Students Social Media Addiction

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```r
library(ggplot2)
library(GGally)
library(tidyverse)
library(lattice)
```

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```r
library(ggplot2)
library(GGally)
library(tidyverse)
library(lattice)

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```r
summary(Social_Media_Addiction)

      Age           Gender          Academic_Level       Country           Daily_Hours   
 Min.   :18.00   Length:705         Length:705         Length:705         Min.   :1.500  
 1st Qu.:19.00   Class :character   Class :character   Class :character   1st Qu.:4.100  
 Median :21.00   Mode  :character   Mode  :character   Mode  :character   Median :4.800  
 Mean   :20.66                                                            Mean   :4.919  
 3rd Qu.:22.00                                                            3rd Qu.:5.800  
 Max.   :24.00                                                            Max.   :8.500  
   Platform         Affects_Academic_Performance  Sleep_Hours    Mental_Health_Score
 Length:705         Length:705                   Min.   :3.800   Min.   :4.000      
 Class :character   Class :character             1st Qu.:6.000   1st Qu.:5.000      
 Mode  :character   Mode  :character             Median :6.900   Median :6.000      
                                                 Mean   :6.869   Mean   :6.227      
                                                 3rd Qu.:7.700   3rd Qu.:7.000      
                                                 Max.   :9.600   Max.   :9.000      
 Relationship_Status   Conflicts    Addicted_Score 
 Length:705          Min.   :0.00   Min.   :2.000  
 Class :character    1st Qu.:2.00   1st Qu.:5.000  
 Mode  :character    Median :3.00   Median :7.000  
                     Mean   :2.85   Mean   :6.437  
                     3rd Qu.:4.00   3rd Qu.:8.000  
                     Max.   :5.00   Max.   :9.000  

ggplot(Social_Media_Addiction)+
  aes(Age)+
  geom_histogram()

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

The bar chart indicates that this survey was done on students.

ggplot(Social_Media_Addiction)+
  aes(Platform)+
  geom_bar()+
  theme(axis.text.x = element_text(angle = 45, hjust = 1))

The bar graph shows that Instagram is the most popular app used by students, followed by TikTok.

ggplot(Social_Media_Addiction)+
  aes(Platform, fill = Gender)+
  geom_bar()+
  theme(axis.text.x = element_text(angle = 45, hjust = 1))

From this graph, I know that more female use Instagram and more male use Facebook.

ggplot(Social_Media_Addiction)+
  aes(Sleep_Hours, Mental_Health_Score)+
  geom_jitter()

By looking at this jitter plot, I know that the more sleep you get, the better your mental health will be.

ggplot(Social_Media_Addiction, aes(x = Daily_Hours, y = Sleep_Hours)) +
  geom_point() +
  facet_wrap(~ Gender)

This graph shows that gender does not play a role in the relationship between sleep duration and social media usage time.

ggplot(Social_Media_Addiction, aes(x = Daily_Hours, y = Sleep_Hours)) +
  geom_point() +
  geom_smooth(method = "lm", se = FALSE) +
  labs(title = "Linear Regression of Sleep Hours on Daily Usage Hours ", x = "Daily usage hours", y = "Sleep hours")

`geom_smooth()` using formula = 'y ~ x'

This graph shows the relationship between daily usage hours and sleep hours. It’s representing that as the amount of time spent using social media increases, the amount of time spent sleeping is decreasing.

ggplot(Social_Media_Addiction, aes(x = Addicted_Score, fill = Affects_Academic_Performance)) +
  geom_bar(position = "fill") +
  labs(title = "Rate of Affection on Academic Performance by Addicted Score", y = "Proportion") +
  scale_fill_manual(values = c("blue", "turquoise")) +
  theme_minimal()

This graph demonstrates that the higher the addiction score, the more likely it is to affect the academic performance.

ggplot(Social_Media_Addiction, aes(x = Mental_Health_Score, fill = Affects_Academic_Performance)) +
  geom_bar(position = "fill") +
  labs(title = "Rate of Affection on Academic Performance by Mental Health Score", y = "Proportion") +
  scale_fill_manual(values = c("blue", "turquoise")) +
  theme_minimal()

This graph demonstrates that the higher the Mental health score, the less likely it is to affect the academic performance.

ggplot(Social_Media_Addiction, aes(x = Affects_Academic_Performance, y = Age, fill = Affects_Academic_Performance)) +
  geom_violin(alpha = 0.5) +
  geom_boxplot(width = 0.1, position = position_dodge(width = 0.9)) +
  labs(title = "Age Distribution by Affection on Academic Performance", y = "Age", x = "Affection on Academic Performance") +
  scale_fill_manual(values = c("blue", "turquoise")) +
  facet_wrap(~Gender) +
  theme(legend.position = "none") +
  theme_minimal()

This graph is showing that males are older than females who answered this survey.