AnkitHw4.Rmd

title: "AnkittHw4"
author: "Ankit"
date: "8/23/2021"
output: html_document
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```

Loading relevant packages

```{r}
library(tidyverse)
```

Reading the dataset into a data frame

```{r}
library(readr)
hotel_bookings <- read_csv("_data/hotel_bookings.csv")
```

Overview of the dataset
```{r}
glimpse(hotel_bookings)
```

Taking a look at the first few rows
```{r}
head(hotel_bookings)
```

Are Na values present in the dataset?

```{r}
hotel_bookings %>% is.na()%>% sum()
```

There are 4 Na values in the data set.

Identifying the positions of NA values in the dataset

```{r}
hotel_bookings %>% is.na() %>% which(arr.ind = TRUE)
```

arr.ind is TRUE because we need the positions of NA values in the Dataset

Na values are present in the 11th column. So, examining the rows which have NA values

```{r}
hotel_bookings[c(40601,40668,40680,41161), ]
```

The children column has NA values for the above rows

Removing the rows which have Na values
```{r}
hotel_bookings <- hotel_bookings %>% filter(!is.na(children))
```

Checking whether data set still has Na values

```{r}
hotel_bookings %>% is.na()%>% sum()
```

```{r}
hotel_bookings %>% mutate(arrival_date_month = factor(arrival_date_month,
    levels = c("January", "February", "March", "April", "May", "June", "July",
    "August", "September", "October", "November", "December"), ordered = TRUE))
```


Frequency table of Hotels in the dataset
```{r}
hotel_bookings %>%
  group_by(hotel) %>% summarise(freq=n())
```