You are to submit either a GitHub (for a statistic visualization) or a shinyapps.io (for an interactive visualization) link to your final project (remember to include the data in your repo so I can...

1 answer below »
You are to submit either a GitHub (for a statistic visualization) or a shinyapps.io (for an interactive visualization) link to your final project (remember to include the data in your repo so I can run your code). In addition, you are to submit a link to the GitHub repository with your code (two links total).


The total number of points assigned to your final project submission is 20, distributed as follows:


2 points for a working link to GitHub (for a statistic visualization) or a shinyapps.io (for an interactive visualization)
2 points for a description of the data and where the data was acquired
6 points, divided into 2 points for each of three plots (for a total of three different types of plot, from the different types we've seen in class)
2 points for the use of colorblind-friendly color schemes
2 points for the use of the appropriate color scheme (categorical, divergent, or continuous) given the variable mapped to the color/fill aesthetics
2 points for appropriate axes scales and labels (meaning they are legible, not overlapping, and clearly state what is being displayed in the plot)
2 points for titles and captions that make it clear what the plot is about
2 points for appropriate ordering of group levels (examples: unordered categorical variables are displayed not according to alphabetical order, but reordered by the numeric variable used; ordered categorical variables are shown in their correct order)
Again, what you need to submit is a LINK to your visualization and a LINK to your GitHub repo with your code.
Answered Same DayOct 15, 2021

Answer To: You are to submit either a GitHub (for a statistic visualization) or a shinyapps.io (for an...

Nithin answered on Oct 15 2021
114 Votes
{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"name": "DataVizualisation.Ipynb",
"provenance": []
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
},
"language_info": {
"name": "python"
}
},
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "qS8EH-GntbnQ"
},
"source": [
"## Data Visualuization Assignment\n",
"\n",
"#### Name : \n",
"\n",
"#### Link : https://colab.research.google.com/drive/1IzkpnxS6bZY7hKeNQ-x0dHK6rrSow0OQ?usp=sharing"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "FwOXPYrHtRj7"
},
"source": [
"## Importing necessary modules"
]
},
{
"cell_type": "code",
"metadata": {
"id": "-qjpjHQKVn_7"
},
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import seaborn as sns"
],
"execution_count": 1,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "SwtEp_FTYkTd"
},
"source": [
"## Loading Dataset"
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 424
},
"id": "Dl3QUnheYDw3",
"outputId": "db0e2e6c-86b9-4a51-c220-61954cd06fbb"
},
"source": [
"dataset = pd.read_csv('Iris.csv')\n",
"dataset"
],
"execution_count": 3,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/html": [
"
\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"
IdSepalLengthCmSepalWidthCmPetalLengthCmPetalWidthCmSpecies
015.13.51.40.2Iris-setosa
124.93.01.40.2Iris-setosa
234.73.21.30.2Iris-setosa
344.63.11.50.2Iris-setosa
455.03.61.40.2Iris-setosa
.....................
1451466.73.05.22.3Iris-virginica
1461476.32.55.01.9Iris-virginica
1471486.53.05.22.0Iris-virginica
1481496.23.45.42.3Iris-virginica
1491505.93.05.11.8Iris-virginica
\n",
"

150 rows × 6 columns

\n",
"
"
],
"text/plain": [
" Id SepalLengthCm ... PetalWidthCm Species\n",
"0 1 5.1 ... 0.2 Iris-setosa\n",
"1 2 4.9 ... 0.2 Iris-setosa\n",
"2 3 4.7 ... 0.2 Iris-setosa\n",
"3 4 4.6 ... 0.2 Iris-setosa\n",
"4 5 5.0 ... 0.2 Iris-setosa\n",
".. ... ... ... ... ...\n",
"145 146 6.7 ... 2.3 Iris-virginica\n",
"146 147 6.3 ... 1.9 Iris-virginica\n",
"147 148 6.5 ... 2.0 Iris-virginica\n",
"148 149 6.2 ... 2.3 Iris-virginica\n",
"149 150 5.9 ... 1.8 Iris-virginica\n",
"\n",
"[150 rows x 6 columns]"
]
},
"metadata": {},
"execution_count": 3
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "sUpDO28FYvYl"
},
"source": [
"## Describing Data"
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 301
},
"id": "qmsq9LS1Yfv9",
"outputId": "3e99439c-6e98-420c-8d36-cfb47c78fe88"
},
"source": [
"dataset.describe()"
],
"execution_count": 5,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/html": [
"
\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"
SepalLengthCmSepalWidthCmPetalLengthCmPetalWidthCm
count150.000000150.000000150.000000150.000000
mean5.8433333.0540003.7586671.198667
std0.8280660.4335941.7644200.763161
min4.3000002.0000001.0000000.100000
25%5.1000002.8000001.6000000.300000
50%5.8000003.0000004.3500001.300000
75%6.4000003.3000005.1000001.800000
max7.9000004.4000006.9000002.500000
\n",
"
"
],
"text/plain": [
" SepalLengthCm SepalWidthCm PetalLengthCm PetalWidthCm\n",
"count 150.000000 150.000000 150.000000 150.000000\n",
"mean 5.843333 3.054000 3.758667 1.198667\n",
"std 0.828066 0.433594 1.764420 0.763161\n",
"min 4.300000 2.000000 1.000000 0.100000\n",
"25% 5.100000 2.800000 1.600000 0.300000\n",
"50% 5.800000 3.000000 4.350000 1.300000\n",
"75% 6.400000 3.300000 5.100000 1.800000\n",
"max 7.900000 4.400000 6.900000 2.500000"
]
},
"metadata": {},
"execution_count": 5
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "XSau8UHZak8t"
},
"source": [
" The data set contains 3 species of 50 instances each, where each class refers to a type of iris plant. One class is linearly separable from the other 2; the latter are NOT linearly separable from each other. "
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 206
},
"id": "XS0nfZ0ma2zX",
"outputId": "e9e6e0bf-9474-4d0e-85bb-8d9b0caed240"
},
"source": [
"dataset = dataset.drop(columns=['Id'])\n",
"dataset.head()"
],
"execution_count": 6,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/html": [
"
\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"
SepalLengthCmSepalWidthCmPetalLengthCmPetalWidthCmSpecies
05.13.51.40.2Iris-setosa
14.93.01.40.2Iris-setosa
24.73.21.30.2Iris-setosa
34.63.11.50.2Iris-setosa
45.03.61.40.2Iris-setosa
\n",
"
"
],
"text/plain": [
" SepalLengthCm SepalWidthCm PetalLengthCm PetalWidthCm Species\n",
"0 5.1 3.5 1.4 0.2 Iris-setosa\n",
"1 4.9 3.0 1.4 0.2 Iris-setosa\n",
"2 4.7 3.2 1.3 0.2 Iris-setosa\n",
"3 4.6 3.1 1.5 0.2 Iris-setosa\n",
"4 5.0 3.6 1.4 0.2 Iris-setosa"
]
},
"metadata": {},
"execution_count": 6
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "b--Y4sLYZMqZ"
},
"source": [
"The columns in this dataset are:\n",
"\n",
" Id\n",
" SepalLengthCm\n",
" SepalWidthCm\n",
" PetalLengthCm\n",
" PetalWidthCm\n",
" Species\n",
"\n",
"Column ID is removed as it doesn't serve any important function"
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "x-VZvE7bb8YQ",
"outputId": "06c5e3ec-eb80-4ce9-9c29-25bb4c5b75b9"
},
"source": [
"dataset.info()"
],
"execution_count": 7,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"\n",
"RangeIndex: 150 entries, 0 to 149\n",
"Data columns (total 5 columns):\n",
" # Column Non-Null Count Dtype \n",
"--- ------ -------------- ----- \n",
" 0 SepalLengthCm 150 non-null float64\n",
" 1 SepalWidthCm 150 non-null float64\n",
" 2 PetalLengthCm 150 non-null float64\n",
" 3 PetalWidthCm 150 non-null float64\n",
" 4 Species 150 non-null object \n",
"dtypes: float64(4), object(1)\n",
"memory usage: 6.0+ KB\n"
]
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "tfv7vGAqbq8f"
},
"source": [
"## Data Source :\n",
"\n",
"Creator:\n",
"\n",
"R.A. Fisher\n",
"\n",
"Donor:\n",
"\n",
"Michael Marshall \n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Fnc_005BaNbn"
},
"source": [
"Dataset can be found at : https://www.kaggle.com/uciml/iris"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "UT4bpKW9coSL"
},
"source": [
"## Data Vizualisation"
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 299
},
"id": "b5C4BNmVayKg",
"outputId": "e65267ae-4715-43e3-e6c6-564744cefab0"
},
"source": [
"## 1. Scatterplot\n",
"\n",
"## Colorblind friendly colours\n",
"color = ['cornflowerblue','lightseagreen','steelblue']\n",
"species=['Iris-setosa','Iris-virginica','Iris-versicolor']\n",
"\n",
"for i in range(3):\n",
" x=dataset[dataset['Species']==species[i]]\n",
" plt.scatter(x['SepalLengthCm'],x['SepalWidthCm'],c=color[i],label=species[i])\n",
"plt.xlabel('SepalLength')\n",
"plt.ylabel('SepalWidth')\n",
"plt.legend()"
],
"execution_count": 22,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
""
]
},
"metadata": {},
"execution_count": 22
},
{
"output_type": "display_data",
"data": {
"image/png": "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\n",
"text/plain": [
"
"
]
},
"metadata": {
"needs_background": "light"
}
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "EilXdyVlkPtH"
},
"source": [
"##### Scatterplot : A scatter plot or graph uses dots to represent values for two different numeric variables to find the relationship between them. Here the relationship between SeapalLength and SepalWidth are shown with repsect to each class of the flower."
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 296
},
"id": "QPX6Yfcqfr1c",
"outputId": "383bd222-6289-4c7f-f63a-3a03bc7b81dc"
},
"source": [
"## 2. Box Plot\n",
"\n",
"## Colourblind friendly pallete : cubehelix\n",
"\n",
"sns.boxplot(x=\"Species\", y=\"PetalLengthCm\", palette=\"cubehelix\", data=dataset)"
],
"execution_count": 30,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
""
]
},
"metadata": {},
"execution_count": 30
},
{
"output_type": "display_data",
"data": {
"image/png": "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\n",
"text/plain": [
"
"
]
},
"metadata": {
"needs_background": "light"
}
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "aaQ4r5m_l0Bm"
},
"source": [
"##### Box Plot : A boxplot is a graph that gives a good indication of how the values in the data are spread out. They consume less space as compared to Histograms and are also useful for identifying outliers."
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 885
},
"id": "f4zBwaHXh5lM",
"outputId": "2e0e98b4-5377-49ab-9e39-608c253d8891"
},
"source": [
"## 3. Pair Plot\n",
"\n",
"sns.pairplot(dataset, hue=\"Species\", palette=\"cubehelix\", height=3)"
],
"execution_count": 24,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
""
]
},
"metadata": {},
"execution_count": 24
},
{
"output_type": "display_data",
"data": {
"image/png":...
SOLUTION.PDF

Answer To This Question Is Available To Download

Related Questions & Answers

More Questions »

Submit New Assignment

Copy and Paste Your Assignment Here