---
title: "Example Markdown File"
author: "Your Name Here"
date: "01/28/2026"
output: html_document
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE, eval = TRUE)
```

## R Markdown 

This is an R Markdown document. Markdown is a simple formatting syntax for authoring HTML, PDF, and MS Word documents. For more details on using R Markdown see <http://rmarkdown.rstudio.com>. Additionally, there is a useful cheat sheet of commands [here.](https://www.rstudio.com/wp-content/uploads/2015/02/rmarkdown-cheatsheet.pdf)

When you click the **Knit** button a document will be generated that includes both content as well as the output of any embedded R code chunks within the document. You can embed an R code chunk like this:

### 1-1
```{r pset1_1, eval=T, echo=T}
data('attitude') #Preloaded dataset
summary(attitude$critical)

#My answer is this as a comment.
```
Or my answer can be written here


### Example: Privileges
```{r, echo = F, eval = T}
summary(attitude$privileges)
```

```{r, echo = T}
plot(attitude$learning, attitude$complaints)
```


## Regularly Scheduled Lab 
```{r}
library(dplyr)
census_data <- read.csv('https://github.com/apodkul/ppol6803_03/raw/main/Data/census_demo.csv')

summary(census_data) 
head(census_data)
```

### Using Piping 
```{r, eval = F, echo = T}
glimpse(census_data) # provides summary of the data 

#... is equivalent to 
census_data %>%
  glimpse()

# why didn't this one print on the page?
```

### Creating New Variables 
```{r, eval = F, echo = T}
census_data %>% 
  mutate(total_aa = AA_MALE + AA_FEMALE)

census_data %>% 
  mutate(maj_white = white_alone_perc > .5)
```

```{r}
census_data <- census_data %>% 
  mutate(total_aa = AA_MALE + AA_FEMALE, 
         maj_white = white_alone_perc > .5)
```

### Subsetting Data 
```{r, eval = F, echo = T}
census_data %>% 
  select(STNAME, TOT_POP, hispanic_perc)

census_data %>% 
  filter(STNAME == 'New Jersey')

census_data %>% 
  filter(STNAME != 'New Jersey' | white_alone_perc < .7)
```


### Joining Data Frames
```{r, eval = F}
census_regions <- read.csv('https://github.com/apodkul/ppol6803_03/raw/main/Data/census_regions.csv')

census_data %>% 
  select(STNAME, CTYNAME, hispanic_perc)

census_data %>% 
  select(STNAME, CTYNAME, hispanic_perc) %>%
  left_join(y = census_regions, 
            by = c('STNAME' = 'State.Name'))

table(census_data$STNAME)
table(census_regions$State.Name)
```

```{r, eval = T, echo = T}
census_regions <- read.csv('https://github.com/apodkul/ppol6803_03/raw/main/Data/census_regions.csv')

census_data <- census_data %>% 
  left_join(y = census_regions, 
            by = c('STNAME' = 'State.Name'))

```



### Collapsing 
```{r eval=F, echo=F}
census_data %>%
  summarise(Population = sum(TOT_POP))

census_data %>%
  group_by(Region) %>%
  summarise(Population = sum(TOT_POP))
```

### Reshaping Data 

```{r, echo = T}
library(tidyr)

#Creating a more manageable dataset 
test_data <- census_data %>% 
  select(STNAME, CTYNAME, TOT_POP, TOT_MALE, TOT_FEMALE)

#From wide to long 
test_data_w <- test_data %>% 
  pivot_longer(cols = TOT_POP:TOT_FEMALE, 
               names_to = "var_name")

#Going back (long to wide)
test_data_l <- test_data_w %>%
  pivot_wider(id_cols = STNAME:CTYNAME, 
              names_from = var_name, 
              values_from = value)
```