U.S. online news coverage of Trump and Harris

Here’s the R script for producing the above output

#Installing and loading the tidyverse package
if (!require("tidyverse"))
  install.packages("tidyverse")
if (!require("dplyr"))
  install.packages("dplyr")
if (!require("plotly"))
  install.packages("plotly")
if (!require("readr"))
  install.packages("readr")
library(tidyverse)
library(dplyr)
library(plotly)
library(ggplot2)
library(readr)

### Date range
startdate <- "20240601"
enddate <- "20240927"

### Topic A
query <- "'Donald Trump' SourceCountry:US"
#Building the Volume dataframe
vp1 <- "https://api.gdeltproject.org/api/v2/doc/doc?query="
vp2 <- "&mode=timelinevolraw&startdatetime="
vp3 <- "000000&enddatetime="
vp4 <- "000000&format=CSV"
text_v_url <- paste0(vp1, query, vp2, startdate, vp3, enddate, vp4)
v_url <- URLencode(text_v_url)
v_url
Volume <- read_csv(v_url)
Volume$Date <- as.Date(Volume$Date, "%Y-%m-%d")
Volume <- Volume %>% 
  filter(Series == "Article Count")
VolumeA <- Volume

### Topic B
query <- "'Kamala Harris' SourceCountry:US"
#Building the Volume dataframe
vp1 <- "https://api.gdeltproject.org/api/v2/doc/doc?query="
vp2 <- "&mode=timelinevolraw&startdatetime="
vp3 <- "000000&enddatetime="
vp4 <- "000000&format=CSV"
text_v_url <- paste0(vp1, query, vp2, startdate, vp3, enddate, vp4)
v_url <- URLencode(text_v_url)
v_url
Volume <- read_csv(v_url)
Volume$Date <- as.Date(Volume$Date, "%Y-%m-%d")
Volume <- Volume %>% 
  filter(Series == "Article Count")
VolumeB <- Volume

### Merging
VolumeAB <- merge(VolumeA, VolumeB, by = "Date")
VolumeAB$VolumeA <- VolumeAB$Value.x
VolumeAB$VolumeB <- VolumeAB$Value.y

#Plotting volume by date
library(plotly)
fig <- plot_ly(
  VolumeAB,
  x = ~ Date,
  y = ~ VolumeA,
  name = 'Trump', # <= Enter label for series A
  type = 'scatter',
  mode = 'lines',
  line = list(color = "#AE2012") # <= Enter color for series A 
)
fig <- fig %>% add_trace(
  y = ~ VolumeB,
  name = 'Harris', # <= Enter label for series B
  mode = 'lines',
  line = list(color = "#005F73") # <= Enter color for series B
)
fig <-
  fig %>% layout(
    title = 'U.S. coverage volume',
    xaxis = list(title = "Date",
                 showgrid = FALSE),
    yaxis = list(title = "Volume",
                 showgrid = TRUE)
  )
fig


### Saving the data to a local .csv file
write_csv(VolumeAB, "VolumeAB.csv")

# Paired-samples t-test

# Read the data
mydata <- VolumeAB

# Specify the two variables involved
mydata$V1 <- mydata$VolumeA
mydata$V2 <- mydata$VolumeB

# Look at the distribution of the pair differences
mydata$PairDifferences <- mydata$V2 - mydata$V1

ggplot(mydata, aes(x = PairDifferences)) +
  geom_histogram(color = "black", fill = "dodgerblue") +
  geom_vline(aes(xintercept = mean(PairDifferences)))

# Get descriptive statistics for pair differences
mydata %>%
  select(PairDifferences) %>%
  summarise(
    count = n(),
    mean = mean(PairDifferences, na.rm = TRUE),
    sd = sd(PairDifferences, na.rm = TRUE),
    min = min(PairDifferences, na.rm = TRUE),
    max = max(PairDifferences, na.rm = TRUE)
  )

# If pair differences look non-normal, you can use a Shapiro-Wilk test to check
# whether their distribution differs significantly from normal. If the
# Shapiro-Wilk test p-value is less than 0.05, #use a Wilcoxon signed rank test
# instead of a paired-samples t-test.

# Shapiro-Wilk test
# options(scipen = 999)
# shapiro.test(mydata$PairDifferences)

# If the pair distribution is non-normal, consider # using a Wilcoxon signed rank test instead of a
# paired-samples t-test.

# mydata %>%
#   select(V1, V2) %>%
#   summarise_all(list(Mean = mean, SD = sd))
# wilcox.test(mydata$V1, mydata$V2, paired = TRUE)

# If the pair differences are normally distributed,
# though, you may use a paired-samples t-test.

mydata %>%
  select(V1, V2) %>%
  summarise_all(list(Mean = mean, SD = sd))
options(scipen = 999)
t.test(mydata$V2, mydata$V1,
       paired = TRUE)