Data source: Top 50 Most Followed Twitter Accounts
简介#
数据列出了Twitter上最受关注的50个账户，每个总数四舍五入到最近的十万，以及每个用户的职业或活动。账户总数和排名月度变化最后更新于2022年5月12日。
粉丝排行榜#
我们试图使用这个数据集制作推特粉丝前50名账户的直观图表
导入#
第一步是将原始数据导入程序代码。
data_csv <- read.csv("Top 50 Most Followed Twitter Accounts.csv")
可视化#
- 由于Twitter账号ID的非重复性，我们自然选择了用户ID作为y轴数据。
- 我们按照粉丝数量重新排列账号，并在图像上从多到少显示。
- 为了使图像比较更直观，我们创造性地使用粉丝数量来绘制渐变色，从深到浅代表粉丝数量从多到少。
dataplot <- data_csv %>%
ggplot(mapping = aes(
x = Followers..millions.,
y = reorder(Account.username, Followers..millions.),
fill = -log(Followers..millions.),
)) +
geom_bar(
stat = "identity",
) +
guides(fill = "none") +
geom_text(mapping = aes(
label = Followers..millions.,
))
# Add auxiliary information.
dataplot +
labs(
x = "Followers (Millions)",
y = "Username",
title = "Top 50 in Twitter",
subtitle = "Information was last updated on May 12, 2022.",
caption = "Data sources: https://www.kaggle.com/datasets/hassanshehzadk/top-50-most-followed-twitter-accounts?resource=download",
) +
theme(
plot.title = element_text(hjust = 0.4, size = 14), # title position
panel.grid.minor = element_blank(), # Secondary grid lines
text = element_text(family = "Hack Nerd Font"), # font
axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)
)
Full code
library(tidyverse)
library(ggplot2)
# Import
data_csv <- read.csv("Top 50 Most Followed Twitter Accounts.csv")
# Plot
dataplot <- data_csv %>%
ggplot(mapping = aes(
x = Followers..millions.,
y = reorder(Account.username, Followers..millions.),
fill = -log(Followers..millions.),
)) +
geom_bar(
stat = "identity",
) +
guides(fill = "none") +
geom_text(mapping = aes(
label = Followers..millions.,
))
# Add auxiliary information.
dataplot +
labs(
x = "Followers (Millions)",
y = "Username",
title = "Top 50 in Twitter",
subtitle = "Information was last updated on May 12, 2022.",
caption = "Data sources: https://www.kaggle.com/datasets/hassanshehzadk/top-50-most-followed-twitter-accounts?resource=download", # nolint
) +
theme(
plot.title = element_text(hjust = 0.4, size = 14), # title position
panel.grid.minor = element_blank(), # Secondary grid lines
text = element_text(family = "Hack Nerd Font"), # font
axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)
)
账户归属分析#
我们还想了解拥有大量粉丝的账户的国家归属，因此绘制一个饼图来检查分布情况
导入#
第一步是将原始数据导入程序代码。
data_csv <- read.csv("Top 50 Most Followed Twitter Accounts.csv")
选择#
快速统计数据集中每个国家出现的频率。
area_count <- data_csv %>%
count(Country, name = "count")
可视化#
- 由于
ggplot中没有内置的饼图绘制方法，我们使用geom_bar和coord_polar来尝试达到相同的效果。
dataplot <- area_count %>%
ggplot(mapping = aes(
x = 1,
y = count,
fill = Country,
))
dataplot +
geom_bar(stat = "identity") +
coord_polar(theta = "y") +
scale_x_continuous(name = NULL, breaks = NULL) +
scale_y_continuous(name = NULL, breaks = NULL) +
scale_fill_viridis_d(option = "inferno")
研究结论#
- 显然，作为美国的本土软件，考虑到美国庞大的人口基数，属于美国的账户所在国家的数量远超其他国家。
- 作为一个人口众多的国家，印度意外地成为了美国的第二位。
- 其他国家的数量基本上是相同的。
Full code
library(tidyverse)
library(ggplot2)
data_csv <- read.csv("Top 50 Most Followed Twitter Accounts.csv")
area_count <- data_csv %>%
count(Country, name = "count")
dataplot <- area_count %>%
ggplot(mapping = aes(
x = 1,
y = count,
fill = Country,
))
dataplot +
geom_bar(stat = "identity") +
coord_polar(theta = "y") +
scale_x_continuous(name = NULL, breaks = NULL) +
scale_y_continuous(name = NULL, breaks = NULL) +
scale_fill_viridis_d(option = "inferno") +
labs(
x = "Followers (Millions)",
y = "Username",
fill = "Country",
title = "Country of Account",
subtitle = "Calculate the top 50 fan accounts on Twitter.\nInformation was last updated on May 12, 2022.", # nolint
caption = "Data sources: https://www.kaggle.com/datasets/hassanshehzadk/top-50-most-followed-twitter-accounts?resource=download", # nolint
) +
theme(
plot.title = element_text(hjust = 0.6, size = 14), # title position
axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1),
plot.caption = element_text(hjust = 0.3),
)