program1

##Aim of the program The objective of this program is to *quickly explore a dataset,perform **categorical (group-wise) analysis* using the ‘group_by()’ command from *dplyr,and **visualize the results* using ggplot2.

develop on r program to quickly explore a given dataset, including categorical analysis using the’ group_by()’ command,and visualize the findings using ‘ggplot2’. ##what we will do In this program, we will: 1. load the required libraries and dataset. 2. Explore the structure of the dataset. 3. convert a numeric varaible into a catrgorical variable. 4. perform categorical analysis using ‘group_by()’ and 5. visualize the results using ‘ggplot2’. ## step 1: load required libraries and dataset

• ‘tidyverse’ is a collection of packages for data science.
• ‘dplyr’ is used for grouping and summarize data.

library(tidyverse)

── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
✔ dplyr     1.2.0     ✔ readr     2.1.6
✔ forcats   1.0.1     ✔ stringr   1.6.0
✔ ggplot2   4.0.2     ✔ tibble    3.3.1
✔ lubridate 1.9.5     ✔ tidyr     1.3.2
✔ purrr     1.2.1     
── 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(dplyr)

# Load built-in dataset
data <- mtcars

##step 2: Explore the dataset

Before performing any analysis, we should understand the dataset.

we will check:

• Number of rows and columns
• column names
• Data types
• Summary statistics
• First few rows

  # Dimensions (rows and columns)
  dim(data)

[1] 32 11

  # Column names
  names(data)

 [1] "mpg"  "cyl"  "disp" "hp"   "drat" "wt"   "qsec" "vs"   "am"   "gear"
[11] "carb"

  # Structure of dataset
  str(data)

'data.frame':   32 obs. of  11 variables:
 $ mpg : num  21 21 22.8 21.4 18.7 18.1 14.3 24.4 22.8 19.2 ...
 $ cyl : num  6 6 4 6 8 6 8 4 4 6 ...
 $ disp: num  160 160 108 258 360 ...
 $ hp  : num  110 110 93 110 175 105 245 62 95 123 ...
 $ drat: num  3.9 3.9 3.85 3.08 3.15 2.76 3.21 3.69 3.92 3.92 ...
 $ wt  : num  2.62 2.88 2.32 3.21 3.44 ...
 $ qsec: num  16.5 17 18.6 19.4 17 ...
 $ vs  : num  0 0 1 1 0 1 0 1 1 1 ...
 $ am  : num  1 1 1 0 0 0 0 0 0 0 ...
 $ gear: num  4 4 4 3 3 3 3 4 4 4 ...
 $ carb: num  4 4 1 1 2 1 4 2 2 4 ...

  # Summary Statistics
  summary(data)

      mpg             cyl             disp             hp       
 Min.   :10.40   Min.   :4.000   Min.   : 71.1   Min.   : 52.0  
 1st Qu.:15.43   1st Qu.:4.000   1st Qu.:120.8   1st Qu.: 96.5  
 Median :19.20   Median :6.000   Median :196.3   Median :123.0  
 Mean   :20.09   Mean   :6.188   Mean   :230.7   Mean   :146.7  
 3rd Qu.:22.80   3rd Qu.:8.000   3rd Qu.:326.0   3rd Qu.:180.0  
 Max.   :33.90   Max.   :8.000   Max.   :472.0   Max.   :335.0  
      drat             wt             qsec             vs        
 Min.   :2.760   Min.   :1.513   Min.   :14.50   Min.   :0.0000  
 1st Qu.:3.080   1st Qu.:2.581   1st Qu.:16.89   1st Qu.:0.0000  
 Median :3.695   Median :3.325   Median :17.71   Median :0.0000  
 Mean   :3.597   Mean   :3.217   Mean   :17.85   Mean   :0.4375  
 3rd Qu.:3.920   3rd Qu.:3.610   3rd Qu.:18.90   3rd Qu.:1.0000  
 Max.   :4.930   Max.   :5.424   Max.   :22.90   Max.   :1.0000  
       am              gear            carb      
 Min.   :0.0000   Min.   :3.000   Min.   :1.000  
 1st Qu.:0.0000   1st Qu.:3.000   1st Qu.:2.000  
 Median :0.0000   Median :4.000   Median :2.000  
 Mean   :0.4062   Mean   :3.688   Mean   :2.812  
 3rd Qu.:1.0000   3rd Qu.:4.000   3rd Qu.:4.000  
 Max.   :1.0000   Max.   :5.000   Max.   :8.000  

  # First six rows
  head(data)

                   mpg cyl disp  hp drat    wt  qsec vs am gear carb
Mazda RX4         21.0   6  160 110 3.90 2.620 16.46  0  1    4    4
Mazda RX4 Wag     21.0   6  160 110 3.90 2.875 17.02  0  1    4    4
Datsun 710        22.8   4  108  93 3.85 2.320 18.61  1  1    4    1
Hornet 4 Drive    21.4   6  258 110 3.08 3.215 19.44  1  0    3    1
Hornet Sportabout 18.7   8  360 175 3.15 3.440 17.02  0  0    3    2
Valiant           18.1   6  225 105 2.76 3.460 20.22  1  0    3    1

step 3: convert numeric variable to categorical

the variable ‘cyl’ represents the number of cylinders in a car.

Although it is numeric (4,6,8).it represents categories.
for categorical analysis. we convert it into a factor.

# convert 'cyl' to factor
datacy11 <- as.factor(data$cyl)


# confirm conversion
str(data$cyl)
levels(data$cyl)

step 4: perform categorical analysis

we calculate the average miles per gallon (‘mpg’) for each cylinder category.

How these functions Work Together

• ’%>% passes output from one function to the next.
• ‘group_by(cyl)’ splits the dataset into groups.
• ‘summarise()’ calculates statistics per group.
• ‘mean(mpq)’ computes average mileage.
• ’.groups = “drop” removes grouping afterward

summary_data <- data %>%
  group_by(cyl) %>%
  summarise(
    avg_mpg = mean(mpg),
    .groups ="drop"
  )
summary_data

# A tibble: 3 × 2
    cyl avg_mpg
  <dbl>   <dbl>
1     4    26.7
2     6    19.7
3     8    15.1

Step 5 : Visualise using a bar plot

ggplot(summary_data, aes(x=cyl, y=avg_mpg,fill = cyl))+
  geom_bar(stat = "identity")+
  labs(
    title="Average MPG by Cylinder Count",
    x= "Number of cylinders",
    y="Average MPG"
  )+
  theme_minimal()

)