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268 lines
8.3 KiB
268 lines
8.3 KiB
---
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title: "Kahoot Report"
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author: "Scary Scarecrow"
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date: "5/4/2022"
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output:
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html_document:
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theme: lumen
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highlight: tango
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self_contained: true
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toc: true
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toc_depth: 4
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toc_float: true
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css: style.css
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---
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```{r setup, include=FALSE}
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knitr::opts_chunk$set(
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echo = FALSE,
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message = FALSE,
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warning = FALSE
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)
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culture<-data.frame(Answer=c("Definitely","Somewhat","No Way","Not Sure","No Answer"),
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Votes=c(0,7,0,0,1))
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training<-data.frame(Answer=c("Definitely","Somewhat","No Way","Not Sure","No Answer"),
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Votes=c(0,3,3,2,0))
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mentor<-data.frame(Answer=c("Definitely","Somewhat","No Way","Not Sure","No Answer"),
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Votes=c(0,4,1,2,1))
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documentation<-data.frame(Answer=c("Fully","Somewhat","Not Much","Not Clue","No Answer"),
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Votes=c(0,3,3,1,0))
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controls<-data.frame(Answer=c("Legislation","Business","Rules","Risk","No Answer"),
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Votes=c(5,6,3,5,0))
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simulations<-data.frame(Answer=c("Definitely","Somewhat","No Way","Not Sure","No Answer"),
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Votes=c(0,1,1,3,2))
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automation<-data.frame(Answer=c("Yes","No"),
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Votes=c(7,0))
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tech_use<-c("AI","AI","excel","BI","Lucy","Recipe predict","Tableau","Cloud")
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tech_challenges<-c("Time","Time","Time","Resistance","Time","People","Knowledge",
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"People","People", "Excel", "Speed", "Data Availability", "Self Exp","Frequency")
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strunstr<-data.frame(Answer=c("Definitely","Somewhat","Nope","No Clue","No Answer"),
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Votes=c(3,2,2,0,0))
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quality<-data.frame(Answer=c("Definitely","Somewhat","Nope","No Clue","No Answer"),
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Votes=c(0,2,4,1,0))
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sources<-data.frame(Answer=c("Definitely","Somewhat","Nope","No Clue","No Answer"),
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Votes=c(2,4,1,0,0))
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library(dplyr)
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library(wordcloud)
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library(highcharter)
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library(tidyr)
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library(d3wordcloud)
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```
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## Technology
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### Tech Use
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```{r}
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tech_use<-as.data.frame(table(tech_use))
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d3wordcloud(tech_use$tech_use, tech_use$Freq, colors = c("#000000", "#0000FF", "#FF0000"), rangesizefont = c(30, 50),color.scale="sqrt",rotate.min=0, rotate.max=0,spiral="rectangular")
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```
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### Challenges in Tech Adoption
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```{r}
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tech_challenges<-as.data.frame(table(tech_challenges))
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d3wordcloud(tech_challenges$tech_challenges, tech_challenges$Freq, colors = c("#000000", "#0000FF", "#FF0000"), rangesizefont = c(20, 50),color.scale="sqrt",rotate.min=0, rotate.max=0,spiral="rectangular")
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```
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## People
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```{r}
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culture |>
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rename(Culture=Votes) |>
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bind_cols(training) |>
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select(1,2,4) |>
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rename(Answer=Answer...1, Training=Votes) |>
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bind_cols(mentor) |>
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select(-4) |>
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rename(Answer=Answer...1, Mentor=Votes) |>
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pivot_longer(!Answer,names_to = "Type",values_to = "values") |>
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#pivot_wider(names_from = Answer, values_from = values)
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mutate(point=case_when(
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Answer=="Definitely" ~ 4,
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Answer=="Somewhat" ~ 3,
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Answer=="No Way" ~ 2,
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Answer=="Not Sure" ~ 1,
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Answer=="No Answer" ~ 0,
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)) |>
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mutate(score=values*point) |>
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group_by(Type) |>
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summarise(score=sum(score)/sum(values)) |>
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mutate(Max=4) |>
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mutate(score=round(score*100/Max,2)) |>
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ungroup() |>
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arrange(score) |>
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hchart("column", hcaes(x=Type, y=score), name="People",
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tooltip = list(pointFormat = "Avg. Score {point.Type}: {point.score}%"),
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dataLabels = list(enabled = TRUE, format= "{point.score}%")) |>
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hc_yAxis(title = list(text = "Average Score (%)"),
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labels = list(format = "{value}%"), max = 100) |>
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hc_title(text = "Mentorship and Training needs a boost") |>
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hc_subtitle(text = "Average score from culture promotion and training and mentorship opportunities") |>
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hc_credits(enabled = TRUE,
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text = "LaNubia Data Science",
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href = "https://www.lanubia.com/") |>
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hc_tooltip() |>
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hc_add_theme(hc_theme_economist()) |>
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hc_exporting(enabled = TRUE, # always enabled
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filename = "People")
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```
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## Data
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```{r}
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strunstr |>
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rename(Data.Type=Votes) |>
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bind_cols(quality) |>
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select(1,2,4) |>
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rename(Answer=Answer...1, Quality=Votes) |>
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bind_cols(sources) |>
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select(-4) |>
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rename(Answer=Answer...1, Sources=Votes) |>
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pivot_longer(!Answer,names_to = "Type",values_to = "values") |>
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#pivot_wider(names_from = Answer, values_from = values)
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mutate(point=case_when(
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Answer=="Definitely" ~ 4,
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Answer=="Somewhat" ~ 3,
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Answer=="Nope" ~ 2,
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Answer=="No Clue" ~ 1,
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Answer=="No Answer" ~ 0,
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)) |>
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mutate(score=values*point) |>
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group_by(Type) |>
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summarise(score=sum(score)/sum(values)) |>
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mutate(Max=4) |>
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mutate(score=round(score*100/Max,2)) |>
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ungroup() |>
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arrange(score) |>
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hchart("column", hcaes(x=Type, y=score), name="Data",
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tooltip = list(pointFormat = "Avg. Score {point.Type}: {point.score}%"),
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dataLabels = list(enabled = TRUE, format= "{point.score}%")) |>
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hc_yAxis(title = list(text = "Average Score (%)"),
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labels = list(format = "{value}%"), max = 100) |>
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hc_title(text = "Data quality is a major issue") |>
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hc_subtitle(text = "Average score from types of data used, its quality and sources") |>
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hc_credits(enabled = TRUE,
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text = "LaNubia Data Science",
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href = "https://www.lanubia.com/") |>
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hc_tooltip() |>
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hc_add_theme(hc_theme_economist()) |>
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hc_exporting(enabled = TRUE, # always enabled
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filename = "Data")
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```
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## Process
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```{r}
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documentation |>
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rename(Documentation=Votes) |>
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bind_cols(simulations) |>
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select(-3) |>
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rename(Answer=Answer...1, Simulations=Votes) |>
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pivot_longer(!Answer,names_to = "Type",values_to = "values") |>
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#pivot_wider(names_from = Answer, values_from = values)
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mutate(point=case_when(
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Answer=="Fully" ~ 4,
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Answer=="Somewhat" ~ 3,
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Answer=="Not Much" ~ 2,
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Answer=="Not Clue" ~ 1,
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Answer=="No Answer" ~ 0,
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)) |>
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mutate(score=values*point) |>
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group_by(Type) |>
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summarise(score=sum(score)/sum(values)) |>
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mutate(Max=4) |>
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mutate(score=round(score*100/Max,2)) |>
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ungroup() |>
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arrange(score) |>
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hchart("column", hcaes(x=Type, y=score), name="Process",
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tooltip = list(pointFormat = "Avg. Score {point.Type}: {point.score}%"),
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dataLabels = list(enabled = TRUE, format= "{point.score}%")) |>
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hc_yAxis(title = list(text = "Average Score (%)"),
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labels = list(format = "{value}%"), max = 100) |>
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hc_title(text = "Opportunity exists in Process Simulation and Documentation") |>
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hc_subtitle(text = "Average score from process documentation and simulations") |>
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hc_credits(enabled = TRUE,
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text = "LaNubia Data Science",
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href = "https://www.lanubia.com/") |>
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hc_tooltip() |>
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hc_add_theme(hc_theme_economist()) |>
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hc_exporting(enabled = TRUE, # always enabled
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filename = "Process")
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```
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### Automation Opportunity
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```{r}
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automation |>
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arrange(Votes) |>
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hchart("column", hcaes(x=Answer, y=Votes), name="ProcessAutomation",
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tooltip = list(pointFormat = "Votes {point.Answer}: {point.Votes}"),
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dataLabels = list(enabled = TRUE, format= "{point.Votes}")) |>
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hc_yAxis(title = list(text = "Count"),
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labels = list(format = "{value}")) |>
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hc_title(text = "Substantial Automation Opportunities Exist") |>
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hc_subtitle(text = "Count of votes (Whether or not process automation opportunities exist)") |>
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hc_credits(enabled = TRUE,
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text = "LaNubia Data Science",
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href = "https://www.lanubia.com/") |>
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hc_tooltip() |>
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hc_add_theme(hc_theme_economist()) |>
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hc_exporting(enabled = TRUE, # always enabled
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filename = "ProcessAuto")
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```
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### Process Controls
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```{r}
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controls |>
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arrange(Votes) |>
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hchart("column", hcaes(x=Answer, y=Votes), name="ProcessControl",
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tooltip = list(pointFormat = "Votes {point.Answer}: {point.Votes}"),
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dataLabels = list(enabled = TRUE, format= "{point.Votes}")) |>
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hc_yAxis(title = list(text = "Count"),
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labels = list(format = "{value}")) |>
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hc_title(text = "Process rules need boost") |>
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hc_subtitle(text = "Count of votes (Which controls exist)") |>
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hc_credits(enabled = TRUE,
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text = "LaNubia Data Science",
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href = "https://www.lanubia.com/") |>
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hc_tooltip() |>
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hc_add_theme(hc_theme_economist()) |>
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hc_exporting(enabled = TRUE, # always enabled
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filename = "ProcessControl")
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```
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