pacman package in R. In order to use the
functions that are contained in the pacman package, we have
to install and load the package to R:install.packages("pacman") # Install pacman package
library("pacman") # Load pacman packagepacman package.In this example, I will illustrate how to apply the p_load function to load multiple add-on packages in one line of code.
Have a look at the following R syntax:
p_load(dplyr, stringr) # Install & load packagesAfter executing the previous code, we have loaded the packages
dplyr, and stringr.
Note that the p_load function also checks whether a package is
installed already. If the package is not installed yet, it is installed
automatically by the pacman package.
Another useful function of the pacman package is the
p_unload function. The p_unload function can be used to detach one or
multiple loaded packages from within R.
Let’s apply the p_unload command to unload the packages
dplyr, and stringr that we have loaded
before:
p_unload(dplyr, stringr) # Unload packagesdplyr,
stringr The previous output of the RStudio console tells us
that our three packages were detached.In this example, I will show how to use the p_update function of
the pacman package to check for outdated packages AND to
update all of these packages.
First, let’s check which of my packages are outdated:
p_update(update = FALSE) # Check for outdated packagesSecond, the pacman package provides an easy solution
to how we can update all outdated packages with only one line of R
code.
Make sure that you have some time before running the following R code. This may take some time, depending on the number of packages you need to update:
p_update() # Update all packagesinstall.packages("devtools")devtools::install_cran("MASS")install.packages("remotes")remotes::install_github("r-lib/conflicted")install.packages("stargazer")# Load your external dataset
data(mtcars)
# Check the summary of dataset
summary(mtcars)
# Create summary statistics table using stargazer
stargazer(mtcars, type='text', title='Summary Statistics')# Fit regression model
fit <- lm(mpg ~ wt + cyl, data = mtcars)
# Create table that summarizes the new regression model
stargazer(fit, type = 'text', title = 'Regression Summary Table',
out = 'C:\\Users\\Downloads\\R.txt')readxl from CRAN:install.packages("readxl")xlsx_example <- readxl_example("datasets.xlsx")
read_excel(xlsx_example)excel_sheets(xlsx_example)read_excel(xlsx_example, sheet = "chickwts")
read_excel(xls_example, sheet = 4)here() uses a reasonable heuristics to find your
project’s files, based on the current working directory at the time when
the package is loaded. Use it as a drop-in replacement for file.path(),
it will always locate the files relative to your project root.install.packages("here")here("data", "penguins.csv")rvest is a package that makes it easy to scrape (or
harvest) data from html web pages.install.packages("rvest")html <- read_html("https://en.wikipedia.org/w/index.php?title=The_Lego_Movie&oldid=998422565")html %>%
html_element(".tracklist") %>%
html_table()install.packages('ggthemes')ggplot(mtcars) + geom_point(aes(x = wt, y = mpg,
colour = factor(gear))) + theme_base() + ggtitle("theme_base()") ggplot(mtcars, aes(x = wt, y = mpg, colour = factor(gear))) +
geom_point() +
theme_calc() +
ggtitle("theme_calc()")install.packages("ggpubr")ggdensity(wdata, x = "weight",
add = "mean", rug = TRUE,
color = "sex", fill = "sex",
palette = c("#00AFBB", "#E7B800"))install.packages("plotly")fig <- plot_ly(midwest, x = ~percollege, color = ~state, type = "box")
figinstall.packages("tidyverse")install.packages("stringr")install.packages("bibliometrix")install.packages("remotes")
remotes::install_github("massimoaria/bibliometrix")Updated on: 2025-02-13