---
title: "Job Application dashboard_Xiaoxiao Lu"
Author: "Xiaoxiao Lu"
Date: "12-06-2022"
output:
flexdashboard::flex_dashboard:
orientation: rows
social: menu
source_code: embed
---
```{r echo = F,warning = FALSE, message = F}
library(plotly)
library(dplyr)
library(ggplot2)
library(RColorBrewer)
library(plot3D)
library(reshape2)
library(tidyr)
library(tidyverse)
```
```{r setup, include=FALSE}
data <- read.csv('C:/Users/xiaox/OneDrive/Documents/HU/512-visualization/courseproject/project_interview.csv')
```
Monthly Job Applications and Interviews Chart{data-orientation=columns}
==========================================================================
Sidebar {.sidebar}
-----------------------------------------------------------------------
### Job Applications and Interviews
Data Collection and methods:
The data was collected during Sep 12 to Nov 30 in 2022. It is based on the numbers of my job applications and interviews varied by different dates and locations.I used Excel and App: Track to follow up numbers of jobs I applied each day. My goal is to apply at least 5 jobs per day. But in real situations, I consider the job descriptions,locations and how long I could invest in daily job searching on the internet.I have applied for 350 jobs in total. Each time I received the interviews, I write it down in the excel as interview_num column and update with industry, location, longitude,latitude.
I categorized the insights by application numbers, interview number, locations and industries. For the applications and interviews, I used line, scatter, bar chart to visualize the report and show the tendency group by month. About locations and industry, I chose ggplot map to visualize the interviews in different locations and industries as time passed by since it gives me a clear overview of which states have more interviews so that I could apply more relevant jobs in specific states in the future.
5 questions are shown as below:
#Q1. What is the tendency number of job application and interviews? What's the difference?
Conclusions:
I choose line and scatter plots to show the tendency of my job applications and interviews. The job application is steady during September and November in this year. It varies from 0 to 10 a day. However, the number of interviews shows decreasing tendency. In September, I received at most as 3 interviews a day, however in November, there are no interviews then.
Column {data-width=400}
------------------------------------------------------------------------------
### Monthly Job Applications and Interviews Chart
```{r}
data$Date <- as.Date( data$Date, '%m/%d/%Y')
p <- ggplot(data, aes(x=`Date`, y=`Apply_num`, group=`Month`, color=`Month`)) +
geom_line() +
labs(
title = "Job Applications during Oct-Dec in 2022",
x = "Month",
y = "Number of Job Applications",
colour = "month") +
theme_bw()
ggplotly(p, tooltip = c("text"))
```
Column {data-width=400}
------------------------------------------------------------------------------
### Job Interviews chart
```{r}
data$Date <- as.Date( data$Date, '%m/%d/%Y')
p <- ggplot(data, aes(x=`Date`, y=`Interview_num`, group=`Month`, color=`Month`)) +
geom_point(size = 1.5, shape = 23) +
labs(
title = "Interview during Oct-Dec in 2022",
x = "Month",
y = "Number of Interviews",
colour = "month") +
theme_bw()
ggplotly(p, tooltip = c("text"))
```
Monthly accumulated Job Applications and Interviews Chart {data-orientation=columns}
==========================================================================
Sidebar {.sidebar}
-----------------------------------------------------------------------
### Number of Job Application of Each Month
#Q2. Which month has the most job applications?
Conclusions:
October has the most job applications, October and November are exceed 125 jobs per month.
#Q3. Which month has the most interviews?
Conclusion:
I use bar charts to show total numbers of applications and interviews sorted by month to see which month has the biggest number. The interesting thing is that September received the most number of interviews compared to October and Nov. I received 7 interviews in Sep and 6 interviews in Oct and 0 in November. There probably existed some processing time but it showed significant lag. Even though the number of job applications does not change drastically, the number of interviews become smaller as time passed by.
Column {data-width=400}
-----------------------------------------------------------------------
### Monthly Job Applications Chart
```{r}
month_data <-
data %>%
mutate(month = format(data$Date,"%m")) %>%
group_by(month) %>%
summarise(total = sum(Apply_num))
ggplot2::qplot(month_data$`month`, month_data$total, geom = "col", fill=month_data$`month`) +
labs(
title = "Number of Applications during Oct-Dec in 2022",
x = "Month",
y = "Number of Applications",
colour = "month") +
theme_bw()
```
------------------------------------------------------------------------------
### Monthly Job Interviews Chart
```{r}
month_data <-
data %>%
mutate(month = format(data$Date,"%m")) %>%
group_by(month) %>%
summarise(total = sum(Interview_num))
ggplot2::qplot(month_data$`month`, month_data$total, geom = "col", fill=month_data$`month`) +
labs(
title = "Number of Interviews during Oct-Dec in 2022",
x = "Month",
y = "Number of Interviews",
colour = "month") +
theme_bw()
```
Job Interviews Map {data-orientation=columns}
==========================================================================
Sidebar {.sidebar}
-----------------------------------------------------------------------
### Number of Interviews and States of Companies
#Q4.which states I received the most interviews?
Conclusions:
New York state has the most interviews compared to Texas and California. There are 5 interviews in New York, 2 in California, and 1 in Texas.
#Q5. Can you tell the difference of jobs and their industry interviews by time series?
Conclusions:
In September, the job interviews are at high tech and accounting. In October, the job interviews are at Accounting and banking. In November, the only interview I got is in high tech company. High tech is the industry has job opportunities even though it laid off many employees in October and November.
Column {data-width=400}
------------------------------------------------------------------------------
### Job Interview Map
```{r}
data <- data %>%
filter(!is.na(data$Lat))
#view(Industry_data)
state_data <- data %>%
group_by(data$Location) %>%
summarise(total_count=n(),
.groups = 'drop')
g <- list(
scope = 'usa',
projection = list(type = 'albers usa')
)
plot_geo() %>%
add_trace(
z = state_data$total_count,
span = I(0),
locations = state_data$`data$Location`,
locationmode = 'USA-states',
size = state_data$total_count,
color = state_data$total_count,
colors = 'YlOrRd') %>%
layout(
title = "Job Interview Map",
geo = g)
```
Column {data-width=400}
------------------------------------------------------------------------------
### Time-Series Interviews Map
```{r echo = F, warning = FALSE, message = F }
data <- data %>%
filter(!is.na(data$Lat))
g <- list(
scope = 'usa',
projection = list(type = 'albers usa')
)
plot_geo(data, locationmode = 'USA-states') %>%
add_markers(
x = ~data$Long,
y = ~data$Lat,
group = data$Location,
size = data$Interview_num,
color = data$Industry,
frame = data$Month,
marker=list(sizeref=0.1, sizemode="area"),
alpha = 0.8) %>%
layout(title = "Time Series about Industries of Interviews",
geo = g)
```
