Introduction:
In this project, I’m exploring the relationship between social media
usage specifically TikTok and academic performance . By analyzing the
Tiktok_Cleaned.csv dataset, I examine variables such as
tiktok_use_hours_mon, study_hours_mon and sleep_quality. I chose this
topic because I am interested in how digital habits affect my own study
efficiency. My analysis involves cleaning the data, and I have used
dplyr to subset my observations to ensure a focused look at
student habits.
# Add this as the first line in your code chunk
setwd("/Users/sadiyasow/Downloads")
# Now your original code will work
data <- haven::read_sav("TikTok_SMSCF_Chinese_teenagers_RAW.sav")
knitr::opts_knit$set(root.dir = "/Users/sadiyasow/Downloads")# Step 1: Install the package
# install.packages("haven")
# Step 2: Load the library
library(haven)
library(readr)
# Step 3: Read the file and store it as a data frame
tiktok_data <- read_sav("TikTok_SMSCF_Chinese_teenagers_RAW.sav")
# Step 4: Write it as a CSV so you have a clean file for the rest of the project
write.csv(tiktok_data, "TikTok_Cleaned.csv", row.names = FALSE)
# Step 5: Now, load that CSV using the command your rubric requires
my_data <- readr::read_csv("TikTok_Cleaned.csv")## Rows: 362 Columns: 72
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (6): UserLanguage, media_use_freq_6_TEXT, phone_4_TEXT, education_part...
## dbl (63): Status, Progress, Duration__in_seconds_, Finished, Q_RecaptchaSco...
## dttm (3): StartDate, EndDate, RecordedDate
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
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## [232] "Zoom.pkg"getwd()## [1] "/Users/sadiyasow/Downloads"Load file as Read.csv:
# Load the necessary libraries
library(readr)
library(tidyverse)## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.2.0 ✔ purrr 1.2.1
## ✔ forcats 1.0.1 ✔ stringr 1.6.0
## ✔ ggplot2 4.0.2 ✔ tibble 3.3.1
## ✔ lubridate 1.9.5 ✔ tidyr 1.3.2
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors# Load the dataset using readr::read_csv()
# We store it in a variable called 'tiktok_data'
tiktok_data <- readr::read_csv("TikTok_Cleaned.csv")## Rows: 362 Columns: 72
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (6): UserLanguage, media_use_freq_6_TEXT, phone_4_TEXT, education_part...
## dbl (63): Status, Progress, Duration__in_seconds_, Finished, Q_RecaptchaSco...
## dttm (3): StartDate, EndDate, RecordedDate
##
## 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.# Inspect the data to make sure it loaded correctly
head(tiktok_data)## # A tibble: 6 × 72
## StartDate EndDate Status Progress Duration__in_seconds_
## <dttm> <dttm> <dbl> <dbl> <dbl>
## 1 2021-01-19 04:36:52 2021-01-19 04:37:16 0 100 24
## 2 2021-01-19 04:36:26 2021-01-19 04:37:20 0 100 54
## 3 2021-01-19 04:31:28 2021-01-19 04:39:45 0 100 496
## 4 2021-01-19 04:31:11 2021-01-19 04:40:25 0 100 553
## 5 2021-01-19 04:33:11 2021-01-19 04:44:57 0 100 705
## 6 2021-01-19 04:38:35 2021-01-19 04:45:00 0 100 385
## # ℹ 67 more variables: Finished <dbl>, RecordedDate <dttm>, UserLanguage <chr>,
## # Q_RecaptchaScore <dbl>, consent_parents <dbl>, consent_children <dbl>,
## # media_use_freq_1 <dbl>, media_use_freq_2 <dbl>, media_use_freq_3 <dbl>,
## # media_use_freq_4 <dbl>, media_use_freq_5 <dbl>, media_use_freq_6 <dbl>,
## # media_use_freq_6_TEXT <chr>, media_use_rank_1 <dbl>,
## # media_use_rank_2 <dbl>, media_use_rank_3 <dbl>, media_use_rank_4 <dbl>,
## # media_use_rank_5 <dbl>, media_use_rank_6 <dbl>, …Data Cleaning:
dplyr): I used the
filter() and mutate() commands from the
dplyr library. The filter() command was
essential for removing missing values (NA) in key columns,
ensuring that the visualization only reflects complete observations. The
mutate() command was used to transform the
gender variable into a factor, which allows
the plotting engine to treat these values as distinct groups (colors)
rather than a continuous numeric range.library(highcharter)## Registered S3 method overwritten by 'quantmod':
## method from
## as.zoo.data.frame zoo## Highcharts (www.highcharts.com) is a Highsoft software product which is## not free for commercial and Governmental uselibrary(tidyverse)
# 1. Prepare data: Convert gender to a factor so the grouping works nicely
df_clean <- read_csv("TikTok_Cleaned.csv") %>%
filter(!is.na(tiktok_use_hours_mon), !is.na(study_hours_mon)) %>%
mutate(gender = as.factor(gender)) ## Rows: 362 Columns: 72## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (6): UserLanguage, media_use_freq_6_TEXT, phone_4_TEXT, education_part...
## dbl (63): Status, Progress, Duration__in_seconds_, Finished, Q_RecaptchaSco...
## dttm (3): StartDate, EndDate, RecordedDate
##
## 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.hchart(df_clean, "scatter", hcaes(x = tiktok_use_hours_mon,
y = study_hours_mon,
size = sleep_quality,
group = gender)) %>%
hc_colors(c("#D67AB1", "#5B8FB9", "#8A7EB9")) %>%
hc_title(text = "TikTok Usage vs. Studying & Sleep") %>%
hc_xAxis(title = list(text = "TikTok Hours (Monday)")) %>%
hc_yAxis(title = list(text = "Study Hours (Monday)")) %>%
hc_add_theme(hc_theme_elementary()) # Correct way to apply a themeThe X-axis represents TikTok Usage in (Hours/Mon)). Moving from the left to right showing how the values are increasing.
The Y-Axis represents shows the Study hours in (Mon) moving from the bottom showing the increase.
The size of the bubble represents the sleep quality and how much A teenager is getting
The Visualization i used was a highcharter: I implemented a
scatter plot using the hchart function. By mapping
tiktok_use_hours_mon to the x-axis and
study_hours_mon to the y-axis, I can visualize the direct
trade-off between leisure and study. Adding sleep_quality
as the size variable provides a dimension to the analysis
which allows us to see if students with poorer sleep (smaller bubbles)
are clustering in specific usage patterns.
Theme and Palette: To meet the project
requirement for a non-default theme, I applied a custom color palette
(#D67AB1, #5B8FB9, #8A7EB9) Which
was Blue, purple and pink. I used the code
hc_theme_elementary(). This ensures the visualization is ,
accessible, and distinct from the standard settings.
library(highcharter)
library(tidyverse)
df_mood <- read_csv("TikTok_Cleaned.csv") %>%
filter(!is.na(tiktok_use_hours_mon), !is.na(well_being_mood))## Rows: 362 Columns: 72
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (6): UserLanguage, media_use_freq_6_TEXT, phone_4_TEXT, education_part...
## dbl (63): Status, Progress, Duration__in_seconds_, Finished, Q_RecaptchaSco...
## dttm (3): StartDate, EndDate, RecordedDate
##
## 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.hchart(df_mood, "scatter", hcaes(x = tiktok_use_hours_mon,
y = well_being_mood)) %>%
hc_colors(c("#D67AB1")) %>%
hc_title(text = "TikTok Usage vs. Reported Well-Being Mood") %>%
hc_xAxis(title = list(text = "TikTok Hours (Monday)")) %>%
hc_yAxis(title = list(text = "Reported Mood (1-5)")) %>%
hc_add_theme(hc_theme_elementary())This second chart compares the wellbeing of students to their tik tok usage, As you can see their tik tok usage is higher then their moods meaning their a negative impact on using tik tok. Their is also a outlier for tik tok hours at 120.
library(highcharter)
library(tidyverse)
# We convert gender to a factor so Highcharts treats them as 2 distinct groups
df_gender <- read_csv("TikTok_Cleaned.csv") %>%
filter(!is.na(tiktok_use_hours_mon), !is.na(scores), !is.na(gender)) %>%
mutate(gender = as.factor(gender))## Rows: 362 Columns: 72
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (6): UserLanguage, media_use_freq_6_TEXT, phone_4_TEXT, education_part...
## dbl (63): Status, Progress, Duration__in_seconds_, Finished, Q_RecaptchaSco...
## dttm (3): StartDate, EndDate, RecordedDate
##
## 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.hchart(df_gender, "scatter", hcaes(x = tiktok_use_hours_mon,
y = scores,
group = gender)) %>%
# Use Red and Black for the two groups
hc_colors(c("#FF0000", "#000000")) %>%
hc_title(text = "TikTok Usage vs. Academic Score by Gender") %>%
hc_xAxis(title = list(text = "TikTok Hours (Monday)")) %>%
hc_yAxis(title = list(text = "Academic Score (GPA Proxy)")) %>%
hc_add_theme(hc_theme_elementary())This scatter plot shows the relationship between time spent on TikTok and academic scores (GPA). The X-axis represents TikTok usage in hours, while the Y-axis represents the academic score. you can see that many of the students GPA falls mediocre and their TikTok usage is clustered with the GPA. To create a better analysis of TikTok usage vs academic scores(GPA) , I categorized the data by gender and mapped each group to a specific color: Red and Black. In this data set their were multiple variables that had (gender_1, gender_2, gender_3, gender_4)This choice allows for an immediate visual comparison between the two groups. These groups demonstrate distinct trends in how their social media habits align with their (GPA).
This project has provided a practical look at how student survey data can be transformed into actionable insights. By cleaning the “Tiktok_Cleaned.csv” dataset and applying interactive visualization techniques, I was able to observe how social media engagement correlates with academic and lifestyle variables.
While the data did not reveal a simple, one-size-fits-all rule, it goes over the diversity of student experiences. Some students maintain their study hours which are relatively high regardless of TikTok usage, while others show a more inverse relationship. This data reinforced how crucial it is to do data cleaning and how to remove the “NA” values and properly characterize variables, this can help prevent errors and improve insights. To conclude , this project aids as a step toward being more considerate our time management.