The Train_R_project.Rmd

---
title: "R_project.Rmd"
output: html_document
date: "2024-03-20"
---

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# The Train Project

```         

Note:
  The train dataset in the Spaceship Titanic records 8693 passengers as a row number with 14 column objects. 
Passenger is specified with a unique number. The Homeplanet is the residence of the passenger and the Destination is the place where the passenger is moved. CryoSleep is for the passenger to be elected for suspended animation during the voyage. The Cabin is the number where the passenger stays.The passager is also age-specified and allowed to pay for VIP and bills for Room service, Food court, shopping mall, Spy, and VRDesk. The dataset also records the name of the passenger and whether the passenger is transported to the destination. 
  Analyzing the data encompasses four steps. Reading the data, cleaning the data, analyzing and visualizing using R libraries and functions.
```

```{r}
#setwd("/Users/ruth/Downloads/train.csv")
df_train <- read.csv("~/Downloads/train_data(in).csv")
View(df_train)

```

```{r}~/Desktop/train.csv}
```

```{r}
df_train
```

# Cleaning Data: Filtering the missing data using filter function.

-   <div>

    ```{r}
    df_train %>%
      select(HomePlanet, CryoSleep, Cabin, Destination, Age, VIP, RoomService, FoodCourt, ShoppingMall, Spa,VRDeck, Name,Transported)%>%
      filter(!complete.cases(.))%>%
      View()
    #As part of cleaning data, we first identify the missing data and removing duplicates #and invalid data. In the case of train data, there is no duplicates and invalid data, but there are missing data. Identfing and visualzing the missing data takes place.
    ```

    </div>

To visualize the missing data by using 'mice' library and the md.pattern(missing data pattern) inside it

```{r fig.width=10, fig.height= 10, size = 30}
library(mice)

md.pattern(df_train)


```

As there are empty data frame cells , that should be replaced by NA value

```{r}
#df_train %>%
  #select(HomePlanet, CryoSleep, Cabin, Destination, Age, VIP, RoomService, FoodCourt, ShoppingMall, Spa, VRDeck, Name,Transported)%>%
 # mutate(df_train = na_if("")) %>%
 # filter(is.na(df_train))
#View()

df_train[df_train == "" | df_train == " "] <- NA
View(df_train)
# we start analyzing by using summary () function that shows the structure, datatype and stastical values of the dataset
```

# Analyzing Data

```{r}
summary(df_train)
```

```{r}
y <- mean(df_train$Age, na.rm = TRUE )
y

```

```{r}
#library(attach)
x <- median(df_train$Age, na.rm = TRUE)
x
```

```{r}

```

```{r}
ggplot(df_train, mapping = aes(x= HomePlanet, y= Destination))+
  geom_point(size = 5)+
  geom_line(colour = 'red')
  

```

# Visualizing Data

```{r}
library(tidyverse)
library(ggplot2)
# using tidyverse library and ggplot function , analyzing and visualizing is taking place.

```

```{r}

pie = ggplot(df_train) +
  geom_bar(mapping = aes(Transported , fill= HomePlanet),
          position = "fill", drop_na(df_train, HomePlanet, Transported) 
          , alpha= 0.5) +
  theme(aspect.ratio = 0.5) +
  labs(title="Transported from Home Planet",
    x= NULL, y= NULL)

pie + coord_polar()



```

```{r}
df_train%>%
  drop_na(HomePlanet)%>%
  drop_na(Destination)%>%
  ggplot(aes(fill= HomePlanet, Destination))+
  geom_bar(position = "dodge",size =5, alpha = 0.5)+
labs(title= "Home Planet and Destination")


```

```{r}
hist(df_train$Age) 

```

```{r}
df_train%>%
  drop_na(HomePlanet) %>%
  drop_na(Destination)%>%
  drop_na(VIP)%>%
  drop_na(CryoSleep)%>%
  ggplot(aes(HomePlanet, Destination))+
  geom_boxplot()+
  geom_point(alpha = 1,
             aes( size= VIP, 
                 colour = HomePlanet)) +
  facet_wrap(~CryoSleep)
  coord_flip()
  theme_bw() 
  labs(title = "VIP Transported")
```

```{r}
df_train%>%
  drop_na(HomePlanet)%>%
  drop_na(VIP)%>%
  ggplot(aes(VIP, HomePlanet),
         ) +
  geom_point(size = 5,alpha = 0.5)+
  coord_flip()
  facet_wrap(~Destination)
  theme_bw()
  
```

```{r}
df_train%>%
  ggplot(aes(RoomService, FoodCourt))+
  geom_line(colour = 'red' )+
  geom_point(size = 0.5, color = 'green')
```

```{r}
# 
Transported_data <- df_train%>%
  drop_na(Transported)%>%
  group_by(PassengerId)%>%
  summarise(PassengerId, Transported
    )
View(Transported_data)
```

```{r}
df_train%>%
  drop_na(Transported)%>%
  ggplot(aes(fill= Transported, PassengerId))+
  geom_bar(position = "dodge",size =5, alpha = 0.5)+
  facet_wrap(~Transported)
labs(title= "How many Transported")

```

```{r}

```

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