survey_analysis.Rmd

---
title: "Crime Survey Analysis"
author: "Eli Nimy"
date: "08/08/2021"
output: html_document
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```


```{r warning=FALSE, message=FALSE}
source("utils/loadpackages.R")
```

```{r warning=FALSE, message=FALSE}
cs_df <- read.csv('Crime Surveillance System - Survey.csv')
```


```{r warning=FALSE, message=FALSE}
cs_df
```


```{r warning=FALSE, message=FALSE}
library(janitor)
# replacing dots(.) in column names with underscores (_)
cs_df <- clean_names(cs_df)
# replacing underscores (_) in column names with spaces
names(cs_df) <- gsub("_", " ", names(cs_df))
```


```{r warning=FALSE, message=FALSE}
names(cs_df)
# [8] Alarm Siren Implementation support (Reason)
# [10] Automated Authority Message Implementation support (Reason)

```


```{r warning=FALSE, message=FALSE}
# Counting the different answers given 
comdata <- cs_df %>% 
  group_by(`select your gender`) %>% 
  summarise(n = n()) %>% 
  mutate(pct = n/sum(n), 
         lbl = scales::percent(pct))
```


```{r warning=FALSE, message=FALSE}
# Reorder Option 1 
# reorder(`select your gender`, -n)
# Reorder Option 2 
# comdata$`select your gender` <- factor(comdata$`select your gender`, levels = unique(comdata$`select your gender`)[order(comdata$n, decreasing = TRUE)])

ggplotly(comdata %>% 
  ggplot(aes(x = `select your gender`, y = n)) +
  geom_bar(fill = "#EA6A47", # peach color hex: #FFE5B4
           stat = "identity") +
  geom_text(aes(label = lbl),
          vjust = -0.25) +
  # scale_y_continuous(labels = percent) +
  labs(x = "", y = "",
       title = paste0("Question", "")) +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 45)))
```

#### Text Mining on the long responses 
```{r warning=FALSE, message=FALSE}
tm_crime <- function(or_text) {
  
  # Text Conversion 
  or_text <- iconv(or_text, "ASCII", "UTF-8", sub="byte")
  
  # Load the data as a corpus
  docs <- Corpus(VectorSource(or_text))
  
  # Text Transformation
  toSpace <- content_transformer(function (x , pattern ) gsub(pattern, " ", x))
  docs <- tm_map(docs, toSpace, "/")
  docs <- tm_map(docs, toSpace, "@")
  docs <- tm_map(docs, toSpace, "\\|")
  docs <- tm_map(docs, function(x) iconv(enc2utf8(x), sub = "byte"))
  
  # Cleaning text 
  
  # Convert the text to lower case
  docs <- tm_map(docs, content_transformer(tolower))
  # Remove numbers
  docs <- tm_map(docs, removeNumbers)
  # Remove english common stopwords
  docs <- tm_map(docs, removeWords, stopwords("english"))
  # Remove your own stop word
  # specify your stopwords as a character vector
  docs <- tm_map(docs, removeWords, c("orientation", "academic", "areas")) 
  # Remove punctuations
  docs <- tm_map(docs, removePunctuation)
  # Eliminate extra white spaces
  docs <- tm_map(docs, stripWhitespace)
  # Text stemming
  # docs <- tm_map(docs, stemDocument)
  
  # term-document matrix
  docs_dtm <- TermDocumentMatrix(docs)
  docs_m <- as.matrix(docs_dtm)
  docs_v <- sort(rowSums(docs_m ),decreasing=TRUE)
  docs_df <- data.frame(word = names(docs_v),freq=docs_v)
  return(docs_df)
}
```

#### Creating term document matrix for the long survey questions 
```{r warning=FALSE, message=FALSE}
cs_alarm <- tm_crime(cs_df[8])
cs_alert <- tm_crime(cs_df[10])
```


##### Reason for: do you think that alarms sounding off are an effective way to discourage criminal activity
```{r warning=FALSE, message=FALSE}
wordcloud2(cs_alarm, color='random-dark')
```


##### Reason for: do you believe that an automated alert from a surveillance system would improve authority response time
```{r warning=FALSE, message=FALSE}
wordcloud2(cs_alert, color='random-dark')
```

#### Most Frequent words
- Based on reason given for answering: do you believe that an automated alert from a surveillance system would improve authority response time
```{r warning=FALSE, message=FALSE}
row.names(cs_alert) <- c()
df_alert <- cs_alert[1:20,]
df_alert$word <- as.character(df_alert$word)
df_alert$word <- factor(df_alert$word, levels = unique(df_alert$word)[order(df_alert$freq, decreasing = TRUE)])
df_alert %>% 
 plot_ly(x = ~word, y = ~freq, type = 'bar', color = I('#56B4E9')) %>% 
  layout(title = "Most Frequent Words based Automated Authority Message Implementation Support (Reason)",
         xaxis = list(title = ""),
         yaxis = list(title = ""))
```


```{r warning=FALSE, message=FALSE}
# Renaming Columns 
names(cs_df)[names(cs_df) == "support your above selection"] <- "Reason: do you think that alarms sounding off are an effective way to discourage criminal activity"
names(cs_df)[names(cs_df) == "support your above selection 1"] <- "Reason: do you believe that an automated alert from a surveillance system would improve authority response time"
select(cs_df, c(8,10))
```


```{r warning=FALSE, message=FALSE}
names(cs_df)
```


```{r warning=FALSE, message=FALSE}
# Renaming Columns 
names(cs_df)[names(cs_df) == "support your above selection"] <- "Reason: do you think that alarms sounding off are an effective way to discourage criminal activity"
names(cs_df)[names(cs_df) == "support your above selection 1"] <- "Reason: do you believe that an automated alert from a surveillance system would improve authority response time"
```


```{r warning=FALSE, message=FALSE}
library(syuzhet)
```

#### Sentiment Analysis 
```{r warning=FALSE, message=FALSE}
# Alarm Question 
# Create a character vector
alarm_emo <- as.character(cs_df[8])
# Get sentiment
alarm_emo <- get_nrc_sentiment(alarm_emo)
# Transform sentiment dataframe
talarm_emo <- as.data.frame(t(alarm_emo))
# Rename the rownames and score value column 
talarm_emo <- rownames_to_column(talarm_emo, var = "Sentiment")
names(talarm_emo)[2] <- "Score"
```


```{r warning=FALSE, message=FALSE}
# Alert Question 
# Create a character vector
alert_emo <- as.character(cs_df[10])
# Get sentiment
alert_emo <- get_nrc_sentiment(alert_emo)
# Transform sentiment dataframe
talert_emo <- as.data.frame(t(alert_emo))
# Rename the rownames and score value column 
talert_emo <- rownames_to_column(talert_emo, var = "Sentiment")
names(talert_emo)[2] <- "Score"
```


```{r warning=FALSE, message=FALSE}
# Get emotion percentage of alarm question
talarm_plot <- talarm_emo %>% 
  mutate(pct = Score/sum(Score), 
         lbl = scales::percent(pct))
```


```{r warning=FALSE, message=FALSE}
# Get emotion percentage of highlights 
talert_plot <- talert_emo %>% 
  mutate(pct = Score/sum(Score), 
         lbl = scales::percent(pct))
```


```{r warning=FALSE, message=FALSE}
amplot <- talarm_plot %>% 
  ggplot(aes(x = reorder(Sentiment, -Score), y = Score)) +
  geom_bar(stat = "identity",
           fill = "#56B4E9") +
  geom_text(aes(label = lbl),
        vjust = -0.25) +
  labs(x = "", y = "",
       title = "Sentiments for Alarm Siren Implementation Support") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 45))

ggplotly(amplot)
```


```{r warning=FALSE, message=FALSE}
atplot <- talert_plot %>% 
  ggplot(aes(x = reorder(Sentiment, -Score), y = Score)) +
  geom_bar(stat = "identity",
           fill = "#56B4E9") +
  geom_text(aes(label = lbl),
        vjust = -0.25) +
  labs(x = "", y = "",
       title = "Sentiments for Automated Authority Message Implementation Support") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 45))

ggplotly(atplot)
```


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