0 3 1
## 3 Datsun 710 22.8 4 108 93 3.85 2.32 18.6 1 1 4 1
## 4 Merc 230 22.8 4 141. 95 3.92 3.15 22.9 1 0 4 2
## 5 Merc 240D 24.4 4 147. 62 3.69 3.19 20 1 0 4 2
## 6 Porsche 914~ 26 4 120. 91 4.43 2.14 16.7 0 1 5 2
## # ... with 26 more rows
##
## # Use as.data.table()/as.data.frame()/as_tibble() to access results
dt[order(cyl, mpg), ]
## car mpg cyl disp hp drat wt qsec vs am gear carb
## 1: Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2
## 2: Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1
## 3: Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1
## 4: Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2
## 5: Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2
## 6: Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2
## 7: Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1
## 8: Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2
## 9: Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2
## 10: Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1
## 11: Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1
## 12: Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4
## 13: Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1
## 14: Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4
## 15: Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6
## 16: Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4
## 17: Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4
## 18: Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1
## 19: Cadillac Fleetwood 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4
## 20: Lincoln Continental 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4
## 21: Camaro Z28 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4
## 22: Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4
## 23: Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4
## 24: Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8
## 25: Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3
## 26: AMC Javelin 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2
## 27: Dodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2
## 28: Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4
## 29: Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3
## 30: Merc 450SL 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3
## 31: Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2
## 32: Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2
## car mpg cyl disp hp drat wt qsec vs am gear carb
# Creating a new column
dt2tb3 = dt2tb %>% mutate(gpm = 1 / mpg) # %>% as_tibble()
dt[, gpm := 1 / mpg, ]
# Deleting a column
dt2tb4 = dt2tb3 %>% select(- gpm)
dt[, gpm := NULL, ]
# Summarizing specific columns
dt2tb %>% summarize(mpg = median(mpg), hp = mean(hp))
## Source: local data table [1 x 2]
## Call: `_DT2`[, .(mpg = median(mpg), hp = mean(hp))]
##
## mpg hp
## <dbl> <dbl>
## 1 19.2 147.
##
## # Use as.data.table()/as.data.frame()/as_tibble() to access results
dt[, .(mpg = median(mpg), hp = mean(hp)), ]
## mpg hp
## 1: 19.2 146.6875
# Summarizing all columns
dt2tb %>% summarize(across(everything(), max))
## Source: local data table [1 x 12]
## Call: `_DT2`[, .(car = max(car), mpg = max(mpg), cyl = max(cyl), disp = max(disp),
## hp = max(hp), drat = max(drat), wt = max(wt), qsec = max(qsec),
## vs = max(vs), am = max(am), gear = max(gear), carb = max(carb))]
##
## 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 Volvo 142E 33.9 8 472 335 4.93 5.42 22.9 1 1 5 8
##
## # Use as.data.table()/as.data.frame()/as_tibble() to access results
dt[, lapply(.SD, max), ]
## car mpg cyl disp hp drat wt qsec vs am gear carb
## 1: Volvo 142E 33.9 8 472 335 4.93 5.424 22.9 1 1 5 8
# Summarizing all columns by group
dt2tb %>% group_by(cyl) %>% summarize(across(everything(), max))
## Source: local data table [3 x 12]
## Call: `_DT2`[, .(car = max(car), mpg = max(mpg), disp = max(disp),
## hp = max(hp), drat = max(drat), wt = max(wt), qsec = max(qsec),
## vs = max(vs), am = max(am), gear = max(gear), carb = max(carb)),
## keyby = .(cyl)]
##
## cyl car mpg disp hp drat wt qsec vs am gear carb
## <dbl> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 4 Volvo 142E 33.9 147. 113 4.93 3.19 22.9 1 1 5 2
## 2 6 Valiant 21.4 258 175 3.92 3.46 20.2 1 1 5 6
## 3 8 Pontiac Fir~ 19.2 472 335 4.22 5.42 18 0 1 5 8
##
## # Use as.data.table()/as.data.frame()/as_tibble() to access results
dt[, lapply(.SD, max), by = cyl]
## cyl car mpg disp hp drat wt qsec vs am gear carb
## 1: 6 Valiant 21.4 258.0 175 3.92 3.460 20.22 1 1 5 6
## 2: 4 Volvo 142E 33.9 146.7 113 4.93 3.190 22.90 1 1 5 2
## 3: 8 Pontiac Firebird 19.2 472.0 335 4.22 5.424 18.00 0 1 5 8
# Pivoting table to long format
dt2tb_L = dt2tb %>%
pivot_longer(cols = - car, names_to = "variable", values_to = "value") %>%
as_tibble()
dt.L = melt(dt, id.vars = c("car"), variable.name = "variable", value.name = "value")
# Pivoting table to wide format
dt2tb_W = dt2tb_L %>%
pivot_wider(names_from = "variable", values_from = "value") %>%
as_tibble()
dt.W = dcast(dt.L, car ~ variable, value.var = "value")
# Joining tables
left_join(dt2tb2, dt2tb1, by = "id")
## Source: local data table [16 x 25]
## Call: setnames(setcolorder(copy(`_DT2`)[, `:=`(id = 1:nrow(..dt2tb))][,
## .(id, car, mpg, cyl, disp, hp, drat, wt, qsec, vs, am, gear,
## carb)][copy(`_DT2`)[, `:=`(id = 1:nrow(..dt2tb))][, .(id,
## car, mpg, cyl, disp, hp, drat, wt, qsec, vs, am, gear, carb)][seq_len(.N) ==
## 17L:32L], on = .(id), allow.cartesian = TRUE], c(1L, 14L,
## 15L, 16L, 17L, 18L, 19L, 20L, 21L, 22L, 23L, 24L, 25L, 2L, 3L,
## 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L)), c("i.car", "i.mpg",
## "i.cyl", "i.disp", "i.hp", "i.drat", "i.wt", "i.qsec", "i.vs",
## "i.am", "i.gear", "i.carb", "car", "mpg", "cyl", "disp", "hp",
## "drat", "wt", "qsec", "vs", "am", "gear", "carb"), c("car.x",
## "mpg.x", "cyl.x", "disp.x", "hp.x", "drat.x", "wt.x", "qsec.x",
## "vs.x", "am.x", "gear.x", "carb.x", "car.y", "mpg.y", "cyl.y",
## "disp.y", "hp.y", "drat.y", "wt.y", "qsec.y", "vs.y", "am.y",
## "gear.y", "carb.y"))
##
## id car.x mpg.x cyl.x disp.x hp.x drat.x wt.x qsec.x vs.x am.x gear.x
## <int> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 17 Chrysle~ 14.7 8 440 230 3.23 5.34 17.4 0 0 3
## 2 18 Fiat 128 32.4 4 78.7 66 4.08 2.2 19.5 1 1 4
## 3 19 Honda C~ 30.4 4 75.7 52 4.93 1.62 18.5 1 1 4
## 4 20 Toyota ~ 33.9 4 71.1 65 4.22 1.84 19.9 1 1 4
## 5 21 Toyota ~ 21.5 4 120. 97 3.7 2.46 20.0 1 0 3
## 6 22 Dodge C~ 15.5 8 318 150 2.76 3.52 16.9 0 0 3
## # ... with 10 more rows, and 13 more variables: carb.x <dbl>, car.y <chr>,
## # mpg.y <dbl>, cyl.y <dbl>, disp.y <dbl>, hp.y <dbl>, drat.y <dbl>,
## # wt.y <dbl>, qsec.y <dbl>, vs.y <dbl>, am.y <dbl>, gear.y <dbl>,
## # carb.y <dbl>
##
## # Use as.data.table()/as.data.frame()/as_tibble() to access results
#dt2tb2[dt2tb1, on = "id"]
# Chaining commands
dt2tb %>%
mutate(gpm = 1/mpg) %>%
group_by(cyl) %>%
summarize(gpm = median(gpm)) %>%
arrange(- gpm)
## Source: local data table [3 x 2]
## Call: copy(`_DT2`)[, `:=`(gpm = 1/mpg)][, .(gpm = median(gpm)), keyby = .(cyl)][order(-gpm)]
##
## cyl gpm
## <dbl> <dbl>
## 1 8 0.0658
## 2 6 0.0508
## 3 4 0.0385
##
## # Use as.data.table()/as.data.frame()/as_tibble() to access results
dt[, gpm := 1/mpg, ][
order(-gpm), .(gpm = median(gpm)), by = cyl]
## cyl gpm
## 1: 8 0.06578947
## 2: 6 0.05076142
## 3: 4 0.03846154