Introduction to RAni Ruhil1 / 36

Agenda

Install R and RStudio
- First install the latest version of from here
- Then install the latest version of from here
Install Rtools (for Windows) from here ... useful if you have to compile packages, which you will have to do from time to time when the package (or its latest version) is not on CRAN
Test installation
Install some packages
Understand how R Markdown works
Read data in various formats
Basic data processing and saving
Fun with leaflet

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Go slow and make sure everyone is able to knit
Minimize panic and keep the environment light

Understand your RStudio Environment

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(1) The Console ... (2) Knitting and Code Chunk options

... key options ...

Console = This is where commands are issued to R, either by typing and hitting enter or running commands from a script (like your R Markdown file)
Environment = stores and shows you all the objects created
History shows you a running list of all commands issued to R
Connections = shows you any databases/servers you are connected to and also allows you to initiate a new connection
Files = shows you files and folders in your current working directory, and you can move up/down in the folder hierarchy
Plots = show you all plots that have been generated
Packages = shows you installed packages
help = allows you to get the help pages by typing in keywords
Viewer = shows you are "live" documents running on the server
Knit = allows you to generated html/pdf/word documents from a script
Insert = allows you to insert a vanilla R chunk. You can (and should) give unique name to code chunks so that you can easily diagnose which chunk is not working
Run = allows you to run lines/chunks

Customize the detachable panes via Tools -> Global Options...

You also have a spellchecker; use it

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Installing packages

Now we install some packages via Tools -> Install Packages... and updated packages via Tools -> Check for Package Updates...¹

install.packages(
  c("devtools", "reshape2", "lubridate", "car", "Hmisc",
    "gapminder", "leaflet", "DT", "data.table", "htmltools",
    "scales", "ggridges", "here", "knitr", "kableExtra",
    "haven", "readr", "readxl", "ggplot2","namer")
  )

Other packages will be installed as needed

Update packages via Tools -> Check for Package Updates...

[1] It is a good idea to update packages on a regular basis but note that every now and then something might break with an update. When this happens check the package's source, usually on github for solutions.

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Make sure they install devtools

Rprojects

(1) Create a folder called ouir

(2) Inside the ouir folder create a subfolder called data. The folder structure will now be as shown below

ouir/
    └── session01.Rmd
    └── session02.Rmd
    └── session03.Rmd    
    └── data/
        └── some data file
        └── another data file

All data you download or create go into the data folder. All R code files reside in the ouir folder.

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

(3) Now create a project via File -> New Project and choose Existing Directory. Browse to the ouir folder and click Create Project. RStudio will restart and when it does you will be in the project folder and will see a file called ouir.Rproj

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Point out that every time they start working they can click on mpa6020.Rproj and everything should work seamlessly unless something breaks

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 ouir 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.

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Emphasize the importance of the YAML YAML Ain't Markup Language
Urge patience again since some packages may have to be installed more than once, perhaps via devtools, and some may not have admin rights (the horror, the horror!!)
Show them how to knit to Word and to PDF
Tell them you will show them how to generate a slide-deck later, if anyone is interested

As the document knits, watch for error messages

... if all goes well ...

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Creating pdf output

If you need to create PDF documents, then you will need a working LaTeX setup on your machine. There are other ways to setup a LaTeX system but the easiest might be to run the following code:

install.packages('tinytex')
tinytex::install_tinytex()
# to uninstall TinyTeX, run tinytex::uninstall_tinytex()

Post-install, restart RStudio and click knit to PDF

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Specific R Markdown code block commands

Golden Rule: Unique name for each chunk (no whitespace in name). Forgot? Use namer()

library(namer)
name_chunks("myfilename.Rmd")

eval = If FALSE, knitr will not run the code in the code chunk.
include = If FALSE, knitr will run the chunk but not include the chunk in the final document.
echo = If FALSE, knitr will not display the code in the code chunk above it’s results in the final document.
error = If FALSE, knitr will not display any error messages generated by the code.
message = If FALSE, knitr will not display any messages generated by the code.
warning = If FALSE, knitr will not display any warning messages generated by the code.
cache = If TRUE, knitr caches the results to reuse in future knits until the code chunk is altered.
dev = The R function name that will be used as a graphical device to record plots, e.g. dev='CairoPDF'.
dpi = A number for knitr to use as the dots per inch (dpi) in graphics (when applicable).
fig.align = 'center', 'left', 'right' alignment in the knit document
fig.height = height of the figure (in inches, for example)
fig.width = width of the figure (in inches, for example)
out.height, out.width = The width and height to scale plots to in the final output.

Other options can be found in the cheatsheet available here

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Working with data11 / 36

Data will generally mirror one of the following types ...integer, numeric/double, character, logical, date, or a factor

library(tibble)
library(lubridate)
data_frame(
    variable1 = c(1L, 2L, 3L, 4L),
    variable2 = c(2.1, 3.4, 5.6, 7.8),
    variable4 = c("Low", "Medium", "High", "Missing"),
    variable5 = c(TRUE, FALSE, FALSE, TRUE),
    variable6 = ymd(c("2017-05-23", "1776/07/04", 
                 "1983-05/31", "1908/04-01")),
    variable7 = as.factor(c("Male", "Female", "Trans", "Trans"))
  )

## # A tibble: 4 x 6
##   variable1 variable2 variable4 variable5 variable6  variable7
##       <int>     <dbl> <chr>     <lgl>     <date>     <fct>    
## 1         1       2.1 Low       TRUE      2017-05-23 Male     
## 2         2       3.4 Medium    FALSE     1776-07-04 Female   
## 3         3       5.6 High      FALSE     1983-05-31 Trans    
## 4         4       7.8 Missing   TRUE      1908-04-01 Trans

A date variable has a very specific meaning for R; the data point must reflect a year, a month, and a day before it is deemed a valid date format

You can convert from most formats to another format (but with care)

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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) # loaded once per session 
df.csv <- read.csv("data/ImportDataCSV.csv", sep=",", header=TRUE) # note sep = ","
df.tab <- read.csv("data/ImportDataTAB.txt", sep="\t", header=TRUE) # note sep = "\t"

If the files were read then 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 csv/txt 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 ouir

Note the assignment operator... df.tab <- read.csv(...) $=$ df.tab = read.cav(...) but I prefer <-

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Point out the importance of setting the data path to data/filename.ext

Excel files can be read via the readxl package

library(readxl)
df.xls <- read_excel("data/ImportDataXLS.xls")
df.xlsx <- read_excel("data/ImportDataXLSX.xlsx")

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

library(haven)
df.stata <- read_stata("data/ImportDataStata.dta")
df.sas <- read_sas("data/ImportDataSAS.sas7bdat")
df.spss <- read_sav("data/ImportDataSPSS.sav")

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Fixed-width files: 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.

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

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

widths specifies how many slots each variable/field occupies
col.names indicates the names to be assigned to each variable/field

Now an example of an even larger fixed-width file

Download the BRFSS data from here
Extract the ascii data file and place it in your data directory
Now copy-and-paste the code I sent you via slack into a single R code-chunk
Run the code chunk

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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.)

fpe <- read.table("http://data.princeton.edu/wws509/datasets/effort.dat")
test <- read.table("https://stats.idre.ucla.edu/stat/data/test.txt", 
                  header = TRUE)
test.csv <- read.csv("https://stats.idre.ucla.edu/stat/data/test.csv", 
                    header = TRUE)
library(foreign)
hsb2.spss <- read.spss("https://stats.idre.ucla.edu/stat/data/hsb2.sav")
df.hsb2.spss <- as.data.frame(hsb2.spss)

hsb2.spss was read with the foreign package², an alternative to haven
foreign calls read.spss while haven calls read_spss

[2] 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.

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Point out that they must have an internet connection or else the file won't be read
Remind them that if the source file's URL change the file may not be read, but it is easy to check if a broken URL is the source of the error by using a browser

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)
oursasdata <- haven::read_sas(unz(temp, "pcen_v2016_y1016.sas7bdat"))
unlink(temp)

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

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

Check your data directory to confirm both files are present

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Your turn
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Your turn

(1) Read in the CSV data found here

(2) Read in the Excel data found here

Make sure you save both files with .RData extension

10:00 minutes

<div class="countdown blink-colon noupdate-15" id="timer_5fde928c" style="right:33%;bottom:0;" data-audio="true" data-warnwhen="0">
<code class="countdown-time"><span class="countdown-digits minutes">10</span><span class="countdown-digits colon">:</span><span class="countdown-digits seconds">00</span></code>
</div>

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Minimal example of data processing

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

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

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

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Notice the absence of value labels

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Value labels in base R

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

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

[3] This is just a quick run through with creating value labels; we will cover this in more detail in a later module.

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save your work!!

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

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

25 / 36

Your turn

(1) Read in the three SAS data-sets sent to you via Slack

(2) In xclass, create value labels for online_flag

0 = Not Online
1 = Online

10:00 minutes

26 / 36

Saving data, objects, and workspaces

You save your data via

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

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

data(mtcars)
save(mtcars, file = "data/mtcars.RData")
{{rm(list = ls())}}# To clear the Environment
load("data/mtcars.RData")

You can also save multiple data files as follows:

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

27 / 36

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 = "data/mydata.RDS")
rm(list = ls()) # To clear the Environment
ourdata = readRDS("data/mydata.RDS")

If instead you did the following, the file will be read with the original name even though you called it with ourdata

data(mtcars)
save(mtcars, file = "data/mtcars.RData")
rm(list = ls())  # To clear the Environment
ourdata = load("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 = "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.

28 / 36

Let them know that if not in a project and they try to close RStudio after some code has been run, they will be prompted to save (or not) the workspace and they should say "no"

Some useful housekeeping commands29 / 36

summary(dataname) will give you a snapshot of your data

glimpse(dataname) does the same if you are using the tidyverse library

dim(dataname) will give you the dimensions of the data frame

str(dataname) will give you the structure of the data frame ... each variable's type and other details

names(dataname) will give you the names of all columns as well as the column position (i.e., number)

head(dataname, x) will give you the first $x$ rows of the data frame

tail(dataname, x) will give you the last $x$ rows of the data frame

clean_names(dataname) from the janitor package will clean up messy column names (i.e., ensuring that all column names are lowercase and have no blank spaces, etc)

30 / 36

Your turn

Run each of the following commands on xstudent

names()
summary()
glimpse() # tidyverse must be loaded for this to work

to get a feel for the output

10:00 minutes

31 / 36

Calculating some basic statistics

mean(dataname$varname, na.rm = TRUE) will give you the mean of a variable

median(dataname$varname, na.rm = TRUE) will give you the median of a variable

sd(dataname$varname, na.rm = TRUE) will give you the standard deviation of a variable

var(dataname$varname, na.rm = TRUE) will give you the variance of a variable

min(dataname$varname, na.rm = TRUE) will give you the minimum of a variable

max(dataname$varname, na.rm = TRUE) will give you the maximum of a variable

quantile(dataname$varname, p = c(0.25, 0.75), na.rm = TRUE) will give you the first and third quartiles of a variable

scale(dataname$varname, na.rm = TRUE) will give you z-score of a variable

Note that na.rm = TRUE drops all cases with missing values before calculating quantities of interest. If you forget this switch you will get nothing or worse, see an error message

32 / 36

Mapping in R with leaflet

33 / 36

leaflet is an easy to learn JavaScript library that generates interactive maps

library(leaflet)
library(leaflet.extras)
library(widgetframe)
leaflet() %>% setView(lat = 39.322577, lng = -82.106336, zoom = 14) %>% 
  addTiles() %>% setMapWidgetStyle() %>%
  frameWidget(height = '275')

<div id="htmlwidget-455622c096e162c2de34" style="width:100%;height:275px;" class="widgetframe html-widget"></div>
<script type="application/json" data-for="htmlwidget-455622c096e162c2de34">{"x":{"url":"Module01_files/figure-html//widgets/widget_leaf1.html","options":{"xdomain":"*","allowfullscreen":false,"lazyload":false}},"evals":[],"jsHooks":[]}</script>

setView() centers the map with given lat/lng
zoom = applies zoom factor

34 / 36

... drop a pin on Building 21

leaflet() %>% setView(lat = 39.322577, lng = -82.106336, zoom = 15) %>% 
  addMarkers(lat = 39.319984, lng = -82.107084, popup = c("The Ridges, Building 21")) %>% 
  addTiles() %>% setMapWidgetStyle() %>%
  frameWidget(height = '325')

<div id="htmlwidget-f8fe59555e5cbaaa37ff" style="width:100%;height:325px;" class="widgetframe html-widget"></div>
<script type="application/json" data-for="htmlwidget-f8fe59555e5cbaaa37ff">{"x":{"url":"Module01_files/figure-html//widgets/widget_leaf2.html","options":{"xdomain":"*","allowfullscreen":false,"lazyload":false}},"evals":[],"jsHooks":[]}</script>

35 / 36

Questions??

Find me at...

@aruhil
aniruhil.org
ruhil@ohio.edu

36 / 36

Agenda

Install R and RStudio

First install the latest version of from here
Then install the latest version of from here

Install Rtools (for Windows) from here ... useful if you have to compile packages, which you will have to do from time to time when the package (or its latest version) is not on CRAN

Test installation

Install some packages

Understand how R Markdown works

Read data in various formats

Basic data processing and saving

Fun with leaflet

2 / 36

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