R_Assignment1_Mansi
2023-09-04
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Part 1:
Working with data and summarize them as: - A numeric summary of data for at least 10 columns of data - For categorical columns, this should include unique values and counts - For numeric columns, this includes min/max, central tendency, and some notion of distribution (e.g., quantiles) - These summaries can be combined.
# Load the necessary libraries
library(tidyverse)## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.1.2 ✔ readr 2.1.4
## ✔ forcats 1.0.0 ✔ stringr 1.5.0
## ✔ ggplot2 3.4.3 ✔ tibble 3.2.1
## ✔ lubridate 1.9.2 ✔ tidyr 1.3.0
## ✔ purrr 1.0.2
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors# Load your data
data <- read.csv("C:\\Users\\mansi\\Downloads\\news+popularity+in+multiple+social+media+platforms\\News_Popularity_in_Multiple_Social_Media_Platforms.csv")
# A numeric summary of data for at least 10 columns
summary(data)## IDLink Title Headline Source
## Min. : 1 Length:93239 Length:93239 Length:93239
## 1st Qu.: 24302 Class :character Class :character Class :character
## Median : 52275 Mode :character Mode :character Mode :character
## Mean : 51561
## 3rd Qu.: 76586
## Max. :104802
## Topic PublishDate SentimentTitle SentimentHeadline
## Length:93239 Length:93239 Min. :-0.950694 Min. :-0.75543
## Class :character Class :character 1st Qu.:-0.079057 1st Qu.:-0.11457
## Mode :character Mode :character Median : 0.000000 Median :-0.02606
## Mean :-0.005411 Mean :-0.02749
## 3rd Qu.: 0.064255 3rd Qu.: 0.05971
## Max. : 0.962354 Max. : 0.96465
## Facebook GooglePlus LinkedIn
## Min. : -1.0 Min. : -1.000 Min. : -1.00
## 1st Qu.: 0.0 1st Qu.: 0.000 1st Qu.: 0.00
## Median : 5.0 Median : 0.000 Median : 0.00
## Mean : 113.1 Mean : 3.888 Mean : 16.55
## 3rd Qu.: 33.0 3rd Qu.: 2.000 3rd Qu.: 4.00
## Max. :49211.0 Max. :1267.000 Max. :20341.00str(data)## 'data.frame': 93239 obs. of 11 variables:
## $ IDLink : num 99248 10423 18828 27788 27789 ...
## $ Title : chr "Obama Lays Wreath at Arlington National Cemetery" "A Look at the Health of the Chinese Economy" "Nouriel Roubini: Global Economy Not Back to 2008" "Finland GDP Expands In Q4" ...
## $ Headline : chr "Obama Lays Wreath at Arlington National Cemetery. President Barack Obama has laid a wreath at the Tomb of the U"| __truncated__ "Tim Haywood, investment director business-unit head for fixed income at Gam, discusses the China beige book and"| __truncated__ "Nouriel Roubini, NYU professor and chairman at Roubini Global Economics, explains why the global economy isn't "| __truncated__ "Finland's economy expanded marginally in the three months ended December, after contracting in the previous qua"| __truncated__ ...
## $ Source : chr "USA TODAY" "Bloomberg" "Bloomberg" "RTT News" ...
## $ Topic : chr "obama" "economy" "economy" "economy" ...
## $ PublishDate : chr "2002-04-02 00:00:00" "2008-09-20 00:00:00" "2012-01-28 00:00:00" "2015-03-01 00:06:00" ...
## $ SentimentTitle : num 0 0.208 -0.425 0 0 ...
## $ SentimentHeadline: num -0.0533 -0.1564 0.1398 0.0261 0.1411 ...
## $ Facebook : int -1 -1 -1 -1 -1 -1 0 -1 -1 -1 ...
## $ GooglePlus : int -1 -1 -1 -1 -1 -1 0 -1 -1 -1 ...
## $ LinkedIn : int -1 -1 -1 -1 -1 -1 0 -1 -1 -1 ...data_types <- sapply(data, class)
print(data_types)## IDLink Title Headline Source
## "numeric" "character" "character" "character"
## Topic PublishDate SentimentTitle SentimentHeadline
## "character" "character" "numeric" "numeric"
## Facebook GooglePlus LinkedIn
## "integer" "integer" "integer"numeric_columns <- sapply(data, is.numeric)
categorical_columns <- sapply(data, function(x) is.factor(x) || is.character(x))
# Create summaries for numeric columns
numeric_summary <- data %>%
select_if(numeric_columns) %>%
summarise_at(vars(everything()),
list(
Min = ~ min(.),
Max = ~ max(.),
Mean = ~ mean(.),
Median = ~ median(.),
Q1 = ~ quantile(., 0.25),
Q3 = ~ quantile(., 0.75)
))
# Create summaries for categorical columns
categorical_summary <- data %>%
select_if(categorical_columns) %>%
summarise_all(list(Unique_Values = ~ length(unique(.)),
Counts = ~ n()))
# Combine the summaries
combined_summary <- bind_cols(numeric_summary, categorical_summary)
# Print the combined summary
print(combined_summary)## IDLink_Min SentimentTitle_Min SentimentHeadline_Min Facebook_Min
## 1 1 -0.9506944 -0.7554334 -1
## GooglePlus_Min LinkedIn_Min IDLink_Max SentimentTitle_Max
## 1 -1 -1 104802 0.9623536
## SentimentHeadline_Max Facebook_Max GooglePlus_Max LinkedIn_Max IDLink_Mean
## 1 0.9646462 49211 1267 20341 51560.65
## SentimentTitle_Mean SentimentHeadline_Mean Facebook_Mean GooglePlus_Mean
## 1 -0.005411366 -0.02749305 113.1413 3.888362
## LinkedIn_Mean IDLink_Median SentimentTitle_Median SentimentHeadline_Median
## 1 16.54796 52275 0 -0.0260643
## Facebook_Median GooglePlus_Median LinkedIn_Median IDLink_Q1 SentimentTitle_Q1
## 1 5 0 0 24301.5 -0.07905694
## SentimentHeadline_Q1 Facebook_Q1 GooglePlus_Q1 LinkedIn_Q1 IDLink_Q3
## 1 -0.1145743 0 0 0 76585.5
## SentimentTitle_Q3 SentimentHeadline_Q3 Facebook_Q3 GooglePlus_Q3 LinkedIn_Q3
## 1 0.06425521 0.0597091 33 2 4
## Title_Unique_Values Headline_Unique_Values Source_Unique_Values
## 1 81259 86695 5757
## Topic_Unique_Values PublishDate_Unique_Values Title_Counts Headline_Counts
## 1 4 82644 93239 93239
## Source_Counts Topic_Counts PublishDate_Counts
## 1 93239 93239 93239Part 2:
Novel questions:
- Which social media platform(s) has the highest average sentiment score (SentimentTitle) for news articles?
- What is the distribution of sentiment scores for the “SentimentTitle” column?
- Are there any correlations between “Facebook” shares and “GooglePlus” shares?
- How does the sentiment in the “SentimentHeadline” column vary by “Topic”?
- Does the length of a news (measured in words) significantly affect its popularity on social media platforms?
Column Summaries: The dataset has 11 columns.
- IDLink: A unique identifier for each news article.
- Title: The title of the news article.
- Headline: The headline or main content of the news article.
- Source: The source or publication where the news article was published.
- Topic: The topic or category of the news article.
- PublishDate: The date and time when the news article was published.
- SentimentTitle: Sentiment score associated with the title of the news article.
- SentimentHeadline: Sentiment score associated with the headline of the news article.
- Facebook: The number of shares on Facebook.
- GooglePlus: The number of shares on GooglePlus.
- LinkedIn: The number of shares on LinkedIn.
Project’s Goal/Purpose:
To identify and infer the key factors that influence the popularity of news articles on different social media platforms (Facebook, Google+, LinkedIn). Overall, the project aims to provide insights into news content performance in the context of social media, helping publishers and content creators optimize their strategies for better engagement and reach.
Part 3:
Use of aggregation functions (other than the ones used from the first bullet, above) i.e., use these explore something interesting about your data
Example: Find the trend in “GooglePlus” shares over time.
# Find the trend in "GooglePlus" shares over time.
# Aggregation to see the trend in "GooglePlus" shares over time
trend_googleplus <- data %>%
group_by(PublishDate) %>%
summarise(Mean_GooglePlus = mean(GooglePlus))
print(trend_googleplus)## # A tibble: 82,644 × 2
## PublishDate Mean_GooglePlus
## <chr> <dbl>
## 1 2002-04-02 00:00:00 -1
## 2 2008-09-20 00:00:00 -1
## 3 2012-01-28 00:00:00 -1
## 4 2015-03-01 00:06:00 -1
## 5 2015-03-01 00:11:00 -1
## 6 2015-03-01 00:19:00 -1
## 7 2015-03-01 00:45:00 -1
## 8 2015-03-01 01:20:00 -1
## 9 2015-03-01 01:32:00 -1
## 10 2015-03-01 02:14:00 -1
## # ℹ 82,634 more rowsPart 4:
A visual summary of at least 5 columns of your data that follows: - This should include distributions at least - In addition, you should consider trends, correlations, and interactions between variables - Use different channels (e.g., color) to show how categorical variables interact with continuous variables
## Loading required package: NLP##
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