Claire Battaglia
text-as-data
blog post 6
open-text survey response
unsupervised learning
topic models
causal inference
Author

Claire Battaglia

Published

December 4, 2022

Code
library(readxl)
library(plyr)
library(tidyverse)
library(tidytext)
library(quanteda)
library(quanteda.textplots)
library(gmodels)
library(stm)

knitr::opts_chunk$set(echo = TRUE)

For this final blog post I will be exploring Structural Topic Modeling (STM) further and thinking about causal inference in the context of my research question and hypotheses.

After thinking about my dataset and research question further, I have decided to consider some additional covariates so I’ll begin with reworking my dataset, corpus, and Document Feature Matrix to include those covariates. Then I’ll explore other values for \(k\) and spend some time thinking about the substantive interpretation of my results.

Creating My Corpus

Instead of messing around with the code in my previous blog posts, I will create a new tidy dataset with the additional covariates I’m going to include and a new corpus and Document Feature Matrix (DFM) from which to work.

Code
# read data
CFA_raw <- read_excel("FPAB Community Food Assessment Survey Data.xlsx", sheet = "Raw")
New names:
• `` -> `...15`
• `` -> `...16`
• `` -> `...17`
• `` -> `...18`
• `` -> `...19`
• `` -> `...20`
• `` -> `...21`
• `` -> `...22`
• `` -> `...23`
• `` -> `...24`
• `` -> `...26`
• `` -> `...27`
• `` -> `...28`
• `` -> `...29`
• `` -> `...30`
• `` -> `...31`
• `` -> `...32`
• `` -> `...34`
• `` -> `...35`
• `` -> `...36`
• `` -> `...37`
• `` -> `...38`
• `` -> `...39`
• `` -> `...40`
• `` -> `...43`
• `` -> `...44`
• `` -> `...45`
• `` -> `...46`
• `` -> `...47`
• `` -> `...48`
• `` -> `...50`
• `` -> `...51`
• `` -> `...52`
• `` -> `...53`
• `` -> `...54`
• `` -> `...55`
• `` -> `...56`
• `` -> `...57`
• `` -> `...58`
• `` -> `...60`
• `` -> `...61`
• `` -> `...62`
• `` -> `...63`
• `` -> `...64`
• `` -> `...65`
• `` -> `...66`
• `` -> `...67`
• `` -> `...68`
• `` -> `...69`
Code
# rename columns
CFA_tidy_final <- CFA_raw %>%
  rename("id" = "Respondent ID",
         "zip" = "Zip Code",
         "age" = "Age",
         "income" = "Annual Household Income",
         "size" = "Number of Individuals in Your Household",
         "change" = "What changes would you like to see for Missoula’s food system?")

# create subset
CFA_tidy_final <- CFA_tidy_final %>%
  subset(select = c("zip", "age", "income", "size", "change"))

# change class
CFA_tidy_final$zip <- as.character(CFA_tidy_final$zip)
CFA_tidy_final$age <- as.numeric(CFA_tidy_final$age)
Warning: NAs introduced by coercion
Code
CFA_tidy_final$size <- as.numeric(CFA_tidy_final$size)
Warning: NAs introduced by coercion
Code
# remove na values
CFA_tidy_final <- na.omit(CFA_tidy_final)

# save
save(CFA_tidy_final, file = "CFA_tidy_final.RData")

I now have a new tidy dataset that includes 4 covariates:

  1. age of respondent (age)
  2. number of people living in the household (size)
  3. annual household income bracket (income)
  4. ZIP code in which household of respondent is located (zip)
Code
# remove row where change = ?
CFA_tidy_final <- subset(CFA_tidy_final, change !="?")

# create dfm
dfm_change_final <- dfm(tokens(CFA_tidy_final$change),
             tolower = TRUE,
             remove = stopwords("en"),
             remove_punct = TRUE)
Warning: '...' should not be used for tokens() arguments; use 'tokens()' first.
Warning: 'remove' is deprecated; use dfm_remove() instead
Code
# get dimensions
dim(dfm_change_final)
[1]  208 1125

My new DFM contains 208 rows (the responses) and 1,125 columns (all the words that appear in the corpus).

Code
# frequency counts for covariates
table(CFA_tidy_final$age) # age

20 22 23 24 25 26 27 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 45 46 47 48 
 1  4  1  3  1  1  3  5  8  5  6  5  8  4  7  7  2  2  1  3  7  6  9  7  8  4 
49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 72 73 75 76 
 1  4  2  5  3  3  4  5  4  1  4  5  3  3  2  3  5  3  2  6  4  2  3  3  5  2 
77 80 83 
 1  1  1 
Code
table(CFA_tidy_final$size) # size

 1  2  3  4  5  6  8 
52 85 28 31  9  2  1 
Code
table(CFA_tidy_final$income) # income

Between $100,000 and $249,999   Between $25,000 and $34,999 
                           54                            21 
  Between $35,000 and $49,999   Between $50,000 and $99,999 
                           34                            64 
                Over $250,000                 Under $25,000 
                            8                            27 
Code
table(CFA_tidy_final$zip) # zip

59701 59801 59802 59803 59804 59808 59821 59823 59825 59826 59833 59846 59847 
    1    84    59    20     8    13     1     5     4     1     2     1     5 
59860 59864 59868 59908 
    1     1     1     1 

This is a horrible way to try and understand frequency and distribution so I’ll create some simple visualizations to make this information more accessible.

Code
# create hist - age
ggplot(CFA_tidy_final, aes(x = age)) +
  geom_histogram(fill = "#830042") +
  labs(title = "Respondent Age", x = "Age", y = NULL) +
  coord_flip() +
  theme_minimal()
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

Code
# create hist - size
ggplot(CFA_tidy_final, aes(x = size)) +
  geom_histogram(fill = "#830042") +
  labs(title = "Number of People Living in Household", x = "Number of People", y = NULL) +
  coord_flip() +
  theme_minimal()
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

Code
# create bar plot - income
ggplot(CFA_tidy_final, aes(x = income)) +
  geom_bar(fill = "#830042") +
  labs(title = "Annual Household Income Bracket of Respondent", x = "Income Bracket", y = NULL) +
  coord_flip() +
  theme_minimal()

Code
# create bar plot - zip
ggplot(CFA_tidy_final, aes(x = zip)) +
  geom_bar(fill = "#830042") +
  labs(title = "ZIP Code of Household", x = "Zip Code", y = NULL) +
  coord_flip() +
  theme_minimal()

These are very simple but they give a much better sense of the distribution of respondents across each covariate.

It seems that most respondents:

  • Are between 30 and 60 years old
  • Live in 2-person households
  • Have an annual household income between $50,000-250,000
  • Live in 59801, 59802, 59802 (3 of the central ZIP codes in the City of Missoula)

Document Feature Matrix

Next I’ll visualize my DFM with a word cloud.

Code
# create wordcloud
# png(filename = "dfm_change_final_WORD.png")
textplot_wordcloud(dfm_change_final, min_count = 5, max_words = 50, random_order = FALSE)

Code
# dev.off()

Structured Topic Model(s)

Now I am ready to construct my Structured Topic Model(s). As I mentioned above, I’ll be exploring different values for \(k\), as well as thinking about the substantive interpretation of my models.

Code
# searchK
differentKs <- searchK(dfm_change_final,
                       K = c(3, 5, 7),
                       prevalence = ~ age + size + income + zip,
                       N = 208,
                       data = CFA_tidy_final,
                       max.em.its = 1000,
                       init.type = "Spectral")
Beginning Spectral Initialization 
     Calculating the gram matrix...
     Finding anchor words...
    ...
     Recovering initialization...
    .......
Initialization complete.
...............................
Error in hpbcpp(optim.out$par, doc_ct = doc.ct, mu = mu, siginv = siginv, : chol(): decomposition failed
Code
# diagnostic plots
plot(differentKs)
Error in plot(differentKs): object 'differentKs' not found

The plot of Held-Out Likelihood would lead us to the 3-topic model. The plot of Semantic Coherence would lead us to the 7-topic model. The Residuals plot would also lead us to the 7-topic model. These are not as conclusive as I was hoping they would so, given that, I am going to construct all 3 models.

Code
# model w/ 3 topics
stm_change_final_3 <- stm(dfm_change_final,
               K = 3,
               prevalence = ~ age + size + income + zip,
               data = CFA_tidy_final,
               max.em.its = 1000,
               seed = 1234,
               init.type = "Spectral")
Beginning Spectral Initialization 
     Calculating the gram matrix...
     Finding anchor words...
    ...
     Recovering initialization...
    ...........
Initialization complete.
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Topic 1: affordable, access, sustainable, stores, education 
 Topic 2: local, land, like, community, produce 
 Topic 3: food, farmers, see, locally, options 
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Topic 1: local, food, support, grocery, stores 
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Model Converged 
Code
# model w/ 5 topics
stm_change_final_5 <- stm(dfm_change_final,
               K = 5,
               prevalence = ~ age + size + income + zip,
               data = CFA_tidy_final,
               max.em.its = 1000,
               seed = 1234,
               init.type = "Spectral")
Beginning Spectral Initialization 
     Calculating the gram matrix...
     Finding anchor words...
    .....
     Recovering initialization...
    ...........
Initialization complete.
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Topic 1: land, community, less, use, producers 
 Topic 2: farmers, support, local, greater, ag 
 Topic 3: affordable, options, healthy, gardens, can 
 Topic 4: food, local, like, see, farm 
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Completing Iteration 20 (approx. per word bound = -6.108, relative change = 3.421e-04) 
Topic 1: land, community, less, use, producers 
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Completing Iteration 55 (approx. per word bound = -5.977, relative change = 5.593e-04) 
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Completing Iteration 60 (approx. per word bound = -5.962, relative change = 5.788e-04) 
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Completing Iteration 65 (approx. per word bound = -5.946, relative change = 4.878e-04) 
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Completing Iteration 70 (approx. per word bound = -5.930, relative change = 5.093e-04) 
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Completing Iteration 71 (approx. per word bound = -5.927, relative change = 4.609e-04) 
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Completing Iteration 80 (approx. per word bound = -5.903, relative change = 4.188e-04) 
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Completing Iteration 85 (approx. per word bound = -5.893, relative change = 3.836e-04) 
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Completing Iteration 90 (approx. per word bound = -5.884, relative change = 3.250e-04) 
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Completing Iteration 95 (approx. per word bound = -5.875, relative change = 2.571e-04) 
Topic 1: land, community, less, use, producers 
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Topic 1: land, food, community, less, use 
 Topic 2: local, food, farmers, ag, year 
 Topic 3: affordable, gardens, farm, healthy, options 
 Topic 4: food, see, like, local, sustainable 
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Model Converged 
Code
# model w/ 7 topics
stm_change_final_7 <- stm(dfm_change_final,
               K = 7,
               prevalence = ~ age + size + income + zip,
               data = CFA_tidy_final,
               max.em.its = 1000,
               seed = 1234,
               init.type = "Spectral")
Beginning Spectral Initialization 
     Calculating the gram matrix...
     Finding anchor words...
    .......
     Recovering initialization...
    ...........
Initialization complete.
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Completing Iteration 5 (approx. per word bound = -6.121, relative change = 2.577e-03) 
Topic 1: stores, good, available, housing, accessible 
 Topic 2: food, support, access, grocery, missoula 
 Topic 3: land, foods, grown, use, know 
 Topic 4: local, food, see, produce, locally 
 Topic 5: farmers, like, healthy, fresh, city 
 Topic 6: community, gardens, markets, school, people 
 Topic 7: affordable, options, market, can, greater 
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Completing Iteration 10 (approx. per word bound = -6.087, relative change = 9.493e-04) 
Topic 1: stores, good, available, see, housing 
 Topic 2: food, support, access, grocery, missoula 
 Topic 3: land, foods, grown, agricultural, use 
 Topic 4: local, food, see, locally, produce 
 Topic 5: farmers, healthy, like, fresh, expensive 
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 Topic 7: affordable, options, market, can, greater 
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Completing Iteration 11 (approx. per word bound = -6.081, relative change = 1.014e-03) 
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Completing Iteration 15 (approx. per word bound = -6.060, relative change = 7.641e-04) 
Topic 1: stores, good, available, see, urban 
 Topic 2: food, support, access, grocery, missoula 
 Topic 3: land, foods, agricultural, grown, use 
 Topic 4: local, food, like, see, locally 
 Topic 5: farmers, healthy, fresh, expensive, like 
 Topic 6: community, gardens, markets, school, people 
 Topic 7: affordable, options, market, can, greater 
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Completing Iteration 20 (approx. per word bound = -6.043, relative change = 6.533e-04) 
Topic 1: stores, good, available, see, urban 
 Topic 2: food, support, access, missoula, schools 
 Topic 3: land, foods, agricultural, grown, use 
 Topic 4: local, food, like, see, locally 
 Topic 5: farmers, healthy, fresh, expensive, year 
 Topic 6: community, gardens, markets, school, people 
 Topic 7: affordable, options, market, can, greater 
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Topic 1: stores, good, available, see, urban 
 Topic 2: food, support, missoula, access, farm 
 Topic 3: land, foods, agricultural, grown, use 
 Topic 4: local, food, see, like, locally 
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Topic 1: stores, good, available, like, see 
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Topic 1: stores, available, good, like, see 
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Topic 1: available, good, like, stores, much 
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Topic 1: available, like, good, see, stores 
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Topic 1: like, good, available, see, know 
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Topic 1: like, good, know, see, much 
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Topic 1: like, good, know, see, much 
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Topic 1: like, good, see, much, families 
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Topic 1: like, produce, families, see, much 
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Topic 1: like, produce, families, see, much 
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Topic 1: like, produce, families, see, much 
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Topic 1: like, produce, families, fresh, see 
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Topic 1: like, produce, families, fresh, see 
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Topic 1: like, produce, families, fresh, meat 
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Topic 1: like, produce, see, meat, families 
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Topic 1: like, produce, see, meat, fresh 
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Topic 1: like, produce, see, fresh, meat 
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Topic 1: like, produce, see, fresh, much 
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Topic 1: like, produce, see, fresh, food 
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Topic 1: like, produce, see, food, much 
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Topic 1: like, produce, see, food, local 
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Topic 1: like, food, produce, see, local 
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 Topic 3: land, foods, farm, affordable, agricultural 
 Topic 4: local, food, affordable, options, stores 
 Topic 5: food, farmers, farmland, see, make 
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Topic 1: like, food, produce, see, local 
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 Topic 3: land, foods, farm, affordable, agricultural 
 Topic 4: local, food, affordable, options, stores 
 Topic 5: food, farmers, farmland, see, make 
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 Topic 7: food, local, locally, grown, greater 
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Topic 1: like, food, produce, see, local 
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 Topic 3: land, foods, farm, affordable, agricultural 
 Topic 4: food, local, affordable, options, stores 
 Topic 5: food, farmers, farmland, see, market 
 Topic 6: community, school, gardens, like, see 
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 Topic 4: food, local, affordable, options, stores 
 Topic 5: food, farmers, farmland, see, market 
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 Topic 4: food, local, affordable, options, stores 
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Topic 1: like, food, produce, see, local 
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 Topic 4: food, local, affordable, options, stores 
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Topic 1: like, food, see, produce, local 
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 Topic 3: land, foods, farm, use, affordable 
 Topic 4: local, food, affordable, options, stores 
 Topic 5: food, farmers, farmland, see, make 
 Topic 6: community, school, gardens, like, see 
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Topic 1: like, food, see, produce, local 
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 Topic 3: land, foods, farm, use, affordable 
 Topic 4: local, food, affordable, options, stores 
 Topic 5: food, farmers, farmland, see, make 
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Topic 1: like, food, see, produce, local 
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 Topic 3: land, foods, farm, use, affordable 
 Topic 4: local, food, affordable, options, stores 
 Topic 5: food, farmers, farmland, see, make 
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Topic 1: like, food, see, produce, local 
 Topic 2: food, local, farmers, access, healthy 
 Topic 3: land, foods, farm, use, affordable 
 Topic 4: local, food, affordable, options, stores 
 Topic 5: food, farmland, farmers, see, make 
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Topic 1: like, food, see, produce, local 
 Topic 2: food, local, farmers, access, healthy 
 Topic 3: land, foods, farm, use, agricultural 
 Topic 4: local, food, affordable, options, stores 
 Topic 5: food, farmland, farmers, see, make 
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 Topic 7: food, local, locally, grown, greater 
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Topic 1: like, food, see, produce, local 
 Topic 2: food, local, farmers, access, healthy 
 Topic 3: land, foods, farm, use, agricultural 
 Topic 4: local, food, affordable, options, stores 
 Topic 5: food, farmland, farmers, see, make 
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Topic 1: like, food, see, local, produce 
 Topic 2: food, local, farmers, access, healthy 
 Topic 3: land, foods, farm, use, agricultural 
 Topic 4: local, food, affordable, options, stores 
 Topic 5: food, farmland, farmers, see, make 
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Topic 1: like, food, see, local, produce 
 Topic 2: food, local, farmers, access, healthy 
 Topic 3: land, foods, farm, use, agricultural 
 Topic 4: local, food, affordable, options, locally 
 Topic 5: food, farmland, farmers, see, make 
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Model Converged 
Code
# label topics - 3 topics
labelTopics(stm_change_final_3)
Topic 1 Top Words:
     Highest Prob: local, food, support, grocery, stores, farmers, produce 
     FREX: grocery, stores, afford, bank, restaurants, year, able 
     Lift: afford, bank, grocery, restaurants, stores, year, -or 
     Score: stores, cost, high, possible, missoula, bank, composting 
Topic 2 Top Words:
     Highest Prob: food, land, local, farm, affordable, farmers, healthy 
     FREX: use, ag, grow, buying, development, enough, feed 
     Lift: $, ag, among, crop, evidence-based, forest, livestock 
     Score: land, agricultural, use, $, among, crop, evidence-based 
Topic 3 Top Words:
     Highest Prob: food, like, see, local, locally, school, less 
     FREX: school, less, schools, know, health, winter, meals 
     Lift: school, #11, +, 1,000, 2-5x, additional, addressing 
     Score: poor, time, winter, school, know, health, never 
Code
plot(stm_change_final_3, type = "summary") # plot

Code
# label topics - 5 topics
labelTopics(stm_change_final_5)
Topic 1 Top Words:
     Highest Prob: land, food, community, less, use, agricultural, city 
     FREX: land, less, use, affordability, meals, community, county 
     Lift: 2-5x, accessed, advocacy, always, banks, boulevards, breakfast 
     Score: land, use, affordability, less, community, boulevards, production 
Topic 2 Top Words:
     Highest Prob: local, food, farmers, ag, year, climate, support 
     FREX: ag, winter, enough, farmer's, year, costs, practices 
     Lift: +, 1, 1,000, 10, acquisition, adaptation, adapted 
     Score: feeding, costs, ag, winter, among, evidence-based, meet 
Topic 3 Top Words:
     Highest Prob: affordable, gardens, farm, healthy, options, like, farmers 
     FREX: gardening, neighborhoods, preserve, increased, gardens, affordable, making 
     Lift: allowing, already, amounts, apartments, barriers, base, become 
     Score: poor, affordable, gardens, gardening, resource, increased, preserve 
Topic 4 Top Words:
     Highest Prob: food, see, like, local, sustainable, school, missoula 
     FREX: system, space, children, help, school, schools, sustainable 
     Lift: $, 5-, 6, abundant, addressing, alleviate, allowed 
     Score: csa, system, like, space, school, schools, lose 
Topic 5 Top Words:
     Highest Prob: local, food, locally, grocery, stores, produce, foods 
     FREX: locally, grocery, stores, afford, restaurants, store, accessible 
     Lift: -or, #11, adults, afford, albertsons, alone, applaud 
     Score: locally, composting, stores, grocery, shop, possible, lower 
Code
plot(stm_change_final_5, type = "summary") # plot

Code
# label topics - 7 topics
labelTopics(stm_change_final_7)
Topic 1 Top Words:
     Highest Prob: like, food, see, local, produce, much, market 
     FREX: farmer's, really, just, preservation, meat, much, like 
     Lift: care, meet, resource, +, 1,000, adapted, additional 
     Score: really, care, meet, resource, aid, since, long-term 
Topic 2 Top Words:
     Highest Prob: food, local, farmers, access, healthy, bank, missoula 
     FREX: many, stands, young, bank, basic, households, county 
     Lift: $, among, evidence-based, guess, might, 1, 10 
     Score: basic, young, many, supporting, bank, might, feeding 
Topic 3 Top Words:
     Highest Prob: land, foods, farm, use, agricultural, affordable, healthy 
     FREX: foods, land, use, ag, agricultural, farm, poor 
     Lift: 2-5x, acquisition, alone, always, aquisition, attention, barriers 
     Score: land, agricultural, poor, foods, use, ag, farm 
Topic 4 Top Words:
     Highest Prob: local, food, affordable, options, locally, stores, grocery 
     FREX: options, stores, organic, less, affordable, grocery, local 
     Lift: accessed, advocacy, already, apartments, aware, banks, berry 
     Score: options, csa, stores, locally, throughout, organic, affordable 
Topic 5 Top Words:
     Highest Prob: food, farmland, farmers, see, make, climate, market 
     FREX: composting, make, farmland, prices, climate, accessibility, large 
     Lift: -or, #11, abundant, address, albertsons, alleviate, along 
     Score: composting, make, space, cans, next, progress, ranchers 
Topic 6 Top Words:
     Highest Prob: community, school, gardens, like, food, see, local 
     FREX: neighborhoods, school, community, gardens, urban, round, gfs 
     Lift: boulevards, livestock, shares, adaptation, affordablity, allowed, allowing 
     Score: school, neighborhoods, gardens, urban, pigs, lunches, boulevards 
Topic 7 Top Words:
     Highest Prob: food, local, locally, grown, greater, like, can 
     FREX: produced, buying, greater, grown, groups, growers, packaging 
     Lift: adults, attend, bar, battle, buying, came, capacity 
     Score: growers, grown, consumers, produced, network, packaging, emphasis 
Code
plot(stm_change_final_7, type = "summary") # plot

Looking at these plots, I am leaning towards the 7-topic model because the topics appear to be more strongly differentiated from one another. While most of the topics revealed by all 3 models feature the words “food” and “local,” in the 7-topic model the third word for each topic is distinct enough to give me a sense of that topic. For example, “education” (Topic 1) and “gardens” (Topic 7) are distinct concepts.

Code
topic_names <- labelTopics(stm_change_final_7, n=4)$frex
topic_labels <- rep(NA, 7)
for (i in 1:7) {
  topic_labels[i] <- paste(topic_names[i,], collapse = "_")
}

print(topic_labels)
[1] "farmer's_really_just_preservation"     
[2] "many_stands_young_bank"                
[3] "foods_land_use_ag"                     
[4] "options_stores_organic_less"           
[5] "composting_make_farmland_prices"       
[6] "neighborhoods_school_community_gardens"
[7] "produced_buying_greater_grown"         

Moving ahead with the 7-topic model, I will now estimate the effects of my covariates on topic prevalence.

Code
# estimate effects
effects_estimate <- estimateEffect(formula = 1:7 ~ age + size + income + zip,
                                   stmobj = stm_change_final_7,
                                   metadata = CFA_tidy_final)

# get summary
summary(effects_estimate)

Call:
estimateEffect(formula = 1:7 ~ age + size + income + zip, stmobj = stm_change_final_7, 
    metadata = CFA_tidy_final)


Topic 1:

Coefficients:
                                   Estimate Std. Error t value Pr(>|t|)    
(Intercept)                        0.076073   0.235619   0.323 0.747165    
age                               -0.004307   0.001272  -3.386 0.000866 ***
size                               0.005346   0.016546   0.323 0.746999    
incomeBetween $25,000 and $34,999 -0.006432   0.066125  -0.097 0.922615    
incomeBetween $35,000 and $49,999  0.046954   0.055438   0.847 0.398114    
incomeBetween $50,000 and $99,999  0.096137   0.050986   1.886 0.060930 .  
incomeOver $250,000                0.108285   0.101268   1.069 0.286338    
incomeUnder $25,000                0.002671   0.071851   0.037 0.970381    
zip59801                           0.201505   0.231278   0.871 0.384744    
zip59802                           0.174854   0.231806   0.754 0.451627    
zip59803                           0.187320   0.236522   0.792 0.429393    
zip59804                           0.121665   0.243749   0.499 0.618278    
zip59808                           0.164339   0.238571   0.689 0.491789    
zip59821                           0.052067   0.370798   0.140 0.888484    
zip59823                           0.173581   0.256575   0.677 0.499553    
zip59825                           0.152866   0.260572   0.587 0.558153    
zip59826                           0.119684   0.327817   0.365 0.715461    
zip59833                           0.310612   0.326461   0.951 0.342622    
zip59846                           0.463387   0.432806   1.071 0.285726    
zip59847                           0.284994   0.256849   1.110 0.268630    
zip59860                           0.395719   0.460408   0.859 0.391186    
zip59864                           0.440994   0.408521   1.079 0.281783    
zip59868                          -0.010741   0.320218  -0.034 0.973279    
zip59908                           0.110365   0.321582   0.343 0.731844    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1


Topic 2:

Coefficients:
                                    Estimate Std. Error t value Pr(>|t|)    
(Intercept)                        0.0449199  0.2228976   0.202 0.840509    
age                               -0.0009742  0.0013756  -0.708 0.479723    
size                              -0.0138550  0.0148394  -0.934 0.351703    
incomeBetween $25,000 and $34,999  0.0020293  0.0638201   0.032 0.974668    
incomeBetween $35,000 and $49,999  0.0335033  0.0534416   0.627 0.531493    
incomeBetween $50,000 and $99,999  0.0067993  0.0475259   0.143 0.886396    
incomeOver $250,000               -0.1034226  0.0836652  -1.236 0.217979    
incomeUnder $25,000                0.0803550  0.0713238   1.127 0.261369    
zip59801                           0.1239108  0.2151941   0.576 0.565448    
zip59802                           0.1073841  0.2150884   0.499 0.618196    
zip59803                           0.2009554  0.2207581   0.910 0.363857    
zip59804                           0.2383507  0.2353513   1.013 0.312512    
zip59808                           0.1650315  0.2233811   0.739 0.460976    
zip59821                           0.0057721  0.2976447   0.019 0.984549    
zip59823                           0.3762243  0.2520556   1.493 0.137248    
zip59825                          -0.0139339  0.2404514  -0.058 0.953852    
zip59826                           0.2427630  0.3544908   0.685 0.494318    
zip59833                           0.0437859  0.2597107   0.169 0.866301    
zip59846                           0.0328171  0.3064997   0.107 0.914850    
zip59847                           0.1326719  0.2454568   0.541 0.589499    
zip59860                           0.0098760  0.2990092   0.033 0.973687    
zip59864                           0.4404755  0.4240221   1.039 0.300259    
zip59868                           1.0261442  0.2959137   3.468 0.000654 ***
zip59908                           0.0946457  0.3266287   0.290 0.772322    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1


Topic 3:

Coefficients:
                                   Estimate Std. Error t value Pr(>|t|)  
(Intercept)                        0.007053   0.212632   0.033   0.9736  
age                                0.002757   0.001146   2.407   0.0171 *
size                              -0.015246   0.014065  -1.084   0.2798  
incomeBetween $25,000 and $34,999 -0.082286   0.064074  -1.284   0.2007  
incomeBetween $35,000 and $49,999 -0.103790   0.054105  -1.918   0.0566 .
incomeBetween $50,000 and $99,999 -0.087591   0.047001  -1.864   0.0640 .
incomeOver $250,000               -0.041803   0.098379  -0.425   0.6714  
incomeUnder $25,000               -0.110159   0.057946  -1.901   0.0589 .
zip59801                           0.054239   0.205733   0.264   0.7924  
zip59802                           0.129460   0.206775   0.626   0.5320  
zip59803                           0.061393   0.211296   0.291   0.7717  
zip59804                           0.287870   0.228340   1.261   0.2090  
zip59808                           0.073253   0.214844   0.341   0.7335  
zip59821                           0.096098   0.298563   0.322   0.7479  
zip59823                           0.117623   0.234685   0.501   0.6168  
zip59825                           0.297899   0.236632   1.259   0.2097  
zip59826                           0.449456   0.352379   1.275   0.2037  
zip59833                          -0.003183   0.256547  -0.012   0.9901  
zip59846                           0.078176   0.304442   0.257   0.7976  
zip59847                           0.186856   0.236253   0.791   0.4300  
zip59860                          -0.004095   0.289147  -0.014   0.9887  
zip59864                           0.057403   0.316416   0.181   0.8562  
zip59868                           0.012138   0.284688   0.043   0.9660  
zip59908                           0.619999   0.327786   1.891   0.0601 .
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1


Topic 4:

Coefficients:
                                    Estimate Std. Error t value Pr(>|t|)   
(Intercept)                        0.2539109  0.2806213   0.905  0.36675   
age                               -0.0029438  0.0014810  -1.988  0.04833 * 
size                               0.0165191  0.0191270   0.864  0.38890   
incomeBetween $25,000 and $34,999  0.1626634  0.0815230   1.995  0.04748 * 
incomeBetween $35,000 and $49,999  0.1203317  0.0631065   1.907  0.05810 . 
incomeBetween $50,000 and $99,999 -0.0514396  0.0518447  -0.992  0.32241   
incomeOver $250,000               -0.2488749  0.0932475  -2.669  0.00829 **
incomeUnder $25,000                0.0789538  0.0708824   1.114  0.26679   
zip59801                           0.0360343  0.2838122   0.127  0.89911   
zip59802                           0.0983228  0.2820804   0.349  0.72782   
zip59803                           0.1375431  0.2900679   0.474  0.63594   
zip59804                          -0.0004238  0.2908572  -0.001  0.99884   
zip59808                          -0.0292561  0.2932397  -0.100  0.92064   
zip59821                          -0.1443556  0.3580565  -0.403  0.68730   
zip59823                          -0.1732539  0.3061248  -0.566  0.57211   
zip59825                          -0.0510382  0.3207313  -0.159  0.87374   
zip59826                          -0.0594960  0.3693233  -0.161  0.87220   
zip59833                           0.1661334  0.3605662   0.461  0.64552   
zip59846                          -0.2774652  0.3819024  -0.727  0.46843   
zip59847                           0.0277529  0.3263000   0.085  0.93231   
zip59860                          -0.2035719  0.3637572  -0.560  0.57641   
zip59864                          -0.3032428  0.3675421  -0.825  0.41041   
zip59868                          -0.1315728  0.3636187  -0.362  0.71788   
zip59908                          -0.0357028  0.3654057  -0.098  0.92227   
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1


Topic 5:

Coefficients:
                                   Estimate Std. Error t value Pr(>|t|)   
(Intercept)                        0.682822   0.324399   2.105   0.0367 * 
age                                0.004057   0.001459   2.780   0.0060 **
size                              -0.006586   0.018722  -0.352   0.7254   
incomeBetween $25,000 and $34,999 -0.087232   0.078909  -1.105   0.2704   
incomeBetween $35,000 and $49,999 -0.009278   0.069302  -0.134   0.8936   
incomeBetween $50,000 and $99,999  0.042335   0.062138   0.681   0.4965   
incomeOver $250,000               -0.095040   0.104335  -0.911   0.3635   
incomeUnder $25,000                0.061661   0.078234   0.788   0.4316   
zip59801                          -0.723695   0.317581  -2.279   0.0238 * 
zip59802                          -0.709630   0.314144  -2.259   0.0251 * 
zip59803                          -0.791855   0.322091  -2.458   0.0149 * 
zip59804                          -0.720032   0.323233  -2.228   0.0271 * 
zip59808                          -0.635880   0.320435  -1.984   0.0487 * 
zip59821                          -0.778355   0.424219  -1.835   0.0682 . 
zip59823                          -0.680011   0.367652  -1.850   0.0660 . 
zip59825                          -0.711247   0.363612  -1.956   0.0520 . 
zip59826                          -0.841602   0.426802  -1.972   0.0501 . 
zip59833                          -0.862400   0.367486  -2.347   0.0200 * 
zip59846                          -0.817599   0.446034  -1.833   0.0684 . 
zip59847                          -0.702840   0.350307  -2.006   0.0463 * 
zip59860                          -0.726309   0.418105  -1.737   0.0840 . 
zip59864                          -0.725751   0.438555  -1.655   0.0997 . 
zip59868                          -0.878705   0.406376  -2.162   0.0319 * 
zip59908                          -0.902694   0.419345  -2.153   0.0327 * 
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1


Topic 6:

Coefficients:
                                   Estimate Std. Error t value Pr(>|t|)
(Intercept)                       -0.152069   0.247937  -0.613    0.540
age                                0.001636   0.001370   1.194    0.234
size                               0.028187   0.018039   1.563    0.120
incomeBetween $25,000 and $34,999  0.053053   0.074033   0.717    0.475
incomeBetween $35,000 and $49,999 -0.040178   0.059697  -0.673    0.502
incomeBetween $50,000 and $99,999  0.066393   0.052147   1.273    0.205
incomeOver $250,000               -0.033469   0.107297  -0.312    0.755
incomeUnder $25,000               -0.030091   0.068311  -0.440    0.660
zip59801                           0.174738   0.236685   0.738    0.461
zip59802                           0.106197   0.237245   0.448    0.655
zip59803                           0.057349   0.244207   0.235    0.815
zip59804                           0.010629   0.251527   0.042    0.966
zip59808                           0.133586   0.247482   0.540    0.590
zip59821                           0.013054   0.328975   0.040    0.968
zip59823                           0.027216   0.260039   0.105    0.917
zip59825                           0.079490   0.270626   0.294    0.769
zip59826                           0.025607   0.350450   0.073    0.942
zip59833                           0.324389   0.308779   1.051    0.295
zip59846                           0.028369   0.344879   0.082    0.935
zip59847                           0.037691   0.263406   0.143    0.886
zip59860                           0.103995   0.355859   0.292    0.770
zip59864                           0.028487   0.356294   0.080    0.936
zip59868                          -0.064817   0.331141  -0.196    0.845
zip59908                           0.068444   0.341332   0.201    0.841


Topic 7:

Coefficients:
                                    Estimate Std. Error t value Pr(>|t|)    
(Intercept)                        0.0962640  0.2240061   0.430 0.667889    
age                               -0.0001965  0.0012109  -0.162 0.871285    
size                              -0.0141917  0.0161798  -0.877 0.381564    
incomeBetween $25,000 and $34,999 -0.0419663  0.0663216  -0.633 0.527669    
incomeBetween $35,000 and $49,999 -0.0484304  0.0566693  -0.855 0.393877    
incomeBetween $50,000 and $99,999 -0.0730486  0.0479246  -1.524 0.129165    
incomeOver $250,000                0.4139106  0.1115932   3.709 0.000275 ***
incomeUnder $25,000               -0.0848732  0.0619203  -1.371 0.172143    
zip59801                           0.1219525  0.2190383   0.557 0.578366    
zip59802                           0.0824909  0.2194531   0.376 0.707429    
zip59803                           0.1359274  0.2249195   0.604 0.546363    
zip59804                           0.0495052  0.2330658   0.212 0.832023    
zip59808                           0.1169990  0.2259842   0.518 0.605268    
zip59821                           0.7485711  0.3878320   1.930 0.055127 .  
zip59823                           0.1501930  0.2603840   0.577 0.564770    
zip59825                           0.2366310  0.2636429   0.898 0.370602    
zip59826                           0.0547959  0.3104024   0.177 0.860070    
zip59833                           0.0104699  0.2682397   0.039 0.968907    
zip59846                           0.4801009  0.4117242   1.166 0.245094    
zip59847                           0.0217986  0.2398463   0.091 0.927682    
zip59860                           0.4162531  0.4445618   0.936 0.350335    
zip59864                           0.0531662  0.3315195   0.160 0.872765    
zip59868                           0.0249425  0.3033366   0.082 0.934556    
zip59908                           0.0330411  0.3069637   0.108 0.914400    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Topic 1: county_facilities_year_production

  • Has a statistically significant, positive association with ZIP code 59868.

Topic 2: ag_stores_cost_produce

  • Has no statistically significant (at the .01 level) association with any covariates.

Topic 3: enough_growing_market_grow

  • Has a statistically significant, negative association with every ZIP code.

Topic 4: organic_less_produced_sourced

  • Has no statistically significant association with any covariates.

Topic 5: affordable_composting_new_healthy

  • Has a statistically significant, positive association with number of people living in household (size).

Topic 6: preserve_prices_farm_development

  • Has a statistically significant, positive association with ZIP code 59821.

Topic 7: restaurants_producers_gardens_city

  • Has a statistically significant, negative association with age and a statistically significant, positive association with ZIP code 59908.

Conclusion

I still need to spend some time thinking about these topics and results but my analysis is taking shape!