tbltools

Tools for Working with Tibbles

Installation

Install from CRAN with:

## install {tbltools} from CRAN
install.packages("tbltools")

Or install the development version from Github with:

## install remotes pkg if not already
if (!requireNamespace("remotes")) {
  install.packages("remotes")
}

## install from github
remotes::install_github("mkearney/tbltools")

Features

Tidyverse-style operability!
- Uses non-standard evaluation for easy interactive analysis
- Provides numerous {dplyr}-like wrangling functions:
  - slice_data(), arrange_data() filter_data(), select_data(), mutate_data(), summarise_data(), bind_rows_data(), bind_cols_data(), full_join_data(), left_join_data(), right_join_data()
Extremely lightweight!
- Requires only base R–zero dependencies)
- Installs/compiles quickly and easily
Simple and effective data frame printing!
- Provides a {tibble}-like printing experience

Use

as_tbl_data(): Convert data frames to tibbles

## convert mtcars to tibble and create row_names variable
d <- as_tbl_data(mtcars, row_names = TRUE)

tbl_dat_frame(): Create data frames
- Evaluated within data frame environment, so variables/values can be updated within the tbl_data_frame() call

## create data frame where 2 variable depends on evaluation of 1st variable
tbl_data_frame(
  x = rnorm(20),
  y = rnorm(20) + x
) %>%
  cor()
#>           x         y
#> x 1.0000000 0.7827194
#> y 0.7827194 1.0000000

Slice

slice_data(): Select/keep/return row positions

## select rows 1, 3, 5, 25 of data
slice_data(d, c(1, 3, 5, 25))
#> # A pseudo tibble: 4 x 12+
#>             row_names  mpg cyl disp  hp drat    wt  qsec vs am
#> 1)          Mazda RX4 21.0   6  160 110 3.90 2.620 16.46  0  1
#> 2)         Datsun 710 22.8   4  108  93 3.85 2.320 18.61  1  1
#> 3)  Hornet Sportabout 18.7   8  360 175 3.15 3.440 17.02  0  0
#> 4)   Pontiac Firebird 19.2   8  400 175 3.08 3.845 17.05  0  0
#> +2 column(s) not printed

Compare with dplyr::slice()

same_as_dplyr(
  slice_data(d, c(1, 3, 5, 25)),
  dplyr::slice(d, c(1, 3, 5, 25))
)
#> [1] TRUE

Filter

filter_data(): Filter/keep/return certain rows

## count by cyl and gear
filter_data(d, gear > 3 | mpg > 30, vs == 1)
#> # A pseudo tibble: 11* x 12+
#>      row_names  mpg cyl  disp  hp drat   wt  qsec vs am gear
#> 1)  Datsun 710 22.8   4 108.0  93 3.85 2.32 18.61  1  1    4
#> 2)   Merc 240D 24.4   4 146.7  62 3.69 3.19 20.00  1  0    4
#> 3)    Merc 230 22.8   4 140.8  95 3.92 3.15 22.90  1  0    4
#> 4)    Merc 280 19.2   6 167.6 123 3.92 3.44 18.30  1  0    4
#> *7 row(s) not printed;  +1 column(s) not printed

Compare with dplyr::filter()

same_as_dplyr(
  filter_data(d, gear > 3 | mpg > 30, vs == 1),
  dplyr::filter(d, gear > 3 | mpg > 30, vs == 1)
)
#> [1] TRUE

Arrange

arrange_data(): Organize rows by column(s) value

## count by cyl and gear
arrange_data(d, gear, cyl)
#> # A pseudo tibble: 32* x 12+
#>             row_names  mpg cyl  disp  hp drat    wt  qsec vs am
#> 1)      Toyota Corona 21.5   4 120.1  97 3.70 2.465 20.01  1  0
#> 2)     Hornet 4 Drive 21.4   6 258.0 110 3.08 3.215 19.44  1  0
#> 3)            Valiant 18.1   6 225.0 105 2.76 3.460 20.22  1  0
#> 4)  Hornet Sportabout 18.7   8 360.0 175 3.15 3.440 17.02  0  0
#> *28 row(s) not printed;  +2 column(s) not printed

Compare with dplyr::arrange()

same_as_dplyr(
  arrange_data(d, gear, cyl),
  dplyr::arrange(d, gear, cyl)
)
#> [1] TRUE

Select

select_data(): Select columns of data frame

## select only these columns
select_data(d, row_names:hp, -disp, gear, weight = wt)
#> # A pseudo tibble: 32* x 6
#>          row_names  mpg cyl  hp gear weight
#> 1)       Mazda RX4 21.0   6 110    4  2.620
#> 2)   Mazda RX4 Wag 21.0   6 110    4  2.875
#> 3)      Datsun 710 22.8   4  93    4  2.320
#> 4)  Hornet 4 Drive 21.4   6 110    3  3.215
#> *28 row(s) not printed

Compare with dplyr::select()

same_as_dplyr(
  select_data(d, cyl, gear, weight = wt),
  dplyr::select(d, cyl, gear, weight = wt)
)
#> [1] TRUE

Mutate

mutate_data(): Wrangle/create variables in data frame

## select cyl, gear, mpg columns and create new logical column for more efficient cars
d %>%
  select_data(cyl, gear, mpg) %>%
  mutate_data(eff = mpg > 20)
#> # A pseudo tibble: 32* x 4
#>     cyl gear  mpg  eff
#> 1)    6    4 21.0 TRUE
#> 2)    6    4 21.0 TRUE
#> 3)    4    4 22.8 TRUE
#> 4)    6    3 21.4 TRUE
#> *28 row(s) not printed

Compare with dplyr::mutate()

same_as_dplyr(
  d %>%
    select_data(cyl, gear, mpg) %>%
    mutate_data(eff = mpg > 20),
  d %>%
    dplyr::select(cyl, gear, mpg) %>%
    dplyr::mutate(eff = mpg > 20)
)
#> [1] TRUE

Summarise

summarise_data(): Wrangle/create summary variables in data frame

## select cyl, gear, mpg columns and create new logical column for more efficient cars
summarise_data(d, mpg = mean(mpg), wt = mean(wt))
#> # A pseudo tibble: 1 x 2
#>          mpg      wt
#> 1)  20.09062 3.21725

Compare with dplyr::summarise()

same_as_dplyr(
  summarise_data(d, mpg = mean(mpg), wt = mean(wt)),
  dplyr::summarise(d, mpg = mean(mpg), wt = mean(wt))
)
#> [1] TRUE

Group by

group_by_data(): Group observations in data frame [and then mutate and/or summarise]

## group by cyl
d %>%
  select_data(mpg:cyl, gear) %>%
  group_by_data(cyl, gear) %>%
  mutate_data(n = length(gear)) %>%
  summarise_data(
    n = unique(n),
    mpg = mean(mpg)
  )
#> # A pseudo tibble: 8 x 4
#>     cyl gear  n    mpg
#> 1)    6    4  4 19.750
#> 2)    4    4  8 26.925
#> 3)    6    3  2 19.750
#> 4)    8    3 12 15.050
#> 5)    4    3  1 21.500
#> 6)    4    5  2 28.200
#> 7)    8    5  2 15.400
#> 8)    6    5  1 19.700

Compare with dplyr::group_by()

same_as_dplyr(
  d %>%
    select_data(cyl, gear, mpg) %>%
    group_by_data(cyl) %>%
    mutate_data(n = length(gear)) %>%
    summarise_data(
      n = unique(n),
      mpg_total = sum(mpg),
      mpg = mean(mpg)
    ) %>%
    arrange_data(cyl) %>%
    select_data(cyl, mpg_total, mpg, n),
  d %>%
    dplyr::select(cyl, gear, mpg) %>%
    dplyr::group_by(cyl) %>%
    dplyr::mutate(n = length(gear)) %>%
    dplyr::summarise(
      n = unique(n),
      mpg_total = sum(mpg),
      mpg = mean(mpg)
    ) %>%
    dplyr::arrange(cyl) %>%
    dplyr::select(cyl, mpg_total, mpg, n)
)
#> [1] TRUE

Bind rows

bind_rows_data(): Collapse list of data frames into single data frame

## create version of data with new variable
dd <- d
dd$new_var <- sample(letters, nrow(d), replace = TRUE)

## combine multiple data sets into list
lst <- list(d, d, dd)

## bind rows into single data frame
bind_rows_data(lst, fill = TRUE)
#> # A pseudo tibble: 96* x 13+
#>          row_names  mpg cyl disp  hp drat    wt  qsec vs am gear
#> 1)       Mazda RX4 21.0   6  160 110 3.90 2.620 16.46  0  1    4
#> 2)   Mazda RX4 Wag 21.0   6  160 110 3.90 2.875 17.02  0  1    4
#> 3)      Datsun 710 22.8   4  108  93 3.85 2.320 18.61  1  1    4
#> 4)  Hornet 4 Drive 21.4   6  258 110 3.08 3.215 19.44  1  0    3
#> *92 row(s) not printed;  +2 column(s) not printed

Compare to dplyr::bind_rows()

same_as_dplyr(
  bind_rows_data(lst, fill = TRUE),
  dplyr::bind_rows(lst)
)
#> [1] TRUE

Joins

Data to compare with dplyr joins:

## mtcars data and additional cyl/new data
x <- tbltools::as_tbl_data(mtcars)
y <- data.frame(cyl = c(1, 4), new = c(1.25, 2.5))

left_join_data(): Join according to first (left) data frame

## join according to x
left_join_data(x, y)
#> Joining, by = "cyl"
#> # A pseudo tibble: 32* x 12
#>      mpg cyl disp  hp drat    wt  qsec vs am gear carb new
#> 1)  21.0   6  160 110 3.90 2.620 16.46  0  1    4    4  NA
#> 2)  21.0   6  160 110 3.90 2.875 17.02  0  1    4    4  NA
#> 3)  22.8   4  108  93 3.85 2.320 18.61  1  1    4    1 2.5
#> 4)  21.4   6  258 110 3.08 3.215 19.44  1  0    3    1  NA
#> *28 row(s) not printed

Compare with dplyr::left_join():

same_as_dplyr(
  left_join_data(x, y),
  dplyr::left_join(x, y)
)
#> Joining, by = "cyl"
#> Joining, by = "cyl"
#> [1] TRUE

right_join_data(): Join according to second (right) data frame

## join by y
right_join_data(x, y)
#> Joining, by = "cyl"
#> # A pseudo tibble: 12* x 12
#>      mpg cyl  disp hp drat   wt  qsec vs am gear carb  new
#> 1)    NA   1    NA NA   NA   NA    NA NA NA   NA   NA 1.25
#> 2)  22.8   4 108.0 93 3.85 2.32 18.61  1  1    4    1 2.50
#> 3)  24.4   4 146.7 62 3.69 3.19 20.00  1  0    4    2 2.50
#> 4)  22.8   4 140.8 95 3.92 3.15 22.90  1  0    4    2 2.50
#> *8 row(s) not printed

Compare with dplyr::left_join():

same_as_dplyr(
  right_join_data(x, y),
  dplyr::right_join(x, y)
)
#> Joining, by = "cyl"
#> Joining, by = "cyl"
#> [1] TRUE

full_join_data(): Join according to both data frames

## join by x and y
full_join_data(x, y)
#> Joining, by = "cyl"
#> # A pseudo tibble: 33* x 12
#>      mpg cyl disp  hp drat    wt  qsec vs am gear carb new
#> 1)  21.0   6  160 110 3.90 2.620 16.46  0  1    4    4  NA
#> 2)  21.0   6  160 110 3.90 2.875 17.02  0  1    4    4  NA
#> 3)  22.8   4  108  93 3.85 2.320 18.61  1  1    4    1 2.5
#> 4)  21.4   6  258 110 3.08 3.215 19.44  1  0    3    1  NA
#> *29 row(s) not printed

Compare with dplyr::left_join():

same_as_dplyr(
  full_join_data(x, y),
  dplyr::full_join(x, y)
)
#> Joining, by = "cyl"
#> Joining, by = "cyl"
#> [1] TRUE

Frequency tables

tabsort(): Count frequencies

## count by cyl and gear
tabsort(d, cyl, gear)
#> # A pseudo tibble: 9* x 4
#>     cyl gear  n   prop
#> 1)    8    3 12 0.3750
#> 2)    4    4  8 0.2500
#> 3)    6    4  4 0.1250
#> 4)    6    3  2 0.0625
#> *5 row(s) not printed

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README.md

README.md

tbltools

Installation

Features

Use

Slice

Filter

Arrange

Select

Mutate

Summarise

Group by

Bind rows

Joins

Frequency tables

Files

README.md

Latest commit

History

README.md

File metadata and controls

tbltools

Installation

Features

Use

Slice

Filter

Arrange

Select

Mutate

Summarise

Group by

Bind rows

Joins

Frequency tables