library(tidyverse)

## -- Attaching packages --------------------------------------- tidyverse 1.3.1 --

## v ggplot2 3.3.5     v purrr   0.3.4
## v tibble  3.1.6     v dplyr   1.0.7
## v tidyr   1.1.4     v stringr 1.4.0
## v readr   2.1.1     v forcats 0.5.1

## -- Conflicts ------------------------------------------ tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag()    masks stats::lag()

library(ggplot2)
library(dplyr)
library(ggthemes)

## Warning: package 'ggthemes' was built under R version 4.1.3

library(wordcloud)

## Warning: package 'wordcloud' was built under R version 4.1.3

## Loading required package: RColorBrewer

library(RColorBrewer)

Downloading the Data from Tidy Tuesday

tuesdata <- tidytuesdayR::tt_load('2022-09-13')

## --- Compiling #TidyTuesday Information for 2022-09-13 ----

## --- There is 1 file available ---

## --- Starting Download ---

## 
##  Downloading file 1 of 1: `bigfoot.csv`

## --- Download complete ---

tuesdata <- tidytuesdayR::tt_load(2022, week = 37)

## --- Compiling #TidyTuesday Information for 2022-09-13 ----

## --- There is 1 file available ---

## --- Starting Download ---

## 
##  Downloading file 1 of 1: `bigfoot.csv`

## --- Download complete ---

bigfoot <- tuesdata$bigfoot
bigfoot <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2022/2022-09-13/bigfoot.csv')

## Rows: 5021 Columns: 28

## -- Column specification --------------------------------------------------------
## Delimiter: ","
## chr  (10): observed, location_details, county, state, season, title, classif...
## dbl  (17): latitude, longitude, number, temperature_high, temperature_mid, t...
## date  (1): date

## 
## i Use `spec()` to retrieve the full column specification for this data.
## i Specify the column types or set `show_col_types = FALSE` to quiet this message.

Cleaning the data

bigfoot2c <- na.omit(bigfoot)
bigfoot2c[!duplicated(bigfoot2c), ]

## # A tibble: 722 x 28
##    observed        location_details county state season title latitude longitude
##    <chr>           <chr>            <chr>  <chr> <chr>  <chr>    <dbl>     <dbl>
##  1 "I was on my w~ Close to Clarem~ Sulli~ New ~ Summer Repo~     43.4     -72.3
##  2 "It was August~ Waterloo Road, ~ Warre~ New ~ Summer Repo~     40.9     -74.8
##  3 "After getting~ Wayne National ~ Washi~ Ohio  Fall   Repo~     39.4     -81.4
##  4 "I've told ver~ In a developmen~ Washi~ Rhod~ Summer Repo~     41.4     -71.7
##  5 "I used to own~ Approximately 4~ Galla~ Mont~ Summer Repo~     45.8    -111. 
##  6 "Saw very larg~ One (1) mile fr~ Galla~ Mont~ Spring Repo~     45.6    -111. 
##  7 "I am an OTR t~ Heading westbou~ Miner~ Mont~ Spring Repo~     47.4    -116. 
##  8 "It has been a~ It was in Monta~ Misso~ Mont~ Summer Repo~     47.0    -114. 
##  9 "In mid july 2~ We live just in~ Misso~ Mont~ Summer Repo~     47.1    -114. 
## 10 "My family and~ 8.6 miles up Fo~ Misso~ Mont~ Summer Repo~     46.9    -114. 
## # ... with 712 more rows, and 20 more variables: date <date>, number <dbl>,
## #   classification <chr>, geohash <chr>, temperature_high <dbl>,
## #   temperature_mid <dbl>, temperature_low <dbl>, dew_point <dbl>,
## #   humidity <dbl>, cloud_cover <dbl>, moon_phase <dbl>,
## #   precip_intensity <dbl>, precip_probability <dbl>, precip_type <chr>,
## #   pressure <dbl>, summary <chr>, uv_index <dbl>, visibility <dbl>,
## #   wind_bearing <dbl>, wind_speed <dbl>

summary(bigfoot2c)

##    observed         location_details      county             state          
##  Length:722         Length:722         Length:722         Length:722        
##  Class :character   Class :character   Class :character   Class :character  
##  Mode  :character   Mode  :character   Mode  :character   Mode  :character  
##                                                                             
##                                                                             
##                                                                             
##     season             title              latitude       longitude      
##  Length:722         Length:722         Min.   :25.14   Min.   :-149.77  
##  Class :character   Class :character   1st Qu.:36.89   1st Qu.:-111.76  
##  Mode  :character   Mode  :character   Median :40.42   Median : -87.25  
##                                        Mean   :40.07   Mean   : -94.35  
##                                        3rd Qu.:44.70   3rd Qu.: -81.49  
##                                        Max.   :64.89   Max.   : -68.23  
##       date                number      classification       geohash         
##  Min.   :1961-09-01   Min.   :   77   Length:722         Length:722        
##  1st Qu.:2002-10-02   1st Qu.: 9333   Class :character   Class :character  
##  Median :2007-10-22   Median :25792   Mode  :character   Mode  :character  
##  Mean   :2006-10-12   Mean   :30196                                        
##  3rd Qu.:2014-11-13   3rd Qu.:49880                                        
##  Max.   :2021-11-27   Max.   :71997                                        
##  temperature_high temperature_mid  temperature_low   dew_point    
##  Min.   : 9.80    Min.   : 8.595   Min.   : 0.09   Min.   :-1.83  
##  1st Qu.:49.60    1st Qu.:43.731   1st Qu.:36.26   1st Qu.:38.01  
##  Median :62.55    Median :55.585   Median :48.23   Median :47.91  
##  Mean   :62.84    Mean   :55.566   Mean   :48.29   Mean   :48.62  
##  3rd Qu.:78.22    3rd Qu.:69.285   3rd Qu.:61.36   3rd Qu.:62.72  
##  Max.   :98.15    Max.   :85.950   Max.   :84.34   Max.   :77.40  
##     humidity       cloud_cover       moon_phase     precip_intensity 
##  Min.   :0.0800   Min.   :0.0000   Min.   :0.0100   Min.   :0.00010  
##  1st Qu.:0.7400   1st Qu.:0.4100   1st Qu.:0.2500   1st Qu.:0.00070  
##  Median :0.8100   Median :0.7000   Median :0.5200   Median :0.00305  
##  Mean   :0.8003   Mean   :0.6419   Mean   :0.4987   Mean   :0.01036  
##  3rd Qu.:0.8800   3rd Qu.:0.9100   3rd Qu.:0.7600   3rd Qu.:0.01080  
##  Max.   :1.0000   Max.   :1.0000   Max.   :0.9900   Max.   :0.20580  
##  precip_probability precip_type           pressure        summary         
##  Min.   :0.010      Length:722         Min.   : 980.3   Length:722        
##  1st Qu.:0.500      Class :character   1st Qu.:1011.7   Class :character  
##  Median :0.950      Mode  :character   Median :1015.1   Mode  :character  
##  Mean   :0.744                         Mean   :1015.2                     
##  3rd Qu.:1.000                         3rd Qu.:1019.2                     
##  Max.   :1.000                         Max.   :1038.0                     
##     uv_index        visibility      wind_bearing     wind_speed    
##  Min.   : 0.000   Min.   : 0.740   Min.   :  0.0   Min.   : 0.100  
##  1st Qu.: 2.000   1st Qu.: 6.822   1st Qu.:134.0   1st Qu.: 1.502  
##  Median : 4.000   Median : 8.940   Median :204.5   Median : 3.420  
##  Mean   : 4.731   Mean   : 8.013   Mean   :199.1   Mean   : 4.024  
##  3rd Qu.: 7.000   3rd Qu.: 9.845   3rd Qu.:260.0   3rd Qu.: 5.630  
##  Max.   :12.000   Max.   :10.000   Max.   :359.0   Max.   :23.940

bigfoot2c <- bigfoot2c %>%
  mutate(year = as.numeric(format(date,'%Y')))
view(bigfoot2c)

How many bigfoot sightings has there been over the years?

bigfoot2c %>%
  ggplot(aes(year)) + 
  geom_histogram(color = "aquamarine4", fill = "aquamarine") +
  labs(x="year of sightings",
         y = "# of sightings",
         title = "Amount of bigfoot sightings")

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

Map of sightings in U.S

bigfoot2c %>%
filter(state != "Alaska") %>%
  ggplot(aes(longitude,latitude, color = season)) +
  geom_point() +
  borders("state") + 
  coord_map() +
  labs(title = "Bigfoot Sightings in the U.S")

So what states was bigfoot seen?

bigfoot2c %>%
  count(state, sort = TRUE)

## # A tibble: 46 x 2
##    state             n
##    <chr>         <int>
##  1 Washington      115
##  2 Ohio             84
##  3 Florida          48
##  4 Illinois         47
##  5 Oregon           31
##  6 West Virginia    26
##  7 Alabama          23
##  8 Kentucky         22
##  9 Michigan         22
## 10 Georgia          20
## # ... with 36 more rows

WA <- bigfoot2c %>%
  filter(state == "Washington") %>%
  select(county,number,season,temperature_mid)
WA

## # A tibble: 115 x 4
##    county           number season temperature_mid
##    <chr>             <dbl> <chr>            <dbl>
##  1 Skamania County   65295 Winter            40.8
##  2 Skamania County   65464 Spring            48.5
##  3 Skamania County   67407 Fall              38.9
##  4 Klickitat County   5953 Fall              46.7
##  5 Klickitat County  23315 Spring            40.4
##  6 Klickitat County  35479 Fall              64.3
##  7 Lewis County       4737 Spring            57.6
##  8 Lewis County      22475 Summer            60.7
##  9 Lewis County      65388 Spring            44.8
## 10 Lewis County      65538 Spring            53.1
## # ... with 105 more rows

Where in WA was bigfoot seen?

attempt <- WA %>%
  count(county, sort = TRUE)
ggplot(attempt, aes(county,n)) +
  geom_point() +
  theme_minimal(base_size = 10)+
  theme(axis.text.x = element_text(size = 8, angle = 90)) +
  labs(title = "WA county sightings", x="counties",y = "# of sightings")

Which seasons did bigfoot appear?

d <- bigfoot2c %>%
  mutate(season = na_if(season, "Unknown")) %>%
  filter(!is.na(season)) %>%
  ggplot(aes(year, fill = season)) + 
  geom_bar() +
  facet_wrap(~season) +
  labs(x="Year",
         y = "# of sightings",
         title = "Seasonal Sightings") +
  theme(axis.text.x = element_text(size = 9, angle = 50))
d

ggplot(bigfoot2c, aes(x = 1, y = season, fill = season)) +
    geom_col() +
    coord_polar(theta='y') +
  theme_void() + 
  ggtitle("Seasons of bigfoot sightings")

Most common words to describe bigfoot observations?

wordcloud(words = bigfoot2c$observed, min.freq = 1,           max.words=1000, colors=brewer.pal(8, "BrBG"))

## Loading required namespace: tm

## Warning in tm_map.SimpleCorpus(corpus, tm::removePunctuation): transformation
## drops documents

## Warning in tm_map.SimpleCorpus(corpus, function(x) tm::removeWords(x,
## tm::stopwords())): transformation drops documents

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thurston <-bigfoot2c %>%
  filter(state == "Washington", county == "Thurston County") %>%
  select(observed)
thurston

## # A tibble: 2 x 1
##   observed                                                                      
##   <chr>                                                                         
## 1 This is how it all started i was walking home from my boyfriends house i woul~
## 2 On wed march 20,2012 My 2, sons J and T, and 2 friends S and T along with mys~