The final

Author

Roksana Shiran

Published

April 25, 2024

Code

# Load libraries
library(here)
library(tidyverse)
library(lubridate)
library(unibeCols)
library(readxl)

##introduction

The aim to visualize the random data of different occupation and income and age in Canada.

##data

Random data collected from Generatedata.com [https://generatedata.com/]

# A tibble: 51 × 6
   Occupation  Average_Experience Median_Experience Average_Age Median_Age Count
   <chr>                    <dbl>             <dbl>       <dbl>      <dbl> <int>
 1 Accountant                4.33               5          27.5       27.5     6
 2 Actor                     7                  7.5        23.8       23       4
 3 Aeroplane …               4.67               5          23.7       23       3
 4 Agronomist                7.25               7          22.5       22.5     4
 5 Archeologi…               4.67               3          24.7       27       3
 6 Architect                 3.25               3          19.5       19       4
 7 Astronomer                5.33               4          24.3       25       3
 8 Auditor                   5.33               4.5        24         24.5     6
 9 Baker                     0.5                0.5        21         21       2
10 Biochemist                6.8                6          23.6       25       5
# ℹ 41 more rows

##conclution This analysis has elucidated significant trends within the workforce regarding experience years, age, and occupation in Canada.

--- title: "The final" author: "Roksana Shiran" date: "`r date()`" format: html: toc: true code-fold: show code-tools: true df-print: paged embed-resources: true pdf: toc: true docx: toc: true --- ```{r} #| output: false #| message: true # Load libraries library(here) library(tidyverse) library(lubridate) library(unibeCols) library(readxl) ``` ##introduction The aim to visualize the random data of different occupation and income and age in Canada. ##data Random data collected from Generatedata.com [https://generatedata.com/] ```{r, echo=FALSE} #| output: false # Read data Book1 <- readxl::read_excel("/Users/roksanashiran/Desktop/course/Book1.xlsx", skip = 1) # Convert 'Experience (Years)' to numeric Book1$`Experience (Years)` <- as.numeric(as.character(Book1$`Experience (Years)`)) # Check and remove NAs Book1 <- filter(Book1, !is.na(`Experience (Years)`)) # Display a summary of the data summary(Book1) # Plotting the distribution of experience ggplot(Book1, aes(x = `Experience (Years)`)) + geom_histogram(binwidth = 1, fill = "blue") + labs(title = "Distribution of Experience Years", x = "Years of Experience", y = "Count") # Set the experience window window_start <- 0 window_end <- 10 # Filter data filtered_data <- Book1 %>% filter(`Experience (Years)` >= window_start & `Experience (Years)` <= window_end) # Display the head of the filtered data head(filtered_data) ``` ```{r,echo=FALSE} library(dplyr) summary_table <- Book1 %>% group_by(Occupation) %>% summarize( Average_Experience = mean(`Experience (Years)`, na.rm = TRUE), Median_Experience = median(`Experience (Years)`, na.rm = TRUE), Average_Age = mean(Age, na.rm = TRUE), Median_Age = median(Age, na.rm = TRUE), Count = n() ) print(summary_table) ``` ```{r,echo=FALSE} library(ggplot2) ggplot(Book1, aes(x = `Experience (Years)`, y = Age, color = Occupation)) + geom_point(alpha = 0.6) + labs(title = "Experience Years vs. Age by Occupation", x = "Years of Experience", y = "Age") + theme_minimal() # Box plot for Experience by Occupation ggplot(Book1, aes(x = Occupation, y = `Experience (Years)`, fill = Occupation)) + geom_boxplot() + labs(title = "Distribution of Experience Years by Occupation", x = "Occupation", y = "Years of Experience") + theme_minimal() # Box plot for Age by Occupation ggplot(Book1, aes(x = Occupation, y = Age, fill = Occupation)) + geom_boxplot() + labs(title = "Distribution of Age by Occupation", x = "Occupation", y = "Age") + theme_minimal() ``` ```{r, echo=FALSE} ggplot(Book1, aes(x = `Experience (Years)`, y = Age, color = Occupation)) + geom_col(alpha = 0.6) + labs(title = "Experience Years vs. Age by Occupation", x = "Years of Experience", y = "Age") + theme_minimal() ``` ```{r,echo=FALSE} ggplot(Book1, aes(x = `Experience (Years)`, y = Age, color = Occupation)) + geom_abline(alpha = 0.6) + labs(title = "Experience Years vs. Age by Occupation", x = "Years of Experience", y = "Age") + theme_minimal() ``` ```{r, echo=FALSE} ggplot(Book1, aes(x = `Experience (Years)`, y = Age, color = Occupation)) + geom_area(alpha=0.6) + # Use geom_point() for scatter plots labs(title = "Experience Years vs. Age by Occupation", x = "Years of Experience", y = "Age") + theme_minimal() ``` ##conclution This analysis has elucidated significant trends within the workforce regarding experience years, age, and occupation in Canada.

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