Overview of the dataset: This dataset is collected from kaggle
datasets and this dataset provides a detailed overview of gym members’
exercise routines, physical attributes, and fitness metrics. It contains
973 samples of gym data, including key performance indicators such as
heart rate, calories burned, and workout duration. Each entry also
includes demographic data and experience levels, allowing for
comprehensive analysis of fitness patterns, athlete progression, and
health trends.
Load libraries:
library(tidyverse)
## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.1.4 ✔ readr 2.1.5
## ✔ forcats 1.0.0 ✔ stringr 1.5.1
## ✔ ggplot2 3.5.1 ✔ tibble 3.2.1
## ✔ lubridate 1.9.3 ✔ tidyr 1.3.1
## ✔ purrr 1.0.2
## ── 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
library(ggplot2)
library(dplyr)
Read the CSV data set:
gymdata<-read.csv("https://raw.githubusercontent.com/zahid607/Tidyverse/refs/heads/main/gym_members_exercise_tracking.csv")
head(gymdata)
## Age Gender Weight..kg. Height..m. Max_BPM Avg_BPM Resting_BPM
## 1 56 Male 88.3 1.71 180 157 60
## 2 46 Female 74.9 1.53 179 151 66
## 3 32 Female 68.1 1.66 167 122 54
## 4 25 Male 53.2 1.70 190 164 56
## 5 38 Male 46.1 1.79 188 158 68
## 6 56 Female 58.0 1.68 168 156 74
## Session_Duration..hours. Calories_Burned Workout_Type Fat_Percentage
## 1 1.69 1313 Yoga 12.6
## 2 1.30 883 HIIT 33.9
## 3 1.11 677 Cardio 33.4
## 4 0.59 532 Strength 28.8
## 5 0.64 556 Strength 29.2
## 6 1.59 1116 HIIT 15.5
## Water_Intake..liters. Workout_Frequency..days.week. Experience_Level BMI
## 1 3.5 4 3 30.20
## 2 2.1 4 2 32.00
## 3 2.3 4 2 24.71
## 4 2.1 3 1 18.41
## 5 2.8 3 1 14.39
## 6 2.7 5 3 20.55
Name of Columns of the data set.
colnames(gymdata)
## [1] "Age" "Gender"
## [3] "Weight..kg." "Height..m."
## [5] "Max_BPM" "Avg_BPM"
## [7] "Resting_BPM" "Session_Duration..hours."
## [9] "Calories_Burned" "Workout_Type"
## [11] "Fat_Percentage" "Water_Intake..liters."
## [13] "Workout_Frequency..days.week." "Experience_Level"
## [15] "BMI"
Summary statistics of gymdata
summary(gymdata)
## Age Gender Weight..kg. Height..m.
## Min. :18.00 Length:973 Min. : 40.00 Min. :1.500
## 1st Qu.:28.00 Class :character 1st Qu.: 58.10 1st Qu.:1.620
## Median :40.00 Mode :character Median : 70.00 Median :1.710
## Mean :38.68 Mean : 73.85 Mean :1.723
## 3rd Qu.:49.00 3rd Qu.: 86.00 3rd Qu.:1.800
## Max. :59.00 Max. :129.90 Max. :2.000
## Max_BPM Avg_BPM Resting_BPM Session_Duration..hours.
## Min. :160.0 Min. :120.0 Min. :50.00 Min. :0.500
## 1st Qu.:170.0 1st Qu.:131.0 1st Qu.:56.00 1st Qu.:1.040
## Median :180.0 Median :143.0 Median :62.00 Median :1.260
## Mean :179.9 Mean :143.8 Mean :62.22 Mean :1.256
## 3rd Qu.:190.0 3rd Qu.:156.0 3rd Qu.:68.00 3rd Qu.:1.460
## Max. :199.0 Max. :169.0 Max. :74.00 Max. :2.000
## Calories_Burned Workout_Type Fat_Percentage Water_Intake..liters.
## Min. : 303.0 Length:973 Min. :10.00 Min. :1.500
## 1st Qu.: 720.0 Class :character 1st Qu.:21.30 1st Qu.:2.200
## Median : 893.0 Mode :character Median :26.20 Median :2.600
## Mean : 905.4 Mean :24.98 Mean :2.627
## 3rd Qu.:1076.0 3rd Qu.:29.30 3rd Qu.:3.100
## Max. :1783.0 Max. :35.00 Max. :3.700
## Workout_Frequency..days.week. Experience_Level BMI
## Min. :2.000 Min. :1.00 Min. :12.32
## 1st Qu.:3.000 1st Qu.:1.00 1st Qu.:20.11
## Median :3.000 Median :2.00 Median :24.16
## Mean :3.322 Mean :1.81 Mean :24.91
## 3rd Qu.:4.000 3rd Qu.:2.00 3rd Qu.:28.56
## Max. :5.000 Max. :3.00 Max. :49.84
Tyepes of Workout and their frequency:
gymdata %>%
count(Workout_Type, sort = TRUE)
## Workout_Type n
## 1 Strength 258
## 2 Cardio 255
## 3 Yoga 239
## 4 HIIT 221
Total Calories burned by Gender
gymdata %>%
group_by(Gender) %>%
summarize(Total_Calories = sum(Calories_Burned, na.rm = TRUE))
## # A tibble: 2 × 2
## Gender Total_Calories
## <chr> <dbl>
## 1 Female 398359
## 2 Male 482617
Gender Differences in Workout Habits:
gymdata %>%
group_by(Gender) %>%
summarize(
avg_duration = mean(Session_Duration..hours., na.rm = TRUE),
avg_calories = mean(Calories_Burned, na.rm = TRUE)
)
## # A tibble: 2 × 3
## Gender avg_duration avg_calories
## <chr> <dbl> <dbl>
## 1 Female 1.26 862.
## 2 Male 1.25 944.
Bar diagram of Average Calories Burned by Workout Type
# Clean + group data: average calories burned per exercise type
calories_by_exercise <- gymdata %>%
group_by(Workout_Type) %>%
summarize(avg_calories = mean(Calories_Burned, na.rm = TRUE)) %>%
arrange(desc(avg_calories))
# Plot it
ggplot(calories_by_exercise, aes(x = reorder(Workout_Type, avg_calories), y = avg_calories)) +
geom_col(fill = "steelblue") +
coord_flip() +
labs(
title = "Average Calories Burned by Workout Type",
x = "Workout Type",
y = "Avg. Calories Burned"
) +
theme_minimal()
Conclusion: This represents a basic analysis of the gym members’
exercise tracking data.
Comment: Females speding average time in gym is slightly more than Males but males are burned more calories than females.