# Chapter 9 Objects, Functions, Loops

### Where are we? Where are we headed?

Up till now, you should have covered:

• R basic programming
• Data Import
• Statistical Summaries
• Visualization

Today we'll cover

• Objects
• Functions
• Loops

## 9.1 What is an object?

Now that we have covered some hands-on ways to use graphics, let's go into some fundamentals of the R language.

Let's first set up

library(dplyr)
library(readr)
library(haven)
library(ggplot2)
cen10 <- read_csv("data/input/usc2010_001percent.csv", col_types = cols())

Objects are abstract symbols in which you store data. Here we will create an object from copy, and assign cen10 to it.

copy <- cen10 

This looks the same as the original dataset:

copy
## # A tibble: 30,871 x 4
##    state         sex      age race
##    <chr>         <chr>  <dbl> <chr>
##  1 New York      Female     8 White
##  2 Ohio          Male      24 White
##  3 Nevada        Male      37 White
##  4 Michigan      Female    12 White
##  5 Maryland      Female    18 Black/Negro
##  6 New Hampshire Male      50 White
##  7 Iowa          Female    51 White
##  8 Missouri      Female    41 White
##  9 New Jersey    Male      62 White
## 10 California    Male      25 White
## # … with 30,861 more rows

What happens if you do this next?

copy <- ""

It got reassigned:

copy
## [1] ""

### 9.1.1 lists

Lists are one of the most generic and flexible type of object. You can make an empty list by the function list()

my_list <- list()
my_list
## list()

And start filling it in. Slots on the list are invoked by double square brackets [[]]

my_list[[1]] <- "contents of the first slot -- this is a string"
my_list[["slot 2"]] <- "contents of slot named slot 2"
my_list
## [[1]]
## [1] "contents of the first slot -- this is a string"
##
## $slot 2 ## [1] "contents of slot named slot 2" each slot can be anything. What are we doing here? We are defining the 1st slot of the list my_list to be a vector c(1, 2, 3, 4, 5) my_list[[1]] <- c(1, 2, 3, 4, 5) my_list ## [[1]] ## [1] 1 2 3 4 5 ## ##$slot 2
## [1] "contents of slot named slot 2"

You can even make nested lists. Let's say we want the 1st slot of the list to be another list of three elements.

my_list[[1]][[1]] <- "subitem 1 in slot 1 of my_list"
my_list[[1]][[2]] <- "subitem 1 in slot 2 of my_list"
my_list[[1]][[3]] <- "subitem 1 in slot 3 of my_list"

my_list
## [[1]]
## [1] "subitem 1 in slot 1 of my_list" "subitem 1 in slot 2 of my_list"
## [3] "subitem 1 in slot 3 of my_list" "4"
## [5] "5"
##
## $slot 2 ## [1] "contents of slot named slot 2" ## 9.2 Making your own objects We've covered one type of object, which is a list. You saw it was quite flexible. How many types of objects are there? There are an infinite number of objects, because people make their own class of object. You can detect the type of the object (the class) by the function class Object can be said to be an instance of a class. Analogies: class - Pokemon, object - Pikachu class - Book, object - To Kill a Mockingbird class - DataFrame, object - 2010 census data class - Character, object - "Programming is Fun" What is type (class) of object is cen10? class(cen10) ## [1] "spec_tbl_df" "tbl_df" "tbl" "data.frame" What about this text? class("some random text") ## [1] "character" To change or create the class of any object, you can assign it. To do this, assign the name of your class to character to an object's class(). We can start from a simple list. For example, say we wanted to store data about pokemon. Because there is no pre-made package for this, we decide to make our own class. pikachu <- list(name = "Pikachu", number = 25, type = "Electric", color = "Yellow") and we can give it any class name we want. class(pikachu) <- "Pokemon" str(pikachu) ## List of 4 ##$ name  : chr "Pikachu"
##  $number: num 25 ##$ type  : chr "Electric"
##  $color : chr "Yellow" ## - attr(*, "class")= chr "Pokemon" pikachu$type
## [1] "Electric"

### 9.2.1 Seeing R through objects

Most of the R objects that you will see as you advance are their own objects. For example, here's a linear regression object (which you will learn more about in Gov 2000):

ols <- lm(mpg ~ wt + vs + gear + carb, mtcars)
class(ols)
## [1] "lm"

Anything can be an object! Even graphs (in ggplot) can be assigned, re-assigned, and edited.

grp_race <- group_by(cen10, race)%>%
summarize(count = n())
## summarise() ungrouping output (override with .groups argument)
grp_race_ordered <- arrange(grp_race, count) %>%
mutate(race = forcats::as_factor(race))

gg_tab <- ggplot(data = grp_race_ordered) +
aes(x = race, y = count) +
geom_col() +
labs(caption = "Source: U.S. Census 2010")

gg_tab

You can change the orientation

gg_tab<- gg_tab + coord_flip()

### 9.2.2 Parsing an object by str()s

It can be hard to understand an R object because it's contents are unknown. The function str, short for structure, is a quick way to look into the innards of an object

str(my_list)
## List of 2
##  $: chr [1:5] "subitem 1 in slot 1 of my_list" "subitem 1 in slot 2 of my_list" "subitem 1 in slot 3 of my_list" "4" ... ##$ slot 2: chr "contents of slot named slot 2"
class(my_list)
## [1] "list"

Same for the object we just made

str(pikachu)
## List of 4
##  $name : chr "Pikachu" ##$ number: num 25
##  $type : chr "Electric" ##$ color : chr "Yellow"
##  - attr(*, "class")= chr "Pokemon"

What does a ggplot object look like? Very complicated, but at least you can see it:

# enter this on your console
str(gg_tab)

## 9.3 Types of variables

In the social science we often analyze variables. As you saw in the tutorial, different types of variables require different care.

A key link with what we just learned is that variables are also types of R objects.

### 9.3.1 scalars

One number. How many people did we count in our Census sample?

nrow(cen10)
## [1] 30871

Question: What proportion of our census sample is Native American? This number is also a scalar

# Enter yourself
unique(cen10$race) ## [1] "White" "Black/Negro" ## [3] "Other race, nec" "American Indian or Alaska Native" ## [5] "Chinese" "Other Asian or Pacific Islander" ## [7] "Two major races" "Three or more major races" ## [9] "Japanese" mean(cen10$race == "American Indian or Alaska Native")
## [1] 0.009555894

Hint: you can use the function mean() to calcualte the sample mean. The sample proportion is the mean of a sequence of number, where your event of interest is a 1 (or TRUE) and others are 0 (or FALSE).

### 9.3.2 numeric vectors

A sequence of numbers.

grp_race_ordered$count ## [1] 77 88 295 354 869 1129 1839 4013 22207 class(grp_race_ordered$count)
## [1] "integer"

Or even, all the ages of the millions of people in our Census. Here are just the first few numbers of the list.

head(cen10$age) ## [1] 8 24 37 12 18 50 ### 9.3.3 characters (aka strings) This can be just one stretch of characters my_name <- "Meg" my_name ## [1] "Meg" class(my_name) ## [1] "character" or more characters. Notice here that there's a difference between a vector of individual characters and a length-one object of characters. my_name_letters <- c("M","e","g") my_name_letters ## [1] "M" "e" "g" class(my_name_letters) ## [1] "character" Finally, remember that lower vs. upper case matters in R! my_name2 <- "shiro" my_name == my_name2 ## [1] FALSE ## 9.4 What is a function? Most of what we do in R is executing a function. read_csv(), nrow(), ggplot() .. pretty much anything with a parentheses is a function. And even things like <- and [ are functions as well. A function is a set of instructions with specified ingredients. It takes an input, then manipulates it -- changes it in some way -- and then returns the manipulated product. One way to see what a function actually does is to enter it without parentheses. # enter this on your console table You'll see below that the most basic functions are quite complicated internally. You'll notice that functions contain other functions. wrapper functions are functions that "wrap around" existing functions. This sounds redundant, but it's an important feature of programming. If you find yourself repeating a command more than two times, you should make your own function, rather than writing the same type of code. ### 9.4.1 Write your own function It's worth remembering the basic structure of a function. You create a new function, call it my_fun by this: my_fun <- function() { } If we wanted to generate a function that computed the number of men in your data, what would that look like? count_men <- function(data) { nmen <- sum(data$sex == "Male")

return(nmen)
}

Then all we need to do is feed this function a dataset

count_men(cen10)
## [1] 15220

The point of a function is that you can use it again and again without typing up the set of constituent manipulations. So, what if we wanted to figure out the number of men in California?

count_men(cen10[cen10$state == "California",]) ## [1] 1876 Let's go one step further. What if we want to know the proportion of non-whites in a state, just by entering the name of the state? There's multiple ways to do it, but it could look something like this nw_in_state <- function(data, state) { s.subset <- data[data$state == state,]
total.s <- nrow(s.subset)
nw.s <- sum(s.subset$race != "White") nw.s / total.s } The last line is what gets generated from the function. To be more explicit you can wrap the last line around return(). (as in return(nw.s/total.s). return() is used when you want to break out of a function in the middle of it and not wait till the last line. Try it on your favorite state! nw_in_state(cen10, "Massachusetts") ## [1] 0.2040185 ## Checkpoint ### 1 Try making your own function, average_age_in_state, that will give you the average age of people in a given state. # Enter on your own ### 2 Try making your own function, asians_in_state, that will give you the number of Chinese, Japanese, and Other Asian or Pacific Islander people in a given state. # Enter on your own ### 3 Try making your own function, 'top_10_oldest_cities', that will give you the names of cities whose population's average age is top 10 oldest. # Enter on your own ## 9.5 What is a package? You can think of a package as a suite of functions that other people have already built for you to make your life easier. help(package = "ggplot2") To use a package, you need to do two things: (1) install it, and then (2) load it. Installing is a one-time thing install.packages("ggplot2") But you need to load each time you start a R instance. So always keep these commands on a script. library(ggplot2) In rstudio.cloud, we already installed a set of packages for you. But when you start your own R instance, you need to have installed the package at some point. ## 9.6 Conditionals Sometimes, you want to execute a command only under certain conditions. This is done through the almost universal function, if(). Inside the if function we enter a logical statement. The line that is adjacent to, or follows, the if() statement only gets executed if the statement returns TRUE. For example, For example, x <- 5 if (x >0) { print("positive number") } else if (x == 0) { print ("zero") } else { print("negative number") } ## [1] "positive number" You can wrap that whole things in a function is_positive <- function(number) { if (number >0) { print("positive number") } else if (number == 0) { print ("zero") } else { print("negative number") } } is_positive(5) ## [1] "positive number" is_positive(-3) ## [1] "negative number" ## 9.7 For-loops Loops repeat the same statement, although the statement can be "the same" only in an abstract sense. Use the for(x in X) syntax to repeat the subsequent command as many times as there are elements in the right-hand object X. Each of these elements will be referred to the left-hand index x First, come up with a vector. fruits <- c("apples", "oranges", "grapes") Now we use the fruits vector in a for loop. for (fruit in fruits) { print(paste("I love", fruit)) } ## [1] "I love apples" ## [1] "I love oranges" ## [1] "I love grapes" Here for() and in must be part of any for loop. The right hand side fruits must be a thing that exists. Finally the left-hand side object is "Pick your favor name." It is analogous to how we can index a sum with any letter. $$\sum_{i=1}^{10}i$$ and sum_{j = 1}^{10}j are in fact the same thing. for (i in 1:length(fruits)) { print(paste("I love", fruits[i])) } ## [1] "I love apples" ## [1] "I love oranges" ## [1] "I love grapes" states_of_interest <- c("California", "Massachusetts", "New Hampshire", "Washington") for( state in states_of_interest){ state_data <- cen10[cen10$state == state,]
nmen <- sum(state_data$sex == "Male") n <- nrow(state_data) men_perc <- round(100*(nmen/n), digits=2) print(paste("Percentage of men in",state, "is", men_perc)) } ## [1] "Percentage of men in California is 49.85" ## [1] "Percentage of men in Massachusetts is 47.6" ## [1] "Percentage of men in New Hampshire is 48.55" ## [1] "Percentage of men in Washington is 48.19" Instead of printing, you can store the information in a vector states_of_interest <- c("California", "Massachusetts", "New Hampshire", "Washington") male_percentages <- c() iter <-1 for( state in states_of_interest){ state_data <- cen10[cen10$state == state,]
nmen <- sum(state_data$sex == "Male") n <- nrow(state_data) men_perc <- round(100*(nmen/n), digits=2) male_percentages <- c(male_percentages, men_perc) names(male_percentages)[iter] <- state iter <- iter + 1 } male_percentages ## California Massachusetts New Hampshire Washington ## 49.85 47.60 48.55 48.19 ## 9.8 Nested Loops What if I want to calculate the population percentage of a race group for all race groups in states of interest? You could probably use tidyverse functions to do this, but let's try using loops! states_of_interest <- c("California", "Massachusetts", "New Hampshire", "Washington") for (state in states_of_interest) { for (race in unique(cen10$race)) {
race_state_num <- nrow(cen10[cen10$race == race & cen10$state == state, ])
state_pop <- nrow(cen10[cen10$state == state, ]) race_perc <- round(100*(race_state_num/(state_pop)), digits=2) print(paste("Percentage of ", race , "in", state, "is", race_perc)) } } ## [1] "Percentage of White in California is 57.61" ## [1] "Percentage of Black/Negro in California is 6.72" ## [1] "Percentage of Other race, nec in California is 15.55" ## [1] "Percentage of American Indian or Alaska Native in California is 1.12" ## [1] "Percentage of Chinese in California is 3.75" ## [1] "Percentage of Other Asian or Pacific Islander in California is 9.54" ## [1] "Percentage of Two major races in California is 4.62" ## [1] "Percentage of Three or more major races in California is 0.37" ## [1] "Percentage of Japanese in California is 0.72" ## [1] "Percentage of White in Massachusetts is 79.6" ## [1] "Percentage of Black/Negro in Massachusetts is 5.87" ## [1] "Percentage of Other race, nec in Massachusetts is 4.02" ## [1] "Percentage of American Indian or Alaska Native in Massachusetts is 0.77" ## [1] "Percentage of Chinese in Massachusetts is 2.32" ## [1] "Percentage of Other Asian or Pacific Islander in Massachusetts is 4.33" ## [1] "Percentage of Two major races in Massachusetts is 2.78" ## [1] "Percentage of Three or more major races in Massachusetts is 0" ## [1] "Percentage of Japanese in Massachusetts is 0.31" ## [1] "Percentage of White in New Hampshire is 93.48" ## [1] "Percentage of Black/Negro in New Hampshire is 0.72" ## [1] "Percentage of Other race, nec in New Hampshire is 0.72" ## [1] "Percentage of American Indian or Alaska Native in New Hampshire is 0.72" ## [1] "Percentage of Chinese in New Hampshire is 0.72" ## [1] "Percentage of Other Asian or Pacific Islander in New Hampshire is 2.17" ## [1] "Percentage of Two major races in New Hampshire is 0.72" ## [1] "Percentage of Three or more major races in New Hampshire is 0" ## [1] "Percentage of Japanese in New Hampshire is 0.72" ## [1] "Percentage of White in Washington is 76.05" ## [1] "Percentage of Black/Negro in Washington is 2.9" ## [1] "Percentage of Other race, nec in Washington is 5.37" ## [1] "Percentage of American Indian or Alaska Native in Washington is 2.03" ## [1] "Percentage of Chinese in Washington is 1.31" ## [1] "Percentage of Other Asian or Pacific Islander in Washington is 6.68" ## [1] "Percentage of Two major races in Washington is 4.79" ## [1] "Percentage of Three or more major races in Washington is 0.29" ## [1] "Percentage of Japanese in Washington is 0.58" ## Exercises ### Exercise 1: Write your own function Write your own function that makes some task of data analysis simpler. Ideally, it would be a function that helps you do either of the previous tasks in fewer lines of code. You can use the three lines of code that was provided in exercise 1 to wrap that into another function too! # Enter yourself ### Exercise 2: Using Loops Using a loop, create a crosstab of sex and race for each state in the set "states_of_interest" states_of_interest <- c("California", "Massachusetts", "New Hampshire", "Washington") # Enter yourself ### Exercise 3: Storing information derived within loops in a global dataframe Recall the following nested loop states_of_interest <- c("California", "Massachusetts", "New Hampshire", "Washington") for (state in states_of_interest) { for (race in unique(cen10$race)) {
race_state_num <- nrow(cen10[cen10$race == race & cen10$state == state, ])
state_pop <- nrow(cen10[cen10\$state == state, ])
race_perc <- round(100*(race_state_num/(state_pop)), digits=2)
print(paste("Percentage of ", race , "in", state, "is", race_perc))
}
}
## [1] "Percentage of  White in California is 57.61"
## [1] "Percentage of  Black/Negro in California is 6.72"
## [1] "Percentage of  Other race, nec in California is 15.55"
## [1] "Percentage of  American Indian or Alaska Native in California is 1.12"
## [1] "Percentage of  Chinese in California is 3.75"
## [1] "Percentage of  Other Asian or Pacific Islander in California is 9.54"
## [1] "Percentage of  Two major races in California is 4.62"
## [1] "Percentage of  Three or more major races in California is 0.37"
## [1] "Percentage of  Japanese in California is 0.72"
## [1] "Percentage of  White in Massachusetts is 79.6"
## [1] "Percentage of  Black/Negro in Massachusetts is 5.87"
## [1] "Percentage of  Other race, nec in Massachusetts is 4.02"
## [1] "Percentage of  American Indian or Alaska Native in Massachusetts is 0.77"
## [1] "Percentage of  Chinese in Massachusetts is 2.32"
## [1] "Percentage of  Other Asian or Pacific Islander in Massachusetts is 4.33"
## [1] "Percentage of  Two major races in Massachusetts is 2.78"
## [1] "Percentage of  Three or more major races in Massachusetts is 0"
## [1] "Percentage of  Japanese in Massachusetts is 0.31"
## [1] "Percentage of  White in New Hampshire is 93.48"
## [1] "Percentage of  Black/Negro in New Hampshire is 0.72"
## [1] "Percentage of  Other race, nec in New Hampshire is 0.72"
## [1] "Percentage of  American Indian or Alaska Native in New Hampshire is 0.72"
## [1] "Percentage of  Chinese in New Hampshire is 0.72"
## [1] "Percentage of  Other Asian or Pacific Islander in New Hampshire is 2.17"
## [1] "Percentage of  Two major races in New Hampshire is 0.72"
## [1] "Percentage of  Three or more major races in New Hampshire is 0"
## [1] "Percentage of  Japanese in New Hampshire is 0.72"
## [1] "Percentage of  White in Washington is 76.05"
## [1] "Percentage of  Black/Negro in Washington is 2.9"
## [1] "Percentage of  Other race, nec in Washington is 5.37"
## [1] "Percentage of  American Indian or Alaska Native in Washington is 2.03"
## [1] "Percentage of  Chinese in Washington is 1.31"
## [1] "Percentage of  Other Asian or Pacific Islander in Washington is 6.68"
## [1] "Percentage of  Two major races in Washington is 4.79"
## [1] "Percentage of  Three or more major races in Washington is 0.29"
## [1] "Percentage of  Japanese in Washington is 0.58"

Instead of printing the percentage of each race in each state, create a dataframe, and store all that information in that dataframe. (Hint: look at how I stored information about male percentage in each state of interest in a vector.)