Introduction to Loops in R

Using for(i in x) is an efficient way to repeatedly execute a code for i number of iterations. In this recitation, we will (1) execute basic loops, (2) create basic functions and (3) make some plots using ggplot

Relevant functions: set.seed(), rnorm(), for(i in x), sample(), cat(), print(), replicate(), sqrt(), prod().

Load the lfpog.csv in R.

setwd("~/Google Drive/Penn/TA/Intro to DS/Rk_Recitation/Data")
lfp <- read.csv("lfpog.csv")

0. Why use loops?

print(paste("The year is", 2010))

## [1] "The year is 2010"

print(paste("The year is", 2011))

## [1] "The year is 2011"

print(paste("The year is", 2012))

## [1] "The year is 2012"

print(paste("The year is", 2013))

## [1] "The year is 2013"

print(paste("The year is", 2014))

## [1] "The year is 2014"

print(paste("The year is", 2015))

## [1] "The year is 2015"

Instead of writing 6 lines of code iteratively, you can create a loop which will do this for you

for (year in c(2010,2011,2012,2013,2014,2015)){
  print(paste("The year is", year))
}

## [1] "The year is 2010"
## [1] "The year is 2011"
## [1] "The year is 2012"
## [1] "The year is 2013"
## [1] "The year is 2014"
## [1] "The year is 2015"

1. Generating Data

1.1 Creating a random normal distribution using `rnorm()`

set.seed(150) # Setting the seed for replication purposes

myData <- rnorm(1000,45,15) # Creating a random normal distribution (n=1000, mean=45, sd=15)

1.2 Performing sanity checks on myData using `length()`, `mean()` and `sd()`

length(myData) # How many observations?

## [1] 1000

mean(myData) # What is the mean?

## [1] 44.52407

sd(myData) # What is the standard deviation?

## [1] 14.85105

2. Basic Loops with `for(i in x)`

2.1 Printing 5 observations from myData using `sample()` and `print()`

set.seed(300) # Setting the seed for replication purposes

for (i in 1:5) # Specifying the number of iterations 
{
    obs <- sample(myData,size=1) # Sampling one observation from the myData vector and storing it into the "obs" object
    print(obs) # Printing the value of that "obs" object
    cat("I have finished", i,"iterations \n") # Printing a string of characters after each iteration
}

## [1] 44.06057
## I have finished 1 iterations 
## [1] 32.19679
## I have finished 2 iterations 
## [1] 34.89336
## I have finished 3 iterations 
## [1] 41.51256
## I have finished 4 iterations 
## [1] 63.42954
## I have finished 5 iterations

2.2 Creating a vector with the square of 5 observations from myData using `sample()` and `sqrt()`

set.seed(300) # Setting the seed for replication purposes

results <- c() # Creating an empty vector to hold the results

for (i in 1:5) # Specifying the number of iterations 
{
    obs <- sample(myData,size=1) # Sampling one observation from the myData vector and storing it into the "obs" object
    results[i] <- (obs)^2 # Calculating the square root of that "obs" object and storing it into the "results" vector
    cat("The square of", obs, "is", results[i],"\n") # Printing a string of characters after each iteration
}

## The square of 44.06057 is 1941.334 
## The square of 32.19679 is 1036.633 
## The square of 34.89336 is 1217.546 
## The square of 41.51256 is 1723.292 
## The square of 63.42954 is 4023.307

2.3 Creating a vector with the mean of 5 pairs of observations from myData using `sample()` and `mean()`

set.seed(300) # Setting the seed for replication purposes

results <- c() # Creating an empty vector to hold the results

for (i in 1:5)
{
    obs <- sample(myData,size=2) # Sampling two observations from the myData vector and storing it into the "obs" object
    results[i] <- mean(obs) # Calculating the sum of the elements encompassed within the "obs" object and storing it into the "results" vector
    cat("The mean of", obs[1], "and", obs[2], "is", results[i],"\n") # Printing a string of characters after each iteration
}

## The mean of 44.06057 and 32.19679 is 38.12868 
## The mean of 34.89336 and 41.51256 is 38.20296 
## The mean of 63.42954 and 30.3382 is 46.88387 
## The mean of 59.36095 and 32.10175 is 45.73135 
## The mean of 59.5734 and 72.12265 is 65.84803

summary(results)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   38.13   38.20   45.73   46.96   46.88   65.85

Plotting the cumulative mean

plot(results,type="l",ylab="Cumulative Mean",xlab="",lwd=2,ylim=c(30,60))
abline(h=mean(results,na.rm=T),col="red",lty=2)

3. Using ggplot

3.1 Creating a blank graph

options(scipen=999)  # turn off scientific notation like 1e+06
library(ggplot2)

# Init Ggplot
ggplot(lfp, aes(x=age, y=inc))

### 3.2 Creating a scatter plot

With these commands you have now created a blank graph with the x and y axis. Now, we need to add points/bars/lines to it.

library(ggplot2)
g<- ggplot(lfp, aes(x=age, y=inc)) + geom_point()

g

###### Now, you can also add a smoothin line to it

library(ggplot2)
g<- g + geom_smooth(method="lm")
g

### 3.3 Changing the axis limits and re-naming the axis

g2<- g + xlim(c(0, 100)) + ylim(c(0, 200)) + labs(title="Relationship between age and income", subtitle="From LFP survey", y="Income in 000s", x="Age of Respondents")
g2

## Warning: Removed 1 rows containing non-finite values (stat_smooth).

## Warning: Removed 1 rows containing missing values (geom_point).

### 3.4 Changing the theme of the graph

g3<- g + theme_bw() + labs(title="Relationship between age and income", subtitle="BW Theme", y="Income in 000s", x="Age of Respondents") 
g4<- g + theme_classic() + labs(title="Relationship between age and income", subtitle="Classic Theme", y="Income in 000s", x="Age of Respondents")
g4

g3

3.5 Changing colours and size of points

g3 + geom_point(aes(col=age), size=3) +  # Set color to vary based on age 
  geom_smooth(method="lm", col="firebrick", size=2)

# Changing the color of ALL the points to red irrespective of age
g3 + geom_point(col = "red", size=1.5) +  # Set color to vary based on age 
  geom_smooth(method="lm", col="steelblue", size=2)

3.6 Creating a barplot

Let’s create the bar plot of the mean income of employed and unemployed women in the survey

library(dplyr)

## 
## Attaching package: 'dplyr'

## The following objects are masked from 'package:stats':
## 
##     filter, lag

## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union

lfp %>% group_by(lfp) %>% summarise(mean(inc,na.rm=T))

## # A tibble: 2 x 2
##   lfp   `mean(inc, na.rm = T)`
##   <fct>                  <dbl>
## 1 no                      21.7
## 2 yes                     18.9

lfp.yn <- as.data.frame(lfp %>% group_by(lfp) %>% summarise(mean(inc,na.rm=T)))
colnames(lfp.yn) <- c("Labour_Force_Participation", "Mean_Income")

ggplot(lfp.yn, aes(Labour_Force_Participation,Mean_Income)) +
  geom_bar(stat="identity", fill="steelblue",width=0.5)+
  geom_text(aes(label=Mean_Income), vjust=-0.3, size=3.5)+
  theme_minimal()

## coord flip
ggplot(lfp.yn, aes(Labour_Force_Participation,Mean_Income)) +
  geom_bar(stat="identity", fill="steelblue",width=0.5)+
  geom_text(aes(label=Mean_Income), vjust=-0.3, size=3.5)+
  theme_minimal() + coord_flip()

Exercise

Using the lfpog.csv data 4 samples of size 5 and calculate the mean income of these respondents.
Store this in a vector and then print them out in order
Calculate the cumulative mean and plot it
Create a bar plot of the mean income of respondents whose husbands work versus those who do not.

PSCI 107 - Recitation #6

Rithika Kumar (adapted from Evelyne Brie)

Fall 2018

Introduction to Loops in R

0. Why use loops?

1. Generating Data

1.1 Creating a random normal distribution using `rnorm()`

1.2 Performing sanity checks on myData using `length()`, `mean()` and `sd()`

2. Basic Loops with `for(i in x)`

2.1 Printing 5 observations from myData using `sample()` and `print()`

2.2 Creating a vector with the square of 5 observations from myData using `sample()` and `sqrt()`

2.3 Creating a vector with the mean of 5 pairs of observations from myData using `sample()` and `mean()`

3. Using ggplot

3.1 Creating a blank graph

3.5 Changing colours and size of points

3.6 Creating a barplot

Exercise

PSCI 107 - Recitation #6

Rithika Kumar (adapted from Evelyne Brie)

Fall 2018

Introduction to Loops in R

0. Why use loops?

1. Generating Data

1.1 Creating a random normal distribution using rnorm()

1.2 Performing sanity checks on myData using length(), mean() and sd()

2. Basic Loops with for(i in x)

2.1 Printing 5 observations from myData using sample() and print()

2.2 Creating a vector with the square of 5 observations from myData using sample() and sqrt()

2.3 Creating a vector with the mean of 5 pairs of observations from myData using sample() and mean()

3. Using ggplot

3.1 Creating a blank graph

3.5 Changing colours and size of points

3.6 Creating a barplot

Exercise

1.1 Creating a random normal distribution using `rnorm()`

1.2 Performing sanity checks on myData using `length()`, `mean()` and `sd()`

2. Basic Loops with `for(i in x)`

2.1 Printing 5 observations from myData using `sample()` and `print()`

2.2 Creating a vector with the square of 5 observations from myData using `sample()` and `sqrt()`

2.3 Creating a vector with the mean of 5 pairs of observations from myData using `sample()` and `mean()`