Publications by Daniel Lee
DALC January Workshop: Data Visualization
library(ggplot2) data(Marriage, package = "mosaicData") # plot the distribution of race ggplot(Marriage, aes(x = race)) + geom_bar(fill = "cornflowerblue", color="black") + labs(x = "Race", y = "Frequency", title = "Participants by race") data(mpg, package = "ggplot2") # stacked bar chart ggplot(mpg, ...
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Common Mental Illness Treatments in the U.S.
Unit 3: Evalution of Metal Illness Treatments Daniel Lee 3/8/2021 Import data Clean data Join data Visualize data Definition of variables Source of data https://www.datafiles.samhsa.gov/study-series/national-mental-health-services-survey-n-mhss-nid13521 The data came from SAMHDA, National Mental Health Services Survey, 2019 and 2010. Selected ar...
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Cultural Representation of Madness
Import data library(readtext) library(dplyr) poems_hunter <- readtext("C:/Users/sclee1/OneDrive/Documents/R/Madness/poems_hunter/*") %>% tibble() poems_hunter ## # A tibble: 10 x 2 ## doc_id text ## <chr> <chr> ...
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Making Sense of Madness: Treatments for Mental Illness
1 Goal Identify underlying trends in treatments for mental illnesses using the National Mental Health Services Survey (N-MHSS). 2 Change between 2010 and 2019 3 Analyses by Treatments and States 3.1 trauma therapy 3.1.1 Map 3.1.2 Data 3.2 dialectical behavior therapy 3.2.1 Map 3.2.2 Data 3.3 substanceuse treatment 3.3.1 Map 3.3.2 Data ...
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Making Sense of Madness: Mental Health and Cultural Representation
1 Goal Identify underlying trends in society’s attitude toward mental illnesses using the words that appear in the lines of books with mad, madness, crazy, manic, hysterical, or melancholy. 2 Senstiment Analysis 3 Examining the Words Used in the Analysis 3.1 Decade of 1830 3.1.1 Positive 3.1.2 Negative 3.2 Decade of 1890 3.2.1 Positive ...
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Making Sense of Madness: Institutional Population in Mental Institutions
1 Goal Identify long-term trends in the institutional population in mental institutions by analyzing decennial census. 2 From Mass Institutionalization to De-Institutionalization 3 Examining by Race and Sex 3.1 By Race 3.1.1 Population Counts 3.1.2 Population Proportions 3.2 By Sex 3.2.1 Population Counts 3.2.2 Population Proportions 4 C...
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Legal Analytics Presentation at ABA
Legal Analytics Presentation at ABA 2022-04-12 Problem Statement Problem: Companies spend millions every year for legal expenses. Solution: Machine Learning algorithms can help companies mitigate the risk of lawsuits by allowing us to mine text data, previously untapped sources of information, from various sources such as newspapers, social ...
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