---
title: "HR ANALYTIC DASHBOARD"
output: 
  flexdashboard::flex_dashboard:
    css: styles.css
    orientation: row
    vertical_layout: scroll
    theme: paper
    source_code: embed
    social: ["menu"]
    
---

```{r setup, include=FALSE}
library(flexdashboard)
library(flextable)
library(tidyverse)
library(rpivotTable)
library(plotly)
hr <- read.csv("HR DATA_Excel.xlsx - Data.csv")
```


Demographics {data-icon="fa-list}
====================================================================

Row
---------------------------------------------------------------------

### Employee Count

```{r}
valueBox(length(hr$Attrition), icon = "ion-android-contacts", color = "red")
```

### Attrition Count
```{r}
valueBox(hr %>% 
  filter(Attrition == "Yes") %>% count(),
  icon = "fa-user", color = "red")
 
```

### Attrition rate

```{r}
yes <- hr %>% 
  filter(Attrition == "Yes")

valueBox(paste(round((length(yes$Attrition)/length(hr$Attrition))*(100),0),"%"),icon = "fa-user", color = "red")
```

### Active Employees
```{r}
valueBox(length(hr$Attrition)- length(yes$Attrition), icon = "fa-user", color = "red")
```
### Average age

```{r}
valueBox(round(mean(hr$Age),0), icon = "fa-user", color = "red")
```

### Maximum monthly rate

```{r}
a <- hr %>% 
  filter(Attrition == "No")
valueBox(max(a$Monthly.Rate), color = "red")
```
### Minimum monthly rate

```{r}
valueBox(min(a$Monthly.Rate), color = "red")
```


Row
------------------------------------------------------------------------

### Attrition by Gender

```{r}
g <- yes %>% select(Attrition,Gender) %>% 
  group_by(Gender) %>% 
  summarise(count = n()) %>% 
  ggplot(aes(Gender, count, label = count, fill = Gender))+geom_col()+geom_text(aes(vjust = 2), size = 5)+
  theme(legend.position = "none",axis.text.x = element_text(face = "bold", colour = "black", size = 10))
g+theme_classic(base_size = 15)
```

### Department wise Attrition

```{r}
yes %>% group_by(Department) %>% 
  summarise(count = n()) %>% 
  plot_ly(labels = ~Department,
          values =~count) %>% 
  add_pie()
  
```
### No of Employees by Age

```{r}
a <- hr %>% 
  filter(Attrition == "No")
p <- a %>% 
  group_by(Age) %>% 
  summarise(age_count = n()) %>% 
  ggplot(aes(Age, age_count, fill = age_count))+geom_col()+
  labs(y = "count")
p+theme_classic(base_size = 15)
```

Row
-----------------------------------------------------

### Education Field wise Attrition

```{r}
y <- yes %>% 
  group_by(Education.Field) %>% 
  summarise(count = n()) %>% 
  ggplot(aes(fct_reorder(Education.Field,count),count, fill = Education.Field))+theme_classic(base_size = 15)+geom_col()+geom_text(aes(label = count, hjust = 2))+
  theme(legend.position = "none")
y2 <- y+theme(axis.text.x = element_text(face = "bold", colour = "black", size = 10),plot.background = element_rect(colour = "grey"))
y2+
  labs(x = "Education Field")+coord_flip()
```

### Education qualification Attrition

```{r}
z <- yes %>% 
  group_by(Education) %>% 
  summarise(count = n()) %>% 
  ggplot(aes(fct_reorder(Education,count),count, fill = Education))+theme_classic(base_size = 15)+
  geom_col()+geom_text(aes(label = count, hjust = 2))+
  theme(legend.position = "none")
z2 <- z+theme(axis.text.x = element_text(face = "bold", colour = "black"))
z2+coord_flip()+labs(x = "Education")
```


### monthly rate per job role

```{r}
a %>% 
  group_by(Job.Role) %>% 
  summarise(average_monthtly_rate = round(mean(Monthly.Rate)),0) %>% 
  ggplot(aes(fct_reorder(Job.Role,average_monthtly_rate),average_monthtly_rate, label =average_monthtly_rate))+theme_classic(base_size = 15)+
  geom_col(fill = "blue")+geom_text(aes(hjust = 1), size = 5)+theme(axis.text.x = element_text(angle = 45, color = "black"))+labs(
    y = "Average Monthly Rate", x ="Job Role",
  )+
  coord_flip()
```


Job Satisfaction
=========================================================

Column {data-height=550}
---------------------------------------------------------
### Job Satisfaction

```{r}

hr1 <- hr %>% select(10,Job.Satisfaction)
t <- table(hr1$Job.Role, hr1$Job.Satisfaction)
t1<- t %>% as.data.frame() %>% pivot_wider(id_cols = 1, names_from = "Var2", values_from = "Freq") %>% data.frame() %>% 
  mutate(Grand_Total= X1+X2+X3+X4)
t_sum <- data.frame(colSums(t1[,-1]))
t_sum <- t_sum %>% rename(Grand_total = "colSums.t1....1..")
t_sum2 <- data.frame(t(t_sum))
tab <- t1 %>% full_join(t_sum2)
tab$Var1 <- as.character(tab$Var1)
tab <- tab %>% replace_na(list(Var1="Grand Total"))
tt <- tab %>% rename(Job_Category = "Var1",
               "1"= "X1",
               "2"="X2",
               "3"="X3",
               "4"="X4") %>% 
  flextable::flextable() 
theme_tron(tt)

```


### Environment Satisfaction

```{r}
hr2 <- hr %>% select(10,Environment.Satisfaction)
e<- table(hr2$Job.Role, hr2$Environment.Satisfaction)
te<- e %>% as.data.frame() %>% pivot_wider(id_cols = 1, names_from = "Var2", values_from = "Freq") %>% data.frame() %>% 
  mutate(Grand_Total= X1+X2+X3+X4)
te_sum <- data.frame(colSums(te[,-1]))
te_sum <- te_sum %>% rename(Grand_total = "colSums.te....1..")
te_sum2 <- data.frame(t(te_sum))
tab_e <- te %>% full_join(te_sum2)
tab_e$Var1 <- as.character(tab_e$Var1)
tab_e <- tab_e %>% replace_na(list(Var1="Grand Total"))
tt_e <- tab_e %>% rename(Job_Category = "Var1",
               "1"= "X1",
               "2"="X2",
               "3"="X3",
               "4"="X4") %>% 
  flextable::flextable() 
theme_tron_legacy(tt_e)
```




Column {data-height=550}
----------------

### Relationship Satisfaction

```{r}
hr3 <- hr %>% select(10, Relationship.Satisfaction)
r<- table(hr3$Job.Role, hr3$Relationship.Satisfaction)
tr<- r %>% as.data.frame() %>% pivot_wider(id_cols = 1, names_from = "Var2", values_from = "Freq") %>% data.frame() %>% 
  mutate(Grand_Total= X1+X2+X3+X4)
tr_sum <- data.frame(colSums(tr[,-1]))
tr_sum <- tr_sum %>% rename(Grand_total = "colSums.tr....1..")
tr_sum2 <- data.frame(t(tr_sum))
tab_r <- tr %>% full_join(tr_sum2)
tab_r$Var1 <- as.character(tab_r$Var1)
tab_r <- tab_r %>% replace_na(list(Var1="Grand Total"))
tt_r <- tab_r %>% rename(Job_Category = "Var1",
               "1"= "X1",
               "2"="X2",
               "3"="X3",
               "4"="X4") %>% 
  flextable::flextable() 
theme_tron(tt_r)
```


###  Work Life Balance

```{r}
hr4 <- hr %>% select(10, Work.Life.Balance)
w<- table(hr4$Job.Role, hr4$Work.Life.Balance)
tw<- w %>% as.data.frame() %>% pivot_wider(id_cols = 1, names_from = "Var2", values_from = "Freq") %>% data.frame() %>% 
  mutate(Grand_Total= X1+X2+X3+X4)
tw_sum <- data.frame(colSums(tw[,-1]))
tw_sum <- tw_sum %>% rename(Grand_total = "colSums.tw....1..")
tw_sum2 <- data.frame(t(tw_sum))
tab_w <- tw %>% full_join(tw_sum2)
tab_w$Var1 <- as.character(tab_w$Var1)
tab_w <- tab_w %>% replace_na(list(Var1="Grand Total"))
tt_w <- tab_w %>% rename(Job_Category = "Var1",
               "1"= "X1",
               "2"="X2",
               "3"="X3",
               "4"="X4") %>% 
  flextable::flextable() 
theme_tron_legacy(tt_w)
```



Job Progress {data-orientation=columns}
=================================================

Column {.tabset}
---------------------------------------------


DEPARTMENTWISE

### Years.In.Current.Role

```{r}
hr_job <- hr %>% 
   select(Years.At.Company,Years.In.Current.Role,Years.Since.Last.Promotion,
          Years.With.Curr.Manager,Department,Gender,Education,Attrition ) %>% 
  filter(Attrition == "No")

hr_job %>% 
  select(Department,Years.In.Current.Role) %>% 
  dplyr::group_by(Department) %>% 
  summarise(year = round(median(Years.In.Current.Role))) %>% 
  ggplot(aes(Department, year, label = year))+geom_col(fill = "red")+
  geom_text(aes(vjust = 2), colour = "white", size = 8)+theme_classic(
    base_size = 15
  )


```

### Years.At.Company

```{r}
hr_job %>% 
  select(Department,Years.At.Company) %>% 
  dplyr::group_by(Department) %>% 
  summarise(year = round(median(Years.At.Company))) %>% 
  ggplot(aes(Department, year,label = year))+geom_col(fill = "steelblue")+
  geom_text(aes(vjust = 2), colour = "white", size = 8)+theme_classic(
    base_size = 15
  )
```

### Years.Since.Last.Promotion

```{r}
hr_job %>% 
  select(Department,Years.Since.Last.Promotion) %>% 
  dplyr::group_by(Department) %>% 
  summarise(year = round(median(Years.Since.Last.Promotion))) %>% 
  ggplot(aes(Department, year,label = year))+geom_col(fill = "green")+
  geom_text(aes(vjust = 2), colour = "white", size = 8)+theme_classic(
    base_size = 15)

```

### Years.With.Curr.Manager

```{r}
hr_job %>% 
  select(Department,Years.With.Curr.Manager) %>% 
  dplyr::group_by(Department) %>% 
  summarise(year = round(median(Years.With.Curr.Manager))) %>% 
  ggplot(aes(Department, year, label = year))+geom_col(fill = "blue")+
  geom_text(aes(vjust = 2), colour = "white", size = 8)+theme_classic(
    base_size = 15)
```

Column {.tabset}
------------------------------------


EDUCATIONWISE


### Years.At.Company


```{r}
hr_job %>% 
  select(Education,Years.At.Company) %>% 
  dplyr::group_by(Education) %>% 
  summarise(year = round(median(Years.At.Company))) %>% 
  ggplot(aes(fct_reorder(Education, year), year, label = year))+geom_col(fill = "steelblue")+
  geom_text(aes(hjust = 2), colour = "white", size = 8)+theme_classic(
    base_size = 15
  )+labs(x = "Education Level")+coord_flip()
```

### Years.Since.Last.Promotion


```{r}
hr_job %>% 
  select(Education,Years.Since.Last.Promotion) %>% 
  dplyr::group_by(Education) %>% 
  summarise(year = round(median(Years.Since.Last.Promotion))) %>% 
  ggplot(aes(fct_reorder(Education, year), year, label = year))+geom_col(fill = "green")+
  geom_text(aes(hjust = 2), colour = "white", size = 8)+theme_classic(
    base_size = 15
  )+labs(x = "Education Level")+coord_flip()
```

### Years.In.Current.Role


```{r}
hr_job %>% 
  select(Education,Years.In.Current.Role) %>% 
  dplyr::group_by(Education) %>% 
  summarise(year = round(median(Years.In.Current.Role))) %>% 
  ggplot(aes(fct_reorder(Education, year), year,label =  year))+geom_col(fill = "red")+
  geom_text(aes(hjust = 2), colour = "white", size = 8)+theme_classic(
    base_size = 15)+labs(x = "Education Level")+coord_flip()
```


### Years.With.Curr.Manager
```{r}
hr_job %>% 
  select(Education,Years.With.Curr.Manager) %>% 
  dplyr::group_by(Education) %>% 
  summarise(year = round(median(Years.With.Curr.Manager))) %>% 
  ggplot(aes(fct_reorder(Education, year), year, label = year))+geom_col(fill = "blue")+
  geom_text(aes(hjust = 2), colour = "white", size = 8)+theme_classic(
    base_size = 15)+labs(x = "Education Level")+coord_flip()
```


Column {.tabset}
------------------------------------

GENDERWISE


### Years.At.Company


```{r}
hr_job %>% 
  select(Gender,Years.At.Company) %>% 
  dplyr::group_by(Gender) %>% 
  summarise(year = round(median(Years.At.Company))) %>% 
  ggplot(aes(fct_reorder(Gender, year), year, label = year))+geom_col(fill = "steelblue")+
  geom_text(aes(vjust = 2), colour = "white", size = 8)+theme_classic(
    base_size = 15
  )+labs(x = "Gender")
```

### Years.Since.Last.Promotion


```{r}
hr_job %>% 
  select(Gender,Years.Since.Last.Promotion) %>% 
  dplyr::group_by(Gender) %>% 
  summarise(year = round(median(Years.Since.Last.Promotion))) %>% 
  ggplot(aes(fct_reorder(Gender, year), year, label = year))+geom_col(fill = "green")+
  geom_text(aes(vjust = 2), colour = "white", size = 8)+theme_classic(
    base_size = 15
  )+labs(x = "Gender")
```

### Years.In.Current.Role


```{r}
hr_job %>% 
  select(Gender,Years.In.Current.Role) %>% 
  dplyr::group_by(Gender) %>% 
  summarise(year = round(median(Years.In.Current.Role))) %>% 
  ggplot(aes(fct_reorder(Gender, year), year, label = year))+geom_col(fill = "red")+
  geom_text(aes(vjust = 2), colour = "white", size = 8)+theme_classic(
    base_size = 15)+labs(x = "Gender")
```


### Years.With.Curr.Manager
```{r}
hr_job %>% 
  select(Gender,Years.With.Curr.Manager) %>% 
  dplyr::group_by(Gender) %>% 
  summarise(year = round(median(Years.With.Curr.Manager))) %>% 
  ggplot(aes(fct_reorder(Gender, year), year, label = year))+geom_col(fill = "blue")+
  geom_text(aes(vjust = 2), colour = "white", size = 8)+theme_classic(
    base_size = 15)+
  labs(x = "Gender")
```






ABOUT
============================================================

Created by Analyst Julius