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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mkearney / tbltools

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tbltools

Installation

Features

Use

Slice

Filter

Arrange

Select

Mutate

Summarise

Group by

Bind rows

Joins

Frequency tables