Vasundhara Sharma 1/22/2022
# reading data files
df_broadband_penetration <- read.csv("data_files/broadband-penetration-by-country.csv", na.string = "")
df_mobile_subscription <- read.csv("data_files/mobile-cellular-subscriptions-per-100-people.csv", na.string = "")
df_internet_users <- read.csv("data_files/number-of-internet-users-by-country.csv")
df_share_indviduals_using_internet <- read.csv("data_files/share-of-individuals-using-the-internet.csv")
df_continents <- read.csv("data_files/continents2.csv")
df_political_indexes <- read.csv("data_files/country-index.csv")str(df_broadband_penetration)## 'data.frame': 4175 obs. of 4 variables:
## $ Entity : chr "Afghanistan" "Afghanistan" "Afghanistan" "Afghanistan" ...
## $ Code : chr "AFG" "AFG" "AFG" "AFG" ...
## $ Year : int 2004 2005 2006 2007 2008 2009 2010 2012 2013 2014 ...
## $ Fixed.broadband.subscriptions..per.100.people.: num 0.000809 0.000858 0.001892 0.001845 0.001804 ...
knitr::kable(df_broadband_penetration %>%
select(everything()) %>%
summarise_all(list(~sum(is.na(.)))),
caption = 'Number of NULL values in each column')| Entity | Code | Year | Fixed.broadband.subscriptions..per.100.people. |
|---|---|---|---|
| 0 | 800 | 0 | 0 |
str(df_mobile_subscription)## 'data.frame': 11895 obs. of 4 variables:
## $ Entity : chr "Afghanistan" "Afghanistan" "Afghanistan" "Afghanistan" ...
## $ Code : chr "AFG" "AFG" "AFG" "AFG" ...
## $ Year : int 1960 1965 1970 1975 1976 1977 1978 1979 1980 1981 ...
## $ Mobile.cellular.subscriptions..per.100.people.: num 0 0 0 0 0 0 0 0 0 0 ...
knitr::kable(df_mobile_subscription %>%
select(everything()) %>%
summarise_all(list(~sum(is.na(.)))),
caption = 'Number of NULL values in each column')| Entity | Code | Year | Mobile.cellular.subscriptions..per.100.people. |
|---|---|---|---|
| 0 | 2250 | 0 | 0 |
str(df_internet_users)## 'data.frame': 4536 obs. of 4 variables:
## $ Entity : chr "Afghanistan" "Afghanistan" "Afghanistan" "Afghanistan" ...
## $ Code : chr "AFG" "AFG" "AFG" "AFG" ...
## $ Year : int 1990 2001 2002 2003 2004 2005 2006 2007 2008 2009 ...
## $ Number.of.internet.users..OWID.based.on.WB...UN.: num 0 990 1003 20272 25520 ...
knitr::kable(df_internet_users %>%
select(everything()) %>%
summarise_all(list(~sum(is.na(.)))),
caption = 'Number of NULL values in each column')| Entity | Code | Year | Number.of.internet.users..OWID.based.on.WB…UN. |
|---|---|---|---|
| 0 | 0 | 0 | 0 |
str(df_share_indviduals_using_internet)## 'data.frame': 7119 obs. of 4 variables:
## $ Entity : chr "Afghanistan" "Afghanistan" "Afghanistan" "Afghanistan" ...
## $ Code : chr "AFG" "AFG" "AFG" "AFG" ...
## $ Year : int 1990 1991 1992 1993 1994 1995 2001 2002 2003 2004 ...
## $ Individuals.using.the.Internet....of.population.: num 0 0 0 0 0 ...
knitr::kable(df_share_indviduals_using_internet %>%
select(everything()) %>%
summarise_all(list(~sum(is.na(.)))),
caption = 'Number of NULL values in each column')| Entity | Code | Year | Individuals.using.the.Internet….of.population. |
|---|---|---|---|
| 0 | 0 | 0 | 0 |
str(df_continents)## 'data.frame': 249 obs. of 11 variables:
## $ ï..name : chr "Afghanistan" "Ã…land Islands" "Albania" "Algeria" ...
## $ alpha.2 : chr "AF" "AX" "AL" "DZ" ...
## $ alpha.3 : chr "AFG" "ALA" "ALB" "DZA" ...
## $ country.code : int 4 248 8 12 16 20 24 660 10 28 ...
## $ iso_3166.2 : chr "ISO 3166-2:AF" "ISO 3166-2:AX" "ISO 3166-2:AL" "ISO 3166-2:DZ" ...
## $ region : chr "Asia" "Europe" "Europe" "Africa" ...
## $ sub.region : chr "Southern Asia" "Northern Europe" "Southern Europe" "Northern Africa" ...
## $ intermediate.region : chr "" "" "" "" ...
## $ region.code : int 142 150 150 2 9 150 2 19 NA 19 ...
## $ sub.region.code : int 34 154 39 15 61 39 202 419 NA 419 ...
## $ intermediate.region.code: int NA NA NA NA NA NA 17 29 NA 29 ...
knitr::kable(df_continents %>%
select(everything()) %>%
summarise_all(list(~sum(is.na(.)))),
caption = 'Number of NULL values in each column')| ï..name | alpha.2 | alpha.3 | country.code | iso\_3166.2 | region | sub.region | intermediate.region | region.code | sub.region.code | intermediate.region.code |
|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 142 |
str(df_political_indexes)## 'data.frame': 2501 obs. of 10 variables:
## $ geo : chr "afg" "afg" "afg" "afg" ...
## $ name : chr "Afghanistan" "Afghanistan" "Afghanistan" "Afghanistan" ...
## $ year : int 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 ...
## $ Democracy.Index : num 30.6 30.4 30.2 27.5 24.8 24.8 24.8 24.8 27.7 27.7 ...
## $ Electoral.process.and.pluralism: num 61.7 56.7 51.7 38.4 25 ...
## $ Functioning.of.government : num 0 3.95 7.9 7.9 7.9 7.9 7.9 7.9 11.4 11.4 ...
## $ Political.participation : num 22.2 22.2 22.2 25 27.8 27.8 27.8 27.8 38.9 38.9 ...
## $ Political.culture : num 25 25 25 25 25 25 25 25 25 25 ...
## $ Civil.liberties : num 44.1 44.1 44.1 41.1 38.2 ...
## $ Change.in.democracy.index : num NA -0.2 -0.2 -2.7 -2.7 0 0 0 2.9 0 ...
knitr::kable(df_political_indexes %>%
select(everything()) %>%
summarise_all(list(~sum(is.na(.)))),
caption = 'Number of NULL values in each column')| geo | name | year | Democracy.Index | Electoral.process.and.pluralism | Functioning.of.government | Political.participation | Political.culture | Civil.liberties | Change.in.democracy.index |
|---|---|---|---|---|---|---|---|---|---|
| 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 320 |
# renaming column names
colnames(df_broadband_penetration) <- c("Country", "Code", "Year", "Fixed_Broadband_Subscription_Per_100_People")
colnames(df_mobile_subscription) <- c("Country", "Code", "Year", "Mobile_Cellular_Subscription_Per_100_People")
colnames(df_internet_users) <- c("Country", "Code", "Year", "Number_Of_Internet_Users")
colnames(df_share_indviduals_using_internet) <- c("Country", "Code", "Year", "Percent_Of_People_Using_Internet")
colnames(df_political_indexes) <- c("Code", "Country", "Year", "Democracy_Index", "Electoral_Process_And_Pluralism", "Functioning_Of_Government", "Political_Participation", "Political_Culture", "Civil_Libterties", "Change_In_Democracy_Index")
colnames(df_continents) <- c("Country", "Alpha-2", "Alpha-3", "Country-code", "ISO_3166-2", "Region", "Sub-region", "Intermediate-region", "Region-code", "Sub-region-code", "Intermediate-region-code")
# fixing data
df_political_indexes$Democracy_Index = (as.numeric(df_political_indexes$Democracy_Index) / 10)knitr::kable(df_mobile_subscription %>%
filter(Code != "") %>%
filter(Year == 2019) %>%
select(Country, Code, Mobile_Cellular_Subscription_Per_100_People)%>%
arrange(desc(Mobile_Cellular_Subscription_Per_100_People)) %>% top_n(5),
caption = "Countries With Maximum Mobile Subscription in 2019")## Selecting by Mobile_Cellular_Subscription_Per_100_People
| Country | Code | Mobile\_Cellular\_Subscription\_Per\_100\_People |
|---|---|---|
| Hong Kong | HKG | 288.5327 |
| United Arab Emirates | ARE | 200.6321 |
| Seychelles | SYC | 198.1522 |
| Thailand | THA | 186.1586 |
| Montenegro | MNE | 183.2837 |
knitr::kable(df_broadband_penetration %>%
filter(Code != "") %>%
filter(Year == 2019) %>%
select(Country, Code, Fixed_Broadband_Subscription_Per_100_People)%>%
arrange(desc(Fixed_Broadband_Subscription_Per_100_People)) %>% top_n(5),
caption = "Countries With Maximum Fixed Broadband Subscription in 2019")## Selecting by Fixed_Broadband_Subscription_Per_100_People
| Country | Code | Fixed\_Broadband\_Subscription\_Per\_100\_People |
|---|---|---|
| Gibraltar | GIB | 57.85288 |
| Monaco | MCO | 52.55107 |
| Andorra | AND | 47.13126 |
| Switzerland | CHE | 46.90110 |
| Malta | MLT | 45.98680 |
knitr::kable(df_internet_users %>%
filter(Code != "") %>%
filter(Year == 2016) %>%
select(Country, Code, Number_Of_Internet_Users)%>%
arrange(desc(Number_Of_Internet_Users)) %>% top_n(5),
caption = "Countries With Maximum Internet users in 2016")## Selecting by Number_Of_Internet_Users
| Country | Code | Number\_Of\_Internet\_Users |
|---|---|---|
| World | OWID\_WRL | 3419398061 |
| China | CHN | 746662194 |
| India | IND | 391255067 |
| United States | USA | 245425910 |
| Brazil | BRA | 126403573 |
knitr::kable(head(df_political_indexes %>%
filter(Year == "2006") %>%
select(Country, Democracy_Index) %>%
arrange(desc(Democracy_Index)), 5),
caption = "Countries With Maximum Democracy Indexes in 2006")| Country | Democracy\_Index |
|---|---|
| Sweden | 9.88 |
| Iceland | 9.71 |
| Netherlands | 9.66 |
| Norway | 9.55 |
| Denmark | 9.52 |
knitr::kable(head(df_political_indexes %>%
filter(Year == "2006") %>%
select(Country, Democracy_Index) %>%
arrange(Democracy_Index), 5),
caption = "Countries With Minimum Democracy Indexes in 2006")| Country | Democracy\_Index |
|---|---|
| North Korea | 1.03 |
| Central African Republic | 1.61 |
| Chad | 1.65 |
| Togo | 1.75 |
| Myanmar | 1.77 |
knitr::kable(head(df_political_indexes %>%
filter(Year == "2020") %>%
select(Country, Democracy_Index) %>%
arrange(desc(Democracy_Index)), 5),
caption = "Countries With Maximum Democracy Indexes in 2020")| Country | Democracy\_Index |
|---|---|
| Norway | 9.81 |
| Iceland | 9.37 |
| Sweden | 9.26 |
| New Zealand | 9.25 |
| Canada | 9.24 |
knitr::kable(head(df_political_indexes %>%
filter(Year == "2020") %>%
select(Country, Democracy_Index) %>%
arrange(Democracy_Index), 5),
caption = "Countries With Minimum Democracy Indexes in 2020")| Country | Democracy\_Index |
|---|---|
| North Korea | 1.08 |
| Congo, Dem. Rep. | 1.13 |
| Central African Republic | 1.32 |
| Syria | 1.43 |
| Chad | 1.55 |
options(repr.plot.width = 12, repr.plot.height = 8)
#Comparing the growth of Mobile Subscriptions from 2009 to 2019
#Removing the regions from the Country column to obtain only the countries
df_mobile_subscription_onlycountries <- df_mobile_subscription %>% filter(Code != "")
#The records for the year 2009
df_mobile_subscription_onlycountries_2009 <- df_mobile_subscription_onlycountries %>% filter(Year == 2009)
#The records for the year 2019
df_mobile_subscription_onlycountries_2019 <- df_mobile_subscription_onlycountries %>% filter(Year == 2019)
#Joining the tables to get the records for only 2009 and 2019
df_mobile_subscription_combined_2009_2019 <- inner_join(df_mobile_subscription_onlycountries_2009,
df_mobile_subscription_onlycountries_2019,
by = c("Country", "Code"))
df_mobile_subscription_combined_change <- mutate(df_mobile_subscription_combined_2009_2019,
Percentage_Change = ((Mobile_Cellular_Subscription_Per_100_People.y - Mobile_Cellular_Subscription_Per_100_People.x)))
#Figuring out the countries which have improved the most in a decade
df_mobile_subscription_combined_change_ordered <- df_mobile_subscription_combined_change %>%
arrange(desc(df_mobile_subscription_combined_change$Percentage_Change))
#Obtaining the top 10 countries to have improved the most
df_mobile_subscription_combined_change_ordered_top10 <- df_mobile_subscription_combined_change_ordered %>% head(20)
# renaming the columns
colnames(df_mobile_subscription_combined_change_ordered_top10)[4] <- "Mobile_Cellular_Subscription_Per_100_People_2009"
colnames(df_mobile_subscription_combined_change_ordered_top10)[6] <- "Mobile_Cellular_Subscription_Per_100_People_2019"
# pivoting the 2009 and 2019 columns
df_mobile_subscription_for_plot <- pivot_longer(df_mobile_subscription_combined_change_ordered_top10,
cols =c("Mobile_Cellular_Subscription_Per_100_People_2019",
"Mobile_Cellular_Subscription_Per_100_People_2009"),
names_to = "Mobile_cellular_Subscriptions_per_100_people",
values_to = "Subscriptions_per_100_people")
# bar plot to visualize the top-10 countries
ggplot(df_mobile_subscription_for_plot,
aes(fill = Mobile_cellular_Subscriptions_per_100_people,
x = Country,
y = Subscriptions_per_100_people,
group = Mobile_cellular_Subscriptions_per_100_people)) +
geom_bar(position = "dodge",
stat = "identity") +
ggtitle("Countries with Highest Change of Mobile subscriptions per 100 people") +
theme(legend.box.background = element_rect(color = "black",
size=0.5),
legend.box.margin = margin(25, 6, 6, 6)) +
theme(axis.text.x = element_text(size=10,
angle=90,
hjust = 0.95,
vjust = 0.2)) 
# figuring out the countries which have improved the lowest
df_mobile_subscription_combined_change_ordered_asc <- df_mobile_subscription_combined_change %>%
arrange(df_mobile_subscription_combined_change$Percentage_Change)
df_mobile_subscription_combined_change_ordered_bottom10 <- df_mobile_subscription_combined_change_ordered_asc %>% head(20)
colnames(df_mobile_subscription_combined_change_ordered_bottom10)[4] <- "Mobile_Cellular_Subscription_Per_100_People_2009"
colnames(df_mobile_subscription_combined_change_ordered_bottom10)[6] <- "Mobile_Cellular_Subscription_Per_100_People_2019"
df_mobile_subscription_for_plot_bottom10 <- pivot_longer(df_mobile_subscription_combined_change_ordered_bottom10,
cols = c("Mobile_Cellular_Subscription_Per_100_People_2019",
"Mobile_Cellular_Subscription_Per_100_People_2009"),
names_to = "Mobile_cellular_Subscriptions_per_100_people",
values_to = "Subscriptions_per_100_people")
# plotting a stacked Bar-Plot to visualize the bottom 10 countries
ggplot(df_mobile_subscription_for_plot_bottom10,
aes(fill = Mobile_cellular_Subscriptions_per_100_people,
x = Subscriptions_per_100_people,
y = Country,
group = Mobile_cellular_Subscriptions_per_100_people)) +
geom_bar(position = "stack",
stat="identity") +
ggtitle("Countries with Lowest Mobile subscriptions per 100 people") +
theme(legend.box.background = element_rect(color="black",
size=0.5),
legend.box.margin = margin(25, 6, 6, 6))
# comparing the growth of broadband from 2009 to 2019
# removing the regions from the Entity column to obtain only the countries
df_broadband_penetration_onlycountries <- df_broadband_penetration %>% filter(Code != "")
# the records for the year 2009
df_broadband_penetration_onlycountries_2009 <- df_broadband_penetration_onlycountries %>% filter(Year == 2009)
# the records for the year 2019
df_broadband_penetration_onlycountries_2019 <- df_broadband_penetration_onlycountries %>% filter(Year == 2019)
# joining the tables to get the records for only 2009 and 2019
df_broadband_penetration_combined_2009_2019 <- inner_join(df_broadband_penetration_onlycountries_2009,
df_broadband_penetration_onlycountries_2019,
by = c("Country", "Code"))
df_broadband_penetration_combined_change <- mutate(df_broadband_penetration_combined_2009_2019,
Percentage_Change = ((Fixed_Broadband_Subscription_Per_100_People.y - Fixed_Broadband_Subscription_Per_100_People.x)))
# figuring out the countries which have improved the most in a decade
df_broadband_penetration_combined_change_ordered <- df_broadband_penetration_combined_change %>%
arrange(desc(df_broadband_penetration_combined_change$Percentage_Change))
# obtaining the top 10 countries to have improved the most
df_broadband_penetration_combined_change_ordered_top10 <- df_broadband_penetration_combined_change_ordered %>% head(20)
# renaming the columns
colnames(df_broadband_penetration_combined_change_ordered_top10)[4] <- "Fixed_Broadband_Subscription_Per_100_People_2009"
colnames(df_broadband_penetration_combined_change_ordered_top10)[6] <- "Fixed_Broadband_Subscription_Per_100_People_2019"
# pivoting the 2009 and 2019 columns
df_broadband_penetration_for_plot <- pivot_longer(df_broadband_penetration_combined_change_ordered_top10,
cols = c("Fixed_Broadband_Subscription_Per_100_People_2019",
"Fixed_Broadband_Subscription_Per_100_People_2009"),
names_to = "Fixed_Broadband_Subscriptions_per_100_people",
values_to = "Subscriptions_per_100_people")
# bar plot to visualize the top-10 countries
ggplot(df_broadband_penetration_for_plot,
aes(fill = Fixed_Broadband_Subscriptions_per_100_people,
x = Country,
y = Subscriptions_per_100_people,
group = Fixed_Broadband_Subscriptions_per_100_people)) +
geom_bar(position = "dodge",
stat = "identity") +
ggtitle("Countries with Highest Change of Broadband subscriptions per 100 people") +
theme(legend.box.background = element_rect(color = "black",
size = 0.5),
legend.box.margin = margin(25, 6, 6, 6)) +
theme(axis.text.x = element_text(size = 10,
angle = 90,
hjust = 0.95,
vjust = 0.2)) 
# figuring out the countries which have improved the lowest
df_broadband_penetration_combined_change_ordered_asc <- df_broadband_penetration_combined_change %>%
arrange(df_broadband_penetration_combined_change$Percentage_Change)
df_broadband_penetration_combined_change_ordered_bottom10 <- df_broadband_penetration_combined_change_ordered_asc %>% head(20)
colnames(df_broadband_penetration_combined_change_ordered_bottom10)[4] <- "Fixed_Broadband_Subscription_Per_100_People_2009"
colnames(df_broadband_penetration_combined_change_ordered_bottom10)[6] <- "Fixed_Broadband_Subscription_Per_100_People_2019"
df_broadband_penetration_for_plot_bottom10 <- pivot_longer(df_broadband_penetration_combined_change_ordered_bottom10,
cols = c("Fixed_Broadband_Subscription_Per_100_People_2019",
"Fixed_Broadband_Subscription_Per_100_People_2009"),
names_to = "Fixed_Broadband_Subscriptions_per_100_people",
values_to = "Subscriptions_per_100_people")
# plotting a stacked Bar-Plot to visualize the bottom 10 countries
ggplot(df_broadband_penetration_for_plot_bottom10,
aes(fill = Fixed_Broadband_Subscriptions_per_100_people,
x = Subscriptions_per_100_people,
y = Country,
group = Fixed_Broadband_Subscriptions_per_100_people))+
geom_bar(position = "stack",
stat = "identity") +
ggtitle("Countries with Lowest Broadband subscriptions per 100 people") +
theme(legend.box.background = element_rect(color = "black",
size = 0.5),
legend.box.margin = margin(25, 6, 6, 6))
options(repr.plot.width = 25, repr.plot.height = 5)
# heatmap of df_mobile_subscription for 2009
# importing the world map
world_map <- map_data("world")
# merging the world map with the mobile subscriptions by country dataset
world_map = merge(world_map,
df_mobile_subscription_onlycountries_2009,
by.x = "region",
by.y = "Country")
# arranging the table on the basis of Mobile subscriptions per 100 people
df_mobile_subscription_onlycountries_top10 <- df_mobile_subscription_onlycountries %>% arrange(desc(Mobile_Cellular_Subscription_Per_100_People)) %>% head(10)
# plotting the heatmap of the world for the year 2009
ggplot(world_map, aes(map_id = region,
fill = Mobile_Cellular_Subscription_Per_100_People)) +
geom_map(map = world_map,
size = 0.1) +
scale_fill_gradient(low = "#fff7bc",
high = "#cc4c02",
name = "Mobile Cellular Subscriptions Per 100 People") +
expand_limits(x = world_map$long,
y = world_map$lat) +
ggtitle("Mobile Cellular Subscriptions Per 100 People Throughout The World - 2009") +
theme(legend.box.background = element_rect(color = "black",
size = 0.5),
legend.box.margin = margin(25, 6, 6, 6))
# heatmap of df_mobile_subscription for 2019
world_map <- map_data("world")
world_map = merge(world_map,
df_mobile_subscription_onlycountries_2019,
by.x = "region",
by.y = "Country")
df_mobile_subscription_onlycountries_top10 <- df_mobile_subscription_onlycountries %>%
arrange(desc(Mobile_Cellular_Subscription_Per_100_People)) %>%
head(10)
# plotting the heatmap of the world for the year 2019
ggplot(world_map,
aes(map_id = region,
fill = Mobile_Cellular_Subscription_Per_100_People)) +
geom_map(map = world_map,
size = 0.25) +
scale_fill_gradient(low = "#fff7bc",
high = "#cc4c02",
name = "Mobile Cellular Subscriptions Per 100 People") +
expand_limits(x = world_map$long,
y = world_map$lat) +
ggtitle("Mobile Cellular Subscriptions Per 100 People Throughout The World - 2019") +
theme(legend.box.background = element_rect(color = "black",
size = 0.5),
legend.box.margin = margin(25, 6, 6, 6))
knitr::opts_chunk$set(warning = FALSE, message = FALSE)
# heatmap of df_broadband_penetration for 2009
# importing the world map
world_map <- map_data("world")
# merging the world map with the broadband penetration by country dataset
world_map = merge(world_map,df_broadband_penetration_onlycountries_2009,
by.x = "region",
by.y = "Country")
# arranging the table on the basis of Fixed Broadband subscriptions per 100 people
df_broadband_penetration_onlycountries_top10 <- df_broadband_penetration_onlycountries %>%
arrange(desc(Fixed_Broadband_Subscription_Per_100_People)) %>% head(10)
# plotting the heatmap of the world for the year 2009
ggplot(world_map,
aes(map_id = region,
fill = Fixed_Broadband_Subscription_Per_100_People)) +
geom_map(map = world_map,
size = 0.1) +
scale_fill_gradient(low = "#fff7bc",
high = "#cc4c02",
name = "Fixed Broadband Subscriptions Per 100 People") +
expand_limits(x = world_map$long,
y = world_map$lat) +
ggtitle("Broadband Subscriptions Per 100 People Throughout the World – 2009") +
theme(legend.box.background = element_rect(color = "black",
size = 0.5),
legend.box.margin = margin(25, 6, 6, 6))
# heatmap of df_broadband_penetration for 2019
world_map <- map_data("world")
world_map = merge(world_map,df_broadband_penetration_onlycountries_2019,
by.x = "region",
by.y = "Country")
df_broadband_penetration_onlycountries_top10 <- df_broadband_penetration_onlycountries %>%
arrange(desc(Fixed_Broadband_Subscription_Per_100_People)) %>% head(10)
# plotting the heatmap of the world for the year 2019
ggplot(world_map,
aes(map_id = region,
fill = Fixed_Broadband_Subscription_Per_100_People)) +
geom_map(map = world_map,
size = 0.1) +
scale_fill_gradient(low = "#fff7bc",
high = "#cc4c02",
name = "Fixed Broadband Subscriptions Per 100 People") +
expand_limits(x = world_map$long,
y = world_map$lat) +
ggtitle("Broadband Subscriptions Per 100 People Throughout the World – 2019") +
theme(legend.box.background = element_rect(color = "black",
size=0.5),
legend.box.margin = margin(6, 6, 6, 6))
options(repr.plot.width = 12, repr.plot.height = 8)
# filtering the Broadband Penetration Dataset for the year 2019
df_broadband_penetration_2019 <- df_broadband_penetration %>% filter(Year == 2019)
#Filtering the Mobile Cellular Dataset for the year 2019
df_mobile_subscription_2019 <- df_mobile_subscription %>% filter(Code != "") %>% filter(Year == 2019)
# df_broadband_penetration and Continents
# joining the Broadband Penetration and Continents dataset using Country as the primary Key
df_broadband_penetration_continents <- inner_join(df_broadband_penetration_2019,
df_continents,
by = "Country")
# grouping the dataset on the basis of region
df_broadband_penetration_continents_grouped <- df_broadband_penetration_continents %>%
group_by(Region) %>%
summarise(Mean_Broadband_Subscriptions_Per_100_people =
mean(Fixed_Broadband_Subscription_Per_100_People))
head(df_broadband_penetration_continents_grouped)## # A tibble: 5 x 2
## Region Mean_Broadband_Subscriptions_Per_100_people
## <chr> <dbl>
## 1 Africa 2.81
## 2 Americas 16.3
## 3 Asia 13.8
## 4 Europe 34.4
## 5 Oceania 8.23
# plotting the Pie-Chart for df_broadband_penetration for different regions
ggplot(df_broadband_penetration_continents_grouped,
aes(x = "",
y = Mean_Broadband_Subscriptions_Per_100_people,
fill = Region)) +
geom_bar(stat = "identity",
width=1) +
coord_polar("y",
start=0) +
theme_void() +
ggtitle("Mean Broadband Subscriptions Per 100 People \nAcross different Regions of the World") +
theme(plot.title = element_text(hjust = 0.3,
size = 15,
face = "bold")) +
guides(fill = guide_legend(reverse = TRUE)) +
scale_fill_brewer(name = "Regions") +
theme(legend.text = element_text(size=10),
legend.title = element_text(hjust = 0.5,
size = 10,
face = "bold")) +
geom_text(aes(label = paste0(round(df_broadband_penetration_continents_grouped$Mean_Broadband_Subscriptions_Per_100_people,
digits = 2))),
position = position_stack(vjust = 0.5),
size = 3,
face = "bold") +
theme(legend.box.background = element_rect(color = "black",
size = 1),
legend.box.margin = margin(25, 6, 6, 6))
# df_mobile_subscription and Democracy of Countries
df_mobile_subscription_continents <- inner_join(df_mobile_subscription_2019,df_continents,by="Country")
df_mobile_subscription_continents_grouped <- df_mobile_subscription_continents %>%
group_by(Region) %>%
summarise(Mean_cellular_Subscriptions_Per_100_people =
mean(Mobile_Cellular_Subscription_Per_100_People))
head(df_mobile_subscription_continents_grouped)## # A tibble: 5 x 2
## Region Mean_cellular_Subscriptions_Per_100_people
## <chr> <dbl>
## 1 Africa 92.8
## 2 Americas 106.
## 3 Asia 128.
## 4 Europe 122.
## 5 Oceania 75.3
# plotting the Pie-Chart for df_mobile_subscription for different regions
ggplot(df_mobile_subscription_continents_grouped,
aes(x = "",
y = Mean_cellular_Subscriptions_Per_100_people,
fill = Region)) +
geom_bar(stat = "identity",
width = 1) +
coord_polar("y",
start = 0) +
theme_void() +
ggtitle("Mean Cellular Subscriptions Per 100 People \nAcross different Regions of the World") +
theme(plot.title = element_text(hjust = 0.3,
size = 15,
face = "bold")) +
guides(fill = guide_legend(reverse = TRUE))+
scale_fill_brewer(name = "Regions") +
theme(legend.text = element_text(size=10),
legend.title = element_text(hjust = 0.5,
size = 10,
face = "bold")) +
geom_text(aes(label = paste0(round(df_mobile_subscription_continents_grouped$Mean_cellular_Subscriptions_Per_100_people,
digits = 2))),
position = position_stack(vjust = 0.5),
size = 3,
face = "bold") +
theme(legend.box.background = element_rect(color="black", size=1),
legend.box.margin = margin(25, 6, 6, 6))
# options(repr.plot.width = 18, repr.plot.height = 20)
# joining data set
df_political_index_broadband_penetration <- merge(x = df_broadband_penetration, y = df_political_indexes, by = c("Country", "Year"))
# Democracy Index VS Fixed Broadband Subscription Per 100 People
df_democracy_index_broadband_penetration <- df_political_index_broadband_penetration %>% select(Country, Year, Democracy_Index, Fixed_Broadband_Subscription_Per_100_People)
plt_demo_indx_2006 <- df_democracy_index_broadband_penetration %>% filter(Year == "2006") %>%
ggplot(aes(x = Democracy_Index, y = Fixed_Broadband_Subscription_Per_100_People)) +
geom_point(color = "deepskyblue") +
xlim(1,11) +
ylim(0,50) +
scale_x_continuous(breaks = c(2,4,6,8,10)) +
theme_minimal() +
geom_smooth(method = "lm", formula = y ~ poly(x, 3), se = FALSE, color = "deeppink") +
ggtitle("Democracy Index VS Fixed Broadband Subscription Per 100 People \n(2006)") +
labs(x = "Democracy Index", y = "Fixed Broadbad Subscription \nPer 100 People") +
theme(plot.title = element_text(size = 10, hjust = 0.5, face="italic"), legend.position = "none", axis.title = element_text(size = 8), axis.text = element_text(size = 5)) +
geom_vline(xintercept = 1) +
geom_hline(yintercept = 0) +
annotate("rect", xmin=7.5, xmax=10, ymin=20, ymax= 32, fill=NA, color="grey40") +
annotate("text", color="grey40", x=5.6, y=30, label="Countries with high democracy index \nand boradband subscriptions", size = 4, face = "bold")
plt_demo_indx_2008 <- df_democracy_index_broadband_penetration %>% filter(Year == "2008") %>%
ggplot(aes(x = Democracy_Index, y = Fixed_Broadband_Subscription_Per_100_People)) +
geom_point(color = "deepskyblue") +
xlim(1,11) +
ylim(0,50) +
scale_x_continuous(breaks = c(2,4,6,8,10)) +
theme_minimal() +
geom_smooth(method = "lm", formula = y ~ poly(x, 3), se = FALSE, color = "deeppink") +
ggtitle("Democracy Index VS Fixed Broadband Subscription Per 100 People \n(2008)") +
labs(x = "Democracy Index", y = "Fixed Broadbad Subscription \nPer 100 People") +
theme(plot.title = element_text(size = 10, hjust = 0.5, face="italic"), legend.position = "none", axis.title = element_text(size = 8), axis.text = element_text(size = 5)) +
geom_vline(xintercept = 1) +
geom_hline(yintercept = 0)
plt_demo_indx_2010 <- df_democracy_index_broadband_penetration %>% filter(Year == "2010") %>%
ggplot(aes(x = Democracy_Index, y = Fixed_Broadband_Subscription_Per_100_People)) +
geom_point(color = "deepskyblue") +
xlim(1,11) +
ylim(0,50) +
scale_x_continuous(breaks = c(2,4,6,8,10)) +
theme_minimal() +
geom_smooth(method = "lm", formula = y ~ poly(x, 3), se = FALSE, color = "deeppink") +
ggtitle("Democracy Index VS Fixed Broadband Subscription Per 100 People \n(2010)") +
labs(x = "Democracy Index", y = "Fixed Broadbad Subscription \nPer 100 People") +
theme(plot.title = element_text(size = 10, hjust = 0.5, face="italic"), legend.position = "none", axis.title = element_text(size = 8), axis.text = element_text(size = 5)) +
geom_vline(xintercept = 1) +
geom_hline(yintercept = 0)
plt_demo_indx_2012 <- df_democracy_index_broadband_penetration %>% filter(Year == "2012") %>%
ggplot(aes(x = Democracy_Index, y = Fixed_Broadband_Subscription_Per_100_People)) +
geom_point(color = "deepskyblue") +
xlim(1,11) +
ylim(0,50) +
scale_x_continuous(breaks = c(2,4,6,8,10)) +
theme_minimal() +
geom_smooth(method = "lm", formula = y ~ poly(x, 3), se = FALSE, color = "deeppink") +
ggtitle("Democracy Index VS Fixed Broadband Subscription Per 100 People \n(2012)") +
labs(x = "Democracy Index", y = "Fixed Broadbad Subscription \nPer 100 People") +
theme(plot.title = element_text(size = 10, hjust = 0.5, face="italic"), legend.position = "none", axis.title = element_text(size = 8), axis.text = element_text(size = 5)) +
geom_vline(xintercept = 1) +
geom_hline(yintercept = 0)
plt_demo_indx_2014 <- df_democracy_index_broadband_penetration %>% filter(Year == "2014") %>%
ggplot(aes(x = Democracy_Index, y = Fixed_Broadband_Subscription_Per_100_People)) +
geom_point(color = "deepskyblue") +
xlim(1,11) +
ylim(0,50) +
scale_x_continuous(breaks = c(2,4,6,8,10)) +
theme_minimal() +
geom_smooth(method = "lm", formula = y ~ poly(x, 3), se = FALSE, color = "deeppink") +
ggtitle("Democracy Index VS Fixed Broadband Subscription Per 100 People \n(2014)") +
labs(x = "Democracy Index", y = "Fixed Broadbad Subscription \nPer 100 People") +
theme(plot.title = element_text(size = 10, hjust = 0.5, face="italic"), legend.position = "none", axis.title = element_text(size = 8), axis.text = element_text(size = 5)) +
geom_vline(xintercept = 1) +
geom_hline(yintercept = 0)
plt_demo_indx_2018 <- df_democracy_index_broadband_penetration %>% filter(Year == "2018") %>%
ggplot(aes(x = Democracy_Index, y = Fixed_Broadband_Subscription_Per_100_People)) +
geom_point(color = "deepskyblue") +
xlim(1,11) +
ylim(0,50) +
scale_x_continuous(breaks = c(2,4,6,8,10)) +
theme_minimal() +
geom_smooth(method = "lm", formula = y ~ poly(x, 3), se = FALSE, color = "deeppink") +
ggtitle("Democracy Index VS Fixed Broadband Subscription Per 100 People \n(2018)") +
labs(x = "Democracy Index", y = "Fixed Broadbad Subscription \nPer 100 People") +
theme(plot.title = element_text(size = 10, hjust = 0.5, face="italic"), legend.position = "none", axis.title = element_text(size = 8), axis.text = element_text(size = 5)) +
geom_vline(xintercept = 1) +
geom_hline(yintercept = 0)
grid.arrange(plt_demo_indx_2006, plt_demo_indx_2008, plt_demo_indx_2010, plt_demo_indx_2012, plt_demo_indx_2014, plt_demo_indx_2018, nrow = 3)
# calculating correlation coefficient
print(paste("Correlation Coefficiant:",
cor(df_democracy_index_broadband_penetration$Democracy_Index, df_democracy_index_broadband_penetration$Fixed_Broadband_Subscription_Per_100_People)))## [1] "Correlation Coefficiant: 0.653504831919655"
# Functioning of Government VS Fixed Broadband Subscription Per 100 People
df_func_govt_broadband_penetration <- df_political_index_broadband_penetration %>% select(Country, Year, Functioning_Of_Government, Fixed_Broadband_Subscription_Per_100_People)
plt_func_govt_2006 <- df_func_govt_broadband_penetration %>% filter(Year == "2006") %>%
ggplot(aes(x = Functioning_Of_Government, y = Fixed_Broadband_Subscription_Per_100_People)) +
geom_point(color = "limegreen") +
xlim(0,100) +
ylim(0,50) +
theme_minimal() +
geom_smooth(method = "lm", formula = y ~ poly(x, 3), se = FALSE, color = "palevioletred") +
ggtitle("Functioning Of Government VS Fixed Broadband Subscription \nPer 100 People (2006)") +
labs(x = "Functioning Of Government", y = "Fixed Broadbad Subscription \nPer 100 People") +
theme(plot.title = element_text(size = 10, hjust = 0.5, face="italic"), legend.position = "none", axis.title = element_text(size = 8), axis.text = element_text(size = 5)) +
geom_vline(xintercept = 0) +
geom_hline(yintercept = 0)
plt_func_govt_2008 <- df_func_govt_broadband_penetration %>% filter(Year == "2008") %>%
ggplot(aes(x = Functioning_Of_Government, y = Fixed_Broadband_Subscription_Per_100_People)) +
geom_point(color = "limegreen") +
xlim(0,100) +
ylim(0,50) +
theme_minimal() +
geom_smooth(method = "lm", formula = y ~ poly(x, 3), se = FALSE, color = "palevioletred") +
ggtitle("Functioning Of Government VS Fixed Broadband Subscription \nPer 100 People (2008)") +
labs(x = "Functioning Of Government", y = "Fixed Broadbad Subscription \nPer 100 People") +
theme(plot.title = element_text(size = 10, hjust = 0.5, face="italic"), legend.position = "none", axis.title = element_text(size = 8), axis.text = element_text(size = 5)) +
geom_vline(xintercept = 0) +
geom_hline(yintercept = 0)
plt_func_govt_2010 <- df_func_govt_broadband_penetration %>% filter(Year == "2010") %>%
ggplot(aes(x = Functioning_Of_Government, y = Fixed_Broadband_Subscription_Per_100_People)) +
geom_point(color = "limegreen") +
xlim(0,100) +
ylim(0,50) +
theme_minimal() +
geom_smooth(method = "lm", formula = y ~ poly(x, 3), se = FALSE, color = "palevioletred") +
ggtitle("Functioning Of Government VS Fixed Broadband Subscription \nPer 100 People (2010)") +
labs(x = "Functioning Of Government", y = "Fixed Broadbad Subscription \nPer 100 People") +
theme(plot.title = element_text(size = 10, hjust = 0.5, face="italic"), legend.position = "none", axis.title = element_text(size = 8), axis.text = element_text(size = 5)) +
geom_vline(xintercept = 0) +
geom_hline(yintercept = 0)
plt_func_govt_2012 <- df_func_govt_broadband_penetration %>% filter(Year == "2012") %>%
ggplot(aes(x = Functioning_Of_Government, y = Fixed_Broadband_Subscription_Per_100_People)) +
geom_point(color = "limegreen") +
xlim(0,100) +
ylim(0,50) +
theme_minimal() +
geom_smooth(method = "lm", formula = y ~ poly(x, 3), se = FALSE, color = "palevioletred") +
ggtitle("Functioning Of Government VS Fixed Broadband Subscription \nPer 100 People (2012)") +
labs(x = "Functioning Of Government", y = "Fixed Broadbad Subscription \nPer 100 People") +
theme(plot.title = element_text(size = 10, hjust = 0.5, face="italic"), legend.position = "none", axis.title = element_text(size = 8), axis.text = element_text(size = 5)) +
geom_vline(xintercept = 0) +
geom_hline(yintercept = 0)
plt_func_govt_2014 <- df_func_govt_broadband_penetration %>% filter(Year == "2014") %>%
ggplot(aes(x = Functioning_Of_Government, y = Fixed_Broadband_Subscription_Per_100_People)) +
geom_point(color = "limegreen") +
xlim(0,100) +
ylim(0,50) +
theme_minimal() +
geom_smooth(method = "lm", formula = y ~ poly(x, 3), se = FALSE, color = "palevioletred") +
ggtitle("Functioning Of Government VS Fixed Broadband Subscription \nPer 100 People (2014)") +
labs(x = "Functioning Of Government", y = "Fixed Broadbad Subscription \nPer 100 People") +
theme(plot.title = element_text(size = 10, hjust = 0.5, face="italic"), legend.position = "none", axis.title = element_text(size = 8), axis.text = element_text(size = 5)) +
geom_vline(xintercept = 0) +
geom_hline(yintercept = 0)
plt_func_govt_2018 <- df_func_govt_broadband_penetration %>% filter(Year == "2018") %>%
ggplot(aes(x = Functioning_Of_Government, y = Fixed_Broadband_Subscription_Per_100_People)) +
geom_point(color = "limegreen") +
xlim(0,100) +
ylim(0,50) +
theme_minimal() +
geom_smooth(method = "lm", formula = y ~ poly(x, 3), se = FALSE, color = "palevioletred") +
ggtitle("Functioning Of Government VS Fixed Broadband Subscription \nPer 100 People (2018)") +
labs(x = "Functioning Of Government", y = "Fixed Broadbad Subscription \nPer 100 People") +
theme(plot.title = element_text(size = 10, hjust = 0.5, face="italic"), legend.position = "none", axis.title = element_text(size = 8), axis.text = element_text(size = 5)) +
geom_vline(xintercept = 0) +
geom_hline(yintercept = 0)
grid.arrange(plt_func_govt_2006, plt_func_govt_2008, plt_func_govt_2010, plt_func_govt_2012, plt_func_govt_2014, plt_func_govt_2018, nrow = 3)
# calculating correlation coefficient
print(paste("Correlation Coefficiant:",
cor(df_political_index_broadband_penetration$Functioning_Of_Government, df_democracy_index_broadband_penetration$Fixed_Broadband_Subscription_Per_100_People)))## [1] "Correlation Coefficiant: 0.635319749543323"