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---
title: "Monthly Numbers"
date: "Last updated: 1/05/2023"
output:
html_document:
toc: true
toc_float: true
collapsed: false
number_sections: false
toc_depth: 1
---
```{css, echo=FALSE}
h1 {
text-align: center;
}
```
```{r setup, echo=FALSE, warning=FALSE, message=FALSE}
library("knitr")
knitr::opts_knit$set(echo=FALSE, warning=FALSE, message=FALSE)
source("monthlyreport_functions.R")
data <- read.csv('December2022TrackingData.csv')
data$Date.Used <- strptime(as.character(data$Date.Used), "%m/%d/%Y")
tracking.data <-reformat_googlesheet(data)
library('tidyr')
tracking.data <- separate_rows(tracking.data, source, sep=",")
tracking.data$source <- trimws(tracking.data$source)
tracking.data$date <- as.Date(tracking.data$date, format = "%Y-%m-%d")
```
Repository for CM/N Programs Monthly Tracking reports.
# December 2022
```{r December 2022 Enrollment, eval=FALSE, echo = FALSE, warning=FALSE, message=FALSE}
rows <- c("Media Meteorologists","Market Penetration (%)", "Local Television Stations", "Spanish-language Meteorologists", "Journalists Receiving CMN")
values <- c(968, 92, 483, 55, 702)
enrollment <- as.data.frame(cbind(rows, values))
names(enrollment) <- c(" ", "Enrollment")
library("formattable")
formattable(enrollment)
```
### Hits by Program
```{r December 2022 Hits by Type and Program, echo = FALSE, warning=FALSE, message=FALSE}
tracking.data.December2022 <- tracking.data[which(tracking.data$month=="December" & tracking.data$year=="2022"),]
#expanded view
library("tidyr")
expanded.hits.draft.December2022 <- hitsbyprogram(tracking.data.December2022)
expanded.hits.December2022 <- expanded.hits.draft.December2022[,c("source", "Twitter",
"online.article", "radio", "tv")]
#restrict data to CC, CM, CMN sources
expanded.hits.CMprograms.December2022 <- subset(expanded.hits.December2022, source=="CC"|source=="CM"|source=="CMN")
#aggregate hits according to nest program model
CMN.December2022 <- expanded.hits.CMprograms.December2022[3,2:5]
CM.December2022 <- CMN.December2022 + expanded.hits.CMprograms.December2022[2,2:5]
CC.December2022 <- CM.December2022 + expanded.hits.CMprograms.December2022[1,2:5]
aggregated.hits.CMprograms.December2022 <- rbind(CC.December2022, CM.December2022, CMN.December2022)
rownames(aggregated.hits.CMprograms.December2022) <- c("CC", "CM", "CMN")
#change column namesg
names(aggregated.hits.CMprograms.December2022) <- c("Twitter",
"Online Articles", "Radio", "TV")
library("formattable")
aggregated.hits.formatted.December2022 <- formattable(aggregated.hits.CMprograms.December2022,
align=c("l", "c", "c", "c", "c"),
list(''))
aggregated.hits.formatted.December2022
```
### Comparison with Last Year
```{r December 2022 Last Year Comparison, echo = FALSE, warning=FALSE, message=FALSE}
tracking.data.December2022 <- tracking.data[which(tracking.data$month=="December" & tracking.data$year=="2022"),]
tracking.data.December2021 <- tracking.data[which(tracking.data$month=="December" & tracking.data$year=="2021"),]
previousYear(tracking.data.December2022, tracking.data.December2021)
```
### Top Releases
Most-used releases this month on TV and Radio (including content released before this month).
```{r December 2022 Top Releases, warning=FALSE, echo = FALSE, warning=FALSE, message=FALSE}
tracking.data.December2022.subsetted <- subset(tracking.data.December2022, source=="CC"|source=="CM"|source=="CMN")
rankedreleases.December2022 <- rankReleasesTVRadioOnly(tracking.data.December2022.subsetted)
top10releases.December2022 <-rankedreleases.December2022[c(1:10),]
names(top10releases.December2022) <- c("Release", "Radio", "TV", "Total")
top5releases.December2022 <-rankedreleases.December2022[c(1:5),]
names(top5releases.December2022) <- c("Release", "Radio", "TV", "Total")
library(DT)
datatable(top10releases.December2022, fillContainer=FALSE, options = list(scrollX="70px", scrollY="200px", pageLength = 5, dom = 'ftp'), rownames=FALSE)
```
### Hits by Region
```{r December 2022 Hits by Region, warning=FALSE, echo = FALSE, message=FALSE}
#compute hits by regions
month.hitsbyregion.December2022 <- hitsbyregion(tracking.data.December2022.subsetted)
month.hitsbyregion.December2022
```
# November 2022
```{r November 2022 Enrollment, eval=FALSE, echo = FALSE, warning=FALSE, message=FALSE}
rows <- c("Media Meteorologists","Market Penetration (%)", "Local Television Stations", "Spanish-language Meteorologists", "Journalists Receiving CMN")
values <- c(968, 92, 483, 55, 702)
enrollment <- as.data.frame(cbind(rows, values))
names(enrollment) <- c(" ", "Enrollment")
library("formattable")
formattable(enrollment)
```
### Hits by Program
```{r November 2022 Hits by Type and Program, echo = FALSE, warning=FALSE, message=FALSE}
tracking.data.November2022 <- tracking.data[which(tracking.data$month=="November" & tracking.data$year=="2022"),]
#expanded view
library("tidyr")
expanded.hits.draft.November2022 <- hitsbyprogram(tracking.data.November2022)
expanded.hits.November2022 <- expanded.hits.draft.November2022[,c("source", "Twitter",
"online.article", "radio", "tv")]
#restrict data to CC, CM, CMN sources
expanded.hits.CMprograms.November2022 <- subset(expanded.hits.November2022, source=="CC"|source=="CM"|source=="CMN")
#aggregate hits according to nest program model
CMN.November2022 <- expanded.hits.CMprograms.November2022[3,2:5]
CM.November2022 <- CMN.November2022 + expanded.hits.CMprograms.November2022[2,2:5]
CC.November2022 <- CM.November2022 + expanded.hits.CMprograms.November2022[1,2:5]
aggregated.hits.CMprograms.November2022 <- rbind(CC.November2022, CM.November2022, CMN.November2022)
rownames(aggregated.hits.CMprograms.November2022) <- c("CC", "CM", "CMN")
#change column namesg
names(aggregated.hits.CMprograms.November2022) <- c("Twitter",
"Online Articles", "Radio", "TV")
library("formattable")
aggregated.hits.formatted.November2022 <- formattable(aggregated.hits.CMprograms.November2022,
align=c("l", "c", "c", "c", "c"),
list(''))
aggregated.hits.formatted.November2022
```
### Comparison with Last Year
```{r November 2022 Last Year Comparison, echo = FALSE, warning=FALSE, message=FALSE}
tracking.data.November2022 <- tracking.data[which(tracking.data$month=="November" & tracking.data$year=="2022"),]
tracking.data.November2021 <- tracking.data[which(tracking.data$month=="November" & tracking.data$year=="2021"),]
previousYear(tracking.data.November2022, tracking.data.November2021)
```
### Top Releases
Most-used releases this month on TV and Radio (including content released before this month).
```{r November 2022 Top Releases, warning=FALSE, echo = FALSE, warning=FALSE, message=FALSE}
tracking.data.November2022.subsetted <- subset(tracking.data.November2022, source=="CC"|source=="CM"|source=="CMN")
rankedreleases.November2022 <- rankReleasesTVRadioOnly(tracking.data.November2022.subsetted)
top10releases.November2022 <-rankedreleases.November2022[c(1:10),]
names(top10releases.November2022) <- c("Release", "Radio", "TV", "Total")
top5releases.November2022 <-rankedreleases.November2022[c(1:5),]
names(top5releases.November2022) <- c("Release", "Radio", "TV", "Total")
library(DT)
datatable(top10releases.November2022, fillContainer=FALSE, options = list(scrollX="70px", scrollY="200px", pageLength = 5, dom = 'ftp'), rownames=FALSE)
```
### Hits by Region
```{r November 2022 Hits by Region, warning=FALSE, echo = FALSE, message=FALSE}
#compute hits by regions
month.hitsbyregion.November2022 <- hitsbyregion(tracking.data.November2022.subsetted)
month.hitsbyregion.November2022
```
# October 2022
```{r October 2022 Enrollment, eval=FALSE, echo = FALSE, warning=FALSE, message=FALSE}
rows <- c("Media Meteorologists","Market Penetration (%)", "Local Television Stations", "Spanish-language Meteorologists", "Journalists Receiving CMN")
values <- c(968, 92, 483, 55, 702)
enrollment <- as.data.frame(cbind(rows, values))
names(enrollment) <- c(" ", "Enrollment")
library("formattable")
formattable(enrollment)
```
### Hits by Program
```{r October 2022 Hits by Type and Program, echo = FALSE, warning=FALSE, message=FALSE}
tracking.data.October2022 <- tracking.data[which(tracking.data$month=="October" & tracking.data$year=="2022"),]
#expanded view
library("tidyr")
expanded.hits.draft.October2022 <- hitsbyprogram(tracking.data.October2022)
expanded.hits.October2022 <- expanded.hits.draft.October2022[,c("source", "Twitter",
"online.article", "radio", "tv")]
#restrict data to CC, CM, CMN sources
expanded.hits.CMprograms.October2022 <- subset(expanded.hits.October2022, source=="CC"|source=="CM"|source=="CMN")
#aggregate hits according to nest program model
CMN.October2022 <- expanded.hits.CMprograms.October2022[3,2:5]
CM.October2022 <- CMN.October2022 + expanded.hits.CMprograms.October2022[2,2:5]
CC.October2022 <- CM.October2022 + expanded.hits.CMprograms.October2022[1,2:5]
aggregated.hits.CMprograms.October2022 <- rbind(CC.October2022, CM.October2022, CMN.October2022)
rownames(aggregated.hits.CMprograms.October2022) <- c("CC", "CM", "CMN")
#change column namesg
names(aggregated.hits.CMprograms.October2022) <- c("Twitter",
"Online Articles", "Radio", "TV")
library("formattable")
aggregated.hits.formatted.October2022 <- formattable(aggregated.hits.CMprograms.October2022,
align=c("l", "c", "c", "c", "c"),
list(''))
aggregated.hits.formatted.October2022
```
### Comparison with Last Year
```{r October 2022 Last Year Comparison, echo = FALSE, warning=FALSE, message=FALSE}
tracking.data.October2022 <- tracking.data[which(tracking.data$month=="October" & tracking.data$year=="2022"),]
tracking.data.October2021 <- tracking.data[which(tracking.data$month=="October" & tracking.data$year=="2021"),]
previousYear(tracking.data.October2022, tracking.data.October2021)
```
### Top Releases
Most-used releases this month on TV and Radio (including content released before this month).
```{r October 2022 Top Releases, warning=FALSE, echo = FALSE, warning=FALSE, message=FALSE}
tracking.data.October2022.subsetted <- subset(tracking.data.October2022, source=="CC"|source=="CM"|source=="CMN")
rankedreleases.October2022 <- rankReleasesTVRadioOnly(tracking.data.October2022.subsetted)
top10releases.October2022 <-rankedreleases.October2022[c(1:10),]
names(top10releases.October2022) <- c("Release", "Radio", "TV", "Total")
top5releases.October2022 <-rankedreleases.October2022[c(1:5),]
names(top5releases.October2022) <- c("Release", "Radio", "TV", "Total")
library(DT)
datatable(top10releases.October2022, fillContainer=FALSE, options = list(scrollX="70px", scrollY="200px", pageLength = 5, dom = 'ftp'), rownames=FALSE)
```
### Hits by Region
```{r October 2022 Hits by Region, warning=FALSE, echo = FALSE, message=FALSE}
#compute hits by regions
month.hitsbyregion.October2022 <- hitsbyregion(tracking.data.October2022.subsetted)
month.hitsbyregion.October2022
```
# September 2022
```{r September 2022 Enrollment, eval=FALSE, echo = FALSE, warning=FALSE, message=FALSE}
rows <- c("Media Meteorologists","Market Penetration (%)", "Local Television Stations", "Spanish-language Meteorologists", "Journalists Receiving CMN")
values <- c(968, 92, 483, 55, 702)
enrollment <- as.data.frame(cbind(rows, values))
names(enrollment) <- c(" ", "Enrollment")
library("formattable")
formattable(enrollment)
```
### Hits by Program
```{r September 2022 Hits by Type and Program, echo = FALSE, warning=FALSE, message=FALSE}
tracking.data.September2022 <- tracking.data[which(tracking.data$month=="September" & tracking.data$year=="2022"),]
#expanded view
library("tidyr")
expanded.hits.draft.September2022 <- hitsbyprogram(tracking.data.September2022)
expanded.hits.September2022 <- expanded.hits.draft.September2022[,c("source", "Twitter",
"online.article", "radio", "tv")]
#restrict data to CC, CM, CMN sources
expanded.hits.CMprograms.September2022 <- subset(expanded.hits.September2022, source=="CC"|source=="CM"|source=="CMN")
#aggregate hits according to nest program model
CMN.September2022 <- expanded.hits.CMprograms.September2022[3,2:5]
CM.September2022 <- CMN.September2022 + expanded.hits.CMprograms.September2022[2,2:5]
CC.September2022 <- CM.September2022 + expanded.hits.CMprograms.September2022[1,2:5]
aggregated.hits.CMprograms.September2022 <- rbind(CC.September2022, CM.September2022, CMN.September2022)
rownames(aggregated.hits.CMprograms.September2022) <- c("CC", "CM", "CMN")
#change column names
names(aggregated.hits.CMprograms.September2022) <- c("Twitter",
"Online Articles", "Radio", "TV")
library("formattable")
aggregated.hits.formatted.September2022 <- formattable(aggregated.hits.CMprograms.September2022,
align=c("l", "c", "c", "c", "c"),
list(''))
aggregated.hits.formatted.September2022
```
### Comparison with Last Year
```{r September 2022 Last Year Comparison, echo = FALSE, warning=FALSE, message=FALSE}
tracking.data.September2022 <- tracking.data[which(tracking.data$month=="September" & tracking.data$year=="2022"),]
tracking.data.September2021 <- tracking.data[which(tracking.data$month=="September" & tracking.data$year=="2021"),]
previousYear(tracking.data.September2022, tracking.data.September2021)
```
### Top Releases
Most-used releases this month on TV and Radio (including content released before this month).
```{r September 2022 Top Releases, warning=FALSE, echo = FALSE, warning=FALSE, message=FALSE}
tracking.data.September2022.subsetted <- subset(tracking.data.September2022, source=="CC"|source=="CM"|source=="CMN")
rankedreleases.September2022 <- rankReleasesTVRadioOnly(tracking.data.September2022.subsetted)
top10releases.September2022 <-rankedreleases.September2022[c(1:10),]
names(top10releases.September2022) <- c("Release", "Radio", "TV", "Total")
top5releases.September2022 <-rankedreleases.September2022[c(1:5),]
names(top5releases.September2022) <- c("Release", "Radio", "TV", "Total")
library(DT)
datatable(top10releases.September2022, fillContainer=FALSE, options = list(scrollX="70px", scrollY="200px", pageLength = 5, dom = 'ftp'), rownames=FALSE)
```
### Hits by Region
```{r September 2022 Hits by Region, warning=FALSE, echo = FALSE, message=FALSE}
#compute hits by regions
month.hitsbyregion.September2022 <- hitsbyregion(tracking.data.September2022.subsetted)
month.hitsbyregion.September2022
```
# August 2022
```{r August 2022 Enrollment, eval=FALSE, echo = FALSE, warning=FALSE, message=FALSE}
rows <- c("Media Meteorologists","Market Penetration (%)", "Local Television Stations", "Spanish-language Meteorologists", "Journalists Receiving CMN")
values <- c(968, 92, 483, 55, 702)
enrollment <- as.data.frame(cbind(rows, values))
names(enrollment) <- c(" ", "Enrollment")
library("formattable")
formattable(enrollment)
```
### Hits by Program
```{r August 2022 Hits by Type and Program, echo = FALSE, warning=FALSE, message=FALSE}
tracking.data.August2022 <- tracking.data[which(tracking.data$month=="August" & tracking.data$year=="2022"),]
#expanded view
library("tidyr")
expanded.hits.draft.August2022 <- hitsbyprogram(tracking.data.August2022)
expanded.hits.August2022 <- expanded.hits.draft.August2022[,c("source", "Twitter",
"online.article", "radio", "tv")]
#restrict data to CC, CM, CMN sources
expanded.hits.CMprograms.August2022 <- subset(expanded.hits.August2022, source=="CC"|source=="CM"|source=="CMN")
#aggregate hits according to nest program model
CMN.August2022 <- expanded.hits.CMprograms.August2022[3,2:5]
CM.August2022 <- CMN.August2022 + expanded.hits.CMprograms.August2022[2,2:5]
CC.August2022 <- CM.August2022 + expanded.hits.CMprograms.August2022[1,2:5]
aggregated.hits.CMprograms.August2022 <- rbind(CC.August2022, CM.August2022, CMN.August2022)
rownames(aggregated.hits.CMprograms.August2022) <- c("CC", "CM", "CMN")
#change column names
names(aggregated.hits.CMprograms.August2022) <- c("Twitter",
"Online Articles", "Radio", "TV")
library("formattable")
aggregated.hits.formatted.August2022 <- formattable(aggregated.hits.CMprograms.August2022,
align=c("l", "c", "c", "c", "c"),
list(''))
aggregated.hits.formatted.August2022
```
### Comparison with Last Year
```{r August 2022 Last Year Comparison, echo = FALSE, warning=FALSE, message=FALSE}
tracking.data.August2022 <- tracking.data[which(tracking.data$month=="August" & tracking.data$year=="2022"),]
tracking.data.August2021 <- tracking.data[which(tracking.data$month=="August" & tracking.data$year=="2021"),]
previousYear(tracking.data.August2022, tracking.data.August2021)
```
### Top Releases
Most-used releases this month on TV and Radio (including content released before this month).
```{r August 2022 Top Releases, warning=FALSE, echo = FALSE, warning=FALSE, message=FALSE}
tracking.data.August2022.subsetted <- subset(tracking.data.August2022, source=="CC"|source=="CM"|source=="CMN")
rankedreleases.August2022 <- rankReleasesTVRadioOnly(tracking.data.August2022.subsetted)
top10releases.August2022 <-rankedreleases.August2022[c(1:10),]
names(top10releases.August2022) <- c("Release", "Radio", "TV", "Total")
top5releases.August2022 <-rankedreleases.August2022[c(1:5),]
names(top5releases.August2022) <- c("Release", "Radio", "TV", "Total")
library(DT)
datatable(top10releases.August2022, fillContainer=FALSE, options = list(scrollX="70px", scrollY="200px", pageLength = 5, dom = 'ftp'), rownames=FALSE)
```
### Hits by Region
```{r August 2022 Hits by Region, warning=FALSE, echo = FALSE, message=FALSE}
#compute hits by regions
month.hitsbyregion.August2022 <- hitsbyregion(tracking.data.August2022.subsetted)
month.hitsbyregion.August2022
```
# July 2022
```{r July 2022 Enrollment, eval=FALSE, echo = FALSE, warning=FALSE, message=FALSE}
rows <- c("Media Meteorologists","Market Penetration (%)", "Local Television Stations", "Spanish-language Meteorologists", "Journalists Receiving CMN")
values <- c(968, 92, 483, 55, 702)
enrollment <- as.data.frame(cbind(rows, values))
names(enrollment) <- c(" ", "Enrollment")
library("formattable")
formattable(enrollment)
```
### Hits by Program
```{r July 2022 Hits by Type and Program, echo = FALSE, warning=FALSE, message=FALSE}
tracking.data.July2022 <- tracking.data[which(tracking.data$month=="July" & tracking.data$year=="2022"),]
#expanded view
library("tidyr")
expanded.hits.draft.July2022 <- hitsbyprogram(tracking.data.July2022)
expanded.hits.July2022 <- expanded.hits.draft.July2022[,c("source", "Twitter",
"online.article", "radio", "tv")]
#restrict data to CC, CM, CMN sources
expanded.hits.CMprograms.July2022 <- subset(expanded.hits.July2022, source=="CC"|source=="CM"|source=="CMN")
#aggregate hits according to nest program model
CMN.July2022 <- expanded.hits.CMprograms.July2022[3,2:5]
CM.July2022 <- CMN.July2022 + expanded.hits.CMprograms.July2022[2,2:5]
CC.July2022 <- CM.July2022 + expanded.hits.CMprograms.July2022[1,2:5]
aggregated.hits.CMprograms.July2022 <- rbind(CC.July2022, CM.July2022, CMN.July2022)
rownames(aggregated.hits.CMprograms.July2022) <- c("CC", "CM", "CMN")
#change column names
names(aggregated.hits.CMprograms.July2022) <- c("Twitter",
"Online Articles", "Radio", "TV")
library("formattable")
aggregated.hits.formatted.July2022 <- formattable(aggregated.hits.CMprograms.July2022,
align=c("l", "c", "c", "c", "c"),
list(''))
aggregated.hits.formatted.July2022
```
### Comparison with Last Year
```{r July 2022 Last Year Comparison, echo = FALSE, warning=FALSE, message=FALSE}
tracking.data.July2022 <- tracking.data[which(tracking.data$month=="July" & tracking.data$year=="2022"),]
tracking.data.July2021 <- tracking.data[which(tracking.data$month=="July" & tracking.data$year=="2021"),]
previousYear(tracking.data.July2022, tracking.data.July2021)
```
### Top Releases
Most-used releases this month on TV and Radio (including content released before this month).
```{r July 2022 Top Releases, warning=FALSE, echo = FALSE, warning=FALSE, message=FALSE}
tracking.data.July2022.subsetted <- subset(tracking.data.July2022, source=="CC"|source=="CM"|source=="CMN")
rankedreleases.July2022 <- rankReleasesTVRadioOnly(tracking.data.July2022.subsetted)
top10releases.July2022 <-rankedreleases.July2022[c(1:10),]
names(top10releases.July2022) <- c("Release", "Radio", "TV", "Total")
top5releases.July2022 <-rankedreleases.July2022[c(1:5),]
names(top5releases.July2022) <- c("Release", "Radio", "TV", "Total")
library(DT)
datatable(top10releases.July2022, fillContainer=FALSE, options = list(scrollX="70px", scrollY="200px", pageLength = 5, dom = 'ftp'), rownames=FALSE)
```
### Hits by Region
```{r July 2022 Hits by Region, warning=FALSE, echo = FALSE, message=FALSE}
#compute hits by regions
month.hitsbyregion.July2022 <- hitsbyregion(tracking.data.July2022.subsetted)
month.hitsbyregion.July2022
```
# June 2022
```{r June 2022 Enrollment, eval=FALSE, echo = FALSE, warning=FALSE, message=FALSE}
rows <- c("Media Meteorologists","Market Penetration (%)", "Local Television Stations", "Spanish-language Meteorologists", "Journalists Receiving CMN")
values <- c(968, 92, 483, 55, 702)
enrollment <- as.data.frame(cbind(rows, values))
names(enrollment) <- c(" ", "Enrollment")
library("formattable")
formattable(enrollment)
```
### Hits by Program
```{r June 2022 Hits by Type and Program, echo = FALSE, warning=FALSE, message=FALSE}
tracking.data.June2022 <- tracking.data[which(tracking.data$month=="June" & tracking.data$year=="2022"),]
#expanded view
library("tidyr")
expanded.hits.draft.June2022 <- hitsbyprogram(tracking.data.June2022)
expanded.hits.June2022 <- expanded.hits.draft.June2022[,c("source", "Twitter",
"online.article", "radio", "tv")]
#restrict data to CC, CM, CMN sources
expanded.hits.CMprograms.June2022 <- subset(expanded.hits.June2022, source=="CC"|source=="CM"|source=="CMN")
#aggregate hits according to nest program model
CMN.June2022 <- expanded.hits.CMprograms.June2022[3,2:5]
CM.June2022 <- CMN.June2022 + expanded.hits.CMprograms.June2022[2,2:5]
CC.June2022 <- CM.June2022 + expanded.hits.CMprograms.June2022[1,2:5]
aggregated.hits.CMprograms.June2022 <- rbind(CC.June2022, CM.June2022, CMN.June2022)
rownames(aggregated.hits.CMprograms.June2022) <- c("CC", "CM", "CMN")
#change column names
names(aggregated.hits.CMprograms.June2022) <- c("Twitter",
"Online Articles", "Radio", "TV")
library("formattable")
aggregated.hits.formatted.June2022 <- formattable(aggregated.hits.CMprograms.June2022,
align=c("l", "c", "c", "c", "c"),
list(''))
aggregated.hits.formatted.June2022
```
### Comparison with Last Year
```{r June 2022 Last Year Comparison, echo = FALSE, warning=FALSE, message=FALSE}
tracking.data.June2022 <- tracking.data[which(tracking.data$month=="June" & tracking.data$year=="2022"),]
tracking.data.June2021 <- tracking.data[which(tracking.data$month=="June" & tracking.data$year=="2021"),]
previousYear(tracking.data.June2022, tracking.data.June2021)
```
### Top Releases
Most-used releases this month on TV and Radio (including content released before this month).
```{r June 2022 Top Releases, warning=FALSE, echo = FALSE, warning=FALSE, message=FALSE}
tracking.data.June2022.subsetted <- subset(tracking.data.June2022, source=="CC"|source=="CM"|source=="CMN")
rankedreleases.June2022 <- rankReleasesTVRadioOnly(tracking.data.June2022.subsetted)
top10releases.June2022 <-rankedreleases.June2022[c(1:10),]
names(top10releases.June2022) <- c("Release", "Radio", "TV", "Total")
top5releases.June2022 <-rankedreleases.June2022[c(1:5),]
names(top5releases.June2022) <- c("Release", "Radio", "TV", "Total")
library(DT)
datatable(top10releases.June2022, fillContainer=FALSE, options = list(scrollX="70px", scrollY="200px", pageLength = 5, dom = 'ftp'), rownames=FALSE)
```
### Hits by Region
```{r June 2022 Hits by Region, warning=FALSE, echo = FALSE, message=FALSE}
#compute hits by regions
month.hitsbyregion.June2022 <- hitsbyregion(tracking.data.June2022.subsetted)
month.hitsbyregion.June2022
```
# May 2022
```{r May 2022 Enrollment, eval=FALSE, echo = FALSE, warning=FALSE, message=FALSE}
rows <- c("Media Meteorologists","Market Penetration (%)", "Local Television Stations", "Spanish-language Meteorologists", "Journalists Receiving CMN")
values <- c(968, 92, 483, 55, 702)
enrollment <- as.data.frame(cbind(rows, values))
names(enrollment) <- c(" ", "Enrollment")
library("formattable")
formattable(enrollment)
```
### Hits by Program
```{r May 2022 Hits by Type and Program, echo = FALSE, warning=FALSE, message=FALSE}
tracking.data.May2022 <- tracking.data[which(tracking.data$month=="May" & tracking.data$year=="2022"),]
#expanded view
library("tidyr")
expanded.hits.draft.May2022 <- hitsbyprogram(tracking.data.May2022)
expanded.hits.May2022 <- expanded.hits.draft.May2022[,c("source", "Twitter",
"online.article", "radio", "tv")]
#restrict data to CC, CM, CMN sources
expanded.hits.CMprograms.May2022 <- subset(expanded.hits.May2022, source=="CC"|source=="CM"|source=="CMN")
#aggregate hits according to nest program model
CMN.May2022 <- expanded.hits.CMprograms.May2022[3,2:5]
CM.May2022 <- CMN.May2022 + expanded.hits.CMprograms.May2022[2,2:5]
CC.May2022 <- CM.May2022 + expanded.hits.CMprograms.May2022[1,2:5]
aggregated.hits.CMprograms.May2022 <- rbind(CC.May2022, CM.May2022, CMN.May2022)
rownames(aggregated.hits.CMprograms.May2022) <- c("CC", "CM", "CMN")
#change column names
names(aggregated.hits.CMprograms.May2022) <- c("Twitter",
"Online Articles", "Radio", "TV")
library("formattable")
aggregated.hits.formatted.May2022 <- formattable(aggregated.hits.CMprograms.May2022,
align=c("l", "c", "c", "c", "c"),
list(''))
aggregated.hits.formatted.May2022
```
### Comparison with Last Year
```{r May 2022 Last Year Comparison, echo = FALSE, warning=FALSE, message=FALSE}
tracking.data.May2022 <- tracking.data[which(tracking.data$month=="May" & tracking.data$year=="2022"),]
tracking.data.May2021 <- tracking.data[which(tracking.data$month=="May" & tracking.data$year=="2021"),]
previousYear(tracking.data.May2022, tracking.data.May2021)
```
### Top Releases
Most-used releases this month on TV and Radio (including content released before this month).
```{r May 2022 Top Releases, warning=FALSE, echo = FALSE, warning=FALSE, message=FALSE}
tracking.data.May2022.subsetted <- subset(tracking.data.May2022, source=="CC"|source=="CM"|source=="CMN")
rankedreleases.May2022 <- rankReleasesTVRadioOnly(tracking.data.May2022.subsetted)
top10releases.May2022 <-rankedreleases.May2022[c(1:10),]
names(top10releases.May2022) <- c("Release", "Radio", "TV", "Total")
top5releases.May2022 <-rankedreleases.May2022[c(1:5),]
names(top5releases.May2022) <- c("Release", "Radio", "TV", "Total")
library(DT)
datatable(top10releases.May2022, fillContainer=FALSE, options = list(scrollX="70px", scrollY="200px", pageLength = 5, dom = 'ftp'), rownames=FALSE)
```
### Hits by Region
```{r May 2022 Hits by Region, warning=FALSE, echo = FALSE, message=FALSE}
#compute hits by regions
month.hitsbyregion.May2022 <- hitsbyregion(tracking.data.May2022.subsetted)
month.hitsbyregion.May2022
```
# April 2022
```{r April 2022 Enrollment, eval=FALSE, echo = FALSE, warning=FALSE, message=FALSE}
rows <- c("Media Meteorologists","Market Penetration (%)", "Local Television Stations", "Spanish-language Meteorologists", "Journalists Receiving CMN")
values <- c(968, 92, 483, 55, 702)
enrollment <- as.data.frame(cbind(rows, values))
names(enrollment) <- c(" ", "Enrollment")
library("formattable")
formattable(enrollment)
```
### Hits by Program
```{r April 2022 Hits by Type and Program, echo = FALSE, warning=FALSE, message=FALSE}
tracking.data.April2022 <- tracking.data[which(tracking.data$month=="April" & tracking.data$year=="2022"),]
#expanded view
library("tidyr")
expanded.hits.draft.April2022 <- hitsbyprogram(tracking.data.April2022)
expanded.hits.April2022 <- expanded.hits.draft.April2022[,c("source", "Twitter",
"online.article", "radio", "tv")]
#restrict data to CC, CM, CMN sources
expanded.hits.CMprograms.April2022 <- subset(expanded.hits.April2022, source=="CC"|source=="CM"|source=="CMN")
#aggregate hits according to nest program model
CMN.April2022 <- expanded.hits.CMprograms.April2022[3,2:5]
CM.April2022 <- CMN.April2022 + expanded.hits.CMprograms.April2022[2,2:5]
CC.April2022 <- CM.April2022 + expanded.hits.CMprograms.April2022[1,2:5]
aggregated.hits.CMprograms.April2022 <- rbind(CC.April2022, CM.April2022, CMN.April2022)
rownames(aggregated.hits.CMprograms.April2022) <- c("CC", "CM", "CMN")
#change column names
names(aggregated.hits.CMprograms.April2022) <- c("Twitter",
"Online Articles", "Radio", "TV")
library("formattable")
aggregated.hits.formatted.April2022 <- formattable(aggregated.hits.CMprograms.April2022,
align=c("l", "c", "c", "c", "c"),
list(''))
aggregated.hits.formatted.April2022
```
### Comparison with Last Year
```{r April 2022 Last Year Comparison, echo = FALSE, warning=FALSE, message=FALSE}
tracking.data.April2022 <- tracking.data[which(tracking.data$month=="April" & tracking.data$year=="2022"),]
tracking.data.April2021 <- tracking.data[which(tracking.data$month=="April" & tracking.data$year=="2021"),]
previousYear(tracking.data.April2022, tracking.data.April2021)
```
### Top Releases
Most-used releases this month on TV and Radio (including content released before this month).
```{r April 2022 Top Releases, warning=FALSE, echo = FALSE, warning=FALSE, message=FALSE}
tracking.data.April2022.subsetted <- subset(tracking.data.April2022, source=="CC"|source=="CM"|source=="CMN")
rankedreleases.April2022 <- rankReleasesTVRadioOnly(tracking.data.April2022.subsetted)
top10releases.April2022 <-rankedreleases.April2022[c(1:10),]
names(top10releases.April2022) <- c("Release", "Radio", "TV", "Total")
top5releases.April2022 <-rankedreleases.April2022[c(1:5),]
names(top5releases.April2022) <- c("Release", "Radio", "TV", "Total")
library(DT)
datatable(top10releases.April2022, fillContainer=FALSE, options = list(scrollX="70px", scrollY="200px", pageLength = 5, dom = 'ftp'), rownames=FALSE)
```
### Hits by Region
```{r April 2022 Hits by Region, warning=FALSE, echo = FALSE, message=FALSE}
#compute hits by regions
month.hitsbyregion.April2022 <- hitsbyregion(tracking.data.April2022.subsetted)
month.hitsbyregion.April2022
```
# March 2022
```{r March 2022 Enrollment, eval=FALSE, echo = FALSE, warning=FALSE, message=FALSE}
rows <- c("Media Meteorologists","Market Penetration (%)", "Local Television Stations", "Spanish-language Meteorologists", "Journalists Receiving CMN")
values <- c(968, 92, 483, 55, 702)
enrollment <- as.data.frame(cbind(rows, values))
names(enrollment) <- c(" ", "Enrollment")
library("formattable")
formattable(enrollment)
```
### Hits by Program
```{r March 2022 Hits by Type and Program, echo = FALSE, warning=FALSE, message=FALSE}
tracking.data.March2022 <- tracking.data[which(tracking.data$month=="March" & tracking.data$year=="2022"),]
#expanded view
library("tidyr")
expanded.hits.draft.March2022 <- hitsbyprogram(tracking.data.March2022)
expanded.hits.March2022 <- expanded.hits.draft.March2022[,c("source", "Twitter",
"online.article", "radio", "tv")]
#restrict data to CC, CM, CMN sources
expanded.hits.CMprograms.March2022 <- subset(expanded.hits.March2022, source=="CC"|source=="CM"|source=="CMN")
#aggregate hits according to nest program model
CMN.March2022 <- expanded.hits.CMprograms.March2022[3,2:5]
CM.March2022 <- CMN.March2022 + expanded.hits.CMprograms.March2022[2,2:5]
CC.March2022 <- CM.March2022 + expanded.hits.CMprograms.March2022[1,2:5]
aggregated.hits.CMprograms.March2022 <- rbind(CC.March2022, CM.March2022, CMN.March2022)
rownames(aggregated.hits.CMprograms.March2022) <- c("CC", "CM", "CMN")
#change column names
names(aggregated.hits.CMprograms.March2022) <- c("Twitter",
"Online Articles", "Radio", "TV")
library("formattable")
aggregated.hits.formatted.March2022 <- formattable(aggregated.hits.CMprograms.March2022,
align=c("l", "c", "c", "c", "c"),
list(''))
aggregated.hits.formatted.March2022
```
### Comparison with Last Year
```{r March 2022 Last Year Comparison, echo = FALSE, warning=FALSE, message=FALSE}
tracking.data.March2022 <- tracking.data[which(tracking.data$month=="March" & tracking.data$year=="2022"),]
tracking.data.March2021 <- tracking.data[which(tracking.data$month=="March" & tracking.data$year=="2021"),]
previousYear(tracking.data.March2022, tracking.data.March2021)
```
### Top Releases
Most-used releases this month on TV and Radio (including content released before this month).
```{r March 2022 Top Releases, warning=FALSE, echo = FALSE, warning=FALSE, message=FALSE}
tracking.data.March2022.subsetted <- subset(tracking.data.March2022, source=="CC"|source=="CM"|source=="CMN")
rankedreleases.March2022 <- rankReleasesTVRadioOnly(tracking.data.March2022.subsetted)
top10releases.March2022 <-rankedreleases.March2022[c(1:10),]
names(top10releases.March2022) <- c("Release", "Radio", "TV", "Total")
top5releases.March2022 <-rankedreleases.March2022[c(1:5),]
names(top5releases.March2022) <- c("Release", "Radio", "TV", "Total")
library(DT)
datatable(top10releases.March2022, fillContainer=FALSE, options = list(scrollX="70px", scrollY="200px", pageLength = 5, dom = 'ftp'), rownames=FALSE)
```
### Hits by Region
```{r March 2022 Hits by Region, warning=FALSE, echo = FALSE, message=FALSE}
#compute hits by regions
month.hitsbyregion.March2022 <- hitsbyregion(tracking.data.March2022.subsetted)
month.hitsbyregion.March2022
```
***
# February 2022
```{r February 2022 Enrollment, eval=FALSE, echo = FALSE, warning=FALSE, message=FALSE}
rows <- c("Media Meteorologists","Market Penetration (%)", "Local Television Stations", "Spanish-language Meteorologists", "Journalists Receiving CMN")
values <- c(968, 92, 483, 55, 702)
enrollment <- as.data.frame(cbind(rows, values))
names(enrollment) <- c(" ", "Enrollment")
library("formattable")
formattable(enrollment)
```
### Hits by Program
```{r February 2022 Hits by Type and Program, echo = FALSE, warning=FALSE, message=FALSE}
tracking.data.February2022 <- tracking.data[which(tracking.data$month=="February" & tracking.data$year=="2022"),]
#expanded view
library("tidyr")
expanded.hits.draft.February2022 <- hitsbyprogram(tracking.data.February2022)
expanded.hits.February2022 <- expanded.hits.draft.February2022[,c("source", "Twitter",
"online.article", "radio", "tv")]
#restrict data to CC, CM, CMN sources
expanded.hits.CMprograms.February2022 <- subset(expanded.hits.February2022, source=="CC"|source=="CM"|source=="CMN")
#aggregate hits according to nest program model
CMN.February2022 <- expanded.hits.CMprograms.February2022[3,2:5]
CM.February2022 <- CMN.February2022 + expanded.hits.CMprograms.February2022[2,2:5]
CC.February2022 <- CM.February2022 + expanded.hits.CMprograms.February2022[1,2:5]
aggregated.hits.CMprograms.February2022 <- rbind(CC.February2022, CM.February2022, CMN.February2022)
rownames(aggregated.hits.CMprograms.February2022) <- c("CC", "CM", "CMN")
#change column names
names(aggregated.hits.CMprograms.February2022) <- c("Twitter",
"Online Articles", "Radio", "TV")
library("formattable")
aggregated.hits.formatted.February2022 <- formattable(aggregated.hits.CMprograms.February2022,
align=c("l", "c", "c", "c", "c"),
list(''))
aggregated.hits.formatted.February2022
```
### Comparison with Last Year
```{r February 2022 Last Year Comparison, echo = FALSE, warning=FALSE, message=FALSE}
tracking.data.February2022 <- tracking.data[which(tracking.data$month=="February" & tracking.data$year=="2022"),]
tracking.data.February2021 <- tracking.data[which(tracking.data$month=="February" & tracking.data$year=="2021"),]
previousYear(tracking.data.February2022, tracking.data.February2021)
```
### Top Releases
Most-used releases this month on TV and Radio (including content released before this month).
```{r February 2022 Top Releases, warning=FALSE, echo = FALSE, warning=FALSE, message=FALSE}
tracking.data.February2022.subsetted <- subset(tracking.data.February2022, source=="CC"|source=="CM"|source=="CMN")
rankedreleases.February2022 <- rankReleasesTVRadioOnly(tracking.data.February2022.subsetted)
top10releases.February2022 <-rankedreleases.February2022[c(1:10),]
names(top10releases.February2022) <- c("Release", "Radio", "TV", "Total")
top5releases.February2022 <-rankedreleases.February2022[c(1:5),]
names(top5releases.February2022) <- c("Release", "Radio", "TV", "Total")
library(DT)
datatable(top10releases.February2022, fillContainer=FALSE, options = list(scrollX="70px", scrollY="200px", pageLength = 5, dom = 'ftp'), rownames=FALSE)
```
### Hits by Region
```{r February 2022 Hits by Region, warning=FALSE, echo = FALSE, message=FALSE}
#compute hits by regions
month.hitsbyregion.February2022 <- hitsbyregion(tracking.data.February2022.subsetted)
month.hitsbyregion.February2022
```
***
# January 2022
```{r January 2022 Enrollment, eval=FALSE, echo = FALSE, warning=FALSE, message=FALSE}
rows <- c("Media Meteorologists","Market Penetration (%)", "Local Television Stations", "Spanish-language Meteorologists", "Journalists Receiving CMN")
values <- c(968, 92, 483, 55, 702)
enrollment <- as.data.frame(cbind(rows, values))
names(enrollment) <- c(" ", "Enrollment")
library("formattable")
formattable(enrollment)
```
### Hits by Program
```{r January 2022 Hits by Type and Program, echo = FALSE, warning=FALSE, message=FALSE}
tracking.data.January2022 <- tracking.data[which(tracking.data$month=="January" & tracking.data$year=="2022"),]
#expanded view
library("tidyr")
expanded.hits.draft.January2022 <- hitsbyprogram(tracking.data.January2022)
expanded.hits.January2022 <- expanded.hits.draft.January2022[,c("source", "Twitter",
"online.article", "radio", "tv")]
#restrict data to CC, CM, CMN sources
expanded.hits.CMprograms.January2022 <- subset(expanded.hits.January2022, source=="CC"|source=="CM"|source=="CMN")
#aggregate hits according to nest program model
CMN.January2022 <- expanded.hits.CMprograms.January2022[3,2:5]
CM.January2022 <- CMN.January2022 + expanded.hits.CMprograms.January2022[2,2:5]
CC.January2022 <- CM.January2022 + expanded.hits.CMprograms.January2022[1,2:5]
aggregated.hits.CMprograms.January2022 <- rbind(CC.January2022, CM.January2022, CMN.January2022)
rownames(aggregated.hits.CMprograms.January2022) <- c("CC", "CM", "CMN")
#change column names
names(aggregated.hits.CMprograms.January2022) <- c("Twitter",
"Online Articles", "Radio", "TV")
library("formattable")
aggregated.hits.formatted.January2022 <- formattable(aggregated.hits.CMprograms.January2022,
align=c("l", "c", "c", "c", "c"),
list(''))
aggregated.hits.formatted.January2022
```
### Comparison with Last Year
```{r January 2022 Last Year Comparison, echo = FALSE, warning=FALSE, message=FALSE}
tracking.data.January2022 <- tracking.data[which(tracking.data$month=="January" & tracking.data$year=="2022"),]
tracking.data.January2021 <- tracking.data[which(tracking.data$month=="January" & tracking.data$year=="2021"),]
previousYear(tracking.data.January2022, tracking.data.January2021)
```
### Top Releases
Most-used releases this month on TV and Radio (including content released before this month).
```{r January 2022 Top Releases, warning=FALSE, echo = FALSE, warning=FALSE, message=FALSE}
tracking.data.January2022.subsetted <- subset(tracking.data.January2022, source=="CC"|source=="CM"|source=="CMN")
rankedreleases.January2022 <- rankReleasesTVRadioOnly(tracking.data.January2022.subsetted)
top10releases.January2022 <-rankedreleases.January2022[c(1:10),]
names(top10releases.January2022) <- c("Release", "Radio", "TV", "Total")
top5releases.January2022 <-rankedreleases.January2022[c(1:5),]
names(top5releases.January2022) <- c("Release", "Radio", "TV", "Total")
library(DT)
datatable(top10releases.January2022, fillContainer=FALSE, options = list(scrollX="70px", scrollY="200px", pageLength = 5, dom = 'ftp'), rownames=FALSE)
```
### Hits by Region
```{r January 2022 Hits by Region, warning=FALSE, echo = FALSE, message=FALSE}
#compute hits by regions
month.hitsbyregion.January2022 <- hitsbyregion(tracking.data.January2022.subsetted)
month.hitsbyregion.January2022
```
***
# December 2021
```{r December 2021 Enrollment, eval=FALSE, echo = FALSE, warning=FALSE, message=FALSE}
rows <- c("Media Meteorologists","Market Penetration (%)", "Local Television Stations", "Spanish-language Meteorologists", "Journalists Receiving CMN")
values <- c(968, 92, 483, 55, 702)
enrollment <- as.data.frame(cbind(rows, values))
names(enrollment) <- c(" ", "Enrollment")
library("formattable")
formattable(enrollment)
```
### Hits by Program
```{r December 2021 Hits by Type and Program, echo = FALSE, warning=FALSE, message=FALSE}
tracking.data.December2021 <- tracking.data[which(tracking.data$month=="December" & tracking.data$year=="2021"),]
#expanded view
library("tidyr")
expanded.hits.draft.December2021 <- hitsbyprogram(tracking.data.December2021)
expanded.hits.December2021 <- expanded.hits.draft.December2021[,c("source", "Twitter",
"online.article", "radio", "tv")]
#restrict data to CC, CM, CMN sources
expanded.hits.CMprograms.December2021 <- subset(expanded.hits.December2021, source=="CC"|source=="CM"|source=="CMN")
#aggregate hits according to nest program model
CMN.December2021 <- expanded.hits.CMprograms.December2021[3,2:5]
CM.December2021 <- CMN.December2021 + expanded.hits.CMprograms.December2021[2,2:5]
CC.December2021 <- CM.December2021 + expanded.hits.CMprograms.December2021[1,2:5]
aggregated.hits.CMprograms.December2021 <- rbind(CC.December2021, CM.December2021, CMN.December2021)
rownames(aggregated.hits.CMprograms.December2021) <- c("CC", "CM", "CMN")
#change column names
names(aggregated.hits.CMprograms.December2021) <- c("Twitter",
"Online Articles", "Radio", "TV")
library("formattable")
aggregated.hits.formatted.December2021 <- formattable(aggregated.hits.CMprograms.December2021,
align=c("l", "c", "c", "c", "c"),
list(''))
aggregated.hits.formatted.December2021
```
### Comparison with Last Year
```{r December 2021 Last Year Comparison, echo = FALSE, eval = FALSE, warning=FALSE, message=FALSE}
tracking.data.December2021 <- tracking.data[which(tracking.data$month=="December" & tracking.data$year=="2021"),]
tracking.data.December2020 <- tracking.data[which(tracking.data$month=="December" & tracking.data$year=="2020"),]
previousYear(tracking.data.December2021, tracking.data.December2020)
```
### Top Releases
Most-used releases this month on TV and Radio (including content released before this month).
```{r December 2021 Top Releases, warning=FALSE, echo = FALSE, warning=FALSE, message=FALSE}
tracking.data.December2021.subsetted <- subset(tracking.data.December2021, source=="CC"|source=="CM"|source=="CMN")
rankedreleases.December2021 <- rankReleasesTVRadioOnly(tracking.data.December2021.subsetted)
top10releases.December2021 <-rankedreleases.December2021[c(1:10),]
names(top10releases.December2021) <- c("Release", "Radio", "TV", "Total")
top5releases.December2021 <-rankedreleases.December2021[c(1:5),]
names(top5releases.December2021) <- c("Release", "Radio", "TV", "Total")
library(DT)
datatable(top10releases.December2021, fillContainer=FALSE, options = list(scrollX="70px", scrollY="200px", pageLength = 5, dom = 'ftp'), rownames=FALSE)
```
### Hits by Region
```{r December 2021 Hits by Region, warning=FALSE, echo = FALSE, message=FALSE}
#compute hits by regions
month.hitsbyregion.December2021 <- hitsbyregion(tracking.data.December2021.subsetted)
month.hitsbyregion.December2021
```
***
# November 2021
```{r November 2021 Enrollment, eval=FALSE, echo = FALSE, warning=FALSE, message=FALSE}
rows <- c("Media Meteorologists","Market Penetration (%)", "Local Television Stations", "Spanish-language Meteorologists", "Journalists Receiving CMN")
values <- c(968, 92, 483, 55, 702)
enrollment <- as.data.frame(cbind(rows, values))
names(enrollment) <- c(" ", "Enrollment")
library("formattable")
formattable(enrollment)
```
### Hits by Program
```{r November 2021 Hits by Type and Program, echo = FALSE, warning=FALSE, message=FALSE}
tracking.data.November2021 <- tracking.data[which(tracking.data$month=="November" & tracking.data$year=="2021"),]
#expanded view
library("tidyr")
expanded.hits.draft.November2021 <- hitsbyprogram(tracking.data.November2021)
expanded.hits.November2021 <- expanded.hits.draft.November2021[,c("source", "Twitter",
"online.article", "radio", "tv")]
#restrict data to CC, CM, CMN sources
expanded.hits.CMprograms.November2021 <- subset(expanded.hits.November2021, source=="CC"|source=="CM"|source=="CMN")
#aggregate hits according to nest program model
CMN.November2021 <- expanded.hits.CMprograms.November2021[3,2:5]
CM.November2021 <- CMN.November2021 + expanded.hits.CMprograms.November2021[2,2:5]
CC.November2021 <- CM.November2021 + expanded.hits.CMprograms.November2021[1,2:5]
aggregated.hits.CMprograms.November2021 <- rbind(CC.November2021, CM.November2021, CMN.November2021)
rownames(aggregated.hits.CMprograms.November2021) <- c("CC", "CM", "CMN")
#change column names
names(aggregated.hits.CMprograms.November2021) <- c("Twitter",
"Online Articles", "Radio", "TV")