Portfolio1
KechenLu
Load dataset
library(ggplot2)
library(dplyr)##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionjoined_data= read.csv("C:/Users/27977/Downloads/433/joined_data.csv")Introduction:
I choose the dataset containing the unemployment rate, gdp and education level of each state in the US. It combines different categories of data from multiple aspects of the US. This dataset is interesting because the information is comprehensive, crossing from education to unemployment rate to reflect the true aspect of the US from a data perspective. These data are recorded on a monthly basis from Dec, 2020 to Nov. 2021, thus given a time span of development through data visualization.
head(joined_data)## X year_mon state Administered_Dose1_Pop_Pct Series_Complete_Pop_Pct
## 1 1 Dec 2020 Alabama 0 0
## 2 2 Dec 2020 Alaska 0 0
## 3 3 Dec 2020 Arizona 0 0
## 4 4 Dec 2020 Arkansas 0 0
## 5 5 Dec 2020 California 0 0
## 6 6 Dec 2020 Colorado 0 0
## value Population_over_25 High_School_or_Higher_Population
## 1 50536 3360058 2926985
## 2 77640 484058 452968
## 3 58945 4944540 4331542
## 4 47597 2036456 1781463
## 5 75235 26937872 22636359
## 6 72331 3974943 3672723
## High_School_or_Higher_Pct Bachelor_or_Higher_Population
## 1 0.8711 885357
## 2 0.9358 146157
## 3 0.8760 1492158
## 4 0.8748 475367
## 5 0.8403 9428484
## 6 0.9240 1695602
## Bachelor_or_Higher_Pct Advanced_Population Advanced_Pct Nominal_GDP_2021
## 1 0.2635 337382 0.1004 243555
## 2 0.3019 56574 0.1169 54020
## 3 0.3018 561120 0.1135 400156
## 4 0.2334 168182 0.0826 143438
## 5 0.3500 3538760 0.1314 3290170
## 6 0.4266 637777 0.1604 416937
## Nominal_GDP_2020 Annual_GDP_Change Annual_GDP_Change_Pct
## 1 229831 13724 0.0387
## 2 52864 1156 0.0334
## 3 377476 22680 0.0450
## 4 131818 11620 0.0416
## 5 3189703 100467 0.0149
## 6 400041 16896 0.0338
## Real_GDP_Growth_Rate_Pct GDP_per_capita_2021 GDP_per_capita_2020
## 1 0.068 48475 46875
## 2 0.054 69336 72263
## 3 0.074 55954 51865
## 4 0.069 47629 43691
## 5 0.063 83213 80727
## 6 0.079 72212 69475
## Pct_of_National_2021 Pct_of_National_2020 unemployment_rate
## 1 0.0107 0.0106 4.7
## 2 0.0024 0.0025 6.5
## 3 0.0178 0.0173 6.8
## 4 0.0062 0.0061 4.9
## 5 0.1477 0.1462 9.3
## 6 0.0186 0.0183 6.9Create the first graph
new=joined_data %>%
filter(state==("Wisconsin")|state==("California")|state==("New York"))%>%
filter(grepl("2021", year_mon))
new%>%
mutate(year_mon =ordered(year_mon, levels = unique(year_mon))) %>%
ggplot(aes(x=year_mon, y=unemployment_rate, group = state,colour=state))+
geom_line()+
theme_classic()+
xlab("Date")+
ylab("Unemployment Rate")+
ggtitle("Unemployment Ratio of Selected States in US")+
theme(legend.title = element_text(size=4),legend.text=element_text(size=4))## Warning: Removed 6 row(s) containing missing values (geom_path).
# ggplot(aes(x=year_mon, y=unemployment_rate, group = state,colour=state))+
# geom_line()+
# theme_classic()+
# xlab("Date")+
# ylab("Unemployment Rate")+
# ggtitle("Unemployment Ratio of Each State in US")+
# theme(legend.title = element_text(size=4),legend.text=element_text(size=4))
# scale_x_discrete(limits = c("Dec 2020","Jan 2021",))The first graph plots the unemployment ratio of selected states in the US during the covid over time. The unemployment rate has risen to an unprecedented level because of the pandemic, causing more than just the finance problem all over the world. By making this plot of the unemployment ratio, we could easily observe when the unemployment problem arose during the pandemic, at what time it arrived at a peak, and how the policy might had an influence on the unemployment ratio. To differentiate each state visually and observe the continual change across time, I used geom_line with different colors to each state, then used scale_x_discrete to avoid overlap of the x label and used theme to reduce the size of the legend.
Create the second graph
joined_data %>%
ggplot(aes(x=Bachelor_or_Higher_Pct, y=reorder(state,Bachelor_or_Higher_Pct),fill=state))+
geom_col()+
labs(title = "Bachelor or higher degree rate of each state in the US",x="rate",y="state")+
theme(panel.grid = element_blank(),legend.title = element_text(size=4),legend.text=element_text(size=4),legend.position="none")