1. Open slides: bit.ly/berd_intro_r

2. Install R

• Windows:
  • Download from https://cran.rstudio.com/bin/windows/base/
• Mac OS X:
  • Download the latest .pkg file (currently R-3.6.2.pkg) from https://cran.rstudio.com/bin/macosx/

3. Install RStudio Desktop Open Source License

• Select download file corresponding to your operating system from https://www.rstudio.com/products/rstudio/download/#download

4. Download folder of data (unzip completely)

• Go to bit.ly/intro_rproj and unzip folder
• Open (double click on) berd_intro_project.Rproj file.

Questions

• Who has used R?
• What other statistical software have you used?
• Has anyone used other programming languages (C, java, python, etc)?
• Why do you want to learn R?

Learning Objectives

• Basic operations in R/RStudio
• Understand data structures
• Be able to load in data
• Basic operations on data
• Be able to make a plot
• Know how to get help

What is R?

For the history and details: Wikipedia

• an interpreted language (run it through a command line)
• procedural programming with functions
• Why "R"?? Scheme (?) inspired S (invented at Bell Labs in 1976) which inspired R (free and open source! in 1992)

What is RStudio?

Modern Dive

Start RStudio

Double click on the berd_intro_project.Rproj file.

Modern Dive

Rstudio demo

R Projects (why .Rproj file?) & Good Practices

Use projects to keep everything together (read this)

• Create an RStudio project for each data analysis project, for each homework assignment, etc.
• A project is associated with a directory folder
  • Keep data files there
  • Keep scripts there; edit them, run them in bits or as a whole
  • Save your outputs (plots and cleaned data) there
• Only use relative paths, never absolute paths
  • relative (good): read.csv("data/mydata.csv")
  • absolute (bad): read.csv("/home/yourname/Documents/stuff/mydata.csv")

Advantages of using projects

• standardizes file paths
• keep everything together
• a whole folder can be easily shared and run on another computer
• when you open the project everything is as you left it

Coding in the console

> 7

[1] 7

> 3 + 5

[1] 8

> "hello"

[1] "hello"

> # this is a comment, nothing happens
> # 5 - 8
> 
> # separate multiple commands with ;
> 3 + 5; 4 + 8

[1] 8

[1] 12

We can do math

> 10^2

[1] 100

> 3 ^ 7

[1] 2187

> 6/9

[1] 0.6666667

> 9-43

[1] -34

> 4^3-2* 7+9 /2

[1] 54.5

Logarithms and exponentials

> log(10)

[1] 2.302585

> log10(10)

[1] 1

> exp(1)

[1] 2.718282

> exp(0)

[1] 1

Check that log() is base $e$

> log(exp(1))

[1] 1

Using functions

• log() is an example of a function
• functions have "arguments"
• ?log in console will show help for log()

> mean(1:4)

[1] 2.5

> seq(1,12,3)

[1]  1  4  7 10

> mean(x = 1:4)

[1] 2.5

> seq(from = 1, to = 12, by = 3)

[1]  1  4  7 10

Variables

Data, information, everything is stored as a variable

> x = 5
> x

[1] 5

> x <- 5
> x

[1] 5

> a <- 3:10
> a

[1]  3  4  5  6  7  8  9 10

> b <- c(5, 12, 2, 100, 8)
> b

[1]   5  12   2 100   8

We can do math with variables

> x <- 5
> x

[1] 5

> x + 3

[1] 8

> y <- x^2
> y

[1] 25

> a <- 3:6
> a

[1] 3 4 5 6

> a+2; a*3

[1] 5 6 7 8

[1]  9 12 15 18

> a*a

[1]  9 16 25 36

Variable can include text (characters)

> hi <- "hello"
> hi

[1] "hello"

> greetings <- c("Guten Tag", "Hola", hi)
> greetings

[1] "Guten Tag" "Hola"      "hello"

Missing values

Missing values are denoted as NA and are handled differently depending on the operation. There are special functions for NA (i.e. is.na(), na.omit()).

> x <- c(1, 2, NA, 5)
> is.na(x)

[1] FALSE FALSE  TRUE FALSE

> mean(x)

[1] NA

> mean(x, na.rm=TRUE)

[1] 2.666667

> x <- c("a", "a", NA, "b")
> table(x)

x
a b 
2 1

> table(x, useNA = "always")

x
   a    b <NA> 
   2    1    1

Viewing list of defined variables

• ls() is the R command to see what objects have been defined.
• This list includes all defined objects (including dataframes, functions, etc.)

> ls()

[1] "a"         "b"         "greetings" "hi"        "x"         "y"

• You can also look at the list in the Environment window:

Removing defined variables

• The R command to delete an object is rm().

> ls()

[1] "a"         "b"         "greetings" "hi"        "x"         "y"

> rm("greetings", hi)  # Can run with or without quotes
> ls()

[1] "a" "b" "x" "y"

• Remove EVERYTHING - Be careful!!

> rm(list=ls())
> ls()

character(0)

• Can also remove everything using the Clear Workspace option in the Session menu.

Common console errors (1/2)

Incomplete commands

> 3 + (2*6
+ )

[1] 15

Common console errors (2/2)

Object is not found

• This happens when text is entered for a non-existent variable (object)

Example:

> hello

Error in eval(expr, envir, enclos): object 'hello' not found

• Can be due to missing quotes

> install.packages(dplyr) # need install.packages("dplyr")

Error in install.packages(dplyr): object 'dplyr' not found

Coding in a script (1/3)

• Create a new script by
  • selecting File -> New File -> R Script,
  • or clicking on (the left most button at the top of the scripting window), and then selecting the first option R Script

• Type code in the script
  • Type each R command on its own line
  • Use # to convert text to comments so that text doesn't accidentally get executed as an R command

Coding in a script (2/3)

• Select code you want to execute, by
  • placing the cursor in the line of code you want to execute,
  • or highlighting the code you want to execute

• Execute code in the script, by
  • clicking on the button in the top right corner of the scripting window,
  • or typing one of the following key combinations to execute the code
    • Windows: ctrl + return
    • Mac: command + return

Coding in a script (3/3)

• The screenshot below shows code in the scripting window (top left window)
• The executed highlighted code and its output appear in the console window (bottom left window)

Useful keyboard shortcuts

y <- 5
y

Others: (see full list)

Saving a script

• Save a script by

  • selecting File -> Save,
  • or clicking on (towards the left above the scripting window)

• You will need to specify

  • a filename to save the script as
    • ALWAYS use .R as the filename extension for R scripts
  • the folder to save the script in

Practice 1

1. Open a new R script and type code/answers for next tasks in it. Save as Practice1.R

2. Create a vector of all integers from 4 to 10, and save it as a1.

3. Create a vector of even integers from 4 to 10, and save it as a2.

4. What is the sum of a1 and a2?

5. What does the command sum(a1) do?

6. What does the command length(a1) do?

7. Use the sum and length commands to calculate the average of the values in a1.

8. Compute the sum of all integers from 1 to 100. Then compare your answer to the one you get using the formula for sum of the first $n$ integers: $n(n+1)/2$.

9. Compute the sum of the squares of all integers from 1 to 100.

10. Take a break!

Data frames

df <- data.frame(
  IDs=1:3, 
  gender=c("male", "female", "Male"), 
  age=c(28, 35.5, 31),
  trt = c("control", "1", "1"),
  Veteran = c(FALSE, TRUE, TRUE)
  )
df

##   IDs gender  age     trt Veteran
## 1   1   male 28.0 control   FALSE
## 2   2 female 35.5       1    TRUE
## 3   3   Male 31.0       1    TRUE

Variable (column) types

• View the structure of our data frame to see what the variable types are:

str(df)

## 'data.frame':    3 obs. of  5 variables:
##  $ IDs    : int  1 2 3
##  $ gender : Factor w/ 3 levels "female","male",..: 2 1 3
##  $ age    : num  28 35.5 31
##  $ trt    : Factor w/ 2 levels "1","control": 2 1 1
##  $ Veteran: logi  FALSE TRUE TRUE

Data frame cells, rows, or columns

df

##   IDs gender  age     trt Veteran
## 1   1   male 28.0 control   FALSE
## 2   2 female 35.5       1    TRUE
## 3   3   Male 31.0       1    TRUE

# Second row, Third column
df[2, 3]

## [1] 35.5

# Third column
df[, 3]

## [1] 28.0 35.5 31.0

# Second row
df[2,]

##   IDs gender  age trt Veteran
## 2   2 female 35.5   1    TRUE

Load a data set

• Read in csv file from file path with code (filepath relative to Rproj directory)

mydata <- read.csv("data/yrbss_demo.csv")

• Or, open saved file using Import Dataset button in Environment window: .
  • If you use this option, then copy and paste the importing code to your script so that you have a record of from where and how you loaded the data set.

View(mydata) 
# Can also view the data by clicking on its name in the Environment tab

About the data

Data from the CDC's Youth Risk Behavior Surveillance System (YRBSS)

• small subset (20 rows) of the full complex survey data
• national school-based survey conducted by CDC and state, territorial, tribal, and local surveys conducted by state, territorial, and local education and health agencies and tribal governments
• monitors health-related behaviors (including alcohol & drug use, unhealthy & dangerous behaviors, sexuality, physical activity); see Questionnaires
• original data in the R package yrbss which includes YRBSS from 1991-2013

Data set summary

summary(mydata)

##        id                             age        sex      grade  
##  Min.   : 335340   14 years old         :1   Female:12   10th:8  
##  1st Qu.: 925193   15 years old         :4   Male  : 8   11th:4  
##  Median :1207132   16 years old         :7               12th:4  
##  Mean   :1093150   17 years old         :7               9th :4  
##  3rd Qu.:1313188   18 years old or older:1                       
##  Max.   :1316123                                                 
##                        race4        bmi          weight_kg    
##  All other races          :5   Min.   :17.48   Min.   :43.09  
##  Black or African American:3   1st Qu.:20.36   1st Qu.:57.27  
##  Hispanic/Latino          :6   Median :22.23   Median :64.86  
##  White                    :4   Mean   :23.01   Mean   :64.09  
##  NA's                     :2   3rd Qu.:26.58   3rd Qu.:70.31  
##                                Max.   :29.35   Max.   :84.82  
##                       text_while_driving_30d smoked_ever bullied_past_12mo
##  0 days                          : 5         No  :10     Mode :logical    
##  1 or 2 days                     : 2         Yes : 6     FALSE:11         
##  3 to 5 days                     : 1         NA's: 4     TRUE :7          
##  All 30 days                     : 1                     NA's :2          
##  I did not drive the past 30 days: 1                                      
##  NA's                            :10

Data set info

dim(mydata)

## [1] 20 10

nrow(mydata)

## [1] 20

ncol(mydata)

## [1] 10

names(mydata)

##  [1] "id"                     "age"                    "sex"                   
##  [4] "grade"                  "race4"                  "bmi"                   
##  [7] "weight_kg"              "text_while_driving_30d" "smoked_ever"           
## [10] "bullied_past_12mo"

Data structure

• What are the different variable types in this data set?

str(mydata)   # structure of data

## 'data.frame':    20 obs. of  10 variables:
##  $ id                    : int  335340 638618 922382 923122 923963 925603 933724 935435 1096564 1108114 ...
##  $ age                   : Factor w/ 5 levels "14 years old",..: 4 3 1 2 2 3 3 4 2 4 ...
##  $ sex                   : Factor w/ 2 levels "Female","Male": 1 1 2 2 2 2 1 1 2 1 ...
##  $ grade                 : Factor w/ 4 levels "10th","11th",..: 1 4 4 4 1 1 1 3 1 4 ...
##  $ race4                 : Factor w/ 4 levels "All other races",..: 4 NA 4 4 2 1 1 1 1 2 ...
##  $ bmi                   : num  27.6 29.3 18.2 21.4 19.6 ...
##  $ weight_kg             : num  66.2 84.8 57.6 60.3 63.5 ...
##  $ text_while_driving_30d: Factor w/ 5 levels "0 days","1 or 2 days",..: NA NA NA NA NA NA NA NA NA NA ...
##  $ smoked_ever           : Factor w/ 2 levels "No","Yes": NA 2 2 2 1 1 2 1 NA 1 ...
##  $ bullied_past_12mo     : logi  NA NA FALSE FALSE TRUE TRUE ...

View the beginning of a data set

head(mydata)

##       id          age    sex grade                     race4     bmi weight_kg
## 1 335340 17 years old Female  10th                     White 27.5671     66.23
## 2 638618 16 years old Female   9th                      <NA> 29.3495     84.82
## 3 922382 14 years old   Male   9th                     White 18.1827     57.61
## 4 923122 15 years old   Male   9th                     White 21.3754     60.33
## 5 923963 15 years old   Male  10th Black or African American 19.5988     63.50
## 6 925603 16 years old   Male  10th           All other races 22.1910     70.31
##   text_while_driving_30d smoked_ever bullied_past_12mo
## 1                   <NA>        <NA>                NA
## 2                   <NA>         Yes                NA
## 3                   <NA>         Yes             FALSE
## 4                   <NA>         Yes             FALSE
## 5                   <NA>          No              TRUE
## 6                   <NA>          No              TRUE

View the end of a data set

tail(mydata)

##         id                   age    sex grade                     race4     bmi
## 15 1313153          16 years old Female  11th           Hispanic/Latino 26.5781
## 16 1313291          16 years old Female  11th                     White 24.8047
## 17 1313477          16 years old Female  10th           All other races 25.0318
## 18 1315121          17 years old Female  11th                      <NA> 22.2687
## 19 1315850          17 years old Female  12th           Hispanic/Latino 19.4922
## 20 1316123 18 years old or older Female  12th Black or African American 27.4894
##    weight_kg           text_while_driving_30d smoked_ever bullied_past_12mo
## 15     68.04                           0 days          No              TRUE
## 16     63.50                      3 to 5 days          No             FALSE
## 17     76.66                           0 days          No              TRUE
## 18     54.89 I did not drive the past 30 days         Yes             FALSE
## 19     49.90                           0 days        <NA>             FALSE
## 20     74.84                      All 30 days         Yes             FALSE

Specify how many rows to view at beginning or end of a data set

head(mydata, 3)

##       id          age    sex grade race4     bmi weight_kg
## 1 335340 17 years old Female  10th White 27.5671     66.23
## 2 638618 16 years old Female   9th  <NA> 29.3495     84.82
## 3 922382 14 years old   Male   9th White 18.1827     57.61
##   text_while_driving_30d smoked_ever bullied_past_12mo
## 1                   <NA>        <NA>                NA
## 2                   <NA>         Yes                NA
## 3                   <NA>         Yes             FALSE

tail(mydata, 1)

##         id                   age    sex grade                     race4     bmi
## 20 1316123 18 years old or older Female  12th Black or African American 27.4894
##    weight_kg text_while_driving_30d smoked_ever bullied_past_12mo
## 20     74.84            All 30 days         Yes             FALSE

The $

Suppose we want to single out the column of BMI values.

• How did we previously learn to do this?

mydata[, 6]

##  [1] 27.5671 29.3495 18.1827 21.3754 19.5988 22.1910 20.9913 17.4814 22.4593
## [10] 26.5781 21.1874 19.4637 20.6121 27.4648 26.5781 24.8047 25.0318 22.2687
## [19] 19.4922 27.4894

The problem with this method, is that we need to know the column number which can change as we make changes to the data set.

• Use the $ instead: DatSetName$VariableName

mydata$bmi

##  [1] 27.5671 29.3495 18.1827 21.3754 19.5988 22.1910 20.9913 17.4814 22.4593
## [10] 26.5781 21.1874 19.4637 20.6121 27.4648 26.5781 24.8047 25.0318 22.2687
## [19] 19.4922 27.4894

Basic plots of numeric data: Histogram

hist(mydata$bmi)

With extra features:

hist(mydata$bmi, xlab = "BMI", main="BMIs of students")

Basic plots of numeric data: Boxplot

boxplot(mydata$bmi)

boxplot(mydata$bmi ~ mydata$sex, 
  horizontal = TRUE, 
  xlab = "BMI", ylab = "sex",
  main = "BMIs of students by sex")

Basic plots of numeric data: Scatterplot

plot(mydata$weight_kg, mydata$bmi)

plot(mydata$weight_kg, mydata$bmi, 
   xlab = "weight (kg)", ylab = "BMI", 
   main = "BMI vs. Weight")

Summary stats of numeric data (1/2)

• Standard R summary command

summary(mydata$bmi)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   17.48   20.36   22.23   23.01   26.58   29.35

• Mean and standard deviation

mean(mydata$bmi)

## [1] 23.00838

sd(mydata$bmi)

## [1] 3.56471

Summary stats of numeric data (2/2)

• Min, max, & median

min(mydata$bmi)

## [1] 17.4814

max(mydata$bmi)

## [1] 29.3495

median(mydata$bmi)

## [1] 22.22985

• Quantiles

quantile(mydata$bmi, prob=c(0, .25, .5, .75, 1))

##       0%      25%      50%      75%     100% 
## 17.48140 20.35878 22.22985 26.57810 29.34950

Add height column to data frame

Since $\text{BMI}=\frac{kg}{m^2}$, we have $\text{height}\left(m\right)=\sqrt{\frac{\text{weight}\left(kg\right)}{\text{BMI}}}$

mydata$height_m <- sqrt( mydata$weight_kg / mydata$bmi )
mydata$height_m

##  [1] 1.550000 1.699999 1.779999 1.680001 1.799998 1.780000 1.469998 1.570002
##  [9] 1.879998 1.600001 1.779998 1.699999 1.730001 1.600001 1.600001 1.600000
## [17] 1.750001 1.569998 1.599999 1.650001

dim(mydata)

## [1] 20 11

names(mydata)

##  [1] "id"                     "age"                    "sex"                   
##  [4] "grade"                  "race4"                  "bmi"                   
##  [7] "weight_kg"              "text_while_driving_30d" "smoked_ever"           
## [10] "bullied_past_12mo"      "height_m"

Access specific columns in data set

mydata[, c(2, 6)] # 2nd & 6th columns

##                      age     bmi
## 1           17 years old 27.5671
## 2           16 years old 29.3495
## 3           14 years old 18.1827
## 4           15 years old 21.3754
## 5           15 years old 19.5988
## 6           16 years old 22.1910
## 7           16 years old 20.9913
## 8           17 years old 17.4814
## 9           15 years old 22.4593
## 10          17 years old 26.5781
## 11          16 years old 21.1874
## 12          17 years old 19.4637
## 13          17 years old 20.6121
## 14          15 years old 27.4648
## 15          16 years old 26.5781
## 16          16 years old 24.8047
## 17          16 years old 25.0318
## 18          17 years old 22.2687
## 19          17 years old 19.4922
## 20 18 years old or older 27.4894

mydata[, c("age", "bmi")]

##                      age     bmi
## 1           17 years old 27.5671
## 2           16 years old 29.3495
## 3           14 years old 18.1827
## 4           15 years old 21.3754
## 5           15 years old 19.5988
## 6           16 years old 22.1910
## 7           16 years old 20.9913
## 8           17 years old 17.4814
## 9           15 years old 22.4593
## 10          17 years old 26.5781
## 11          16 years old 21.1874
## 12          17 years old 19.4637
## 13          17 years old 20.6121
## 14          15 years old 27.4648
## 15          16 years old 26.5781
## 16          16 years old 24.8047
## 17          16 years old 25.0318
## 18          17 years old 22.2687
## 19          17 years old 19.4922
## 20 18 years old or older 27.4894

Access specific rows in data set

• Rows for 14 year olds only

mydata[mydata$age == "14 years old",] # 1 row since there is only one 14 year old

##       id          age  sex grade race4     bmi weight_kg text_while_driving_30d
## 3 922382 14 years old Male   9th White 18.1827     57.61                   <NA>
##   smoked_ever bullied_past_12mo height_m
## 3         Yes             FALSE 1.779999

• Rows for teens with BMI less than 19

mydata[mydata$bmi < 19,]

##       id          age    sex grade           race4     bmi weight_kg
## 3 922382 14 years old   Male   9th           White 18.1827     57.61
## 8 935435 17 years old Female  12th All other races 17.4814     43.09
##   text_while_driving_30d smoked_ever bullied_past_12mo height_m
## 3                   <NA>         Yes             FALSE 1.779999
## 8                   <NA>          No             FALSE 1.570002

Access specific values in data set

• Grade and race for 15 year olds only

mydata[mydata$age == "15 years old", c("age", "grade", "race4")]

##             age grade                     race4
## 4  15 years old   9th                     White
## 5  15 years old  10th Black or African American
## 9  15 years old  10th           All other races
## 14 15 years old  10th           Hispanic/Latino

• Age, sex, and BMI for students with BMI less than 19

mydata[mydata$bmi < 19, c("age", "sex", "bmi")]

##            age    sex     bmi
## 3 14 years old   Male 18.1827
## 8 17 years old Female 17.4814

Practice 2

1. Create a new script and save it as Practice2.R

2. Create data frames for males and females separately.

3. Do males and females have similar BMIs? Weights? Compares means, standard deviations, range, and boxplots.

4. Plot BMI vs. weight for each gender separately. Do they have similar relationships?

5. Are males or females more likely to be bullied in the past 12 months? Calculate the percentage bullied for each gender.

Save data frame

• Save .RData file: the standard R format, which is recommended if saving data for future use in R

save(mydata, file = "data/mydata.RData")  # saving mydata within the data folder

You can load .RData files using the load() command:

load("data/mydata.RData")

• Save csv file: comma-separated values

write.csv(mydata, file = "data/mydata.csv", col.names = TRUE, row.names = FALSE)

Installing and using packages

• Packages are to R like apps are to your phone/OS
• Packages contain additional functions and data
• Install packages with install.packages()
  • Also can use the "Packages" tab in Files/Plots/Packages/Help/Viewer window
  • Only install once (unless you want to update)
  • Installs from Comprehensive R Archive Network (CRAN) = package mothership

install.packages("dplyr")   # only do this ONCE, use quotes

• Load packages: At the top of your script include library() commands to load each required package every time you open Rstudio.

library(dplyr)    # run this every time you open Rstudio

• Use a function without loading the package with ::

dplyr::arrange(mydata, bmi)

##         id                   age    sex grade                     race4     bmi
## 1   935435          17 years old Female  12th           All other races 17.4814
## 2   922382          14 years old   Male   9th                     White 18.1827
## 3  1307481          17 years old   Male  12th           Hispanic/Latino 19.4637
## 4  1315850          17 years old Female  12th           Hispanic/Latino 19.4922
## 5   923963          15 years old   Male  10th Black or African American 19.5988
## 6  1307872          17 years old   Male  11th           Hispanic/Latino 20.6121
## 7   933724          16 years old Female  10th           All other races 20.9913
## 8  1306150          16 years old   Male  10th           Hispanic/Latino 21.1874
## 9   923122          15 years old   Male   9th                     White 21.3754
## 10  925603          16 years old   Male  10th           All other races 22.1910
## 11 1315121          17 years old Female  11th                      <NA> 22.2687
## 12 1096564          15 years old   Male  10th           All other races 22.4593
## 13 1313291          16 years old Female  11th                     White 24.8047
## 14 1313477          16 years old Female  10th           All other races 25.0318
## 15 1108114          17 years old Female   9th Black or African American 26.5781
## 16 1313153          16 years old Female  11th           Hispanic/Latino 26.5781
## 17 1311617          15 years old Female  10th           Hispanic/Latino 27.4648
## 18 1316123 18 years old or older Female  12th Black or African American 27.4894
## 19  335340          17 years old Female  10th                     White 27.5671
## 20  638618          16 years old Female   9th                      <NA> 29.3495
##    weight_kg           text_while_driving_30d smoked_ever bullied_past_12mo
## 1      43.09                             <NA>          No             FALSE
## 2      57.61                             <NA>         Yes             FALSE
## 3      56.25                      1 or 2 days          No             FALSE
## 4      49.90                           0 days        <NA>             FALSE
## 5      63.50                             <NA>          No              TRUE
## 6      61.69                      1 or 2 days          No             FALSE
## 7      45.36                             <NA>         Yes              TRUE
## 8      67.13                           0 days        <NA>             FALSE
## 9      60.33                             <NA>         Yes             FALSE
## 10     70.31                             <NA>          No              TRUE
## 11     54.89 I did not drive the past 30 days         Yes             FALSE
## 12     79.38                             <NA>        <NA>              TRUE
## 13     63.50                      3 to 5 days          No             FALSE
## 14     76.66                           0 days          No              TRUE
## 15     68.04                             <NA>          No             FALSE
## 16     68.04                           0 days          No              TRUE
## 17     70.31                           0 days          No              TRUE
## 18     74.84                      All 30 days         Yes             FALSE
## 19     66.23                             <NA>        <NA>                NA
## 20     84.82                             <NA>         Yes                NA
##    height_m
## 1  1.570002
## 2  1.779999
## 3  1.699999
## 4  1.599999
## 5  1.799998
## 6  1.730001
## 7  1.469998
## 8  1.779998
## 9  1.680001
## 10 1.780000
## 11 1.569998
## 12 1.879998
## 13 1.600000
## 14 1.750001
## 15 1.600001
## 16 1.600001
## 17 1.600001
## 18 1.650001
## 19 1.550000
## 20 1.699999

Installing packages from other places (i.e. github, URLs)

• Need to have remotes package installed first:

install.packages("remotes")

• To install a package from github (often in development) use install_github() from the remotes package

# https://github.com/hadley/yrbss
remotes::install_github("hadley/yrbss")
# Load it the same way
library(yrbss)

How to get help (1/2)

Use ? in front of function name in console. Try this:

How to get help (2/2)

• Use ?? (i.e ??dplyr or ??read_csv) for searching all documentation in installed packages (including unloaded packages)
• search Stack Overflow #r tag
• googlequestion + rcran or + r (i.e. "make a boxplot rcran" "make a boxplot r")
• google error in quotes (i.e. "Evaluation error: invalid type (closure) for variable '***'")
• search github for your function name (to see examples) or error
• Rstudio community
• twitter #rstats

Resources

• Click on this List of resources for learning R
• Watch recordings of our other workshops
• Highly recommend Data Wrangling in R with Tidyverse

Getting started:

• RStudio IDE Cheatsheet
• Install R/RStudio help video
• Basic Basics

Some of this is drawn from materials in online books/lessons:

• Intro to R/RStudio by Emma Rand
• Modern Dive - An Introduction to Statistical and Data Sciences via R by Chester Ismay & Albert Kim
• Cookbook for R by Winston Chang

Local resources

Allison Horst

Contact info:

Jessica Minnier: minnier@ohsu.edu

Meike Niederhausen: niederha@ohsu.edu

This workshop info:

• Code for these slides on github: jminnier/berd_r_courses
• all the R code in an R script
• answers to practice problems can be found here: html, pdf
• The project folder of examples can be downloaded at github.com/jminnier/berd_intro_project & the solutions are in the solns/ folder.