Agenda

• Install R and RStudio, and the RStudio Cloud alternative
• Understand the RStudio panes and functionalities
• Installing packages
• Understand how R Markdown works
• Read and write data in various formats
• Brief overview of variable types and labeling values
• Saving data in R format

Installing R & RStudio vs. RStudio Cloud

Understand your RStudio Environment

Installing packages

Open the Rmd file I sent you: Module01.Rmd and save it in the code folder Save the data I sent you to the data folder

Now we install some packages via Tools -> Install Packages...

and update packages via Tools -> Check for Package Updates...¹

devtools, ggplot2, dplyr, reshape2, lubridate, car, Hmisc, 
gapminder, leaflet, prettydoc, DT, data.table, htmltools, 
scales, ggridges

Other packages will be installed as needed.

Note: If you are running some code I have provided or you have found online and you get a message saying

Error in library(xyz) : there is no package called ‘xyz’

go ahead and install that library.

[1] It is a good idea to update packages regularly. Every now and then an update might break something but it is usually fixed sooner rather than later by the developer.

Rprojects (ONLY if installing on your own computer)

• Create a folder called mpa5830. Inside mpa5830 create ONE sub-folder called data. The folder structure will now be

mpa5830/ 
    └── data/
          └── datafile-1
          └── datafile-2
          └── ...

R Markdown files

• Go to New File -> R Markdown ... and enter a My First Rmd File in title and your name.

• Click OK.
• Now File -> Save As.. and save it as testing_rmd in the code sub-folder
• Click this button:

  You may see a message that says some packages need to be installed/updated. Allow these to be installed/updated.

Reading data

Make sure you have the following data-sets in the data folder. If you don't then the commands that follow will not work. We start by reading a simple comma-separated variable format file and then a tab-delimited variable format file.

library(here)
read.csv(
  here("data", "ImportDataCSV.csv"),
  sep = ",",
  header = TRUE) -> df.csv # note sep = ","
read.csv(
    here("data", "ImportDataTAB.txt"),
    sep = "\t", 
    header = TRUE) -> df.tab # note sep = "\t"

If the files were read then Global Environment should show objects called df.csv and df.tab. If you don't see these then check the following:

• Make sure you have the files in your data folder
• Make sure the folder has been correctly named (no blank spaces before or after, all lowercase, etc)
• Make sure the data folder is inside mpa5830

(a) Excel files can be read via the readxl package

library(readxl)
read_excel(
  here("data", "ImportDataXLS.xls")
  ) -> df.xls 
read_excel(
  here("data", "ImportDataXLSX.xlsx")
  ) -> df.xlsx

(b) SPSS, Stata, SAS files can be read via the haven package

library(haven)
read_stata(
  here("data", "ImportDataStata.dta")
  ) -> df.stata # Stata data file 
read_sas(
  here("data", "ImportDataSAS.sas7bdat")
  ) -> df.sas # SAS data file 
read_sav(
  here("data", "ImportDataSPSS.sav")
  ) -> df.spss # SPSS data file

(c) It is also common to encounter fixed-width files where the raw data are stored without any gaps between successive variables. However, these files will come with documentation that will tell you where each variable starts and ends, along with other details about each variable.

read.fwf(
  here("data", "fwfdata.txt"),
  widths = c(4, 9, 2, 4),
  header = FALSE,
  col.names = c("Name", "Month", "Day", "Year")
  ) -> df.fw

Notice we need widths = c() and col.names = c()

Reading Files from the Web

It is possible to specify the full web-path for a file and read it in, rather than storing a local copy. This is often useful when updated by the source (Census Bureau, Bureau of Labor, Bureau of Economic Analysis, etc.)

read.table(
  "http://data.princeton.edu/wws509/datasets/effort.dat"
  ) -> fpe 
read.table(
  "https://stats.idre.ucla.edu/stat/data/test.txt",
  header = TRUE
  ) -> test 
read.csv(
  "https://stats.idre.ucla.edu/stat/data/test.csv",
  header = TRUE
  ) -> test.csv

The foreign package will also read Stata and other formats. I end up defaulting to haven now. There are other packages for reading SPSS, SAS, etc. files ... sas7bdat, rio, data.table, xlsx, XLConnect, gdata and others. ]

Reading compressed files

temp <- tempfile()
download.file(
  "ftp://ftp.cdc.gov/pub/Health_Statistics/NCHS/Datasets/NVSS/bridgepop/2016/pcen_v2016_y1016.sas7bdat.zip",
  temp
  )
haven::read_sas(
  unz(
    temp,
    "pcen_v2016_y1016.sas7bdat"
    )
  ) -> oursasdata 
unlink(temp)

You can save your data in a format that R will recognize, giving it the RData or rdata extension

save(
  oursasdata,
  file = here("data", "oursasdata.RData")
  )
save(
  oursasdata,
  file = here("data", "oursasdata.rdata")
  )

Check your data directory to confirm both files are present

Minimal example of data processing

Working with the hsb2 data: 200 students from the High school and Beyond study

read.table(
  'https://stats.idre.ucla.edu/stat/data/hsb2.csv',
  header = TRUE,
  sep = ","
  ) -> hsb2

• female = (0/1)
• race = (1=hispanic 2=asian 3=african-amer 4=white)
• ses = socioeconomic status (1=low 2=middle 3=high)
• schtyp = type of school (1=public 2=private)
• prog = type of program (1=general 2=academic 3=vocational)
• read = standardized reading score
• write = standardized writing score
• math = standardized math score
• science = standardized science score
• socst = standardized social studies score

There are no label values for the various qualitative variables (female, race, ses, schtyp, and prog) so we create these.³

factor(hsb2$female,
       levels = c(0, 1),
       labels = c("Male", "Female")
       ) -> hsb2$female.f 
factor(hsb2$race,
       levels = c(1:4),
       labels = c("Hispanic", "Asian", "African American", "White")
       ) -> hsb2$race.f
factor(hsb2$ses,
       levels = c(1:3),
       labels = c("Low", "Middle", "High")
       ) -> hsb2$ses.f
factor(hsb2$schtyp,
       levels = c(1:2),
       labels = c("Public", "Private")
       ) -> hsb2$schtyp.f
factor(hsb2$prog,
       levels = c(1:3),
       labels = c("General", "Academic", "Vocational")
       ) -> hsb2$prog.f

save your work!!

Having added labels to the factors in hsb2 we can now save the data for later use.

save(hsb2, file = here("data", "hsb2.RData"))

Let us test if this R Markdown file will to html

If all is good then we can Close Project

• RStudio will close your project and reopen in a vanilla session

Data in packages

Almost all R packages come bundled with data-sets, too many of them to walk you through but

• see here for standard ones
• here are some more
• and some more

To load data from a package, if you know the data-set's name, run

library(HistData)
data("Galton")
names(Galton)

## [1] "parent" "child"

or you can run

data("GaltonFamilies", package = "HistData")
names(GaltonFamilies)

## [1] "family"          "father"          "mother"          "midparentHeight"
## [5] "children"        "childNum"        "gender"          "childHeight"

Saving data and workspaces

You can certainly save your data via

• save(dataname, file = "filepath/filename.RData") or
• save(dataname, file = "filepath/filename.rdata")

data(mtcars)
names(mtcars)
save(mtcars, file = here("data", "mtcars.RData"))
rm(list = ls())
load(here("data", "mtcars.RData"))

You can also save multiple data files as follows:

data(mtcars)
library(ggplot2)
data(diamonds)
save(mtcars, diamonds, file = here("data", "mydata.RData"))
rm(list = ls()) # To clear the Environment
load(here("data", "mydata.RData"))

If you want to save just a single object from the environment and then load it in a later session, maybe with a different name, then you should use saveRDS() and readRDS()

data(mtcars)
saveRDS(mtcars, file = here("data", "mydata.RDS"))
rm(list = ls()) # To clear the Environment
ourdata <- readRDS(here("data", "mydata.RDS"))

If instead you did the following, note that you have to did the following, the file will be read with the name when saved

data(mtcars)
save(mtcars, file = here("data", "mtcars.RData"))
rm(list = ls())  # To clear the Environment
ourdata <- load(here("data", "mtcars.RData")) # Note ourdata is listed as "mtcars"

If you want to save everything you have done in the work session you can via save.image()

save.image(file = here("data", "mywork_jan182018.RData"))

• The next time you start RStudio this image will be automatically loaded
• Useful if you have a lot of R code you have written and various objects generated and do not want to start from scratch the next time around.

RStudio webinars

The fantastic team at RStudio runs free webinar that are often very helpful so be sure to signup with your email. Here are some video recordings of webinars that are relevant to what we have covered so far.

• Programming Part 1 (Writing code in RStudio)
• Programming Part 2 (Debugging code in RStudio)
• Managing Change Part 1 (Projects in RStudio)
• Importing Data into R
• Whats new with readxl
• Getting your data into R
• Getting Started with R Markdown