What is R?

R is the language of data analysis.

-It works with objects and functions.

-Its basic functions are similar to what you can do in Excel.

-But it has almost every statistical and data analytics tool available.

-It easily allows people to create their own functions and share them with the world.

-It is free, available on any operating system and growing in popularity in business.

-It can be hard to learn at first, but it gets easier the more you use it.

Objects allow things to be created and stored.

Objects allow things to be created and stored.

They can be variables,

x <- 5
x + 1

## [1] 6

Objects allow things to be created and stored.

They can be variables,

x <- 5
x + 1

## [1] 6

or they can be vectors, matrices, and entire datasets.

y <- c(1, 4, 2, 6, 5, 0)
y

## [1] 1 4 2 6 5 0

z <- matrix(y, nrow = 2, ncol = 3)
z

##      [,1] [,2] [,3]
## [1,]    1    2    5
## [2,]    4    6    0

Objects allow things to be created and stored.

They can be variables,

x <- 5
x + 1

## [1] 6

or they can be vectors, matrices, and entire datasets.

y <- c(1, 4, 2, 6, 5, 0)
y

## [1] 1 4 2 6 5 0

z <- matrix(y, nrow = 2, ncol = 3)
z

##      [,1] [,2] [,3]
## [1,]    1    2    5
## [2,]    4    6    0

An object can be almost everything!

Functions are a set of instructions to turn an input into an output.

myMean <- function(x){
  N <- length(x)
  mean <- sum(x) / N
  return(mean)
}
x <- c(4, 1, 6, -3, 7, 1)
myMean(x)

## [1] 2.666667

Functions are a set of instructions to turn an input into an output.

myMean <- function(x){
  N <- length(x)
  mean <- sum(x) / N
  return(mean)
}
x <- c(4, 1, 6, -3, 7, 1)
myMean(x)

## [1] 2.666667

It's easy to give them multiple inputs.

addAndDouble <- function(x, y){
  2 * (x + y)
}
addAndDouble(5, 2)

## [1] 14

R comes with many built in functions

x <- c(4, 1, 6, -3, 7, 1)
mean(x)

## [1] 2.666667

R comes with many built in functions

x <- c(4, 1, 6, -3, 7, 1)
mean(x)

## [1] 2.666667

max(x)

## [1] 7

R comes with many built in functions

x <- c(4, 1, 6, -3, 7, 1)
mean(x)

## [1] 2.666667

max(x)

## [1] 7

sd(x)

## [1] 3.723797

R comes with many built in functions

x <- c(4, 1, 6, -3, 7, 1)
mean(x)

## [1] 2.666667

max(x)

## [1] 7

sd(x)

## [1] 3.723797

cor(x, c(0, -5, 1, 3, 11, 2))

## [1] 0.4016281

R comes with many built in functions

x <- c(4, 1, 6, -3, 7, 1)
mean(x)

## [1] 2.666667

max(x)

## [1] 7

sd(x)

## [1] 3.723797

cor(x, c(0, -5, 1, 3, 11, 2))

## [1] 0.4016281

sort(x)

## [1] -3  1  1  4  6  7

R can do all of the regression you would do in eViews, Excel, Stata, SAS etc.

x <- rnorm(1000)
y <- 2 + 3*x + rnorm(1000, sd = 0.1)
linReg <- lm(y ~ x)
summary(linReg)

## 
## Call:
## lm(formula = y ~ x)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.35405 -0.06416 -0.00191  0.06639  0.28754 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 2.002005   0.003126   640.5   <2e-16 ***
## x           3.002633   0.003196   939.5   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.09884 on 998 degrees of freedom
## Multiple R-squared:  0.9989,    Adjusted R-squared:  0.9989 
## F-statistic: 8.826e+05 on 1 and 998 DF,  p-value: < 2.2e-16

There are thousands of packages people have written with extra functions and data.

The dataset economics is part of the ggplot2 package and the function glimpse is part of the dplyr package. Install these once per computer.

install.packages("ggplot2")
install.packages("dplyr")

There are thousands of packages people have written with extra functions and data.

The dataset economics is part of the ggplot2 package and the function glimpse is part of the dplyr package. Install these once per computer.

install.packages("ggplot2")
install.packages("dplyr")

Then use library(ggplot2) and library(dplyr) to load their contents into R.

library(ggplot2)
library(dplyr)
data(economics, package="ggplot2")

There are thousands of packages people have written with extra functions and data.

The dataset economics is part of the ggplot2 package and the function glimpse is part of the dplyr package. Install these once per computer.

install.packages("ggplot2")
install.packages("dplyr")

Then use library(ggplot2) and library(dplyr) to load their contents into R.

library(ggplot2)
library(dplyr)
data(economics, package="ggplot2")

Now we have access to everything in dplyr and ggplot2 until you close R!

Now we can look at this economics data

glimpse(economics)

## Observations: 574
## Variables: 6
## $ date     <date> 1967-07-01, 1967-08-01, 1967-09-01, 1967-10-01, 1967...
## $ pce      <dbl> 507.4, 510.5, 516.3, 512.9, 518.1, 525.8, 531.5, 534....
## $ pop      <int> 198712, 198911, 199113, 199311, 199498, 199657, 19980...
## $ psavert  <dbl> 12.5, 12.5, 11.7, 12.5, 12.5, 12.1, 11.7, 12.2, 11.6,...
## $ uempmed  <dbl> 4.5, 4.7, 4.6, 4.9, 4.7, 4.8, 5.1, 4.5, 4.1, 4.6, 4.4...
## $ unemploy <int> 2944, 2945, 2958, 3143, 3066, 3018, 2878, 3001, 2877,...

Alternatively, you can use any of these to get an idea about what the dataset looks like

head(economics)
tail(economics)
summary(economics)
str(economics)

Histogram

# histogram
ggplot(data = economics) +
  geom_histogram(aes(pce))

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

Scatterplot

ggplot(data= economics) +
  geom_point(aes(x = psavert, y = pce))

If you're ever stuck on how to use a function, you can get help with ?

?mean
?ggplot

There's also tonnes of online resources available,

• https://stackoverflow.com/ is a great resource for programming help

• https://cran.r-project.org/ has documentation on almost every function

• https://www.datacamp.com/ has nice interactive courses on learning R.