library(knitr)
library(kableExtra)
#' Pretty formatted HTML tables
#'
#' @param df The dataframe to format as a table
#' @param names Names to use for columns if not using the df column names. Vector
#' @param highlight_cols Columns to highlight. Default is c(1) for highlighting the first row. Use c() to remove.
#' @param highlight_rows Rows to highlight. Default is c() to not highlight any rows.
#' @param color_rows Rows to highlight in color. Default is c() to not highlight any rows.
#' @param highlight_end_rows Rows to highlight. Uses a reverse indexing where 1 is the last row, 2 is second to last, etc.
#' @param full Use to span the full width of a slide. FALSE by default.
#' @param fixed_header Whether the first row should be treated as a header row. TRUE by default.
html_df <- function(df, names=NULL, highlight_cols=c(1), highlight_rows=c(), color_rows=c(), highlight_end_rows=c(), full=F, fixed_header=T) {
if(!length(names)) {
names=colnames(df)
}
kbl(df,"html", col.names = names, align = c("l",rep('c',length(cols)-1))) %>%
kable_paper(c("striped","hover"), full_width=full, fixed_thead=F, html_font = "\"Source Sans Pro\", Helvetica, sans-serif") %>%
{if(length(highlight_cols)) column_spec(., highlight_cols,bold=T, background = "#ffffff99") else .} %>%
{if(length(highlight_rows)) row_spec(., highlight_rows,bold=T, background = "#ffffff99") else .} %>%
{if(length(color_rows)) row_spec(., color_rows,bold=T, background = "#aaaaff99") else .} %>%
{if(length(highlight_end_rows)) row_spec(., nrow(df) + 1 - highlight_end_rows,bold=T, background = "#ffffff99") else .}
}
library(tidyverse)Code for Session 7
Note that the directories used to store data are likely different on your computer, and such references will need to be changed before using any such code.
# Read text from a .txt file using read_file()
doc <- read_file("../../Data/0001564590-21-039151.txt")
# str_wrap is from stringr from tidyverse
# The first 500 characters of the second paragraph
cat(str_wrap(substring(doc,1728,2228), 80))Microsoft is a technology company whose mission is to empower every person
and every organization on the planet to achieve more. We strive to create
local opportunity, growth, and impact in every country around the world. Our
platforms and tools help drive small business productivity, large business
competitiveness, and public-sector efficiency. They also support new startups,
improve educational and health outcomes, and empower human ingenuity. We bring
technology and products together into exlibrary(httr)
library(XML)
httpResponse <- GET('https://coinmarketcap.com/currencies/ethereum/')
html = httr::content(httpResponse, "text")
paste0('...', str_wrap(substring(html, 11305, 11363), 80), '...')[1] "...ntent=\"The live Ethereum price today is $1,592.03 USD with..."xpath <- '//*[@id="section-coin-overview"]/div[2]/span/text()'
hdoc = htmlParse(html, asText=TRUE) # from XML
price <- xpathSApply(hdoc, xpath, xmlValue)
print(paste0("Ethereum was priced at ", price,
" when these slides were compiled"))[1] "Ethereum was priced at $1,592.10 when these slides were compiled"# The actual version I use (with caching to avoid repeated lookups) is in the appendix
cryptoMC <- function(name) {
httpResponse <- GET(paste('https://coinmarketcap.com/currencies/',name,'/',sep=''))
html = httr::content(httpResponse, "text")
xpath <- '//*[@id="section-coin-overview"]/div[2]/span/text()'
hdoc = htmlParse(html, asText=TRUE)
plain.text <- xpathSApply(hdoc, xpath, xmlValue)
plain.text
}paste("Ethereum was priced at", cryptoMC("ethereum"))[1] "Ethereum was priced at $1,592.10"paste("Litecoin was priced at", cryptoMC("litecoin"))[1] "Litecoin was priced at $64.56"In fiscal year 2021, the COVID-19 pandemic continued to impact our business
operations and financial results. Cloud usage and demand benefited as customers
accelerate their digital transformation priorities.Operating expenses increased $2.0 billion or 4% driven by investments in cloud
engineering and commercial sales, offset in part by savings related to COVID-19
across each of our segments, prior year charges associated with the closing of
our Microsoft Store physical locations, and a reduction in bad debt expense.sentence <- str_sub(doc, 138287, 138384)
str_to_lower(sentence)[1] "cloud usage and demand benefited as customers accelerate their digital transformation priorities. "str_to_upper(sentence)[1] "CLOUD USAGE AND DEMAND BENEFITED AS CUSTOMERS ACCELERATE THEIR DIGITAL TRANSFORMATION PRIORITIES. "str_to_title(sentence)[1] "Cloud Usage And Demand Benefited As Customers Accelerate Their Digital Transformation Priorities. "board <- c("Reid Hoffman", "Hugh Johnston", "Teri List", "Satya Nadella", "Sandra E. Peterson", "Penny Pritzker", "Carlos Rodriguez", "Charles W. Scharf", "John W. Stanton", "John W. Thompson", "Emma Walmsley", "Padmasree Warrior")
titles <- c("Partner, Greylock Partners", "Vice Chairman and Chief Financial Officer, PepsiCo", "Former Executive Vice President and Chief Financial Officer, Gap Inc.", "Chairman and Chief Executive Officer", "Lead Independent Director", "Founder and Chairman, PSP Partners", "Executive Chair, ADP, Inc.", "CEO and President, Wells Fargo & Company", "Chairman, Trilogy Partnerships", "Partner, Lightspeed Venture Partners", "CEO, GSK", "Founder, President and CEO, Fable Group Inc.")# board is a list of director names
# titles is a list of the director's titles
paste(board, titles, sep=", ") [1] "Reid Hoffman, Partner, Greylock Partners"
[2] "Hugh Johnston, Vice Chairman and Chief Financial Officer, PepsiCo"
[3] "Teri List, Former Executive Vice President and Chief Financial Officer, Gap Inc."
[4] "Satya Nadella, Chairman and Chief Executive Officer"
[5] "Sandra E. Peterson, Lead Independent Director"
[6] "Penny Pritzker, Founder and Chairman, PSP Partners"
[7] "Carlos Rodriguez, Executive Chair, ADP, Inc."
[8] "Charles W. Scharf, CEO and President, Wells Fargo & Company"
[9] "John W. Stanton, Chairman, Trilogy Partnerships"
[10] "John W. Thompson, Partner, Lightspeed Venture Partners"
[11] "Emma Walmsley, CEO, GSK"
[12] "Padmasree Warrior, Founder, President and CEO, Fable Group Inc." cat(str_wrap(paste0("Microsoft's board consists of: ",
paste(board[1:length(board)-1], collapse=", "),
", and ", board[length(board)], "."), 80))Microsoft's board consists of: Reid Hoffman, Hugh Johnston, Teri List, Satya
Nadella, Sandra E. Peterson, Penny Pritzker, Carlos Rodriguez, Charles W.
Scharf, John W. Stanton, John W. Thompson, Emma Walmsley, and Padmasree Warrior.sentence[1] "Cloud usage and demand benefited as customers accelerate their digital transformation priorities. "str_replace_all(sentence, "digital transformation", "data science")[1] "Cloud usage and demand benefited as customers accelerate their data science priorities. "[1] 474The following Management’s Discussion and Analysis of Financial Condition
and Results of Operations (“MD&A”) is intended to help the reader understand
the results of operations and financial condition of Microsoft Corporation.
MD&A is provided as a supplement to, and should be read in conjunction with,
our consolidated financial statements and the accompanying Notes to Financial
Statements (Part II, Item 8 of this Form 10-K). This section generally discusses
the results of our operations for the year ended June 30, 2021 compared to
the year ended June 30, 2020. For a discussion of the year ended June 30, 2020
compared to the year ended June 30, 2019, please refer to Part II, Item 7,
“Management’s Discussion and Analysis of Financial Condition and Results of
Operations” in our Annual Report on Form 10-K for the year ended June 30, 2020.str_locate_all(str_to_lower(doc), "net income")[[1]]
start end
[1,] 139992 140001
[2,] 142476 142485
[3,] 144664 144673
[4,] 144834 144843
[5,] 148712 148721
[6,] 151464 151473
[7,] 177859 177868
[8,] 216135 216144
[9,] 217104 217113
[10,] 218151 218160
[11,] 219629 219638x <- str_detect(str_to_lower(paragraphs), "revenue")
x[51:60] [1] FALSE FALSE FALSE TRUE TRUE TRUE TRUE FALSE TRUE FALSEsum(x)[1] 86str_locate(str_to_lower(doc), "revenue") start end
[1,] 31243 31249str_extract(str_to_lower(doc), "revenue")[1] "revenue"# Extract all sentences mentioning COVID from the annual report
str_extract_all(doc, '(?<=^|\\.\\s{1,5})[^.]*?COVID[^.]*?\\.')[[1]]
[1] "\nIn March 2020, the World Health Organization declared the outbreak of COVID-19 to be a pandemic."
[2] "The COVID-19 pandemic continues to have widespread and unpredictable impacts on global society, economies, financial markets, and business practices, and continues to impact our business operations, including our employees, customers, partners, and communities."
[3] "Refer to Management’s Discussion and Analysis of Financial Condition and Results of Operations (Part II, Item 7 of this Form 10-K) for further discussion regarding the impact of COVID-19 on our fiscal year 2021 financial results."
[4] "The extent to which the COVID-19 pandemic impacts our business going forward will depend on numerous evolving factors we cannot reliably predict."
[5] "\nWith a continued focus on digital transformation, Microsoft is helping to ensure that no one is left behind, particularly as economies recover from the COVID-19 pandemic."
[6] "During fiscal year 2021, our Daily Pulse surveys gave us invaluable insights into ways we could support employees through the COVID-19 pandemic and addressing racial injustice."
[7] "\nWe took a wide variety of measures to protect the health and well-being of our employees, suppliers, and customers during the COVID-19 pandemic."
[8] "We implemented a global Paid Pandemic School and Childcare Closure Leave to support working parents, added wellbeing days for those who needed to take time off for mental health and wellness, implemented on-demand COVID-19 testing and vaccinations on our Redmond, Washington campus, and extended full medical plan coverage for coronavirus testing, treatment, and telehealth services."
[9] "\nIn addition to the extraordinary steps and programs relating to COVID-19, our comprehensive benefits package includes many physical, emotional, and financial wellness programs including counseling through the Microsoft CARES Employee Assistance Program, flexible fitness benefits, savings and investment tools, adoption assistance, and back-up care for children and elders."
[10] "The COVID-19 pandemic continues to have widespread, rapidly evolving, and unpredictable impacts on global society, economies, financial markets, and business practices."
[11] "The COVID-19 pandemic has impacted and may continue to impact our business operations, including our employees, customers, partners, and communities, and there is substantial uncertainty in the nature and degree of its continued effects over time."
[12] "\nThe extent to which the COVID-19 pandemic impacts our business going forward will depend on numerous evolving factors we cannot reliably predict, including the duration and scope of the pandemic; governmental, business, and individuals' actions in response to the pandemic; and the impact on economic activity including the possibility of recession or financial market instability."
[13] "\nAs the world continues to respond to COVID-19, we are working to do our part by ensuring the safety of our employees, striving to protect the health and well-being of the communities in which we operate, and providing technology and resources to our customers to help them do their best work while remote."
[14] "\nIn fiscal year 2021, the COVID-19 pandemic continued to impact our business operations and financial results."
[15] "We saw improvement in customer advertising spend and savings in operating expenses related to COVID-19, but experienced weakness in transactional licensing."
[16] "The COVID-19 pandemic may continue to impact our business operations and financial operating results, and there is uncertainty in the nature and degree of its continued effects over time."
[17] "These estimates and assumptions are affected by management’s application of accounting policies, as well as uncertainty in the current economic environment due to COVID-19."
[18] "Actual results and outcomes may differ from management’s estimates and assumptions due to risks and uncertainties, including uncertainty in the current economic environment due to COVID-19." text <- c("abcde", 'ABCDE', '12345', '!?!?.', 'ABC123?', "With space", "New\nline")
html_df(data.frame(
text=text,
alpha=str_detect(text,'[:alpha:]'),
lower=str_detect(text,'[:lower:]'),
upper=str_detect(text,'[:upper:]'),
digit=str_detect(text,'[:digit:]'),
alnum=str_detect(text,'[:alnum:]')))| text | alpha | lower | upper | digit | alnum |
|---|---|---|---|---|---|
| abcde | TRUE | TRUE | FALSE | FALSE | TRUE |
| ABCDE | TRUE | FALSE | TRUE | FALSE | TRUE |
| 12345 | FALSE | FALSE | FALSE | TRUE | TRUE |
| !?!?. | FALSE | FALSE | FALSE | FALSE | FALSE |
| ABC123? | TRUE | FALSE | TRUE | TRUE | TRUE |
| With space | TRUE | TRUE | TRUE | FALSE | TRUE |
| New line | TRUE | TRUE | TRUE | FALSE | TRUE |
text <- c("abcde", 'ABCDE', '12345', '!?!?.', 'ABC123?', "With space", "New\nline")
html_df(data.frame(
text=text,
punct=str_detect(text,'[:punct:]'),
graph=str_detect(text,'[:graph:]'),
space=str_detect(text,'[:space:]'),
blank=str_detect(text,'[:blank:]'),
period=str_detect(text,'.')))| text | punct | graph | space | blank | period |
|---|---|---|---|---|---|
| abcde | FALSE | TRUE | FALSE | FALSE | TRUE |
| ABCDE | FALSE | TRUE | FALSE | FALSE | TRUE |
| 12345 | FALSE | TRUE | FALSE | FALSE | TRUE |
| !?!?. | TRUE | TRUE | FALSE | FALSE | TRUE |
| ABC123? | TRUE | TRUE | FALSE | FALSE | TRUE |
| With space | FALSE | TRUE | TRUE | TRUE | TRUE |
| New line | FALSE | TRUE | TRUE | FALSE | TRUE |
# Real estate firm names with 3 vowels in a row
str_subset(RE_names, '[AEIOU]{3}')[1] "STADLAUER MALZFABRIK" "ELECT ET EAUX DE MADAGASCAR"
[3] "JOAO FORTES ENGENHARIA SA" # Real estate firm names with no vowels
str_subset(RE_names, '^[^AEIOU]+$')[1] "FGP LTD" "MBK PCL" "MYP LTD" "R T C L LTD"# Real estate firm names with at least 12 vowels
str_subset(RE_names, '([^AEIOU]*[AEIOU]){11,}') [1] "INTERNATIONAL ENTERTAINMENT" "PREMIERE HORIZON ALLIANCE"
[3] "ELECT ET EAUX DE MADAGASCAR" "HUA YIN INTERNATIONAL HOLDIN"
[5] "JOAO FORTES ENGENHARIA SA" "TIANJIN TROILA INFORMATION"
[7] "OVERSEAS CHINESE TOWN (ASIA)" "ASIA-PACIFIC STRATEGIC INVES"
[9] "FUTURA CONSORCIO INMOBILIARI" "FRANCE TOURISME IMMOBILIER"
[11] "BONEI HATICHON CIVIL ENGINE" # Real estate firm names with a repeated 4 letter pattern
str_subset(RE_names, '([:upper:]{4}).*\\1')[1] "INTERNATIONAL ENTERTAINMENT" "SHANDONG XINNENG TAISHAN"
[3] "CHONG HONG CONSTRUCTION CO" "DEUTSCHE GEOTHERMISCHE IMMOB"# Compustat firm names example
df_RE_names <- df_RE %>%
group_by(isin) %>%
slice(1) %>%
mutate(SG_in_name = str_detect(conm, "(SG|SINGAPORE)"),
name_length = str_length(conm),
SG_firm = ifelse(fic=="SGP",1,0)) %>%
ungroup()
df_RE_names %>%
group_by(SG_firm) %>%
mutate(pct_SG = mean(SG_in_name) * 100) %>%
slice(1) %>%
ungroup() %>%
select(SG_firm, pct_SG)# A tibble: 2 × 2
SG_firm pct_SG
<dbl> <dbl>
1 0 0.746
2 1 4.76 library(DT)
df_RE_names %>%
group_by(fic) %>%
mutate(avg_name_length = mean(name_length)) %>%
slice(1) %>%
ungroup() %>%
select(fic, avg_name_length) %>%
arrange(desc(avg_name_length), fic) %>%
datatable(options = list(pageLength = 5))library(quanteda)
library(quanteda.textstats)
textstat_readability(doc, "Flesch.Kincaid") document Flesch.Kincaid
1 text1 16.85874textstat_readability(doc, "FOG") document FOG
1 text1 20.88005textstat_readability(doc, "Coleman.Liau.short") document Coleman.Liau.short
1 text1 15.48359# Example of "tokenizing"
library(tidytext)
df_doc <- data.frame(ID=c("0001564590-21-039151"), text=c(doc)) %>%
unnest_tokens(word, text)
# word is the name for the new column
# text is the name of the string column in the input datahtml_df(head(df_doc))| ID | word |
|---|---|
| 0001564590-21-039151 | this |
| 0001564590-21-039151 | report |
| 0001564590-21-039151 | includes |
| 0001564590-21-039151 | estimates |
| 0001564590-21-039151 | projections |
| 0001564590-21-039151 | statements |
# get a list of stopwords
stop_en <- stopwords::stopwords("english") # Snowball English
paste0(length(stop_en), " words: ", paste(stop_en[1:5], collapse=", "))[1] "175 words: i, me, my, myself, we"stop_SMART <- stopwords::stopwords(source="smart") # SMART English
paste0(length(stop_SMART), " words: ", paste(stop_SMART[1:5], collapse=", "))[1] "571 words: a, a's, able, about, above"stop_fr <- stopwords::stopwords("french") # Snowball French
paste0(length(stop_fr), " words: ", paste(stop_fr[1:5], collapse=", "))[1] "164 words: au, aux, avec, ce, ces"df_doc_stop <- df_doc %>%
anti_join(data.frame(word=stop_SMART))Joining with `by = join_by(word)`nrow(df_doc)[1] 37234nrow(df_doc_stop)[1] 21171terms <- df_doc_stop %>%
count(ID, word, sort=TRUE) %>%
ungroup()
total_terms <- terms %>%
group_by(ID) %>%
summarize(total = sum(n))
tf <- left_join(terms, total_terms) %>% mutate(tf=n/total)
tf ID word n total tf
1 0001564590-21-039151 services 250 21171 1.180861e-02
2 0001564590-21-039151 products 194 21171 9.163478e-03
3 0001564590-21-039151 financial 166 21171 7.840914e-03
4 0001564590-21-039151 business 144 21171 6.801757e-03
5 0001564590-21-039151 tax 140 21171 6.612819e-03
6 0001564590-21-039151 revenue 137 21171 6.471116e-03
7 0001564590-21-039151 customers 132 21171 6.234944e-03
8 0001564590-21-039151 software 130 21171 6.140475e-03
9 0001564590-21-039151 cloud 124 21171 5.857069e-03
10 0001564590-21-039151 2021 118 21171 5.573662e-03
11 0001564590-21-039151 year 113 21171 5.337490e-03
12 0001564590-21-039151 based 110 21171 5.195787e-03
13 0001564590-21-039151 billion 109 21171 5.148552e-03
14 0001564590-21-039151 microsoft 105 21171 4.959615e-03
15 0001564590-21-039151 income 104 21171 4.912380e-03
16 0001564590-21-039151 statements 92 21171 4.345567e-03
17 0001564590-21-039151 including 88 21171 4.156629e-03
18 0001564590-21-039151 fiscal 84 21171 3.967692e-03
19 0001564590-21-039151 operating 84 21171 3.967692e-03
20 0001564590-21-039151 related 83 21171 3.920457e-03
21 0001564590-21-039151 information 70 21171 3.306410e-03
22 0001564590-21-039151 development 68 21171 3.211941e-03
23 0001564590-21-039151 fair 66 21171 3.117472e-03
24 0001564590-21-039151 significant 66 21171 3.117472e-03
25 0001564590-21-039151 include 65 21171 3.070238e-03
26 0001564590-21-039151 security 65 21171 3.070238e-03
27 0001564590-21-039151 years 65 21171 3.070238e-03
28 0001564590-21-039151 2020 63 21171 2.975769e-03
29 0001564590-21-039151 data 63 21171 2.975769e-03
30 0001564590-21-039151 product 62 21171 2.928534e-03
31 0001564590-21-039151 u.s 62 21171 2.928534e-03
32 0001564590-21-039151 costs 61 21171 2.881300e-03
33 0001564590-21-039151 devices 61 21171 2.881300e-03
34 0001564590-21-039151 foreign 61 21171 2.881300e-03
35 0001564590-21-039151 investments 61 21171 2.881300e-03
36 0001564590-21-039151 assets 60 21171 2.834065e-03
37 0001564590-21-039151 consolidated 59 21171 2.786831e-03
38 0001564590-21-039151 impact 59 21171 2.786831e-03
39 0001564590-21-039151 june 58 21171 2.739597e-03
40 0001564590-21-039151 sales 58 21171 2.739597e-03
41 0001564590-21-039151 employees 55 21171 2.597893e-03
42 0001564590-21-039151 30 54 21171 2.550659e-03
43 0001564590-21-039151 internal 54 21171 2.550659e-03
44 0001564590-21-039151 period 54 21171 2.550659e-03
45 0001564590-21-039151 stock 53 21171 2.503424e-03
46 0001564590-21-039151 net 52 21171 2.456190e-03
47 0001564590-21-039151 provide 52 21171 2.456190e-03
48 0001564590-21-039151 recognized 52 21171 2.456190e-03
49 0001564590-21-039151 operations 50 21171 2.361721e-03
50 0001564590-21-039151 part 49 21171 2.314487e-03
51 0001564590-21-039151 reporting 48 21171 2.267252e-03
52 0001564590-21-039151 solutions 48 21171 2.267252e-03
53 0001564590-21-039151 support 48 21171 2.267252e-03
54 0001564590-21-039151 cash 46 21171 2.172784e-03
55 0001564590-21-039151 included 46 21171 2.172784e-03
56 0001564590-21-039151 management 46 21171 2.172784e-03
57 0001564590-21-039151 server 45 21171 2.125549e-03
58 0001564590-21-039151 increase 44 21171 2.078315e-03
59 0001564590-21-039151 organizations 44 21171 2.078315e-03
60 0001564590-21-039151 taxes 44 21171 2.078315e-03
61 0001564590-21-039151 technology 44 21171 2.078315e-03
62 0001564590-21-039151 ability 42 21171 1.983846e-03
63 0001564590-21-039151 audit 42 21171 1.983846e-03
64 0001564590-21-039151 benefits 42 21171 1.983846e-03
65 0001564590-21-039151 customer 42 21171 1.983846e-03
66 0001564590-21-039151 subject 41 21171 1.936611e-03
67 0001564590-21-039151 cost 40 21171 1.889377e-03
68 0001564590-21-039151 future 40 21171 1.889377e-03
69 0001564590-21-039151 primarily 40 21171 1.889377e-03
70 0001564590-21-039151 windows 40 21171 1.889377e-03
71 0001564590-21-039151 accounting 39 21171 1.842143e-03
72 0001564590-21-039151 estimated 39 21171 1.842143e-03
73 0001564590-21-039151 generally 39 21171 1.842143e-03
74 0001564590-21-039151 market 39 21171 1.842143e-03
75 0001564590-21-039151 result 39 21171 1.842143e-03
76 0001564590-21-039151 results 39 21171 1.842143e-03
77 0001564590-21-039151 service 39 21171 1.842143e-03
78 0001564590-21-039151 time 39 21171 1.842143e-03
79 0001564590-21-039151 company 38 21171 1.794908e-03
80 0001564590-21-039151 growth 38 21171 1.794908e-03
81 0001564590-21-039151 million 38 21171 1.794908e-03
82 0001564590-21-039151 systems 38 21171 1.794908e-03
83 0001564590-21-039151 global 37 21171 1.747674e-03
84 0001564590-21-039151 expense 36 21171 1.700439e-03
85 0001564590-21-039151 liabilities 36 21171 1.700439e-03
86 0001564590-21-039151 term 36 21171 1.700439e-03
87 0001564590-21-039151 affect 35 21171 1.653205e-03
88 0001564590-21-039151 agreements 35 21171 1.653205e-03
89 0001564590-21-039151 control 35 21171 1.653205e-03
90 0001564590-21-039151 due 35 21171 1.653205e-03
91 0001564590-21-039151 lease 35 21171 1.653205e-03
92 0001564590-21-039151 purchase 35 21171 1.653205e-03
93 0001564590-21-039151 continue 34 21171 1.605970e-03
94 0001564590-21-039151 investment 34 21171 1.605970e-03
95 0001564590-21-039151 licenses 34 21171 1.605970e-03
96 0001564590-21-039151 required 34 21171 1.605970e-03
97 0001564590-21-039151 research 34 21171 1.605970e-03
98 0001564590-21-039151 commercial 33 21171 1.558736e-03
99 0001564590-21-039151 item 33 21171 1.558736e-03
100 0001564590-21-039151 performance 33 21171 1.558736e-03
101 0001564590-21-039151 programs 33 21171 1.558736e-03
102 0001564590-21-039151 risks 33 21171 1.558736e-03
103 0001564590-21-039151 10 32 21171 1.511502e-03
104 0001564590-21-039151 applications 32 21171 1.511502e-03
105 0001564590-21-039151 content 32 21171 1.511502e-03
106 0001564590-21-039151 debt 32 21171 1.511502e-03
107 0001564590-21-039151 equipment 32 21171 1.511502e-03
108 0001564590-21-039151 hardware 32 21171 1.511502e-03
109 0001564590-21-039151 office 32 21171 1.511502e-03
110 0001564590-21-039151 property 32 21171 1.511502e-03
111 0001564590-21-039151 resources 32 21171 1.511502e-03
112 0001564590-21-039151 securities 32 21171 1.511502e-03
113 0001564590-21-039151 2019 31 21171 1.464267e-03
114 0001564590-21-039151 ai 31 21171 1.464267e-03
115 0001564590-21-039151 form 31 21171 1.464267e-03
116 0001564590-21-039151 includes 31 21171 1.464267e-03
117 0001564590-21-039151 laws 31 21171 1.464267e-03
118 0001564590-21-039151 current 30 21171 1.417033e-03
119 0001564590-21-039151 increased 30 21171 1.417033e-03
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3288 0001564590-21-039151 status 1 21171 4.723442e-05
3289 0001564590-21-039151 stayed 1 21171 4.723442e-05
3290 0001564590-21-039151 step 1 21171 4.723442e-05
3291 0001564590-21-039151 stepped 1 21171 4.723442e-05
3292 0001564590-21-039151 stimulate 1 21171 4.723442e-05
3293 0001564590-21-039151 storytelling 1 21171 4.723442e-05
3294 0001564590-21-039151 stream 1 21171 4.723442e-05
3295 0001564590-21-039151 strength 1 21171 4.723442e-05
3296 0001564590-21-039151 strengthen 1 21171 4.723442e-05
3297 0001564590-21-039151 strengths 1 21171 4.723442e-05
3298 0001564590-21-039151 strike 1 21171 4.723442e-05
3299 0001564590-21-039151 striking 1 21171 4.723442e-05
3300 0001564590-21-039151 striving 1 21171 4.723442e-05
3301 0001564590-21-039151 strongest 1 21171 4.723442e-05
3302 0001564590-21-039151 subjected 1 21171 4.723442e-05
3303 0001564590-21-039151 submitted 1 21171 4.723442e-05
3304 0001564590-21-039151 subscribers 1 21171 4.723442e-05
3305 0001564590-21-039151 substantive 1 21171 4.723442e-05
3306 0001564590-21-039151 substitute 1 21171 4.723442e-05
3307 0001564590-21-039151 substituted 1 21171 4.723442e-05
3308 0001564590-21-039151 succession 1 21171 4.723442e-05
3309 0001564590-21-039151 successor 1 21171 4.723442e-05
3310 0001564590-21-039151 suits 1 21171 4.723442e-05
3311 0001564590-21-039151 summary 1 21171 4.723442e-05
3312 0001564590-21-039151 supervision 1 21171 4.723442e-05
3313 0001564590-21-039151 supplement 1 21171 4.723442e-05
3314 0001564590-21-039151 supplemental 1 21171 4.723442e-05
3315 0001564590-21-039151 supplies 1 21171 4.723442e-05
3316 0001564590-21-039151 supportable 1 21171 4.723442e-05
3317 0001564590-21-039151 surpassed 1 21171 4.723442e-05
3318 0001564590-21-039151 surrounding 1 21171 4.723442e-05
3319 0001564590-21-039151 surveillance 1 21171 4.723442e-05
3320 0001564590-21-039151 susceptible 1 21171 4.723442e-05
3321 0001564590-21-039151 suspended 1 21171 4.723442e-05
3322 0001564590-21-039151 sustain 1 21171 4.723442e-05
3323 0001564590-21-039151 swaps 1 21171 4.723442e-05
3324 0001564590-21-039151 switching 1 21171 4.723442e-05
3325 0001564590-21-039151 symbol 1 21171 4.723442e-05
3326 0001564590-21-039151 synapse 1 21171 4.723442e-05
3327 0001564590-21-039151 syria 1 21171 4.723442e-05
3328 0001564590-21-039151 systemic 1 21171 4.723442e-05
3329 0001564590-21-039151 tablet 1 21171 4.723442e-05
3330 0001564590-21-039151 tabs 1 21171 4.723442e-05
3331 0001564590-21-039151 tangible 1 21171 4.723442e-05
3332 0001564590-21-039151 target 1 21171 4.723442e-05
3333 0001564590-21-039151 targeted 1 21171 4.723442e-05
3334 0001564590-21-039151 targeting 1 21171 4.723442e-05
3335 0001564590-21-039151 targets 1 21171 4.723442e-05
3336 0001564590-21-039151 tariffs 1 21171 4.723442e-05
3337 0001564590-21-039151 task 1 21171 4.723442e-05
3338 0001564590-21-039151 tech 1 21171 4.723442e-05
3339 0001564590-21-039151 tenancy 1 21171 4.723442e-05
3340 0001564590-21-039151 tencent 1 21171 4.723442e-05
3341 0001564590-21-039151 tend 1 21171 4.723442e-05
3342 0001564590-21-039151 terminated 1 21171 4.723442e-05
3343 0001564590-21-039151 termination 1 21171 4.723442e-05
3344 0001564590-21-039151 testers 1 21171 4.723442e-05
3345 0001564590-21-039151 text 1 21171 4.723442e-05
3346 0001564590-21-039151 theft 1 21171 4.723442e-05
3347 0001564590-21-039151 thousands 1 21171 4.723442e-05
3348 0001564590-21-039151 threat 1 21171 4.723442e-05
3349 0001564590-21-039151 threatened 1 21171 4.723442e-05
3350 0001564590-21-039151 thrive 1 21171 4.723442e-05
3351 0001564590-21-039151 today 1 21171 4.723442e-05
3352 0001564590-21-039151 totaled 1 21171 4.723442e-05
3353 0001564590-21-039151 track 1 21171 4.723442e-05
3354 0001564590-21-039151 tracking 1 21171 4.723442e-05
3355 0001564590-21-039151 trademark 1 21171 4.723442e-05
3356 0001564590-21-039151 trades 1 21171 4.723442e-05
3357 0001564590-21-039151 transact 1 21171 4.723442e-05
3358 0001564590-21-039151 transactional 1 21171 4.723442e-05
3359 0001564590-21-039151 transcend 1 21171 4.723442e-05
3360 0001564590-21-039151 transformations 1 21171 4.723442e-05
3361 0001564590-21-039151 transforms 1 21171 4.723442e-05
3362 0001564590-21-039151 transitions 1 21171 4.723442e-05
3363 0001564590-21-039151 translating 1 21171 4.723442e-05
3364 0001564590-21-039151 translation 1 21171 4.723442e-05
3365 0001564590-21-039151 translations 1 21171 4.723442e-05
3366 0001564590-21-039151 treasury 1 21171 4.723442e-05
3367 0001564590-21-039151 trend 1 21171 4.723442e-05
3368 0001564590-21-039151 trust 1 21171 4.723442e-05
3369 0001564590-21-039151 tumors 1 21171 4.723442e-05
3370 0001564590-21-039151 tying 1 21171 4.723442e-05
3371 0001564590-21-039151 ubiquitous 1 21171 4.723442e-05
3372 0001564590-21-039151 ultimately 1 21171 4.723442e-05
3373 0001564590-21-039151 unchanged 1 21171 4.723442e-05
3374 0001564590-21-039151 unconditional 1 21171 4.723442e-05
3375 0001564590-21-039151 unconscious 1 21171 4.723442e-05
3376 0001564590-21-039151 unconsolidated 1 21171 4.723442e-05
3377 0001564590-21-039151 uncovered 1 21171 4.723442e-05
3378 0001564590-21-039151 underlie 1 21171 4.723442e-05
3379 0001564590-21-039151 undermine 1 21171 4.723442e-05
3380 0001564590-21-039151 underrepresented 1 21171 4.723442e-05
3381 0001564590-21-039151 underserved 1 21171 4.723442e-05
3382 0001564590-21-039151 undue 1 21171 4.723442e-05
3383 0001564590-21-039151 unfair 1 21171 4.723442e-05
3384 0001564590-21-039151 unfavorably 1 21171 4.723442e-05
3385 0001564590-21-039151 unfold 1 21171 4.723442e-05
3386 0001564590-21-039151 union 1 21171 4.723442e-05
3387 0001564590-21-039151 universities 1 21171 4.723442e-05
3388 0001564590-21-039151 university 1 21171 4.723442e-05
3389 0001564590-21-039151 unix 1 21171 4.723442e-05
3390 0001564590-21-039151 unknown 1 21171 4.723442e-05
3391 0001564590-21-039151 unlicensed 1 21171 4.723442e-05
3392 0001564590-21-039151 unlocks 1 21171 4.723442e-05
3393 0001564590-21-039151 unsatisfactory 1 21171 4.723442e-05
3394 0001564590-21-039151 unsuccessful 1 21171 4.723442e-05
3395 0001564590-21-039151 upgrade 1 21171 4.723442e-05
3396 0001564590-21-039151 ups 1 21171 4.723442e-05
3397 0001564590-21-039151 urban 1 21171 4.723442e-05
3398 0001564590-21-039151 usability 1 21171 4.723442e-05
3399 0001564590-21-039151 utilized 1 21171 4.723442e-05
3400 0001564590-21-039151 utilizes 1 21171 4.723442e-05
3401 0001564590-21-039151 vaccination 1 21171 4.723442e-05
3402 0001564590-21-039151 vaccinations 1 21171 4.723442e-05
3403 0001564590-21-039151 valid 1 21171 4.723442e-05
3404 0001564590-21-039151 validation 1 21171 4.723442e-05
3405 0001564590-21-039151 validity 1 21171 4.723442e-05
3406 0001564590-21-039151 valley 1 21171 4.723442e-05
3407 0001564590-21-039151 valuable 1 21171 4.723442e-05
3408 0001564590-21-039151 valuing 1 21171 4.723442e-05
3409 0001564590-21-039151 var 1 21171 4.723442e-05
3410 0001564590-21-039151 variants 1 21171 4.723442e-05
3411 0001564590-21-039151 variation 1 21171 4.723442e-05
3412 0001564590-21-039151 variations 1 21171 4.723442e-05
3413 0001564590-21-039151 varied 1 21171 4.723442e-05
3414 0001564590-21-039151 ve 1 21171 4.723442e-05
3415 0001564590-21-039151 vehicles 1 21171 4.723442e-05
3416 0001564590-21-039151 verizon 1 21171 4.723442e-05
3417 0001564590-21-039151 versatility 1 21171 4.723442e-05
3418 0001564590-21-039151 vertically 1 21171 4.723442e-05
3419 0001564590-21-039151 vests 1 21171 4.723442e-05
3420 0001564590-21-039151 videoconferencing 1 21171 4.723442e-05
3421 0001564590-21-039151 viewed 1 21171 4.723442e-05
3422 0001564590-21-039151 views 1 21171 4.723442e-05
3423 0001564590-21-039151 vigorously 1 21171 4.723442e-05
3424 0001564590-21-039151 violates 1 21171 4.723442e-05
3425 0001564590-21-039151 violation 1 21171 4.723442e-05
3426 0001564590-21-039151 violations 1 21171 4.723442e-05
3427 0001564590-21-039151 violent 1 21171 4.723442e-05
3428 0001564590-21-039151 virginia 1 21171 4.723442e-05
3429 0001564590-21-039151 virtual 1 21171 4.723442e-05
3430 0001564590-21-039151 virtually 1 21171 4.723442e-05
3431 0001564590-21-039151 visibility 1 21171 4.723442e-05
3432 0001564590-21-039151 visio 1 21171 4.723442e-05
3433 0001564590-21-039151 visual 1 21171 4.723442e-05
3434 0001564590-21-039151 vital 1 21171 4.723442e-05
3435 0001564590-21-039151 viva 1 21171 4.723442e-05
3436 0001564590-21-039151 volumes 1 21171 4.723442e-05
3437 0001564590-21-039151 warrant 1 21171 4.723442e-05
3438 0001564590-21-039151 waste 1 21171 4.723442e-05
3439 0001564590-21-039151 water 1 21171 4.723442e-05
3440 0001564590-21-039151 weakening 1 21171 4.723442e-05
3441 0001564590-21-039151 weakest 1 21171 4.723442e-05
3442 0001564590-21-039151 weather 1 21171 4.723442e-05
3443 0001564590-21-039151 western 1 21171 4.723442e-05
3444 0001564590-21-039151 we’ve 1 21171 4.723442e-05
3445 0001564590-21-039151 white 1 21171 4.723442e-05
3446 0001564590-21-039151 widely 1 21171 4.723442e-05
3447 0001564590-21-039151 witnessed 1 21171 4.723442e-05
3448 0001564590-21-039151 words 1 21171 4.723442e-05
3449 0001564590-21-039151 workbench 1 21171 4.723442e-05
3450 0001564590-21-039151 worked 1 21171 4.723442e-05
3451 0001564590-21-039151 workflow 1 21171 4.723442e-05
3452 0001564590-21-039151 works 1 21171 4.723442e-05
3453 0001564590-21-039151 worksite 1 21171 4.723442e-05
3454 0001564590-21-039151 workstyles 1 21171 4.723442e-05
3455 0001564590-21-039151 workweeks 1 21171 4.723442e-05
3456 0001564590-21-039151 worldview 1 21171 4.723442e-05
3457 0001564590-21-039151 worsening 1 21171 4.723442e-05
3458 0001564590-21-039151 write 1 21171 4.723442e-05
3459 0001564590-21-039151 yammer 1 21171 4.723442e-05
3460 0001564590-21-039151 yields 1 21171 4.723442e-05
3461 0001564590-21-039151 these 1 21171 4.723442e-05# Need to install the `textdata` package for the below to work
get_sentiments("afinn") %>%
group_by(value) %>%
slice(1) %>%
ungroup()# A tibble: 11 × 2
word value
<chr> <dbl>
1 bastard -5
2 ass -4
3 abhor -3
4 abandon -2
5 absentee -1
6 some kind 0
7 aboard 1
8 abilities 2
9 admire 3
10 amazing 4
11 breathtaking 5# A tibble: 2 × 2
word sentiment
<chr> <chr>
1 2-faces negative
2 abound positive # A tibble: 10 × 2
word sentiment
<chr> <chr>
1 abandoned anger
2 abundance anticipation
3 aberration disgust
4 abandon fear
5 absolution joy
6 abandon negative
7 abba positive
8 abandon sadness
9 abandonment surprise
10 abacus trust # A tibble: 6 × 2
word sentiment
<chr> <chr>
1 abide constraining
2 abovementioned litigious
3 abandon negative
4 able positive
5 aegis superfluous
6 abeyance uncertainty tf_sent <- tf %>% left_join(get_sentiments("loughran"))Joining with `by = join_by(word)`tf_sent[1:5,] ID word n total tf sentiment
1 0001564590-21-039151 services 250 21171 0.011808606 <NA>
2 0001564590-21-039151 products 194 21171 0.009163478 <NA>
3 0001564590-21-039151 financial 166 21171 0.007840914 <NA>
4 0001564590-21-039151 business 144 21171 0.006801757 <NA>
5 0001564590-21-039151 tax 140 21171 0.006612819 <NA>tf_sent[!is.na(tf_sent$sentiment),][1:5,] ID word n total tf sentiment
96 0001564590-21-039151 required 34 21171 0.001605970 constraining
102 0001564590-21-039151 risks 33 21171 0.001558736 uncertainty
117 0001564590-21-039151 laws 31 21171 0.001464267 litigious
141 0001564590-21-039151 effective 27 21171 0.001275329 positive
144 0001564590-21-039151 losses 27 21171 0.001275329 negativetf_sent %>%
spread(sentiment, tf, fill=0) %>%
select(constraining, litigious, negative, positive, superfluous, uncertainty) %>%
colSums()constraining litigious negative positive superfluous uncertainty
1.284776e-02 1.690992e-02 3.226111e-02 1.785461e-02 4.723442e-05 1.218648e-02 tf_sent %>% filter(!is.na(sentiment)) %>%
group_by(sentiment) %>%
arrange(desc(n)) %>% mutate(row = row_number()) %>% filter(row < 10) %>%
ungroup() %>% mutate(word = reorder(word, n)) %>%
ggplot(aes(y=n, x=word)) + geom_col() + theme(axis.text.x = element_text(angle=90, hjust=1)) +
facet_wrap(~sentiment, ncol=3, scales="free_x")
library(quanteda.textplots)
corp <- cast_dfm(tf, ID, word, n)
textplot_wordcloud(dfm(corp), color = RColorBrewer::brewer.pal(9, "Set1"))
corp_no_stop <- cast_dfm(tf_no_stop, ID, word, n)
textplot_wordcloud(dfm(corp_no_stop), color = RColorBrewer::brewer.pal(9, "Set1"))