program 8

SUMMARY OF ALL 7 PROGRAMS

step 1:load the required libraries

library(ggplot2)
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
✔ lubridate 1.9.4     ✔ tibble    3.2.1
✔ purrr     1.0.4     ✔ tidyr     1.3.1
── 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(tidyr)

Step 2:load dataset

temp<-mtcars
#temp$cyl
#class(temp)
#class(temp$cyl)
#mtcars[3]
#str(temp)
#(temp$cyl)
temp$cyl<-as.factor(temp$cyl)
str(temp)

'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 : Factor w/ 3 levels "4","6","8": 2 2 1 2 3 2 3 1 1 2 ...
 $ 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 ...

library(dplyr)
summary_data<- temp %>% group_by(cyl) %>%
  summarise(avg_mpg = mean(mpg), .groups = 'drop')
#Dipsplay summary
print(summary_data)

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

#create a bar plot using ggplot2
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()

# Load the iris dataset
data<- iris
# display first few rows

head(data, n=10)

   Sepal.Length Sepal.Width Petal.Length Petal.Width Species
1           5.1         3.5          1.4         0.2  setosa
2           4.9         3.0          1.4         0.2  setosa
3           4.7         3.2          1.3         0.2  setosa
4           4.6         3.1          1.5         0.2  setosa
5           5.0         3.6          1.4         0.2  setosa
6           5.4         3.9          1.7         0.4  setosa
7           4.6         3.4          1.4         0.3  setosa
8           5.0         3.4          1.5         0.2  setosa
9           4.4         2.9          1.4         0.2  setosa
10          4.9         3.1          1.5         0.1  setosa

table(data$Species)

    setosa versicolor  virginica 
        50         50         50 

ggplot(data, aes(x = Sepal.Length, y = Sepal.Width, color = Species))+
  geom_point(size = 3, alpha = 0.7) + 
  labs(title = "Scatter plot ofsepal Dimensions", 
       x = "Sepal Length",
       y = "Sepal Width",
       color = "Species") + 
  theme_minimal() +
  theme(legend.position = "top")

# Convert time-series data to a dataframe
data <- data.frame(
  Date = seq(as.Date("1949-01-01"), by = "month", length.out = length(AirPassengers)),
  Passengers = as.numeric(AirPassengers),
  Year = as.factor(format(seq(as.Date("1949-01-01"), by = "month", length.out = length(AirPassengers)), "%Y"))
)

# Display first few rows
head(data, n=20)

         Date Passengers Year
1  1949-01-01        112 1949
2  1949-02-01        118 1949
3  1949-03-01        132 1949
4  1949-04-01        129 1949
5  1949-05-01        121 1949
6  1949-06-01        135 1949
7  1949-07-01        148 1949
8  1949-08-01        148 1949
9  1949-09-01        136 1949
10 1949-10-01        119 1949
11 1949-11-01        104 1949
12 1949-12-01        118 1949
13 1950-01-01        115 1950
14 1950-02-01        126 1950
15 1950-03-01        141 1950
16 1950-04-01        135 1950
17 1950-05-01        125 1950
18 1950-06-01        149 1950
19 1950-07-01        170 1950
20 1950-08-01        170 1950

# Function to plot time-series trend
plot_time_series <- function(data, x_col, y_col, group_col, title="Air Passenger Trends") {
  ggplot(data, aes_string(x = x_col, y = y_col, color = group_col, group = group_col)) +
    geom_line(size = 1.2) +  # Line graph
    geom_point(size = 2) +   # Add points for clarity
    labs(title = title,
         x = "Year",
         y = "Number of Passengers",
         color = "Year") +  # Legend title
    theme_minimal() +
    theme(legend.position = "top")
}

# Call the function
plot_time_series(data, "Date", "Passengers", "Year", "Trend of Airline Passengers Over Time")

Warning: `aes_string()` was deprecated in ggplot2 3.0.0.
ℹ Please use tidy evaluation idioms with `aes()`.
ℹ See also `vignette("ggplot2-in-packages")` for more information.

Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
ℹ Please use `linewidth` instead.

# Load dataset
data <- mtcars

# Display first few 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

data$cyl <- as.factor(data$cyl) 
data$gear <- as.factor(data$gear)

# Create a bar graph
ggplot(data, aes(x = cyl, fill = gear)) +
  geom_bar(position = "dodge") +  # Grouped bar chart
  labs(title = "Frequency of Cylinders Grouped by Gear Type",
       x = "Number of Cylinders",
       y = "Count",
       fill = "Gears") +  # Legend title
  theme_minimal()

str(iris)

'data.frame':   150 obs. of  5 variables:
 $ Sepal.Length: num  5.1 4.9 4.7 4.6 5 5.4 4.6 5 4.4 4.9 ...
 $ Sepal.Width : num  3.5 3 3.2 3.1 3.6 3.9 3.4 3.4 2.9 3.1 ...
 $ Petal.Length: num  1.4 1.4 1.3 1.5 1.4 1.7 1.4 1.5 1.4 1.5 ...
 $ Petal.Width : num  0.2 0.2 0.2 0.2 0.2 0.4 0.3 0.2 0.2 0.1 ...
 $ Species     : Factor w/ 3 levels "setosa","versicolor",..: 1 1 1 1 1 1 1 1 1 1 ...

head(iris)

  Sepal.Length Sepal.Width Petal.Length Petal.Width Species
1          5.1         3.5          1.4         0.2  setosa
2          4.9         3.0          1.4         0.2  setosa
3          4.7         3.2          1.3         0.2  setosa
4          4.6         3.1          1.5         0.2  setosa
5          5.0         3.6          1.4         0.2  setosa
6          5.4         3.9          1.7         0.4  setosa

p <- ggplot(data = iris,aes(x=Petal.Length,fill=Species))
p

p <- p + geom_histogram(aes(y = ..density..),
    alpha = 0.4,
    position = "identity",
    bins = 30)
p

Warning: The dot-dot notation (`..density..`) was deprecated in ggplot2 3.4.0.
ℹ Please use `after_stat(density)` instead.

p <- p +
  geom_density(aes(colour = Species),
               size=1.2)
p

p<-p+ 
  labs(title="Distribution of Petal Length with Group-wise Density curves",
       x="Petal Length",
       y="Density")+
  theme_minimal()
p

# Initialize ggplot with data and aesthetic mappings

p <- ggplot(data = iris, aes(x = Species, y = Petal.Width, fill = Species))
p

# Add the box plot layer

p <- p + geom_boxplot()
p

# Add title and labels and use a minimal theme

p <- p + labs(title = "Box Plot of Petal Width by Species",
              x = "Species",
              y = "Petal Width") +
         theme_minimal()
p

#Create a sequence of x values ringing from -2pi to 2pi
x <- seq(-2*pi, 2*pi, length.out = 500)

#Evaluate sin(x) and cos(x) over the x range
y1 <- sin(x)
y2 <- cos(x)

#Combine data into one data frame
df <- data.frame(
  x = rep(x,2),
  y = c(y1,y2),
  group = rep(c("sin(x)", "cos(x)"), each = length(x))
)
df

               x             y  group
1    -6.28318531  2.449213e-16 sin(x)
2    -6.25800220  2.518045e-02 sin(x)
3    -6.23281909  5.034492e-02 sin(x)
4    -6.20763598  7.547747e-02 sin(x)
5    -6.18245288  1.005622e-01 sin(x)
6    -6.15726977  1.255831e-01 sin(x)
7    -6.13208666  1.505244e-01 sin(x)
8    -6.10690356  1.753702e-01 sin(x)
9    -6.08172045  2.001048e-01 sin(x)
10   -6.05653734  2.247125e-01 sin(x)
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17   -5.88025559  3.921151e-01 sin(x)
18   -5.85507248  4.151547e-01 sin(x)
19   -5.82988937  4.379310e-01 sin(x)
20   -5.80470627  4.604296e-01 sin(x)
21   -5.77952316  4.826362e-01 sin(x)
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930   4.52036779 -1.908433e-01 cos(x)
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937   4.69664954 -1.573879e-02 cos(x)
938   4.72183265  9.443525e-03 cos(x)
939   4.74701575  3.461985e-02 cos(x)
940   4.77219886  5.977423e-02 cos(x)
941   4.79738197  8.489070e-02 cos(x)
942   4.82256508  1.099533e-01 cos(x)
943   4.84774818  1.349462e-01 cos(x)
944   4.87293129  1.598536e-01 cos(x)
945   4.89811440  1.846595e-01 cos(x)
946   4.92329751  2.093484e-01 cos(x)
947   4.94848061  2.339045e-01 cos(x)
948   4.97366372  2.583122e-01 cos(x)
949   4.99884683  2.825562e-01 cos(x)
950   5.02402993  3.066210e-01 cos(x)
951   5.04921304  3.304913e-01 cos(x)
952   5.07439615  3.541520e-01 cos(x)
953   5.09957926  3.775882e-01 cos(x)
954   5.12476236  4.007849e-01 cos(x)
955   5.14994547  4.237274e-01 cos(x)
956   5.17512858  4.464013e-01 cos(x)
957   5.20031169  4.687920e-01 cos(x)
958   5.22549479  4.908855e-01 cos(x)
959   5.25067790  5.126676e-01 cos(x)
960   5.27586101  5.341247e-01 cos(x)
961   5.30104412  5.552430e-01 cos(x)
962   5.32622722  5.760092e-01 cos(x)
963   5.35141033  5.964102e-01 cos(x)
964   5.37659344  6.164329e-01 cos(x)
965   5.40177655  6.360647e-01 cos(x)
966   5.42695965  6.552932e-01 cos(x)
967   5.45214276  6.741061e-01 cos(x)
968   5.47732587  6.924915e-01 cos(x)
969   5.50250898  7.104377e-01 cos(x)
970   5.52769208  7.279335e-01 cos(x)
971   5.55287519  7.449676e-01 cos(x)
972   5.57805830  7.615292e-01 cos(x)
973   5.60324141  7.776080e-01 cos(x)
974   5.62842451  7.931936e-01 cos(x)
975   5.65360762  8.082762e-01 cos(x)
976   5.67879073  8.228463e-01 cos(x)
977   5.70397384  8.368945e-01 cos(x)
978   5.72915694  8.504120e-01 cos(x)
979   5.75434005  8.633903e-01 cos(x)
980   5.77952316  8.758210e-01 cos(x)
981   5.80470627  8.876962e-01 cos(x)
982   5.82988937  8.990086e-01 cos(x)
983   5.85507248  9.097508e-01 cos(x)
984   5.88025559  9.199162e-01 cos(x)
985   5.90543870  9.294981e-01 cos(x)
986   5.93062180  9.384906e-01 cos(x)
987   5.95580491  9.468880e-01 cos(x)
988   5.98098802  9.546848e-01 cos(x)
989   6.00617113  9.618763e-01 cos(x)
990   6.03135423  9.684578e-01 cos(x)
991   6.05653734  9.744251e-01 cos(x)
992   6.08172045  9.797745e-01 cos(x)
993   6.10690356  9.845026e-01 cos(x)
994   6.13208666  9.886063e-01 cos(x)
995   6.15726977  9.920831e-01 cos(x)
996   6.18245288  9.949308e-01 cos(x)
997   6.20763598  9.971475e-01 cos(x)
998   6.23281909  9.987319e-01 cos(x)
999   6.25800220  9.996829e-01 cos(x)
1000  6.28318531  1.000000e+00 cos(x)

p <- ggplot(df, aes(x = x, y = y, color = group, linetype = group))
p

p <- p + geom_line(size = 1.2)
p

p <- p + theme_minimal()
p