Challenge 9 - Wild Bird

challenge_9

wild_bird

srujan_kagitala

Creating a function

Author

Srujan Kagitala

Published

July 11, 2023

library(tidyverse)
library(ggplot2)
library(readxl)

knitr::opts_chunk$set(echo = TRUE, warning=FALSE, message=FALSE)

Challenge Overview

Today’s challenge is simple. Create a function, and use it to perform a data analysis / cleaning / visualization task:

Examples of such functions are: 1) A function that reads in and cleans a dataset.
2) A function that computes summary statistics (e.g., computes the z score for a variable).
3) A function that plots a histogram.

That’s it!

Read

Using “wild_bird_data.xlsx” dataset.

wild_bird_data <- read_excel("_data/wild_bird_data.xlsx", skip = 1)
wild_bird_data

Function

Creating a function to show statistics like mean, median, min, max, IQR, standard deviation and variance

#Function to give statistics
statistics <- function(data, col_name){
  data %>%
    select({{col_name}}) %>%
    summarise_all(list(mean = mean,
                       median = median,
                       min = min,
                       max = max,
                       IQR = IQR,
                       sd = sd,
                       var = var), na.rm = TRUE)
}

Statistics of wet body weight of wild birds.

statistics(wild_bird_data,`Wet body weight [g]`)

---
title: "Challenge 9 - Wild Bird"
author: "Srujan Kagitala"
description: "Creating a function"
date: "7/11/2023"
format:
  html:
    toc: true
    code-copy: true
    code-tools: true
    df-print: paged
categories:
  - challenge_9
  - wild_bird
  - srujan_kagitala
---

```{r}
#| label: setup
#| warning: false
#| message: false

library(tidyverse)
library(ggplot2)
library(readxl)

knitr::opts_chunk$set(echo = TRUE, warning=FALSE, message=FALSE)
```

## Challenge Overview

Today's challenge is simple. Create a function, and use it to perform a data analysis / cleaning / visualization task:

Examples of such functions are:
1) A function that reads in and cleans a dataset.  
2) A function that computes summary statistics (e.g., computes the z score for a variable).  
3) A function that plots a histogram.  

That's it!

## Read

Using "wild_bird_data.xlsx" dataset.
```{r}
#| label: read

wild_bird_data <- read_excel("_data/wild_bird_data.xlsx", skip = 1)
wild_bird_data
```

## Function

Creating a function to show statistics like mean, median, min, max, IQR, standard deviation and variance
```{r}
#| label: func

#Function to give statistics
statistics <- function(data, col_name){
  data %>%
    select({{col_name}}) %>%
    summarise_all(list(mean = mean,
                       median = median,
                       min = min,
                       max = max,
                       IQR = IQR,
                       sd = sd,
                       var = var), na.rm = TRUE)
}
```


Statistics of wet body weight of wild birds.
```{r}
statistics(wild_bird_data,`Wet body weight [g]`)
```