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https://github.com/mkearney/tbltools
🗜🔢 Tools for Working with Tibbles
https://github.com/mkearney/tbltools
frequency-table mkearney-r-package r r-package rstats table tibble tidyverse
Last synced: 27 days ago
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🗜🔢 Tools for Working with Tibbles
- Host: GitHub
- URL: https://github.com/mkearney/tbltools
- Owner: mkearney
- License: gpl-3.0
- Created: 2018-10-08T17:55:43.000Z (about 6 years ago)
- Default Branch: master
- Last Pushed: 2019-02-08T16:29:24.000Z (almost 6 years ago)
- Last Synced: 2024-08-13T07:14:37.578Z (4 months ago)
- Topics: frequency-table, mkearney-r-package, r, r-package, rstats, table, tibble, tidyverse
- Language: R
- Homepage: https://github.com/mkearney/tbltools
- Size: 584 KB
- Stars: 36
- Watchers: 3
- Forks: 0
- Open Issues: 3
-
Metadata Files:
- Readme: README.Rmd
- Changelog: NEWS.md
- License: LICENSE.md
Awesome Lists containing this project
- jimsghstars - mkearney/tbltools - 🗜🔢 Tools for Working with Tibbles (R)
README
---
output: github_document
---
```{r setup, include = FALSE}
knitr::opts_chunk$set(echo = TRUE, collapse = TRUE, comment = "#>")
library(tbltools)
options(tbltools.print_tibble = FALSE, tbltools.print_n = 4)
## function to compare with dplyr (ignore non data.frame class diffs)
same_as_dplyr <- function(d1, d2) {
if (identical(d1, d2)) return(TRUE)
if (is.data.frame(d1) && is.data.frame(d2)) {
d1 <- as.data.frame(d1)
d1at <- attributes(d1)
attributes(d1) <- d1at[order(names(d1at))]
d2 <- as.data.frame(d2)
d2at <- attributes(d2)
attributes(d2) <- d2at[order(names(d2at))]
}
identical(d1, d2)
}
set.seed(12)
```
# tbltools 
[](https://travis-ci.org/mkearney/tbltools)
[](https://cran.r-project.org/package=tbltools)
[](https://codecov.io/github/mkearney/tbltools?branch=master)
[](https://www.tidyverse.org/lifecycle/#experimental)
Tools for Working with Tibbles
## Installation
Install from CRAN with:
```{r, eval=FALSE}
## install {tbltools} from CRAN
install.packages("tbltools")
```
Or install the development version from Github with:
```{r, eval=FALSE}
## install remotes pkg if not already
if (!requireNamespace("remotes")) {
install.packages("remotes")
}
## install from github
remotes::install_github("mkearney/tbltools")
```
## Features
+ [Tidyverse](https://tidyverse.org)-style operability!
- Uses non-standard evaluation for **easy interactive analysis**
- Provides numerous [{dplyr}](https://dplyr.tidyverse.org)-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}](https://tibble.tidyverse.org)-like printing experience
## Use
+ **`as_tbl_data()`**: Convert data frames to tibbles
```{r}
## 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
```{r}
## create data frame where 2 variable depends on evaluation of 1st variable
tbl_data_frame(
x = rnorm(20),
y = rnorm(20) + x
) %>%
cor()
```
### Slice
+ **`slice_data()`**: Select/keep/return row positions
```{r}
## select rows 1, 3, 5, 25 of data
slice_data(d, c(1, 3, 5, 25))
```
Compare with `dplyr::slice()`
```{r}
same_as_dplyr(
slice_data(d, c(1, 3, 5, 25)),
dplyr::slice(d, c(1, 3, 5, 25))
)
```
### Filter
+ **`filter_data()`**: Filter/keep/return certain rows
```{r}
## count by cyl and gear
filter_data(d, gear > 3 | mpg > 30, vs == 1)
```
Compare with `dplyr::filter()`
```{r}
same_as_dplyr(
filter_data(d, gear > 3 | mpg > 30, vs == 1),
dplyr::filter(d, gear > 3 | mpg > 30, vs == 1)
)
```
### Arrange
+ **`arrange_data()`**: Organize rows by column(s) value
```{r}
## count by cyl and gear
arrange_data(d, gear, cyl)
```
Compare with `dplyr::arrange()`
```{r}
same_as_dplyr(
arrange_data(d, gear, cyl),
dplyr::arrange(d, gear, cyl)
)
```
### Select
+ **`select_data()`**: Select columns of data frame
```{r}
## select only these columns
select_data(d, row_names:hp, -disp, gear, weight = wt)
```
Compare with `dplyr::select()`
```{r}
same_as_dplyr(
select_data(d, cyl, gear, weight = wt),
dplyr::select(d, cyl, gear, weight = wt)
)
```
### Mutate
+ **`mutate_data()`**: Wrangle/create variables in data frame
```{r}
## 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)
```
Compare with `dplyr::mutate()`
```{r}
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)
)
```
### Summarise
+ **`summarise_data()`**: Wrangle/create summary variables in data frame
```{r}
## select cyl, gear, mpg columns and create new logical column for more efficient cars
summarise_data(d, mpg = mean(mpg), wt = mean(wt))
```
Compare with `dplyr::summarise()`
```{r}
same_as_dplyr(
summarise_data(d, mpg = mean(mpg), wt = mean(wt)),
dplyr::summarise(d, mpg = mean(mpg), wt = mean(wt))
)
```
### Group by
+ **`group_by_data()`**: Group observations in data frame [and then mutate and/or summarise]
```{r}
## 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)
)
```
Compare with `dplyr::group_by()`
```{r}
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)
)
```
### Bind rows
+ **`bind_rows_data()`**: Collapse list of data frames into single data frame
```{r}
## 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)
```
Compare to `dplyr::bind_rows()`
```{r}
same_as_dplyr(
bind_rows_data(lst, fill = TRUE),
dplyr::bind_rows(lst)
)
```
### Joins
+ Data to compare with [dplyr](https://github.com/tidyverse/dplyr) joins:
```{r}
## 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
```{r}
## join according to x
left_join_data(x, y)
```
Compare with `dplyr::left_join()`:
```{r}
same_as_dplyr(
left_join_data(x, y),
dplyr::left_join(x, y)
)
```
+ **`right_join_data()`**: Join according to second (right) data frame
```{r}
## join by y
right_join_data(x, y)
```
Compare with `dplyr::left_join()`:
```{r}
same_as_dplyr(
right_join_data(x, y),
dplyr::right_join(x, y)
)
```
+ **`full_join_data()`**: Join according to both data frames
```{r}
## join by x and y
full_join_data(x, y)
```
Compare with `dplyr::left_join()`:
```{r}
same_as_dplyr(
full_join_data(x, y),
dplyr::full_join(x, y)
)
```
### Frequency tables
+ **`tabsort()`**: Count frequencies
```{r}
## count by cyl and gear
tabsort(d, cyl, gear)
```