Day 1

My First Code

My R First Code
My R First Code

My First Histogram

Age of the Orange Tree

Day 2 Introduction to R and R Studio

Day 2

Data Visualization in Base R

Learning Functions and arguments

Day 1 Basic Statistics with R

Day 3

Descriptive Statistics

Summary

   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
   35.0    63.0   103.0   121.8   163.8   373.0 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
   0.00    4.00   10.00   10.72   16.00   21.00 

Day 2 Basic Statistics with R

Day 4

T test


    One Sample t-test

data:  ChickWeight$weight
t = -0.061451, df = 577, p-value = 0.951
alternative hypothesis: true mean is not equal to 122
95 percent confidence interval:
 116.0121 127.6246
sample estimates:
mean of x 
 121.8183 
Effect sizes were labelled following Cohen's (1988) recommendations.

The One Sample t-test testing the difference between ChickWeight$weight (mean =
121.82) and mu = 122 suggests that the effect is negative, statistically not
significant, and very small (difference = -0.18, 95% CI [116.01, 127.62],
t(577) = -0.06, p = 0.951; Cohen's d = -2.56e-03, 95% CI [-0.08, 0.08])

Day 3 Basic Statistics with R

Day 5

Correlation

Speed Vs Distance

---
title: "My R Learning Journey"
output: 
  flexdashboard::flex_dashboard:
    orientation: columns
    vertical_layout: fill
    social: menu 
    source_code: embed
    self_contained: true
    theme: cosmo
---

```{r setup, include=FALSE}
library(flexdashboard)
library(ggplot2)
library(summarytools)
library(report)

##Data 
data("ChickWeight")
data("Orange")
data("mtcars")
data(cars)
```

# Sidebar {.sidebar}

### 2025 Year-End Accomplishment💫

It is very efficient and beneficial for me to learn such a great skill. I would like to say thank you very much to my teachers.

🎄Merry Christmas and 🎆 Happy New Year!

## Day 1 Introduction to R and R Studio

# Day 1

### My First Code

![My R First Code](images/Screenshot%20(226).png){width="385"}

## My First Histogram

### Age of the Orange Tree

```{r}
hist(Orange$age,col=rainbow(5))
```

## Day 2 Introduction to R and R Studio

# Day 2

### Data Visualization in Base R


### Learning Functions and arguments

```{r}
barplot(table(mtcars$gear),
        main = "Distribution of No. of Gears",
        xlab = "No of Gears",
        ylab = "Frequency",
        col = c("violet", "gold", "lightgreen"),
        names.arg = c("3 gears", "4 gears", "5 gears"))

```

## Day 1 Basic Statistics with R

# Day 3

### Descriptive Statistics


### Summary

```{r}

data("ChickWeight")
summary(ChickWeight$weight)
summary(ChickWeight$Time)
```

## Day 2 Basic Statistics with R

# Day 4

### T test

```{r}
t.test(ChickWeight$weight,mu=122, data=ChickWeight)
report(t.test(ChickWeight$weight,mu=122, data=ChickWeight))
```

## Day 3 Basic Statistics with R

# Day 5

## Correlation

### Speed Vs Distance

```{r}
ggplot(cars, 
       aes(speed, dist)) + 
  geom_point(shape=15, color="gold", size=2)+
  labs(x="Speed",y="Distance", title="Speed vs Distance",
       subtitle = "cars dataset")+
  theme_classic(16)+
  theme(plot.title = element_text(hjust=0.5, 
                                  face="bold"))

```