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.
skimr::skim(employed)
Data summary
Name employed
Number of rows 8184
Number of columns 7
_______________________
Column type frequency:
character 4
numeric 3
________________________
Group variables None

Variable type: character

skim_variable n_missing complete_rate min max empty n_unique whitespace
industry 330 0.96 3 46 0 25 0
major_occupation 0 1.00 19 60 0 5 0
minor_occupation 0 1.00 22 59 0 12 0
race_gender 0 1.00 3 25 0 6 0

Variable type: numeric

skim_variable n_missing complete_rate mean sd p0 p25 p50 p75 p100 hist
industry_total 660 0.92 5077105.3 6056215.81 18000 767250 2484000.0 7643000 35894000 ▇▂▁▁▁
employ_n 660 0.92 461551.6 1267564.10 0 9000 65000.0 373000 20263000 ▇▁▁▁▁
year 0 1.00 2017.5 1.71 2015 2016 2017.5 2019 2020 ▇▃▃▃▃

#Convert Data Ton Standarized 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 %>%
    
    
    
    #Standarize 
    group_by(race_gender) %>%
    mutate(pct = pct %>% scale() %>% as.numeric()) %>%
    ungroup()  %>%
    mutate(total = total %>% log() %>% scale() %>% as.numeric())
 employed_standard
## # A tibble: 1,160 × 4
##    occupation                                          race_gender  total    pct
##    <chr>                                               <chr>        <dbl>  <dbl>
##  1 Agriculture and related Construction and extractio… Asian       -1.30  -0.539
##  2 Agriculture and related Construction and extractio… Black or A… -1.30  -0.405
##  3 Agriculture and related Construction and extractio… Men         -1.30   1.31 
##  4 Agriculture and related Construction and extractio… White       -1.30   0.725
##  5 Agriculture and related Construction and extractio… Women       -1.30  -1.30 
##  6 Agriculture and related Farming, fishing, and fore… Asian        0.819 -0.928
##  7 Agriculture and related Farming, fishing, and fore… Black or A…  0.819 -1.21 
##  8 Agriculture and related Farming, fishing, and fore… Men          0.819  0.510
##  9 Agriculture and related Farming, fishing, and fore… White        0.819  1.38 
## 10 Agriculture and related Farming, fishing, and fore… Women        0.819 -0.503
## # ℹ 1,150 more rows

#Spread to Object Characteristics format

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

#Perform K means clustering

occupation_cluster <- kmeans(occupation_demo_tbl %>%
  select(-occupation), centers = 3, nstart = 20)
#  visualize 
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  1.47e7 0.0684                  0.120 0.554 0.779 0.446    26  5.46e14 1      
## 2  5.41e7 0.0654                  0.124 0.533 0.774 0.467     8  4.86e15 2      
## 3  1.80e6 0.0424                  0.104 0.693 0.816 0.305   198  8.13e14 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()

#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()

#Reduce Dimensions using UMAP

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`.
## ℹ The deprecated feature was likely used in the tibble package.
##   Please report the issue at <https://github.com/tidyverse/tibble/issues>.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
umap_results_tbl
## # A tibble: 232 × 3
##         V1    V2 occupation                                                     
##      <dbl> <dbl> <chr>                                                          
##  1 -5.58   -7.85 Agriculture and related Construction and extraction occupations
##  2 -1.40    7.03 Agriculture and related Farming, fishing, and forestry occupat…
##  3  0.117  -9.32 Agriculture and related Installation, maintenance, and repair …
##  4  6.28   -1.94 Agriculture and related Manage-ment, business, and financial o…
##  5 -0.736   6.95 Agriculture and related Management, business, and financial op…
##  6  3.86   -5.85 Agriculture and related Office and administrative support occu…
##  7  0.0694 -9.24 Agriculture and related Production occupations                 
##  8  1.67   -8.06 Agriculture and related Professional and related occupations   
##  9 -5.49   -7.96 Agriculture and related Protective service occupations         
## 10 -5.32   -8.17 Agriculture and related Sales and related occupations          
## # ℹ 222 more rows
umap_results_tbl %>%
  ggplot(aes(V1, V2)) +
  geom_point()

#Visualize Clusters

kmeans_umap_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_umap_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 Agricultur… 3        -5.58   -7.85 0.0274                 0.0822  0.973 0.863
##  2 Agricultur… 5        -1.40    7.03 0.0139                 0.0342  0.789 0.911
##  3 Agricultur… 3         0.117  -9.32 0.0155                 0.0309  0.985 0.918
##  4 Agricultur… 3         6.28   -1.94 0.00992                0.00794 0.739 0.967
##  5 Agricultur… 5        -0.736   6.95 0.00997                0.00882 0.741 0.962
##  6 Agricultur… 3         3.86   -5.85 0.0233                 0.0155  0.159 0.938
##  7 Agricultur… 3         0.0694 -9.24 0.0332                 0.104   0.815 0.820
##  8 Agricultur… 3         1.67   -8.06 0.0339                 0.0373  0.675 0.902
##  9 Agricultur… 3        -5.49   -7.96 0                      0.0682  0.864 0.875
## 10 Agricultur… 3        -5.32   -8.17 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_umap_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")