Introduction

The article from 538 talks about the bump in media coverage of Joe Biden following the breaking of the Trump-Ukraine story. After about a week, the bump subsided and Joe Biden went back to regular mentions in the media.
Link to 538 article

Getting the Data

I started by loading the .csv files into two data frames.

cable_data <- data.frame(read.csv("media-mentions-2020/cable_weekly.csv"))
online_data <- data.frame(read.csv("media-mentions-2020/online_weekly.csv"))

The next step was to combine the relevant columns into a new data frame titled all_data and provide relevant column names.

all_data <- data.frame(cable_data$date, cable_data$name, 
                       cable_data$pct_of_all_candidate_clips,
                       online_data$pct_of_all_candidate_stories)
names(all_data) <- c("Date","Name","Clips","Stories")

Finally, I subsetted the data to only include mentions of Joe Biden. At the same time, I converted the Date column into Date objects.

all_biden <- subset(all_data, Name == "Joe Biden")
all_biden$Date <- as.Date(all_biden$Date)

Looking at the Data

I started here by looking at the enitre set of data.

plot(all_biden$Date, all_biden$Clips + all_biden$Stories,
     type = "l", main = "Biden in the Media", xlab = "Date",
     ylab = "% of Mentions")

After looking at the data, it’s clear that there is a sharp decrease in Joe Biden mentions starting somewhere in September With this in mind, I subsetted the data again, looking just at mentions from September on.

all_biden <- subset(all_biden, Date >= "2019-09-01")
plot(all_biden$Date, all_biden$Clips + all_biden$Stories,
     type = "l", main = "Biden in the Media", xlab = "Date",
     ylab = "% of Mentions")

Basic Loading and Transformations

David Moste

2/1/2020

Introduction

Getting the Data

Looking at the Data

Conclusions