Set up

library(tidyverse)
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library(broom) # Tidy model results
library(umap) #dimension reduction
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library(plotly) # Interactive Visualization
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employed <- read_csv("https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2021/2021-02-23/employed.csv")
## Rows: 8184 Columns: 7
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (4): industry, major_occupation, minor_occupation, race_gender
## dbl (3): industry_total, employ_n, year
## 
## 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.

1 Convert data to standardized form

employed_grouped <- employed %>%
  filter(!is.na(employ_n)) %>%
  group_by(occupation = paste(industry, minor_occupation), race_gender) %>%
  summarise(n = sum(employ_n)) %>%
  ungroup()
## `summarise()` has grouped output by 'occupation'. You can override using the
## `.groups` argument.
 employed_tidy <- employed_grouped %>%


#Remove total category
filter(race_gender != "TOTAL") %>%

# Add total column
  left_join(employed_grouped %>%
              filter(race_gender == "TOTAL") %>%
              select(occupation, total = n)) %>%
  
  # Get pct in total
  mutate(pct = n/total) %>%
  
    # Remove Outliers
  
  filter(total >1000) %>%
  select(-n)
## Joining with `by = join_by(occupation)`
 employed_tidy
## # A tibble: 1,160 × 4
##    occupation                                          race_gender  total    pct
##    <chr>                                               <chr>        <dbl>  <dbl>
##  1 Agriculture and related Construction and extractio… Asian       7.3 e4 0.0274
##  2 Agriculture and related Construction and extractio… Black or A… 7.3 e4 0.0822
##  3 Agriculture and related Construction and extractio… Men         7.3 e4 0.973 
##  4 Agriculture and related Construction and extractio… White       7.3 e4 0.863 
##  5 Agriculture and related Construction and extractio… Women       7.3 e4 0.0274
##  6 Agriculture and related Farming, fishing, and fore… Asian       5.74e6 0.0139
##  7 Agriculture and related Farming, fishing, and fore… Black or A… 5.74e6 0.0342
##  8 Agriculture and related Farming, fishing, and fore… Men         5.74e6 0.789 
##  9 Agriculture and related Farming, fishing, and fore… White       5.74e6 0.911 
## 10 Agriculture and related Farming, fishing, and fore… Women       5.74e6 0.211 
## # ℹ 1,150 more rows
 employed_standard <- employed_tidy %>%

# Standardize

group_by(race_gender) %>%
  mutate(pct = pct %>% scale() %>% as.numeric())

2 Spread to object - characteristic format

library(janitor)
## Warning: package 'janitor' was built under R version 4.4.3
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## Attaching package: 'janitor'
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##     chisq.test, fisher.test
occupation_demo_tbl <- employed_tidy %>%
  pivot_wider(names_from = race_gender, values_from = pct) %>%
  janitor::clean_names()


occupation_demo_tbl
## # A tibble: 232 × 7
##    occupation            total   asian black_or_african_ame…¹   men white  women
##    <chr>                 <dbl>   <dbl>                  <dbl> <dbl> <dbl>  <dbl>
##  1 Agriculture and rel… 7.3 e4 0.0274                 0.0822  0.973 0.863 0.0274
##  2 Agriculture and rel… 5.74e6 0.0139                 0.0342  0.789 0.911 0.211 
##  3 Agriculture and rel… 1.94e5 0.0155                 0.0309  0.985 0.918 0.0103
##  4 Agriculture and rel… 1.01e6 0.00992                0.00794 0.739 0.967 0.261 
##  5 Agriculture and rel… 5.22e6 0.00997                0.00882 0.741 0.962 0.259 
##  6 Agriculture and rel… 5.15e5 0.0233                 0.0155  0.159 0.938 0.841 
##  7 Agriculture and rel… 2.11e5 0.0332                 0.104   0.815 0.820 0.185 
##  8 Agriculture and rel… 2.95e5 0.0339                 0.0373  0.675 0.902 0.329 
##  9 Agriculture and rel… 8.80e4 0                      0.0682  0.864 0.875 0.136 
## 10 Agriculture and rel… 9.40e4 0                      0.0213  0.585 0.968 0.426 
## # ℹ 222 more rows
## # ℹ abbreviated name: ¹​black_or_african_american

3 Perform k-means clustering

Occupation_cluster <- kmeans(occupation_demo_tbl %>% select(-occupation), centers = 3, nstart = 20)
summary(Occupation_cluster)
##              Length Class  Mode   
## cluster      232    -none- numeric
## centers       18    -none- numeric
## totss          1    -none- numeric
## withinss       3    -none- numeric
## tot.withinss   1    -none- numeric
## betweenss      1    -none- numeric
## size           3    -none- numeric
## iter           1    -none- numeric
## ifault         1    -none- numeric
tidy(Occupation_cluster)
## # A tibble: 3 × 9
##     total  asian black_or_african_ame…¹   men white women  size withinss cluster
##     <dbl>  <dbl>                  <dbl> <dbl> <dbl> <dbl> <int>    <dbl> <fct>  
## 1  1.80e6 0.0424                  0.104 0.693 0.816 0.305   198  8.13e14 1      
## 2  5.41e7 0.0654                  0.124 0.533 0.774 0.467     8  4.86e15 2      
## 3  1.47e7 0.0684                  0.120 0.554 0.779 0.446    26  5.46e14 3      
## # ℹ abbreviated name: ¹​black_or_african_american
glance(Occupation_cluster)
## # A tibble: 1 × 4
##     totss tot.withinss betweenss  iter
##     <dbl>        <dbl>     <dbl> <int>
## 1 3.00e16      6.22e15   2.37e16     3
augment(Occupation_cluster, occupation_demo_tbl) %>%
  ggplot(aes(total, asian, color = .cluster)) +
  geom_point()

4 Select optimal number of clusters

kclusts <- tibble(k = 1:9) %>%
  mutate(kclust = map(.x = k, .f = ~  kmeans(occupation_demo_tbl %>% select(-occupation), centers = .x, nstart = 20)), glanced = map(.x = kclust, .f = glance))

kclusts %>%
  unnest(glanced) %>%
  ggplot(aes(k, tot.withinss))+
  geom_point() +
  geom_line()

final_cluster <- kmeans(occupation_demo_tbl %>% select(-occupation), centers = 5, nstart = 20)
augment(final_cluster, occupation_demo_tbl) %>%
  ggplot(aes(total, asian, color = .cluster)) +
  geom_point()

5 Reduce Dimensions using UMAP

library(dplyr)

umap_results <- occupation_demo_tbl %>%
  select(-occupation) %>%
  umap()

umap_results_tbl <- umap_results$layout %>%
  as.tibble() %>%
  bind_cols(occupation_demo_tbl %>% select(occupation))
## Warning: `as.tibble()` was deprecated in tibble 2.0.0.
## ℹ Please use `as_tibble()` instead.
## ℹ The signature and semantics have changed, see `?as_tibble`.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
## Warning: The `x` argument of `as_tibble.matrix()` must have unique column names if
## `.name_repair` is omitted as of tibble 2.0.0.
## ℹ Using compatibility `.name_repair`.
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## generated.
umap_results_tbl
## # A tibble: 232 × 3
##        V1    V2 occupation                                                      
##     <dbl> <dbl> <chr>                                                           
##  1 -5.99  -7.34 Agriculture and related Construction and extraction occupations 
##  2 -1.18   7.29 Agriculture and related Farming, fishing, and forestry occupati…
##  3 -1.91  -7.64 Agriculture and related Installation, maintenance, and repair o…
##  4  7.30  -1.81 Agriculture and related Manage-ment, business, and financial op…
##  5 -0.599  7.04 Agriculture and related Management, business, and financial ope…
##  6  2.71  -5.84 Agriculture and related Office and administrative support occup…
##  7 -1.54  -7.74 Agriculture and related Production occupations                  
##  8  0.175 -7.08 Agriculture and related Professional and related occupations    
##  9 -5.74  -7.55 Agriculture and related Protective service occupations          
## 10 -5.51  -7.81 Agriculture and related Sales and related occupations           
## # ℹ 222 more rows
umap_results_tbl %>%
  ggplot(aes(V1, V2, text = occupation)) +
  geom_point()

6 Visualize Clusters by adding k-means results

kmeans_umaps_tbl <- final_cluster %>%
  augment(occupation_demo_tbl) %>%
  select(occupation, .cluster) %>%


# Add umap results 

left_join(umap_results_tbl) %>%
  
  # Add employment info
  left_join(employed_tidy %>%
              select(-total) %>% 
              pivot_wider(names_from = race_gender, values_from = pct) %>%
              janitor::clean_names())
## Joining with `by = join_by(occupation)`
## Joining with `by = join_by(occupation)`
kmeans_umaps_tbl
## # A tibble: 232 × 9
##    occupation   .cluster     V1    V2   asian black_or_african_ame…¹   men white
##    <chr>        <fct>     <dbl> <dbl>   <dbl>                  <dbl> <dbl> <dbl>
##  1 Agriculture… 1        -5.99  -7.34 0.0274                 0.0822  0.973 0.863
##  2 Agriculture… 3        -1.18   7.29 0.0139                 0.0342  0.789 0.911
##  3 Agriculture… 1        -1.91  -7.64 0.0155                 0.0309  0.985 0.918
##  4 Agriculture… 1         7.30  -1.81 0.00992                0.00794 0.739 0.967
##  5 Agriculture… 3        -0.599  7.04 0.00997                0.00882 0.741 0.962
##  6 Agriculture… 1         2.71  -5.84 0.0233                 0.0155  0.159 0.938
##  7 Agriculture… 1        -1.54  -7.74 0.0332                 0.104   0.815 0.820
##  8 Agriculture… 1         0.175 -7.08 0.0339                 0.0373  0.675 0.902
##  9 Agriculture… 1        -5.74  -7.55 0                      0.0682  0.864 0.875
## 10 Agriculture… 1        -5.51  -7.81 0                      0.0213  0.585 0.968
## # ℹ 222 more rows
## # ℹ abbreviated name: ¹​black_or_african_american
## # ℹ 1 more variable: women <dbl>
g <- kmeans_umaps_tbl %>%
  #Create text label
  mutate(text_label = str_glue("Occupation: {occupation}
                               Cluster: {.cluster}
                               Asian: {asian %>% scales::percent(1)}
                               Women: {women %>% scales::percent(1)}")) %>%
  # Plot
  ggplot(aes(V1,V2,color = .cluster, text = text_label))+
           geom_point()

g %>% ggplotly(tooltip = "text")