Assignment 3
Damaris Montecinos
2024-09-03
Load Dataset
library(ggplot2)
MxMH_Data <- read.csv("mxmh_survey_results.csv")
head(MxMH_Data, 15)## Timestamp Age Primary.streaming.service Hours.per.day
## 1 8/27/2022 19:29:02 18 Spotify 3.0
## 2 8/27/2022 19:57:31 63 Pandora 1.5
## 3 8/27/2022 21:28:18 18 Spotify 4.0
## 4 8/27/2022 21:40:40 61 YouTube Music 2.5
## 5 8/27/2022 21:54:47 18 Spotify 4.0
## 6 8/27/2022 21:56:50 18 Spotify 5.0
## 7 8/27/2022 22:00:29 18 YouTube Music 3.0
## 8 8/27/2022 22:18:59 21 Spotify 1.0
## 9 8/27/2022 22:33:05 19 Spotify 6.0
## 10 8/27/2022 22:44:03 18 I do not use a streaming service. 1.0
## 11 8/27/2022 22:51:15 18 Spotify 3.0
## 12 8/27/2022 23:00:32 19 YouTube Music 8.0
## 13 8/27/2022 23:04:00 NA Spotify 3.0
## 14 8/27/2022 23:12:03 19 Spotify 2.0
## 15 8/27/2022 23:16:06 18 Spotify 4.0
## While.working Instrumentalist Composer Fav.genre Exploratory
## 1 Yes Yes Yes Latin Yes
## 2 Yes No No Rock Yes
## 3 No No No Video game music No
## 4 Yes No Yes Jazz Yes
## 5 Yes No No R&B Yes
## 6 Yes Yes Yes Jazz Yes
## 7 Yes Yes No Video game music Yes
## 8 Yes No No K pop Yes
## 9 Yes No No Rock No
## 10 Yes No No R&B Yes
## 11 Yes Yes No Country Yes
## 12 Yes No No EDM Yes
## 13 Yes No No Hip hop Yes
## 14 Yes No No Country Yes
## 15 Yes Yes No Jazz Yes
## Foreign.languages BPM Frequency..Classical. Frequency..Country.
## 1 Yes 156 Rarely Never
## 2 No 119 Sometimes Never
## 3 Yes 132 Never Never
## 4 Yes 84 Sometimes Never
## 5 No 107 Never Never
## 6 Yes 86 Rarely Sometimes
## 7 Yes 66 Sometimes Never
## 8 Yes 95 Never Never
## 9 No 94 Never Very frequently
## 10 Yes 155 Rarely Rarely
## 11 No NA Never Very frequently
## 12 No 125 Rarely Never
## 13 Yes NA Rarely Never
## 14 No 88 Never Very frequently
## 15 Yes 148 Very frequently Rarely
## Frequency..EDM. Frequency..Folk. Frequency..Gospel. Frequency..Hip.hop.
## 1 Rarely Never Never Sometimes
## 2 Never Rarely Sometimes Rarely
## 3 Very frequently Never Never Rarely
## 4 Never Rarely Sometimes Never
## 5 Rarely Never Rarely Very frequently
## 6 Never Never Never Sometimes
## 7 Rarely Sometimes Rarely Rarely
## 8 Rarely Never Never Very frequently
## 9 Never Sometimes Never Never
## 10 Rarely Rarely Sometimes Rarely
## 11 Never Never Never Never
## 12 Very frequently Never Never Sometimes
## 13 Rarely Never Never Very frequently
## 14 Rarely Sometimes Never Sometimes
## 15 Never Never Never Never
## Frequency..Jazz. Frequency..K.pop. Frequency..Latin. Frequency..Lofi.
## 1 Never Very frequently Very frequently Rarely
## 2 Very frequently Rarely Sometimes Rarely
## 3 Rarely Very frequently Never Sometimes
## 4 Very frequently Sometimes Very frequently Sometimes
## 5 Never Very frequently Sometimes Sometimes
## 6 Very frequently Very frequently Rarely Very frequently
## 7 Sometimes Never Rarely Rarely
## 8 Rarely Very frequently Never Sometimes
## 9 Never Never Never Never
## 10 Rarely Never Rarely Rarely
## 11 Never Never Never Never
## 12 Rarely Rarely Rarely Rarely
## 13 Never Sometimes Never Very frequently
## 14 Never Never Never Rarely
## 15 Very frequently Never Sometimes Rarely
## Frequency..Metal. Frequency..Pop. Frequency..R.B. Frequency..Rap.
## 1 Never Very frequently Sometimes Very frequently
## 2 Never Sometimes Sometimes Rarely
## 3 Sometimes Rarely Never Rarely
## 4 Never Sometimes Sometimes Never
## 5 Never Sometimes Very frequently Very frequently
## 6 Rarely Very frequently Very frequently Very frequently
## 7 Rarely Rarely Rarely Never
## 8 Never Sometimes Sometimes Rarely
## 9 Very frequently Never Never Never
## 10 Never Sometimes Sometimes Rarely
## 11 Never Rarely Rarely Never
## 12 Never Rarely Rarely Sometimes
## 13 Never Sometimes Sometimes Rarely
## 14 Never Rarely Never Very frequently
## 15 Sometimes Sometimes Never Never
## Frequency..Rock. Frequency..Video.game.music. Anxiety Depression Insomnia
## 1 Never Sometimes 3 0 1
## 2 Very frequently Rarely 7 2 2
## 3 Rarely Very frequently 7 7 10
## 4 Never Never 9 7 3
## 5 Never Rarely 7 2 5
## 6 Very frequently Never 8 8 7
## 7 Never Sometimes 4 8 6
## 8 Never Rarely 5 3 5
## 9 Very frequently Never 2 0 0
## 10 Sometimes Sometimes 2 2 5
## 11 Rarely Never 7 7 4
## 12 Rarely Rarely 1 0 0
## 13 Rarely Never 9 3 2
## 14 Never Never 2 1 2
## 15 Sometimes Rarely 6 4 7
## OCD Music.effects Permissions
## 1 0 I understand.
## 2 1 I understand.
## 3 2 No effect I understand.
## 4 3 Improve I understand.
## 5 9 Improve I understand.
## 6 7 Improve I understand.
## 7 0 Improve I understand.
## 8 3 Improve I understand.
## 9 0 Improve I understand.
## 10 1 Improve I understand.
## 11 7 No effect I understand.
## 12 1 Improve I understand.
## 13 7 Improve I understand.
## 14 0 Improve I understand.
## 15 0 Improve I understand.Clean data
I will be using na.omit() to remove all rows with NA values in the dataset and give the clean dataset a new name
Clean_MxMHData <- na.omit(MxMH_Data)Histogram for Age per Person in Data
Will create a histogram that shows the Age frequency for every person in the dataset
ggplot(Clean_MxMHData, aes(x = Age)) + geom_histogram(binwidth = 1, fill = "pink", color = "black") + labs(title = "Histogram for Age per person", x = "Age", y = "Frequency")
Scatter Plot for Age and Hours Per Day listening to music
Will create a Scatter Plot that shows the correlation between Age and Hours per day someone listens to music
ggplot(Clean_MxMHData, aes(x = Age, y = `Hours.per.day`)) + geom_point(color = "blue") + labs(title = "Scatter Plot for Age and Hours per day listening to music", x = "Age", y = "Hours per day")
Bar Graph based on Music Genres listened to
This bar graph will show the peoples fav genre and count them according to the genre that was picked
ggplot(Clean_MxMHData, aes(x = `Fav.genre` )) + geom_bar(fill = "pink", color = "black") + labs (title = "Bar graph for Music genres listened to", x = "Fav genre", y = "Count") + theme_minimal() + theme(axis.text = element_text(angle = 45, hjust = 1))
Hypothesis
My Hypothesis is that People with an “Age” less than or equal to 25 have a higher “Hours.per.day” number compared to people older than 25
Split data into two groups
I will split the data into two groups. one group where age <= 25 and the second group will show where age > 25
Group1 <- Clean_MxMHData$Hours.per.day[Clean_MxMHData$Age <= 25 ]
Group2 <- Clean_MxMHData$Hours.per.day[Clean_MxMHData$Age > 25 ]Calculate Mean
I will calculate the mean for both groups
MU_Group1 <- mean(Group1)
MU_Group1## [1] 3.866441MU_Group2 <- mean(Group2)
MU_Group2## [1] 3.337838T-Test
I will conduct a T-Test to compare the means of the two groups, likely corresponding to the age groups (Age <= 25 and Age > 25) and their respective “Hours.per.day.”
T_TestResults <- t.test(Group1,Group2)
T_TestResults##
## Welch Two Sample t-test
##
## data: Group1 and Group2
## t = 1.9504, df = 340.23, p-value = 0.05195
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.004485226 1.061692433
## sample estimates:
## mean of x mean of y
## 3.866441 3.337838Conclusion
While the mean “Hours.per.day” is higher for people with Age <= 25 (3.87) compared to those with Age > 25 (3.34), the difference is not statistically significant at the 0.05 level (p-value = 0.05195). In this case, the p-value shows borderline significance, so it’s unclear whether there is a real difference to support the alternative hypothesis.