{include=FALSE}
knitr::opts_chunk$set(error = TRUE)

install.packages("tidyverse")
library(tidyverse)

candidate_level <- pres_elections %>%
  filter(party_simplified != "OTHER") %>%
  group_by(year, candidate, totalvotes, party_simplified candidatevotes) %>%
  summarize(all_states_total = sum(candidatevotes))

## Error in parse(text = input): <text>:6:58: unexpected symbol
## 5:   filter(party_simplified != "OTHER") %>%
## 6:   group_by(year, candidate, totalvotes, party_simplified candidatevotes
##                                                             ^

POL 280 - Warnings, Errors, and Debugging

Date: March 4, 2026

The difference between warnings and errors

- Error: R does not understand your code and cannot carry it out

- Warning: R is not sure about your code but it is still running your code

- Unexpected responses: R gives you output but it isn’t what you expected

- No response: You executed your code, but R gives you nothing. Zilch. Nada.

Preliminaries

Set your working directory. YOURS WILL BE DIFFERENT!

setwd(“/Users/susan/Library/CloudStorage/Dropbox/Teaching/Political Science/POL280_researchDesign/2026_Spring”)

setwd("/Users/susan/Library/CloudStorage/Dropbox/Teaching/Political Science/POL280_researchDesign/2026_Spring")

## Error in setwd("/Users/susan/Library/CloudStorage/Dropbox/Teaching/Political Science/POL280_researchDesign/2026_Spring"): cannot change working directory

Load the dataset

pres_elections <- read.csv(“1976-2020-president.csv”)

For variable information, open and preview the file “codebook-us-president-1976-2024.md”

Basics

How can I perform the basic calculation of adding five and ten?

5 + 10

## [1] 15

How can I save that result as an object?

fifteen <- 5+10 fifteen <- c(10,18,35,48,60)

ones <- (1,1,1,1,1)

fifteen + ones

Exercise: how to decode error messages

(1) Run the command below to look at the presidential election data you just loaded. What is the unit of observation? How do you know?

View(pres_elections) # This command will allow you to see the data in a spreadsheet format.

View(pres_elections)

## Error: object 'pres_elections' not found

The unit of observation is “state_fips” since they are ranked in order 1-60 using this unit.

(2) Assume that you ran this code:

install.packages(“tidyverse”) library(tidyverse)

candidate_level <- pres_elections %>% filter(party_simplified != “OTHER”) %>% group_by(year, candidate, totalvotes, party_simplified candidatevotes) %>% summarize(all_states_total = sum(candidatevotes))

install.packages("tidyverse")

## Error in contrib.url(repos, "source"): trying to use CRAN without setting a mirror

library(tidyverse)

## Warning: package 'readr' was built under R version 4.4.3

## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr     1.1.4     ✔ readr     2.2.0
## ✔ forcats   1.0.1     ✔ stringr   1.5.1
## ✔ ggplot2   4.0.2     ✔ tibble    3.2.1
## ✔ lubridate 1.9.5     ✔ tidyr     1.3.2
## ✔ purrr     1.2.1     
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag()    masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors

candidate_level <- pres_elections %>%
  filter(party_simplified != "OTHER") %>%
  group_by(year, candidate, totalvotes, party_simplified, candidatevotes) %>%
  summarize(all_states_total = sum(candidatevotes))

## Error: object 'pres_elections' not found

(a) What did R return?

I received an error message in return. # (b) Is this an error or a warning? Did your code run? The error message said that there is an unexpected symbol in: ” filter(party_simplified != “OTHER”) %>% group_by(year, candidate, totalvotes, party_simplified candidatevotes” No, my code did not run. # (c) What is the content of the bug? What is the location/position? The bug is probably due to the open parantheses without a closed parantheses in the second line of the error message. # (d) What do you think the solution might be? (Do your best! We will talk about it!) The solution might be adding a closed parantheses at the end of the line. Upon running the code a bunch of times with no luck, I have realized the issue is not a closed parantheses but the lack of a comma.

install.packages("tidyverse")
library(tidyverse)

candidate_level <- pres_elections %>%
  filter(party_simplified != "OTHER") %>%
  group_by(year, candidate, totalvotes, party_simplified candidatevotes) %>%
  summarize(all_states_total = sum(candidatevotes)

## Error in parse(text = input): <text>:6:58: unexpected symbol
## 5:   filter(party_simplified != "OTHER") %>%
## 6:   group_by(year, candidate, totalvotes, party_simplified candidatevotes
##                                                             ^

install.packages("tidyverse")
library(tidyverse)

candidate_level <- pres_elections %>%
  filter(party_simplified != "OTHER") %>%
  group_by(year, candidate, totalvotes, partysimplified candidatevotes) %>%
  summarize(all_states_total = sum(candidatevotes))

## Error in parse(text = input): <text>:6:57: unexpected symbol
## 5:   filter(party_simplified != "OTHER") %>%
## 6:   group_by(year, candidate, totalvotes, partysimplified candidatevotes
##                                                            ^

install.packages("tidyverse")
library("tidyverse"")

candidate_level <- pres_elections %>%
  filter(party_simplified != "OTHER") %>%
  group_by(year, candidate, totalvotes, party_simplified candidatevotes) %>%
  summarize(all_states_total = sum(candidatevotes))

## Error in parse(text = input): <text>:2:20: unexpected string constant
## 4: candidate_level <- pres_elections %>%
## 5:   filter(party_simplified != "
##                       ^

install.packages("tidyverse")
library("tidyverse"")

candidate_level <- pres_elections %>%
  filter(party_simplified(!= "OTHER")) %>%
  group_by(year, candidate, totalvotes, party_simplified candidatevotes) %>%
  summarize(all_states_total = sum(candidatevotes))

## Error in parse(text = input): <text>:2:20: unexpected string constant
## 4: candidate_level <- pres_elections %>%
## 5:   filter(party_simplified(!= "
##                       ^

install.packages("tidyverse")

## Error in contrib.url(repos, "source"): trying to use CRAN without setting a mirror

library(tidyverse)

candidate_level <- pres_elections %>%
  filter(party_simplified != "OTHER") %>%
  group_by(year, candidate, totalvotes, party_simplified, candidatevotes) %>%
  summarize(all_states_total = sum(candidatevotes))

## Error: object 'pres_elections' not found

(e) After you fix the issue, run the code again. What does R return? Is this a warning or an error? How would you describe it?

I think if this were between an error and warning this would be a warning since R states where the downloaded packages are but it also states that it has grouped out certain units and gives you a way to override. I would describe this as a suggestion.

(2) Assume that the code above intended to calculate the total number of popular votes won by a presidential candidate at the national level in each election. How should that data frame look? Did we achieve our goal?

View(candidate_level)

View(candidate_level)

## Error: object 'candidate_level' not found

We would need to add all values assocaited with each name in each year. I’m not exactly sure how that data frame would look, possibly adding candidate votes for columns 1-51, as an example for 1976 Jimmy Carter. We did not achieve our goal, as we were not provided any additional numbers.

(3) Run the code below.

new_df <- pres_elections %>% filter(party_simplified %in% c(“DEMOCRAT”, “REPUBLICAN”)) %>% mutate(candidate = na_if(candidate, ““)) %>% filter(!is.na(candidate)) %>% filter(writein == FALSE) %>% group_by(year, party_simplified, candidate) %>% summarize(all_states_total = sum(candidatevotes), national_vote_total = sum(totalvotes)) %>% pivot_wider( id_cols = c(year, national_vote_total), names_from = party_simplified, values_from = c(candidate, all_states_total), names_glue =”{party_simplified}_{.value}” )

new_df <- pres_elections %>%
  filter(party_simplified %in% c("DEMOCRAT", "REPUBLICAN")) %>%
  mutate(candidate = na_if(candidate, "")) %>%
  filter(!is.na(candidate)) %>%
  filter(writein == FALSE) %>%
  group_by(year, party_simplified, candidate) %>%
  summarize(all_states_total = sum(candidatevotes),
            national_vote_total = sum(totalvotes)) %>%
  pivot_wider(
    id_cols = c(year, national_vote_total),
    names_from = party_simplified,
    values_from = c(candidate, all_states_total),
    names_glue = "{party_simplified}_{.value}"
  )

## Error: object 'pres_elections' not found

How has the unit of observation changed?

The unit of obsevation has changed to year and party. # (4) Let’s say that you wanted to calculate the proportion of the popular vote share won by each candidate in 2016 and 2020. In future classes, we will learn how to do this with the names of variables. But let’s do it by hand today. Use view() to open the data frame, then find the relevant vote totals. Calculate the percentage for each candidate below. For each line of code, please use narrative debugging. In other words, please use a # and write the purpose of each line of code next to the code itself. Include your answers below.

CODE HERE:

Calculating Hillary’s 2016 proportion of votes

 65853514/128838342

## [1] 0.5111329

51.1%

Calculating Trump’s 2016 proportion of votes

62984828/128838342

## [1] 0.4888671

48.89%

Calculating Biden’s 2020 proportion of votes

81283501/155507476

## [1] 0.5226983

52.27%

Calculating Trump’s 2020 proportion of votes

74223975/155507476

## [1] 0.4773017

47.7%

View the data frame

Find 2016 and 2020 data

2016 total votes: 128838342 2020 total votes: 155507476

Find candidate totals

Hillary 2016: 65853514 Trump 2016: 62984828 Biden 2020: 81283501 Trump 2020: 74223975 # Mini-R HW: Please complete Problem 4.3 and 4.4 (Sumner, Chapter 4, p. 51) and turn in this whole script on Canvas.

knitr::opts_chunk$set(error = TRUE)

5 + x

## Error: object 'x' not found

Will your code produce an output in the console? How do you know? Because an error message is the computer’s way of saying that it doesn’t understand what I’m asking it to do, the code will not produce an output in the console. I also did not define what the value of x is, so there is no way for the computer to produce an output. This is different from a warning message in which the computer would produce an output but would warn that it may not be what I’m looking for.

x = 2
4 + x

## [1] 6

Once we give a value for x, the code will produce an output.

sd(c(47,29,90,1@))

## Error in parse(text = input): <text>:1:17: unexpected ')'
## 1: sd(c(47,29,90,1@)
##                     ^

sd(c(47,29,90,1@)

## Error in parse(text = input): <text>:1:17: unexpected ')'
## 1: sd(c(47,29,90,1@)
##                     ^

sd(c(47,29,90,1@

## Error in parse(text = input): <text>:2:0: unexpected end of input
## 1: sd(c(47,29,90,1@
##    ^

sdc(47,29,90,1)

## Error in sdc(47, 29, 90, 1): could not find function "sdc"

sd(c(47,29,90,1@))

## Error in parse(text = input): <text>:1:17: unexpected ')'
## 1: sd(c(47,29,90,1@)
##                     ^

unexpected ‘)’ probably means that R encountered an unexpected closing parantheses in the code. Though I did try to exclude one of the closing parantheses to correct it, it returned with the same error message.

sd(c(47,29,90,1@))

## Error in parse(text = input): <text>:1:17: unexpected ')'
## 1: sd(c(47,29,90,1@)
##                     ^

sd(c47,29,90,1@))

## Error in parse(text = input): <text>:1:16: unexpected ')'
## 1: sd(c47,29,90,1@)
##                    ^

sd(c(47,29,90,1))

## [1] 37.3218

Upon retrying the code without the ‘@’ symbol next to the fourth value, I’ve realized the error is the @ symbol instead of the closed parantheses.
With the @ symbol included, the computer does not calculate a standard deviation. It only returns with the error message.
The way to fix the error would be to take out the ‘@’ symbol and retry.

Sumner4.3and4.4

Anderson Patry

2026-03-17