Assignment 2

library(haven) 
TEDS_2016 <-
  read_stata("https://github.com/datageneration/home/blob/master/DataProgramming/data/TEDS_2016.dta?raw=true")
#TEDS_2016
TEDS_2016$PartyID <- factor(TEDS_2016$PartyID, labels=c("KMT","DPP","NP","PFP", "TSU", "NPP","NA"))
attach(TEDS_2016)
head(PartyID)

[1] NA  NA  KMT NA  NA  DPP
Levels: KMT DPP NP PFP TSU NPP NA

tail(PartyID)

[1] NA  NA  DPP NA  NA  NA 
Levels: KMT DPP NP PFP TSU NPP NA

install.packages("descr", repos = "http://cran.us.r-project.org")

The downloaded binary packages are in
    /var/folders/fg/l9nwkw5954b_7xv794xmt5_40000gn/T//Rtmps4QTUQ/downloaded_packages

library(descr)
freq(TEDS_2016$PartyID)

TEDS_2016$PartyID 
      Frequency  Percent
KMT         388  22.9586
DPP         591  34.9704
NP            3   0.1775
PFP          32   1.8935
TSU           5   0.2959
NPP          43   2.5444
NA          628  37.1598
Total      1690 100.0000

# Plot the Party ID variable
library(ggplot2)

ggplot(TEDS_2016, aes(PartyID)) + 
  geom_bar()

ggplot(TEDS_2016, aes(PartyID)) + 
  geom_bar(aes(y = (..count..)/sum(..count..))) + 
  scale_y_continuous(labels=scales::percent) +
  ylab("Party Support (%)") + 
  xlab("Taiwan Political Parties")

Warning: The dot-dot notation (`..count..`) was deprecated in ggplot2 3.4.0.
ℹ Please use `after_stat(count)` instead.

ggplot(TEDS_2016, aes(PartyID)) + 
  geom_bar(aes(y = (..count..)/sum(..count..),fill=PartyID)) + 
  scale_y_continuous(labels=scales::percent) +
  ylab("Party Support (%)") + 
  xlab("Taiwan Political Parties") +
  theme_bw()

ggplot(TEDS_2016, aes(PartyID)) + 
  geom_bar(aes(y = (..count..)/sum(..count..),fill=PartyID)) + 
  scale_y_continuous(labels=scales::percent) +
  ylab("Party Support (%)") + 
  xlab("Taiwan Political Parties") +
  theme_bw() +
  scale_fill_manual(values=c("steel blue","forestgreen","khaki1","orange","goldenrod","yellow","grey"))

library(tidyverse)

── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
✔ dplyr     1.1.0     ✔ readr     2.1.4
✔ forcats   1.0.0     ✔ stringr   1.5.0
✔ lubridate 1.9.2     ✔ tibble    3.2.1
✔ purrr     1.0.1     ✔ tidyr     1.3.0
── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag()    masks stats::lag()
ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors

TEDS_2016 %>% 
  count(PartyID) %>% 
  mutate(perc = n / nrow(TEDS_2016)) -> T2
ggplot(T2, aes(x = reorder(PartyID, -perc),y = perc,fill=PartyID)) + 
  geom_bar(stat = "identity") +
  ylab("Party Support (%)") + 
  xlab("Taiwan Political Parties") +
  theme_bw() +
  scale_fill_manual(values=c("steel blue","forestgreen","khaki1","orange","goldenrod","yellow","grey"))

TEDS_2016$Tondu<-factor(TEDS_2016$Tondu,labels=c("Unification now","Status quo, unif. in >future","Status quo, decide later","Status quo forever", "Status quo, indep. in future", "Independence now","No >response"))
library(descr)
freq(TEDS_2016$Tondu)

TEDS_2016$Tondu 
                             Frequency Percent
Unification now                     27   1.598
Status quo, unif. in >future       180  10.651
Status quo, decide later           546  32.308
Status quo forever                 328  19.408
Status quo, indep. in future       380  22.485
Independence now                   108   6.391
No >response                       121   7.160
Total                             1690 100.000

library(ggplot2)
ggplot(TEDS_2016, aes(Tondu)) + 
  geom_bar()

ggplot(TEDS_2016, aes(Tondu)) + 
  geom_bar(aes(y = (..count..)/sum(..count..))) + 
  scale_y_continuous(labels=scales::percent) +
  ylab("Count") + 
  xlab("Tondu")

library(tidyverse)
TEDS_2016 %>% 
  count(Tondu) %>% 
  mutate(perc = n / nrow(TEDS_2016)) -> T2
ggplot(T2, aes(x = reorder(Tondu, -perc),y = perc,fill=Tondu)) + 
  geom_bar(stat = "identity") +
  ylab("Count") + 
  xlab("Tondu Variables") +
  theme_bw() +
  scale_fill_manual(values=c("steel blue","forestgreen","khaki1","orange","goldenrod","yellow","grey"))

Problems with the dataset:

There are missing values in this dataset. A solution would be to replace these with “NA”.

TEDS_2016$votetsai <- as.factor(TEDS_2016$votetsai)
ggplot(TEDS_2016, aes(Tondu, fill = votetsai)) +
  geom_bar() +
  theme(axis.text.x = element_text(angle = 90),
        legend.position = "bottom")

#age variable
TEDS_2016$age <- as.factor(TEDS_2016$age)
ggplot(TEDS_2016, aes(Tondu, fill = age)) +
  geom_bar() +
  theme(axis.text.x = element_text(angle = 90),  
        legend.title = element_blank(),  # remove the legend title
        legend.text = element_text(size = 10))