R Projects are a feature of RStudio that can help you stay organized. They are pretty straightforward to set up, but are not required. You can learn more about R Projects here:

https://daseh.org/resources/R_Projects.html

• ‘Reading in’ data is the first step of any real project/analysis
• R can read almost any file format, especially via add-on packages
• We are going to focus on simple delimited files first
  • comma separated (e.g. ‘.csv’)
  • tab delimited (e.g. ‘.txt’)
  • Microsoft Excel (e.g. ‘.xlsx’)

• We have added functionality to load some datasets directly in the dasehr package

CalEnviroScreen Dataset:

CalEnviroScreen is a project that ranks census tracts in California based on potential exposures to pollutants, adverse environmental conditions, socioeconomic factors and the prevalence of certain health conditions. Data used in the CalEnviroScreen model come from national and state sources.

• Check out the data at: https://calenviroscreen-oehha.hub.arcgis.com/#Data

• > File
• > Import Dataset
• > From Text (readr)
• > paste the url (https://daseh.org/data/CalEnviroScreen_data.csv)
• > click “Update” and “Import”

You see a preview of the data on the top left pane.

You see a new object called CalEnviroScreen_data in your environment pane (top right). The table button opens the data for you to view.

R ran some code in the console (bottom left).

Pros: easy!!

Cons: obscures some of what’s happening, others will have difficulty running your code

# load library `readr` that contains function `read_csv`
library(readr)
dat <- read_csv(
  file = "https://daseh.org/data/CalEnviroScreen_data.csv"
)

# `head` displays first few rows of a data frame. `tail()` works the same way.
head(dat, n = 5)

# A tibble: 5 × 68
   ...1 CensusTract CaliforniaCounty   ZIP Longitude Latitude ApproxLocation
  <dbl>       <dbl> <chr>            <dbl>     <dbl>    <dbl> <chr>         
1     1  6001400100 Alameda          94704     -122.     37.9 Oakland       
2     2  6001400200 Alameda          94618     -122.     37.8 Oakland       
3     3  6001400300 Alameda          94618     -122.     37.8 Oakland       
4     4  6001400400 Alameda          94609     -122.     37.8 Oakland       
5     5  6001400500 Alameda          94609     -122.     37.8 Oakland       
# ℹ 61 more variables: CES4.0Score <dbl>, CES4.0Percentile <dbl>,
#   CES4.0PercRange <chr>, Ozone <dbl>, OzonePctl <dbl>, PM2.5 <dbl>,
#   PM2.5.Pctl <dbl>, DieselPM <dbl>, DieselPMPctl <dbl>, DrinkingWater <dbl>,
#   DrinkingWaterPctl <dbl>, Lead <dbl>, LeadPctl <dbl>, Pesticides <dbl>,
#   PesticidesPctl <dbl>, ToxRelease <dbl>, ToxReleasePctl <dbl>,
#   Traffic <dbl>, TrafficPctl <dbl>, CleanupSites <dbl>,
#   CleanupSitesPctl <dbl>, GroundwaterThreats <dbl>, …

dat <- read_csv(
  file = "https://daseh.org/data/CalEnviroScreen_data.csv"
)
# EQUIVALENT TO
dat <- read_csv(
  "https://daseh.org/data/CalEnviroScreen_data.csv"
)

read_csv() needs an argument file =.

• file is the path to your file, in quotation marks
• can be path to a file on a website (URL)
• can be path in your local computer – absolute file path or relative file path

# Examples

dat <- read_csv(file = "www.someurl.com/table1.csv")

dat <- read_csv(file = "/Users/avahoffman/Downloads/CalEnviroScreen_data.csv")

dat <- read_csv(file = "CalEnviroScreen_data.csv")

What is a file path ????

When we work in R, we automatically have a working directory.

Working directory is a folder (directory) that RStudio assumes “you are working in”.

It’s where R looks for files.

Run the getwd() function to determine your working directory.

# Get the working directory
getwd()

Let’s say my data is in a folder called “data” in my working directory.

data/my_data.csv would be the relative path. It’s relative to the working directory.

The whole address, for example /Users/avahoffman/Downloads/data/my_data.csv is the absolute path.

You can set the working directory manually with the setwd() function:

# set the working directory
setwd("/Users/avahoffman/Desktop")

• the View() function shows your data in a new tab, in spreadsheet format
• be careful if your data is big!

View(dat)

read_csv() is a special case of read_delim() – a general function to read a delimited file into a data frame

read_delim() needs path to your file and file’s delimiter, will return a tibble

• file is the path to your file, in quotes
• delim is what separates the fields within a record

## Examples
dat <- read_delim(file = "www.someurl.com/table1.tsv", delim = "\t")

dat <- read_delim(file = "data.txt", delim = "|")

• You cannot read in an excel file from a URL.
• Need to load the readxl package with library().
• The argument is path (not file).

library(readxl)

read_excel(path = "nitrate.xlsx")

• haven package has functions to read SAS, SPSS, Stata formats

• There are also resources for REDCap : REDCapR

There are also data importing functions provided in base R (rather than the readr package), like read.delim() and read.csv().

These functions have slightly different syntax for reading in data (e.g. header argument).

However, while many online resources use the base R tools, the latest version of RStudio switched to use these new readr data import tools, so we will use them in the class for slides. They are also up to two times faster for reading in large datasets, and have a progress bar which is nice.

If you are trying to knit your work, it might help to set the knit directory to the “Current Working Directory”:

• The str() function can tell you about data/objects.
• We will also discuss the glimpse() function later, which does something very similar.
• head() shows first few rows
• tail() shows the last few rows

R Projects can make it easier to find files. Check out this resource.

Importing data manually:

• File > Import Dataset > From Text (readr)
• Paste the url
• Click “Update” and “Import”
• Review the process: https://youtu.be/LEkNfJgpunQ

Importing data programmatically:

• read_csv() function from readr package
• Use getwd() to check your working directory, where R looks for your data files

Look at your data!

• Check the environment for a data object
• View() gives you a preview of the data in a new tab

Other file types

• readr package: read_delim() for general delimited files
• readxl package: read_excel() for Excel files

Don’t forget to use <- to assign your data to an object!

🏠 Class Website

💻 Data Input Lab

Image by Gerd Altmann from Pixabay