The goal of {dataedu} is to provide readers of Data Science in Education Using R with a package with useful functions, data, and references from the book.
First, let’s install {remotes}. If you already have {remotes} installed, you can move on to the next step.
install.packages("remotes")You can install the development version of {dataedu} by running this in your RStudio console:
remotes::install_github("data-edu/dataedu")-
{dataedu} requires R 3.6 or above to be installed.
-
{dataedu} has other packages that it needs to be able to run. You can see the full list under “Imports” (imported when downloading the package) and “Suggests” (we think you should include these too!) in the DESCRIPTION file.
-
We recommend first checking to see if your packages are all up-to-date if you are running into issues with installation. If you have installed the imported/suggested packages previously and have not updated them in a while, RStudio may prompt you to update them. You can choose to (1) ignore this prompt, (2) exit the prompt and update your packages, or (3) try to update your packages through the prompt. It’s usually easier to exit and update your packages outside the prompt (one way to do this is to go to the RStudio Packages pane and click Update, then select the packages you’d like to update).
Before you can use the package, make sure to call it using library():
library(dataedu)We created this package to provide our readers an opportunity to jump into R however they see fit.
- Mass installation of all the packages used in the book
- Reproducible code for the walkthroughs
- Access to the data used in each of the walkthroughs
- The dataedu theme and color palette for reuse
We strived to use packages that we use in our daily work when creating the walkthroughs in the book. Because we covered a variety of subjects, that means we used a lot of packages! As described in the Foundational Skills chapter, you can install the packages individually as they suit your needs.
However, if you want to get started quickly and download all the
packages at once, please use install_dataedu().
dataedu::install_dataedu()To see the packages used in the book, run:
dataedu::dataedu_packages
#> [1] "apaTables" "caret" "dummies" "e1071" "ggraph"
#> [6] "here" "janitor" "lme4" "lubridate" "performance"
#> [11] "ranger" "readxl" "rtweet" "randomNames" "sjPlot"
#> [16] "textdata" "tidygraph" "tidylog" "tidyverse" "tidytext"A special note on {tabulizer}: One of the walkthroughs uses
tabulizer, created by ROpenSci
to read PDFs. {tabulizer} requires the installation of
RJava, which
can be a tricky process. {tabulizer} is not included in
install_dataedu() and we recommend reading through the notes on its
Github repo if installing.
Coming soon!
To get the data, run dataedu:: then the dataset as it is named in the
book:
dataedu::course_dataTo see all the datasets available in the package, run data(package = "dataedu").
# this is to print the results for the README
# only `data(package = "dataedu")` is needed to see this list
a <- data(package = "dataedu")
a$result[ , 3:4]
#> Item
#> [1,] "all_files"
#> [2,] "bchildcountandedenvironments2012"
#> [3,] "bchildcountandedenvironments2013"
#> [4,] "bchildcountandedenvironments2014"
#> [5,] "bchildcountandedenvironments2015"
#> [6,] "bchildcountandedenvironments2016"
#> [7,] "bchildcountandedenvironments2017_18"
#> [8,] "child_counts"
#> [9,] "course_data"
#> [10,] "course_minutes"
#> [11,] "district_merged_df"
#> [12,] "district_tidy_df"
#> [13,] "frpl_pdf"
#> [14,] "ma_data_init"
#> [15,] "pre_survey"
#> [16,] "race_pdf"
#> [17,] "sci_mo_processed"
#> [18,] "sci_mo_with_text"
#> [19,] "tt_tweets"
#> Title
#> [1,] "Walkthrough 04 - Students with Disabilities Counts - Combined List"
#> [2,] "Walkthrough 04 - Students with Disabilities Counts - 2012"
#> [3,] "Walkthrough 04 - Students with Disabilities Counts - 2013"
#> [4,] "Walkthrough 04 - Students with Disabilities Counts - 2014"
#> [5,] "Walkthrough 04 - Students with Disabilities Counts - 2015"
#> [6,] "Walkthrough 04 - Students with Disabilities Counts - 2016"
#> [7,] "Walkthrough 04 - Students with Disabilities Counts - 2017-18"
#> [8,] "Walkthrough 04 - Students with Disabilities Counts - Combined Data Frame"
#> [9,] "Walkthrough 01 - Course Data"
#> [10,] "Walkthrough 01 - Course Minutes"
#> [11,] "Walkthrough 03 - Merged Ethnicity and FRPL District Data"
#> [12,] "Walkthrough 03 - Merged and Tidy Ethnicity and FRPL District Data"
#> [13,] "Walkthrough 03 - Tabulizer Output from FRPL PDF"
#> [14,] "Foundational Skills Data"
#> [15,] "Walkthrough 01 - Pre-Survey"
#> [16,] "Walkthrough 03 - Tabulizer Output from Race PDF"
#> [17,] "Walkthrough 01 - Student Motivation (Processed)"
#> [18,] "Walkthrough 01 - Student Motivation (Processed and With Text)"
#> [19,] "Walkthrough 12 - Tweet Data"If you would like to download the data in non-.Rds (RData) format, the
CSV and JSON formats are available under
inst/extdata.
Please note that all_files is not included because of how large the
file would be.
Add the theme and palette to ggplot2-based plots using theme_dataedu()
and scale_*_dataedu().
- Note: The DataEdu theme uses {showtext} to render the font. If
you would like to use it in an R markdown chunk, please ensure that
the chunk lists
fig.showtext = TRUE. If you would like to use it in a standalone R script, then you will need to use a differnet graphic device. More information is available in the documentation here.
library(ggplot2)
library(dataedu)
ggplot(midwest, aes(x = area, y = popdensity, color = state)) +
geom_point() +
theme_dataedu() +
scale_color_dataedu()
The font for the DSIEUR graphs is Cabin and available here. The code to load the font with the package is heavily based on the code from Guangchuang Yu’s extrafont package - thank you!
- Please report ideas or issues on the Issues page. If possible, make your post reproducible using the {reprex} package.
- Send any pull requests on the Github repository.
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent