Import Data


February 19, 2024

Did you start with a Project?

Reproducibility starts at the foundation! Always begin by opening a project. See New Projects for tips.

We recommend entering your code in code-chunks within coding notebooks.

In this workshop we recommend the Tidyverse approach to learning and using R

Below are some of the core tidyverse packages that are loaded with the function: library(tidyverse).

package use package use
dplyr data wrangling forcats categorical data / factors
ggplot2 visualization lubridate dates and times
readr import CSV stringr regular expressions / strings
purrr iteration / functional programing tidyr pivot data
readxl import Excel files haven import SPSS/Stata/SAS

Data import wizard

The data import wizard is a quick and easy way to import your data

import dataset

Import dataset

It’s actually way better to follow the reproducible steps – and hardly any more effort – below…

Load library packages

Open a Quarto document, Insert a code-chunk (Ctrl-Alt-I) and copy the following code. Then execute the code. You may first have to install the tidyverse-package if you have not already1.


Import data

In RStudio, in the Files quadrant and tab, click the data folder, then left-click the brodhead_center.csv file. Using the context menu, choose the Import Dataset… option. Once inside the data wizard, you can copy the code int he code-preview window, then paste the code into the code chunk of your quarto document or r script.

# library(readr)
brodhead_center <- read_csv("data/brodhead_center.csv")
# view(brodheadCenter)
Composing the data import code…

Writing the import data function can be tricky. Try the import wizard pictured above. THEN, paste the code from the Code Preview section into your script.

Easily write import data function

Easily write import data function

Excel, SPSS, SAS, etc.

The data import wizard will help you find the proper package for importing your data. For example, use…

  • library(readxl) for Excel data
  • library(haven) for SPSS, SAS, Stata
  • lirary(readr) for CSV or other delimeters

Just start with File > Import Dataset to get started composing that code, then paste your code into a script.

Look at the data object

Now that you’ve assigned the output from the read_csv function to the name brodhead_center, simply call that object name in a code chunk.


Visualize your data with {ggplot2}

Here’s a quick teaser on visualizing data. Read more in the visualization chapter.

brodhead_center |> 
  ggplot(aes(x = name, y = cost)) +

brodhead_center |> 
  filter(name != "Tandoor") |> 
  ggplot(aes(x = rating, y = cost)) +
  geom_jitter(aes(color = name))

Show the code
brodhead_center |> 
  drop_na(rating, cost, name) |> 
  filter(name != "Tandoor") |> 
  ggplot(aes(x = factor(rating), y = cost)) +
  geom_tile(aes(fill = name), alpha = .3) +
  scale_y_continuous(label = scales::dollar) +
  scale_fill_brewer(palette = "Dark2") + 
  labs(x = "rating", y = NULL, title = "Heatmap: cost over ratings", 
       caption = "Source:",
       fill = "Restaurant name") +
  theme_classic() +
  theme(plot.title.position = "plot")


  1. In R, a package is a collection of R functions, and/or data, and/or documentation. R users find and install packages via centralized package-hubs (e.g. Metacran, CRAN, Bioconductor, R-universe, Github) to aid in the specialization and efficiency of R coding.↩︎