Untitled
Marjete
2022-10-06
Data 607 - Project 2:The goal of this assignment is to give you practice in preparing different datasets for downstream analysis work.
library(tidyverse)## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.2 ──
## ✔ ggplot2 3.3.6 ✔ purrr 0.3.4
## ✔ tibble 3.1.8 ✔ dplyr 1.0.10
## ✔ tidyr 1.2.0 ✔ stringr 1.4.1
## ✔ readr 2.1.2 ✔ forcats 0.5.2
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()#Dataset 1
cyber <- read.csv("https://raw.githubusercontent.com/marjete/Project2.607/main/Cyber_Threats.Brown%2CT.csv")
cyber## Year Adware Backdoor Ransomware Trojan
## 1 2022 2,242 356 2352 680
## 2 2016 2,679 664 3634 429
## 3 2020 1,853 523 3,122 534
## 4 2019 1634 354 2,685 254
## 5 2015 1,263 235 1,547 336
## 6 2017 856 273 1785 346
## 7 2021 945 195 2,073 264
## 8 2018 735 152 1,863 174# Analysis: Compare the frequency of cyber crime in each year.#columns Adware and Ransomware should be an numeric not character; also #s with commas change to in NA when attempting to do so. First drop the ','
col_conv <- c("Ransomware", "Adware")
cyber_updated <- cyber # Duplicate data
cyber_updated[ , col_conv] <- lapply(cyber_updated[ , col_conv], # Convert data
function(x){ as.numeric(as.character(gsub(",", "", x))) })
cyber_updated ## Year Adware Backdoor Ransomware Trojan
## 1 2022 2242 356 2352 680
## 2 2016 2679 664 3634 429
## 3 2020 1853 523 3122 534
## 4 2019 1634 354 2685 254
## 5 2015 1263 235 1547 336
## 6 2017 856 273 1785 346
## 7 2021 945 195 2073 264
## 8 2018 735 152 1863 174cyber_updated <- cyber_updated %>%
gather(type, attack.count, 2:5) #change wide structure to long
cyber_updated## Year type attack.count
## 1 2022 Adware 2242
## 2 2016 Adware 2679
## 3 2020 Adware 1853
## 4 2019 Adware 1634
## 5 2015 Adware 1263
## 6 2017 Adware 856
## 7 2021 Adware 945
## 8 2018 Adware 735
## 9 2022 Backdoor 356
## 10 2016 Backdoor 664
## 11 2020 Backdoor 523
## 12 2019 Backdoor 354
## 13 2015 Backdoor 235
## 14 2017 Backdoor 273
## 15 2021 Backdoor 195
## 16 2018 Backdoor 152
## 17 2022 Ransomware 2352
## 18 2016 Ransomware 3634
## 19 2020 Ransomware 3122
## 20 2019 Ransomware 2685
## 21 2015 Ransomware 1547
## 22 2017 Ransomware 1785
## 23 2021 Ransomware 2073
## 24 2018 Ransomware 1863
## 25 2022 Trojan 680
## 26 2016 Trojan 429
## 27 2020 Trojan 534
## 28 2019 Trojan 254
## 29 2015 Trojan 336
## 30 2017 Trojan 346
## 31 2021 Trojan 264
## 32 2018 Trojan 174LineGraph <- ggplot(cyber_updated, aes(x = Year, y = attack.count))
LineGraph <- LineGraph + geom_line(aes(color=factor(type), group = type))
LineGraph <- LineGraph + scale_color_discrete(name = "type")
LineGraph <- LineGraph + labs(title = "Cyber attacks from 2016-22", x = "Year", y = "Total Number of attacks")
LineGraph
#from the graph below, we can see the breakdown per type of attack per year, the discussion question didnt ask to compare companies so next we will sum the attack count to compare the frequency per yearcyber_updated %>%
group_by(Year) %>%
summarise(Freq = sum(attack.count)) #use dyplr to find the sum by year## # A tibble: 8 × 2
## Year Freq
## <int> <dbl>
## 1 2015 3381
## 2 2016 7406
## 3 2017 3260
## 4 2018 2924
## 5 2019 4927
## 6 2020 6032
## 7 2021 3477
## 8 2022 5630Conclusion: Year 2016 had the highest frequency of cyber attacks (7406), with 2020 being the next highest count of attacks at 6032. Technically 2022 has not ended so more data could be included in the next few months.
#Dataset 2
stroke <- read.csv("https://raw.githubusercontent.com/marjete/Project2.607/main/brain_stroke.csv")
glimpse(stroke)## Rows: 4,981
## Columns: 11
## $ gender <chr> "Male", "Male", "Female", "Female", "Male", "Male", …
## $ age <dbl> 67, 80, 49, 79, 81, 74, 69, 78, 81, 61, 54, 79, 50, …
## $ hypertension <int> 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 1…
## $ heart_disease <int> 1, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0…
## $ ever_married <chr> "Yes", "Yes", "Yes", "Yes", "Yes", "Yes", "No", "Yes…
## $ work_type <chr> "Private", "Private", "Private", "Self-employed", "P…
## $ Residence_type <chr> "Urban", "Rural", "Urban", "Rural", "Urban", "Rural"…
## $ avg_glucose_level <dbl> 228.69, 105.92, 171.23, 174.12, 186.21, 70.09, 94.39…
## $ bmi <dbl> 36.6, 32.5, 34.4, 24.0, 29.0, 27.4, 22.8, 24.2, 29.7…
## $ smoking_status <chr> "formerly smoked", "never smoked", "smokes", "never …
## $ stroke <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1…#Analysis: Predict risk of brain stroke#the data seems pretty clean already; there are no NA in the dataset
colSums(is.na(stroke))## gender age hypertension heart_disease
## 0 0 0 0
## ever_married work_type Residence_type avg_glucose_level
## 0 0 0 0
## bmi smoking_status stroke
## 0 0 0as.data.frame(table(stroke$smoking_status))## Var1 Freq
## 1 formerly smoked 867
## 2 never smoked 1838
## 3 smokes 776
## 4 Unknown 1500#unknown smoking status is somewhat like NA so it should be replaced with the highest category present (never smoked)
stroke <- stroke %>% mutate(smoking_status = replace(smoking_status, smoking_status == "Unknown", "never smoked"))
as.data.frame(table(stroke$smoking_status))## Var1 Freq
## 1 formerly smoked 867
## 2 never smoked 3338
## 3 smokes 776stroke %>%
group_by(gender) %>%
summarise(Freq = sum(stroke))## # A tibble: 2 × 2
## gender Freq
## <chr> <int>
## 1 Female 140
## 2 Male 108stroke %>%
group_by(hypertension) %>%
summarise(Freq = sum(stroke))## # A tibble: 2 × 2
## hypertension Freq
## <int> <int>
## 1 0 182
## 2 1 66stroke %>%
group_by(heart_disease) %>%
summarise(Freq = sum(stroke))## # A tibble: 2 × 2
## heart_disease Freq
## <int> <int>
## 1 0 201
## 2 1 47stroke %>%
group_by(ever_married) %>%
summarise(Freq = sum(stroke))## # A tibble: 2 × 2
## ever_married Freq
## <chr> <int>
## 1 No 29
## 2 Yes 219f5 <-ggplot(data = stroke) +geom_bar(mapping = aes(x = smoking_status))
f5
stroke %>%
group_by(smoking_status) %>%
summarise(Freq = sum(stroke))## # A tibble: 3 × 2
## smoking_status Freq
## <chr> <int>
## 1 formerly smoked 70
## 2 never smoked 136
## 3 smokes 42f6 <-ggplot(data = stroke) +geom_bar(mapping = aes(x = work_type))
f6
stroke %>%
group_by(work_type) %>%
summarise(Freq = sum(stroke)) ## # A tibble: 4 × 2
## work_type Freq
## <chr> <int>
## 1 children 2
## 2 Govt_job 33
## 3 Private 148
## 4 Self-employed 65ggplot(data = stroke, aes(x= age, y = smoking_status, color = stroke))+geom_point()
stroke <- stroke %>%
gather(factors, result, 1:10) #change wide structure to long; I'm not sure if this layout of data would be best or if there should in fact be more columns
stroke## stroke factors result
## 1 1 gender Male
## 2 1 gender Male
## 3 1 gender Female
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## 7 1 gender Female
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## [ reached 'max' / getOption("max.print") -- omitted 16477 rows ]stroke %>%
group_by(factors) %>%
ggplot(aes(stroke = 1)) +
facet_wrap(.~result) #this could be improved
Conclusion: A lot of the factors that indicate a higher risk of having a stroke. At this time it seems unclear if, for example, folks who work in the private sector have a higher risk for stroke, even though its the category with the highest frequency of having a stoke, due to the survey sample happen to work in this field more so. Similarly, it would not be correct to say that women have a higher chance of stoke because the sample size of women in this data set is just larger. Ideally, I would attempt the find the statistical significance of each factor to better compare which factors lead to higher risk.
#dataset 3
nba <- read.csv("https://raw.githubusercontent.com/marjete/Project2.607/main/NBA%20Distance%20Traveled.csv")
glimpse(nba)## Rows: 11
## Columns: 10
## $ Player <chr> "Chris Smith", "Paul Pierce", "James Harden", "David West"…
## $ Team <chr> "NYK", "BKN", "HOU", "IND", "IND", "MEM", "HOU", "CHI", "N…
## $ GP <int> 2, 74, 73, 80, 26, 59, 1, 76, 32, 2, 77
## $ W <int> 0, 41, 48, 54, 11, 40, 0, 43, 13, 2, 35
## $ L <int> 2, 33, 25, 26, 15, 19, 1, 33, 19, 0, 42
## $ Min <dbl> 1.0, 28.0, 38.0, 30.9, 19.8, 33.4, 19.4, 28.2, 19.8, 2.4, …
## $ Dist..Feet <dbl> 350.0, 9004.4, 12381.5, 10072.8, 6405.0, 10943.2, 6561.0, …
## $ Dist..Miles <dbl> 0.07, 1.71, 2.34, 1.91, 1.21, 2.07, 1.24, 1.75, 1.24, 0.14…
## $ Avg.Speed <dbl> 3.06, 3.67, 3.71, 3.71, 3.72, 3.74, 3.74, 3.75, 3.75, 3.75…
## $ Season <int> 2013, 2013, 2013, 2013, 2013, 2013, 2013, 2013, 2013, 2013…#analysis- Look at the relationship between distance traveled and average speed#Drop rows not needed; doing this changed the formatting
nba <- nba[, -c(2:7, 11)]
head(nba)## Player Dist..Miles Avg.Speed Season
## 1 Chris Smith 0.07 3.06 2013
## 2 Paul Pierce 1.71 3.67 2013
## 3 James Harden 2.34 3.71 2013
## 4 David West 1.91 3.71 2013
## 5 Andrew Bynum 1.21 3.72 2013
## 6 Marc Gasol 2.07 3.74 2013nba <- nba[, -c(4)]
head(nba)## Player Dist..Miles Avg.Speed
## 1 Chris Smith 0.07 3.06
## 2 Paul Pierce 1.71 3.67
## 3 James Harden 2.34 3.71
## 4 David West 1.91 3.71
## 5 Andrew Bynum 1.21 3.72
## 6 Marc Gasol 2.07 3.74colnames(nba) <- c("player", "distance", "speed")
head(nba) #renaming columns## player distance speed
## 1 Chris Smith 0.07 3.06
## 2 Paul Pierce 1.71 3.67
## 3 James Harden 2.34 3.71
## 4 David West 1.91 3.71
## 5 Andrew Bynum 1.21 3.72
## 6 Marc Gasol 2.07 3.74ggplot(data = nba, aes(x= distance, y = speed, color = player))+geom_point() 
#not sure why additional players not in the dataset appear in the plot belowConclusion: the graph above does not seem to reflect a correlation between speed and distance(miles). I am unsure how to compare distance and speed, maybe investigating the time it took could be helpful by doing distance divided by speed.