Weeks 10 and 11

Introduction To Geographic Data and Quiz 2 Review

Housekeeping

Upcoming Dates

• HW 5 - Part 1 is due on Wed. 3/26 - Grace Period ends 3/28

• Draft Proposals are due Thursday 3/27

• Thursday 3/27: In-class work day and NO OFFICE HOURS

• Tuesday 4/1: Review using practice questions

• Quiz 2 is on Thursday, 4/3

  • Mostly on Weeks 5 through 9, but material is cumulative.

  • Mostly on HW Assignments 4 and 5 , but material is cumulative.

  • Data Mgmt. tasks will requires 2 or 3 steps and then you will answer questions.

• HW 5 - Part 2 will be posted after Quiz 2.

Today, Thursday, and Tuesday, 4/1

• Practice Questions for Quiz 2 are posted.

• Tue. 4/1: Skills/Concepts Review for Quiz 2

  • Putting skills together for different goals

  • Come with questions or submit them as Engagement Questions.

• Today: Intro to Managing and Plotting Geographic Data

  • Today’s geographic maps will not be on Quiz 2.

  • Mapping data is an effective data curation tool that may be useful in this class, other classes, your career.

• We will cover more about geographic data and map visualizations after Quiz 2.

• If you want help with mapping project data, please reach out to me or TA.

In-class Exercise - Week 10

Purpose:

• To gain some experience and understanding of map data available in R and elsewhere.

• To experiment with mapping data

• Students are encouraged to use domestic or international map data in their dashboards if appropriate.

Data Preparation

• The data for today’s in-class exercise is part of R.

• These geographic data are useful if you have information by state or county and you want to show a choropleth map of your data.

• R also has world information, e.g., countries, continents, etc.

```{r us data prep}
us_states <- map_data("state") |>               # state polygons (not used today)
  rename("state" = "region")
us_counties <- map_data("county") |>            # county polygons
  rename("state" = "region", "county" = "subregion") |>
  mutate(county = gsub(" ", "", county), 
         county = gsub("'","", county) |> tolower())
#unique(us_counties$county[us_counties$state=="louisiana"]) # note issue Louisiana counties
cnty2019_all <- county_2019
#unique(cnty2019_all$name[cnty2019_all$state=="Louisiana"]) # note issue Louisiana counties

cnty2019_all <- cnty2019_all |> 
  mutate(state = tolower(state),
         county = tolower(name),
         county = gsub(" county", "", county),
         county = gsub(" parish", "", county),
         county = gsub("\\.", "", county),          # \\ is required because . used in R coding
         county = gsub(" ", "", county),
         county = gsub("'","", county)) |>
  relocate(county, .before=name)

cnty2019_all <- full_join(us_counties,cnty2019_all)  # geo data and demographic data
```

County Demographic Plots

Data & Plot 1

• Creating a new dataset not required, but helpful

• Plot code could be converted into a function

```{r}
#|label: select data and plot 1 map
cnty_data1 <- cnty2019_all |> 
  select(long:county, hs_grad, bachelors, 
         household_has_computer, 
         household_has_broadband) 
cnty_hs_grad <- cnty_data1 |>
  ggplot(aes(x=long, y=lat, 
             group=group, fill=hs_grad)) +
    geom_polygon() +
    theme_map() +
    coord_map("albers", lat0 = 39, lat1 = 45) +
    labs(fill= "", title="Percent with High School Degree") +
    scale_fill_continuous(type = "viridis") + 
    theme(plot.background = element_rect(fill = "lightgrey", color = NA),
          legend.key.size = unit(.4, 'cm'),
          plot.title = element_text(size = 10),
          legend.text= element_text(size = 8))
```

Font in plot adjusted for screen.

Plot 2

```{r}
#|label: plot 2 code
cnty_bachelors <- cnty_data1 |>
  ggplot(aes(x=long, y=lat, 
             group=group, 
             fill=bachelors)) +
    geom_polygon() +
    theme_map() +
    coord_map("albers", lat0 = 39, lat1 = 45) +
    labs(fill= "", title="Percent with Bachelor's Degree") +
    scale_fill_continuous(type = "viridis") + 
    theme(plot.background = element_rect(fill = "lightgrey", color = NA),
          legend.key.size = unit(.4, 'cm'),
          plot.title = element_text(size = 10),
          legend.text= element_text(size = 8))
```

Font in plot adjusted for screen.

Plot 3

```{r}
#|label:  plot 3 code
cnty_computer <- cnty_data1 |>
  ggplot(aes(x=long, y=lat, 
             group=group, 
             fill=household_has_computer)) +
    geom_polygon() +
    theme_map() +
    coord_map("albers", lat0 = 39, lat1 = 45) +
    labs(fill= "", title="Percent of Households with A Computer")+
    scale_fill_continuous(type = "viridis") + 
    theme(plot.background = element_rect(fill = "lightgrey", color = NA),
          legend.key.size = unit(.4, 'cm'),
          plot.title = element_text(size = 10),
          legend.text= element_text(size = 8))
```

Font in plot adjusted for screen

Plot 4

```{r plot 4 code}
#|label:  plot 4 code
cnty_brdbd <- cnty_data1 |>
  ggplot(aes(x=long, y=lat, 
             group=group, 
             fill=household_has_broadband)) +
    geom_polygon() +
    theme_map() +
    coord_map("albers", lat0 = 39, lat1 = 45) +
    labs(fill= "", title="Percent of Households with BroadBand") +
    scale_fill_continuous(type = "viridis") + 
    theme(plot.background = element_rect(fill = "lightgrey", color = NA),
          legend.key.size = unit(.4, 'cm'),
          plot.title = element_text(size = 10),
          legend.text= element_text(size = 8))
```

Font in plot adjusted for screen

Demographic Plot Grid - Order Matters

Grid is populated left to right and then top to bottom unless otherwise specified.

```{r eval=F}
#|label: grid of pct plots

# note alternative to grid.arrange used
# code shown is for export version of plots
grid <- plot_grid(cnty_hs_grad, cnty_computer, cnty_bachelors, cnty_brdbd, ncol=2)
```

In-class Exercise - Plot Grid (Steps 1 & 2)

Steps to Follow

1. Examine the available variables in the cnty_2019_all dataset saved from R.

2. Create Individual Plots. Variable names in R dataset and definitions:

   • household_has_smartphone: Households with Smart Phones
   • median_age: Median Age
   • median_household_income: Median Household Income
   • median_individual_income: Median Individual Income

• You can copy provided plot code and modify it for these variables or you could try to create a function (not required today).

```{r}
#|label: usmaps demographic maps exercise
# select data for plot (not required, but helpful)
# cnty_data2 <- 

# create four individual plots, one for each variable
# use provided plot code and modify
```

In-class Exercise - Plot Grid (Steps 3 & 4)

3. Create 2x2 plot grid of these four variables by US County as part of your class participation credit for this week.

• For full credit, plots must be in the order specified.

  • Row 1: should have smartphone variable and median age.
  • Row 2: median household and individual income.

• Create Plot Grid using plot_grid command.

```{r}
#|label: in-class exercise 2x2 plot grid
# for full credit grid of plots must be in order specified
# use plot_grid command
# export plots to img folder using save_plot 
```

4. Right click on plot grid, then save as… and save to img folder with correct name.

• or use save_plot command

Mapping Log Transformed Data

• The previous examples above are all percent data.

• No transformations are needed

• In contrast, population data or financial data are often right-skewed and need to be log transformed.

• Recall from MAS 261, BUA 345 (and perhaps FIN classes):

  • An effective transformation for right skewed data is the natural log (LN) transformation.

  • The following demo shows how useful it is for mapping right skewed data.

Plot of Skewed Data

```{r}
#|label: untransformed pop. map code

cnty_data3 <- cnty2019_all |> 
  select(long:county, pop) |>
  mutate(pop1k = pop/1000)
       
cnty_pop <- cnty_data3 |>
    ggplot(aes(x=long, y=lat, group=group, fill=pop1k)) +
    geom_polygon() +
    theme_map() +
    coord_map("albers", lat0 = 39, lat1 = 45) +
    labs(fill= "", title="Population by County",
         subtitle="Unit is 1000 People") +
    scale_fill_continuous(type = "viridis") +
    theme(legend.key.width = unit(.4, "cm"))
```

Histogram Clarifies Data Skewness

```{r}
#|label: untransformed pop hist code
hist_pop <- cnty_data3 |>
  ggplot() +
  geom_histogram(aes(x=pop1k),
                 fill="lightblue", 
                 col="darkblue") +
  labs(x="Population", title="Histogram of US Population Data",
       y = "Count",subtitle="Unit is 1000 People") +
  theme_classic()
```

The Problem: We see we have skewed data BUT presenting log transformed data in a map may complicate data interpretation.

Solution - Natural Log Transformed Plot

• Data are not transformed, but data axes and scale are

• Options specified in scale_fill_continuous:

  • trans = "log"
  • breaks = c(...)

• break intervals determined by examining data

```{r}
#|label: log transformed map plot code
cnty_lpop <- cnty_data3 |>
  ggplot(aes(x=long, y=lat, group=group, fill=pop1k)) +
    geom_polygon() +
    theme_map() +
    coord_map("albers", lat0 = 39, lat1 = 45) +
    labs(fill= "", title="Population by County",
         subtitle="Unit is 1000 People and Date are Log-transformed") +
    scale_fill_continuous(type = "viridis",trans="log",
                          breaks=c(1,10,100,1000,10000)) +
    theme(legend.key.width = unit(.4, "cm"))
```

Histogram of Log transformed Data

• Final dashboard doesn’t include exporatory plots
• Data exploration plots:
  • Histograms, Scatterplots and boxplots are all useful

```{r}
#|label: log transformed pop hist code
hist_lpop <- cnty_data3 |>
  ggplot() +
  geom_histogram(aes(x=pop1k),fill="lightblue", col="darkblue") +
  labs(x="Population", 
       title="Histogram of Natural Log of US Population Data",
       y = "Count",
       subtitle="Unit is 1000 People and Data are Log-transformed") +
  theme_classic() + 
  scale_x_continuous(trans="log", breaks=c(1,10,100,1000,10000))
```

Histogram of Log-transformed Data

Population Plot Grid

```{r eval=F}
#|label: plot code for pop grid
grid.arrange(cnty_pop, hist_pop,cnty_lpop, hist_lpop, ncol=2) 
```

When and How to Log Transform

• Log transformation are useful if you have right skewed POSITIVE data such as

  • Prices
  • Population
  • Sales
  • Income

• Note: If data (x) have zeros, a good option is to use log(x + 1)

  • ln(1) = 0 (In R log(1) = 0)

  • 0 values in the data will still be zeros

• In the following example we will create plots for number of households by county:

  • Histograms with and without LN transformation

  • Map plots of with and without LN transformation

Number of Households Per County

Without Transformation

Untransformed Data Histogram

```{r}
#|label: data and untransformed hh data histogram code
#|
cnty_data4 <- cnty2019_all |> 
  select(long:county, households) |>
  mutate(households1K = households/1000)

hist_hholds <- cnty_data4 |>
  ggplot() +
  geom_histogram(aes(x=households1K),
                 fill="lightblue", 
                 col="darkblue") +
  labs(x="Number of Households", title="Histogram of US Household Data",
       y = "Count",subtitle="Unit is 1000 Households") +
  theme_classic()
```

Number of Households is highly right-skewed.

Number of Households Per County

With Transformation

Log transformed Data Histogram

```{r}
#|label: log transformed hh data histogram code
hist_lhholds <- cnty_data4 |>
  ggplot() +
  geom_histogram(aes(x=households1K),
                 fill="lightblue", 
                 col="darkblue") +
  labs(x="Number of Households", 
       title="Histogram of Natural Log of US Household Data",
       y = "Count",
       subtitle="Unit is 1000 Households and Data are Log-transformed") +
  theme_classic() + 
  scale_x_continuous(trans="log", breaks=c(1,10,100,1000))
```

Log-transformed data appear normally distributed

Number of Households Per County

```{r untransformed hh plot map code}
cnty_hholds <- cnty_data4 |>
  ggplot(aes(x=long, y=lat, group=group, fill=households1K)) +
  geom_polygon() +
  theme_map() +
  coord_map("albers", lat0 = 39, lat1 = 45) +
  labs(fill= "", title="Number of Households by County",
       subtitle="Unit is 1000 Households") +
  scale_fill_continuous(type = "viridis") +
  theme(legend.key.width = unit(.4, "cm"))
```

Map of untransformed households per county data is uninformative.

In-class Exercise 2

Log-transformed data map is more more informative about geographic variability.

Submit R code to create log transformed households map in a text (.txt) file with your name.

Households per County Plot Grid

```{r household grid plot code, eval=F}
grid.arrange(hist_hholds, hist_lhholds, cnty_hholds, cnty_lhholds, ncol=2)
```

Quiz 2 Information

• Questions and Material from Quiz 1 may be on Quiz 2

• Practice Questions will be posted by 10/31

  • Review Quiz 1 and Quiz 1 Practice Questions

  • Review Week HW 4 and HW 5 - Part 1 and recent lectures

• Study Tip: Feel free to add on to practice questions .qmd file with extra chunks and notes so that all of your notes are in one place.

Quiz 2 Information Cont’d

• Converting text (character) date information to a date using lubridate commands (Week 5)

  • Example R commands:ymd, dmy, mdy, ym combined with paste to combine columns

• Extracting year, month, or day from the date variable using lubridate commands (Week 6)

  • Example R commands: year, month, quarter, wday, day

• Converting an xts dataset to a tibble (standard R dataset) (Week 7)

  • Creating a lineplot from time series (non-xts) dataset

• Converting a tibble to an xts dataset

  • Creating an interactive hchart

Quiz 2 Info Cont’d

• You should be familiar with the bls_tidy function we created and how to use it to import similar datasets.

• There will be datasets to be imported AND combined (joined)

  • Data sets can be joined by row.

  • You should know how to do the different joins we covered and what each one does:

    • full_join

    • right_join

    • left_join

    • inner_join

• Data sets can be stacked by column if the columns are identical

  • In BUA 455 we covered bind_rows

Quiz 2 Info Cont’d

• Cleaning messy data (Week 5 and Weeks 8-9)

  • Dealing with text (character variables)

    • gsub

    • separate

    • unite or paste or paste0

    • ifelse can be be used for text OR for numeric data

    • ifelse followed by factor allows you to make any categorical variable you want.

• Other commands for modifying text:

  • tolower and toupper

  • str_trim, str_squish and str_pad

Additional Text Commands

Additional skills for Quiz 2 from HW 5 - Part 1:

• summing across rows using sum(c_across(...))

• using pivot_wider and then pivot_longer and then replacing NAs with 0 to create a ‘complete’ data set

  • Useful for area plots

• Plotting skills for Quiz 2

  • unformatted line plot, area plot, or grouped bar chart

  • hchart in highcharter package

NOT ON QUIZ 2:

• Commands to covert case

• str_to_title: First letter of each word

• str_to_sentence: First letter of first word

Key Points

Introduction to Geographic Data

• Use skills already covered to

  • clean data and check text variables

  • join datasets

  • create plot grids for comparing variables

• Determine if variables need to be log-transformed.

• Quiz 2 Practice Questions are posted.

• Quiz 2 will be on Thursday, 4/3.

You may submit an ‘Engagement Question’ about each lecture until midnight on the day of the lecture. A minimum of four submissions are required during the semester.