USCOTS

Author

Rachel Saidi

Useful Models

geocode using geoapify

https://www.geoapify.com/tools/geocoding-online/

# A tibble: 6 × 11
  City      Address.x  Zip   Institution Primary Affiliation.…¹ Position.x State
  <chr>     <chr>      <chr> <chr>       <chr>                  <chr>      <chr>
1 Allendale A-1-178 M… 49401 Grand Vall… 4-year College         Faculty    MI   
2 Allendale A-1-178 M… 49401 Grand Vall… 4-year College         Faculty    MI   
3 Allendale 1 Campus … 4940… Grand Vall… 4-year College         Faculty    MI   
4 Allendale 1 Campus … 4940… Grand Vall… 4-year College         Faculty    MI   
5 Ames      2438 Osbo… 5001… Iowa State… Research Institution   Faculty    IA   
6 Ames      2438 Osbo… 5001… Iowa State… Research Institution   Faculty    IA   
# ℹ abbreviated name: ¹`Primary Affiliation.x`
# ℹ 4 more variables: `Work ZIP/Postal Code` <chr>, region <chr>, lat <dbl>,
#   lon <dbl>

Add regions and arrow end points

registra <- registr |>
  filter(Institution != "Other?(not?a?CAUSE?institutional?member")|>
  rename(count = n) |>
  mutate(
    region = case_when(
      State %in% c("CT", "ME", "MA", "NH", "RI", "VT", "NJ", "NY", "PA") ~ "Northeast",
      State %in% c("IL", "IN", "MI", "OH", "WI", "IA", "KS", "MN", "MO", "NE", "ND", "SD") ~ "Midwest",
      State %in% c("DE", "FL", "GA", "MD", "NC", "SC", "VA", "DC", "WV", "AL", "KY", "MI", "TN", "AR", "LA", "OK", "TX", "PR") ~ "South",
      State %in% c("AZ", "CO", "ID", "MT", "NV", "NM", "UT", "WY", "AK", "CA", "HI", "OR", "WA") ~ "West",
      FALSE ~ "Other")
  )|>
  filter(!is.na(region))

cause1 <- cause |>
  filter(!is.na(region))

Jitter the points

cause1$lat_jitter <- jitter(cause1$lat, factor = 0.0001)  # Adjust factor.
cause1$lon_jitter <- jitter(cause1$lon, factor = 0.0001)

Create a color palette for the regions

library(wesanderson) wes_palette(“FantasticFox1”)

pal <- colorFactor(palette = c("#7538a1", "#c7b816","#169bc7", "#c4313d"), 
  domain = cause1$region)

Set the default lat/long for the map

lat <-38.7946 
lon <- -97.5348 
popup = paste0(
        "<b>Institution:</b> ", cause1$Institution, "<br>",
        "<b>City:</b> ", cause1$City, "<br>",
        "<b>State:</b> ", cause1$State, "<br>")

m <- leaflet(data = cause1) |>
  addProviderTiles(providers$Thunderforest.Landscape) |>
  setView(lng = lon, lat = lat, zoom = 3.5) |>
  addTiles() |>
  addMarkers(
      lng = -93.6465, 
      lat = 42.0267,
      label = "ISU")|>
  addCircles(
    color = pal(cause1$region),
    popup = popup,
    radius = 1500,
    opacity = 0.6)|>
  addLegend("bottomright", pal = pal, values = cause1$region,
            title = "Region", opacity = 1)

Assuming "lon" and "lat" are longitude and latitude, respectively

calculate distance from Iowa State U

library(sf)

Warning: package 'sf' was built under R version 4.5.1

Linking to GEOS 3.13.1, GDAL 3.11.0, PROJ 9.6.0; sf_use_s2() is TRUE

df_sf <- st_as_sf(cause1, coords = c("lat", "lon"), crs = 4326)

# 4326 is WGS 84, a common CRS for geographic coordinates
df_sf <- st_set_crs(df_sf, 4326)

Use Haversine formula

library(geosphere)

Warning: package 'geosphere' was built under R version 4.5.1

ames_lon <- -93.616669
ames_lat <- 42.034737
cause1$distance <- distHaversine(
  p1 = cbind(cause1$lon, cause1$lat),
  p2 = c(ames_lon, ames_lat)
)
cause1$distance_miles <- cause1$distance/1609.344

Proportion of Faculty to students for each Primary Affiliation

library(knitr)
library(kableExtra)


Attaching package: 'kableExtra'

The following object is masked from 'package:dplyr':

    group_rows

cause_table <- cause1 |>
  filter(`Primary Affiliation.x` != "Other")|>
  mutate(type = case_when(`Primary Affiliation.x` %in% c("Research Institution", "4-year College") ~ "four-year",
                               `Primary Affiliation.x` == "2-year College" ~ "two-year",
                               TRUE ~ "Other"))|>
  select(type, Position.x) |>
  table()
kbl(cause_table, caption = "Two-Way Table") |> 
  kable_styling() |> 
  row_spec(row = 0, color = "#C41E3A")

Two-Way Table
	Faculty	Other	Student
four-year	183	5	30
Other	0	4	0
two-year	7	0	0

table of states

state_table <- cause1 |>
  group_by(State) |>
  tally() |>
  arrange(desc(n))
kbl(state_table, caption = "Two-Way Table") |> 
  kable_styling() |> 
  row_spec(row = 0, color = "#C41E3A")|>
   column_spec(1, width = "0.5in")

Two-Way Table
State	n
IA	33
CA	28
MN	28
MI	23
NY	19
PA	17
IL	15
NC	15
MO	9
OH	9
GA	5
KY	4
OR	4
MA	3
AZ	2
FL	2
MD	2
NJ	2
WI	2
CO	1
IN	1
KS	1
NE	1
NM	1
TX	1
VA	1
VT	1
WA	1

State	n
IA	33
CA	28
MN	28
MI	23
NY	19
PA	17
IL	15
NC	15
MO	9
OH	9
GA	5
KY	4
OR	4
MA	3
AZ	2
FL	2
MD	2
NJ	2
WI	2
CO	1
IN	1
KS	1
NE	1
NM	1
TX	1
VA	1
VT	1
WA	1

State	n
IA	33
CA	28
MN	28
MI	23
NY	19
PA	17
IL	15
NC	15
MO	9
OH	9
GA	5
KY	4
OR	4
MA	3
AZ	2
FL	2
MD	2
NJ	2
WI	2
CO	1
IN	1
KS	1
NE	1
NM	1
TX	1
VA	1
VT	1
WA	1

State	n
IA	33
CA	28
MN	28
MI	23
NY	19
PA	17
IL	15
NC	15
MO	9
OH	9
GA	5
KY	4
OR	4
MA	3
AZ	2
FL	2
MD	2
NJ	2
WI	2
CO	1
IN	1
KS	1
NE	1
NM	1
TX	1
VA	1
VT	1
WA	1