Tara Sperber

3 of my favourite graphs

  1. Graph 1

    A graph of the American Indian population in the midwest USA.

ggplot(midwest, aes(x = poptotal, y = popamerindian)) + geom_point(colour = "red") + geom_smooth(colour = "yellow") 
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

  1. Graph 2
bbox_l <- bbox <- osmplotr::get_bbox(c(77.56,12.93,77.63,12.96))
bbox_l
##     min   max
## x 77.56 77.63
## y 12.93 12.96
dat_B <- extract_osm_objects(key = "building", bbox = bbox_l) 
## Issuing query to Overpass API ...
## Rate limit: 2
## Query complete!
## converting OSM data to sf format
dat_H <- extract_osm_objects(key = 'highway', bbox = bbox_l)
## Issuing query to Overpass API ...
## Rate limit: 2
## Query complete!
## converting OSM data to sf format
dat_P <- extract_osm_objects(key = 'park', bbox = bbox_l)
## Issuing query to Overpass API ...
## Rate limit: 2
## Query complete!
## converting OSM data to sf format
dat_G <- extract_osm_objects(key = 'landuse', value = 'grass', bbox = bbox_l)
## Issuing query to Overpass API ...
## Rate limit: 2
## Request failed [429]. Retrying in 1.5 seconds...
## Query complete!
## converting OSM data to sf format
dat_T <- extract_osm_objects(key = 'natural', value = 'tree', bbox = bbox_l)
## Issuing query to Overpass API ...
## Rate limit: 2
## Query complete!
## converting OSM data to sf format

Bangalore map

This map visualises the highways, parks and trees around Bangalore, my hometown. The gray are the highways, orange the parks and purple are the trees.

tm_shape(dat_B) + tm_polygons(col = "gray40") +
tm_shape(dat_G) + tm_fill(size = 4, col = "orange") +
tm_shape(dat_H) + tm_dots(col = "purple") +
tm_shape(dat_T) + tm_dots(col = "green") +

tm_layout(title = "Bangalore", title.size = 6, frame = TRUE, frame.lwd = 5, bg.color = "lightyellow")

Network Graph

bojack_nodes <- read_csv("./Data/bojack-nodes.csv",trim_ws = TRUE) %>% 
  select(1:4) %>% 
  drop_na()
## Rows: 15 Columns: 4
## -- Column specification --------------------------------------------------------
## Delimiter: ","
## chr (3): Name, Sex, Animal
## dbl (1): Season
## 
## i Use `spec()` to retrieve the full column specification for this data.
## i Specify the column types or set `show_col_types = FALSE` to quiet this message.
bojack_edges <- read_csv("./Data/bojack-edges.csv",trim_ws = TRUE)%>% 
  select(1:4) %>% 
  drop_na()
## Rows: 20 Columns: 4
## -- Column specification --------------------------------------------------------
## Delimiter: ","
## chr (3): from, to, Type
## dbl (1): weight
## 
## i Use `spec()` to retrieve the full column specification for this data.
## i Specify the column types or set `show_col_types = FALSE` to quiet this message.
bojack_nodes
## # A tibble: 15 x 4
##    Name                Sex    Animal       Season
##    <chr>               <chr>  <chr>         <dbl>
##  1 Bojack Horseman     male   Horse             1
##  2 Princess Carolyn    female Cat               1
##  3 Diane Nguyen        female Human             1
##  4 Todd Chavez         male   Human             1
##  5 Mr. Peanutbutter    male   Dog               1
##  6 Vincent Adultman    male   Human             1
##  7 Secretariat         male   Horse             1
##  8 Dick Cavett         male   Human             1
##  9 Random character    female Human             1
## 10 Random character #2 female Human             1
## 11 Sebastian St. Clair male   Snow Leopard      1
## 12 Random character #3 male   Horse             1
## 13 Lenny Turtletaub    male   Turtle            1
## 14 Kelsey Jannings     female Human             1
## 15 Photographers       male   Human             1
bojack_edges
## # A tibble: 20 x 4
##    from             to                  weight Type        
##    <chr>            <chr>                <dbl> <chr>       
##  1 Dick Cavett      Secretariat              1 Professional
##  2 Mr. Peanutbutter Bojack Horseman          3 Friends     
##  3 Bojack Horseman  Todd Chavez              3 Friends     
##  4 Princess Carolyn Bojack Horseman          3 Friends     
##  5 Vincent Adultman Bojack Horseman          1 Friends     
##  6 Vincent Adultman Princess Carolyn         4 Partners    
##  7 Mr. Peanutbutter Vincent Adultman         1 Friends     
##  8 Princess Carolyn Mr. Peanutbutter         1 Friends     
##  9 Bojack Horseman  Random character         1 Acquaintance
## 10 Bojack Horseman  Random character #2      1 Acquaintance
## 11 Bojack Horseman  Random character #3      1 Acquaintance
## 12 Diane Nguyen     Photographers            1 Professional
## 13 Diane Nguyen     Sebastian St. Clair      2 Professional
## 14 Mr. Peanutbutter Todd Chavez              4 Friends     
## 15 Diane Nguyen     Mr. Peanutbutter         1 Partners    
## 16 Bojack Horseman  Lenny Turtletaub         2 Professional
## 17 Bojack Horseman  Kelsey Jannings          2 Professional
## 18 Kelsey Jannings  Lenny Turtletaub         1 Professional
## 19 Princess Carolyn Diane Nguyen             1 Friends     
## 20 Bojack Horseman  Diane Nguyen             1 Friends
  1. Graph 3

    This graph shows the connections/interactions between characters appearing in Season 1, Episode 12 of Bojack Horseman.

bojack <- tbl_graph(nodes = bojack_nodes, 
                    edges = bojack_edges,
                    directed = FALSE)

ggraph(graph = bojack,  layout = "kk") +
geom_edge_link(width = 2, color = "black") +
geom_node_point(shape = 21, size = 8, fill = "pink", color = "red", stroke = 2) 

Reflection

Learning this particular language of code was a different experience for me. I’ve had several encounters with code and they were always very stressful. This course was also stressful but one thing different is that I enjoyed learning and working with it.