forked from tidyverse/tidyverse
-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathREADME.Rmd
115 lines (86 loc) · 4.17 KB
/
README.Rmd
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
---
output: github_document
---
<!-- README.md is generated from README.Rmd. Please edit that file -->
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "README-"
)
options(cli.hyperlink = FALSE)
```
# tidyverse <a href='https://tidyverse.tidyverse.org'><img src='man/figures/logo.png' align="right" height="138" /></a>
<!-- badges: start -->
[](https://cran.r-project.org/package=tidyverse)
[](https://github.com/tidyverse/tidyverse/actions/workflows/R-CMD-check.yaml)
[](https://app.codecov.io/gh/tidyverse/tidyverse?branch=main)
<!-- badges: end -->
## Overview
The tidyverse is a set of packages that work in harmony because they share common data representations and API design. The __tidyverse__ package is designed to make it easy to install and load core packages from the tidyverse in a single command.
If you'd like to learn how to use the tidyverse effectively, the best place to start is [R for Data Science (2e)](https://r4ds.hadley.nz/).
## Installation
::: .pkgdown-release
```{r eval = FALSE}
# Install from CRAN
install.packages("tidyverse")
```
:::
::: .pkgdown-devel
```{r eval = FALSE}
# Install the development version from GitHub
# install.packages("pak")
pak::pak("tidyverse/tidyverse")
```
:::
If you're compiling from source, you can run `pak::pkg_system_requirements("tidyverse")`, to see the complete set of system packages needed on your machine.
## Usage
`library(tidyverse)` will load the core tidyverse packages:
* [ggplot2](https://ggplot2.tidyverse.org), for data visualisation.
* [dplyr](https://dplyr.tidyverse.org), for data manipulation.
* [tidyr](https://tidyr.tidyverse.org), for data tidying.
* [readr](https://readr.tidyverse.org), for data import.
* [purrr](https://purrr.tidyverse.org), for functional programming.
* [tibble](https://tibble.tidyverse.org), for tibbles, a modern re-imagining of data frames.
* [stringr](https://github.com/tidyverse/stringr), for strings.
* [forcats](https://github.com/tidyverse/forcats), for factors.
* [lubridate](https://github.com/tidyverse/lubridate), for date/times.
You also get a condensed summary of conflicts with other packages you have loaded:
```{r example}
library(tidyverse)
```
You can see conflicts created later with `tidyverse_conflicts()`:
```{r conflicts}
library(MASS)
tidyverse_conflicts()
```
And you can check that all tidyverse packages are up-to-date with `tidyverse_update()`:
```{r update, eval = FALSE}
tidyverse_update()
#> The following packages are out of date:
#> * broom (0.4.0 -> 0.4.1)
#> * DBI (0.4.1 -> 0.5)
#> * Rcpp (0.12.6 -> 0.12.7)
#>
#> Start a clean R session then run:
#> install.packages(c("broom", "DBI", "Rcpp"))
```
## Packages
As well as the core tidyverse, installing this package also installs a selection of other packages that you're likely to use frequently, but probably not in every analysis. This includes packages for:
* Working with specific types of vectors:
* [hms](https://github.com/tidyverse/hms), for times.
* Importing other types of data:
* [feather](https://github.com/wesm/feather), for sharing with Python and other languages.
* [haven](https://github.com/tidyverse/haven), for SPSS, SAS and Stata files.
* [httr](https://github.com/r-lib/httr), for web apis.
* [jsonlite](https://github.com/jeroen/jsonlite) for JSON.
* [readxl](https://github.com/tidyverse/readxl), for `.xls` and `.xlsx` files.
* [rvest](https://github.com/tidyverse/rvest), for web scraping.
* [xml2](https://github.com/r-lib/xml2), for XML.
* Modelling
* [modelr](https://github.com/tidyverse/modelr), for modelling within a pipeline
* [broom](https://github.com/tidymodels/broom), for turning models into
tidy data
## Code of Conduct
Please note that the tidyverse project is released with a [Contributor Code of Conduct](https://tidyverse.tidyverse.org/CODE_OF_CONDUCT.html).
By contributing to this project, you agree to abide by its terms.