Week 2: dplyr and Census Data

table function and acs data.

Published

September 5, 2026

What We Did

quiz
set up portfolio for today
lesson about dplyr and census
practice
GO HOME!!!

Key Concepts

saving result in R

result <- filter(counties, population > 5000)

packages

install.packages("dplyr")
library(dplyr)

Data frames and dplyr

Main verbs: - filter() — choose rows based on conditions. - select() — choose columns. - mutate() — add or modify computed columns. - arrange() — sort rows; use desc() for descending order. - group_by() + summarize() — calculate statistics by group, similar to SQL aggregation.

Pipelines

new_data <- counties |>
  filter(moe_pct > 10) |>
  arrange(desc(moe_pct)) |>
  select(name, moe_pct)

Getting ACS data

library(tidycensus)

data <- get_acs(
  geography = "county",
  variables = c("VARIABLE_ID_1", "VARIABLE_ID_2"),
  state = "PA",
  year = 2014,
  survey = "acs5",
  output = "wide"
)

GEOIDs

Treat GEOIDs as strings, not numbers, so leading zeros are preserved. Typical components: - State: 2 digits - County: 3 digits - Census tract: 6 digits - Block group: 1 additional digit when applicable A state + county + tract GEOID is usually 11 digits.

Proving R Works

Reflection

Questions I Still Have

---
title: "Week 2: dplyr and Census Data"
description: "table function and acs data."
date: 2026-09-05
---

## What We Did

- quiz
- set up portfolio for today
- lesson about dplyr and census
- practice
- GO HOME!!!

## Key Concepts

### saving result in R
```R
result <- filter(counties, population > 5000)
```

### packages

```R
install.packages("dplyr")
library(dplyr)
```

### Data frames and dplyr
Main verbs:
- filter() — choose rows based on conditions.
- select() — choose columns.
- mutate() — add or modify computed columns.
- arrange() — sort rows; use desc() for descending order.
- group_by() + summarize() — calculate statistics by group, similar to SQL aggregation.


### Pipelines

```R
new_data <- counties |>
  filter(moe_pct > 10) |>
  arrange(desc(moe_pct)) |>
  select(name, moe_pct)
```

### Getting ACS data

```R
library(tidycensus)

data <- get_acs(
  geography = "county",
  variables = c("VARIABLE_ID_1", "VARIABLE_ID_2"),
  state = "PA",
  year = 2014,
  survey = "acs5",
  output = "wide"
)
```


### GEOIDs

Treat GEOIDs as strings, not numbers, so leading zeros are preserved.
Typical components:
- State: 2 digits
- County: 3 digits
- Census tract: 6 digits
- Block group: 1 additional digit when applicable
A state + county + tract GEOID is usually 11 digits.


## Proving R Works


## Reflection


## Questions I Still Have