Official unemployment rates by Gender, based on 4 week job search period

*Data: Economic Active Survey from Korean Statistical Information Service (KOSIS)

library(ggplot2)
library(tidyr)
## Warning: package 'tidyr' was built under R version 4.0.5
library(RColorBrewer)
library(ggsci)
## Warning: package 'ggsci' was built under R version 4.0.5
# load econ_active
econ_active <- read.csv("econ_active.csv")
colnames(econ_active)[colnames(econ_active) == "ï..Month"] <- "Month" #change colname
econ_active
##     Month   All  Male Female
## 1  Apr-20 27734 15912  11822
## 2  May-20 28209 16119  12090
## 3  Jun-20 28283 16125  12158
## 4  Jul-20 28244 16103  12141
## 5  Aug-20 27949 15953  11996
## 6  Sep-20 28012 16018  11994
## 7  Oct-20 28116 16060  12056
## 8  Nov-20 28208 16063  12145
## 9  Dec-20 27661 15838  11823
## 10 Jan-21 27388 15728  11660
## 11 Feb-21 27718 15889  11829
## 12 Mar-21 28138 16099  12040
## 13 Apr-21 28361 16167  12194
# commit x-bar to prevent alphabetical reordering
econ_active$Month <- factor(econ_active$Month, levels = econ_active$Month)
# econ_active plot
econ_active %>%
  gather("Gender", "Population", -Month) %>%
  ggplot(aes(x= Month, y = Population, fill = Gender)) +
  geom_bar(width = 0.7, position = "dodge", stat = "identity") +
  theme_minimal() +
  labs(title = "Economically Active Population By Gender | Korea 2020 - 2021", 
       x = "Month", y = "Population (thousands)") + 
  theme(plot.title = element_text(face = "bold"),
        axis.title.x = element_text(face = "bold"),
        axis.title.y = element_text(face = "bold"),
        legend.title = element_text(face = "bold")
  ) +
  coord_cartesian(ylim = c(5000, 30000)) +
  scale_y_continuous(limits = c(0, 30000), breaks=seq(0, 30000, 5000)) +
  scale_fill_manual(values = c("Female" = "#ff69b4", 
                               "Male" = "#00bfff",
                               "All" = "#90ee90"))

# employed csv
employed <- read.csv("Employed.csv")
colnames(employed)[colnames(employed) == "ï..Month"] <- "Month" #change colname
employed
##     Month   All  Male Female
## 1  20-Apr 26562 15237  11325
## 2  20-May 26930 15382  11549
## 3  20-Jun 27055 15431  11624
## 4  20-Jul 27106 15461  11645
## 5  20-Aug 27085 15469  11615
## 6  20-Sep 27012 15430  11582
## 7  20-Oct 27088 15456  11633
## 8  20-Nov 27241 15523  11718
## 9  20-Dec 26526 15244  11281
## 10 21-Jan 25818 14939  10879
## 11 21-Feb 26365 15149  11215
## 12 21-Mar 26923 15417  11506
## 13 21-Apr 27214 15514  11699
# commit x-bar to prevent alphabetical reordering
employed$Month <- factor(employed$Month, levels = employed$Month)
# econ_active plot
employed %>%
  gather("Gender", "Population", -Month) %>%
  ggplot(aes(x= Month, y = Population, fill = Gender)) +
  geom_bar(width = 0.7, position = "dodge", stat = "identity") +
  theme_minimal() +
  labs(title = "Employed Population By Gender | Korea 2020 - 2021", 
       x = "Month", y = "Population (thousands)") + 
  theme(plot.title = element_text(face = "bold"),
        axis.title.x = element_text(face = "bold"),
        axis.title.y = element_text(face = "bold"),
        legend.title = element_text(face = "bold")
  ) +
  coord_cartesian(ylim = c(5000, 30000)) +
  scale_y_continuous(limits = c(0, 30000), breaks=seq(0, 30000, 5000)) +
  scale_fill_manual(values = c("Female" = "#ff69b4", 
                               "Male" = "#00bfff",
                               "All" = "#90ee90"))

# unemployed csv
unemployed <- read.csv("Unemployed.csv")
colnames(unemployed)[colnames(unemployed) == "ï..Month"] <- "Month" #change colname
unemployed
##     Month  All Male Female
## 1  20-Apr 1172  675    497
## 2  20-May 1278  737    541
## 3  20-Jun 1228  694    534
## 4  20-Jul 1138  642    496
## 5  20-Aug  864  484    381
## 6  20-Sep 1000  587    413
## 7  20-Oct 1028  605    423
## 8  20-Nov  967  540    427
## 9  20-Dec 1135  594    542
## 10 21-Jan 1570  789    781
## 11 21-Feb 1353  739    614
## 12 21-Mar 1215  682    534
## 13 21-Apr 1147  653    494
# commit x-bar to prevent alphabetical reordering
unemployed$Month <- factor(unemployed$Month, levels = unemployed$Month)
# econ_active plot
unemployed %>%
  gather("Gender", "Population", -Month) %>%
  ggplot(aes(x= Month, y = Population, fill = Gender)) +
  geom_bar(width = 0.7, position = "dodge", stat = "identity") +
  theme_minimal() +
  labs(title = "Unemployed Population By Gender | Korea 2020 - 2021", 
       x = "Month", y = "Population (thousands)") + 
  theme(plot.title = element_text(face = "bold"),
        axis.title.x = element_text(face = "bold"),
        axis.title.y = element_text(face = "bold"),
        legend.title = element_text(face = "bold")
  ) + 
  coord_cartesian(ylim = c(300,1600)) +
  scale_y_continuous(limits = c(0, 1600), breaks=seq(0, 1600, 100)) +
  scale_fill_manual(values = c("Female" = "#ff69b4", 
                               "Male" = "#00bfff",
                               "All" = "#90ee90"))

# perc participation csv
participation <- read.csv("perc_participation.csv")
colnames(participation)[colnames(participation) == "ï..Month"] <- "Month" #change colname
participation
##     Month  All Male Female
## 1  20-Apr 62.0 72.3   52.0
## 2  20-May 63.0 73.2   53.2
## 3  20-Jun 63.2 73.2   53.5
## 4  20-Jul 63.1 73.1   53.4
## 5  20-Aug 62.4 72.4   52.7
## 6  20-Sep 62.5 72.6   52.7
## 7  20-Oct 62.7 72.8   52.9
## 8  20-Nov 62.8 72.7   53.3
## 9  20-Dec 61.6 71.6   51.8
## 10 21-Jan 60.9 71.0   51.1
## 11 21-Feb 61.6 71.7   51.8
## 12 21-Mar 62.5 72.6   52.7
## 13 21-Apr 63.0 72.9   53.4
# commit x-bar to prevent alphabetical reordering
participation$Month <- factor(participation$Month, levels = participation$Month)
# econ_active plot
participation %>%
  gather("Gender", "Percentage", -Month) %>%
  ggplot(aes(x= Month, y = Percentage, fill = Gender)) +
  geom_bar(width = 0.7, position = "dodge", stat = "identity") +
  theme_minimal() +
  labs(title = "Percentage Workforce Participation By Gender | Korea 2020 - 2021", 
       x = "Month", y = "Percentage") + 
  theme(plot.title = element_text(face = "bold"),
        axis.title.x = element_text(face = "bold"),
        axis.title.y = element_text(face = "bold"),
        legend.title = element_text(face = "bold")
  ) + 
  coord_cartesian(ylim = c(45, 75)) +
  scale_y_continuous(breaks=seq(45, 75, 5), labels = scales::percent_format(scale = 1)) +
  scale_fill_manual(values = c("Female" = "#ff69b4", 
                               "Male" = "#00bfff",
                               "All" = "#90ee90"))

# unemployed% csv
unemployed_perc <- read.csv("Unemployed%.csv")
colnames(unemployed_perc)[colnames(unemployed_perc) == "ï..Month"] <- "Month" #change colname
unemployed_perc
##     Month All Male Female
## 1  20-Apr 4.2  4.2    4.2
## 2  20-May 4.5  4.6    4.5
## 3  20-Jun 4.3  4.3    4.4
## 4  20-Jul 4.0  4.0    4.1
## 5  20-Aug 3.1  3.0    3.2
## 6  20-Sep 3.6  3.7    3.4
## 7  20-Oct 3.7  3.8    3.5
## 8  20-Nov 3.4  3.4    3.5
## 9  20-Dec 4.1  3.7    4.6
## 10 21-Jan 5.7  5.0    6.7
## 11 21-Feb 4.9  4.7    5.2
## 12 21-Mar 4.3  4.2    4.4
## 13 21-Apr 4.0  4.0    4.1
# commit x-bar to prevent alphabetical reordering
unemployed_perc$Month <- factor(unemployed_perc$Month, levels = unemployed_perc$Month)
# econ_active plot
unemployed_perc %>%
  gather("Gender", "Percentage", -Month) %>%
  ggplot(aes(x= Month, y = Percentage, fill = Gender)) +
  geom_bar(width = 0.7, position = "dodge", stat = "identity") +
  theme_minimal() +
  labs(title = "Percentage Unemployment By Gender | Korea 2020 - 2021", 
       x = "Month", y = "Percentage") + 
  theme(plot.title = element_text(face = "bold"),
        axis.title.x = element_text(face = "bold"),
        axis.title.y = element_text(face = "bold"),
        legend.title = element_text(face = "bold")
  ) + 
  coord_cartesian(ylim = c(3, 7)) +
  scale_y_continuous(breaks = seq(3, 7, 0.5), labels = scales::percent_format(scale = 1)) +
  scale_fill_manual(values = c("Female" = "#ff69b4", 
                               "Male" = "#00bfff",
                               "All" = "#90ee90"))

# Employed% csv
employed_perc <- read.csv("Employed%.csv")
colnames(employed_perc)[colnames(employed_perc) == "ï..Month"] <- "Month" #change colname
employed_perc
##     Month  All Male Female
## 1  20-Apr 59.4 69.3   49.8
## 2  20-May 60.2 69.9   50.8
## 3  20-Jun 60.4 70.1   51.1
## 4  20-Jul 60.5 70.2   51.2
## 5  20-Aug 60.4 70.2   51.0
## 6  20-Sep 60.3 70.0   50.9
## 7  20-Oct 60.4 70.0   51.1
## 8  20-Nov 60.7 70.3   51.4
## 9  20-Dec 59.1 68.9   49.5
## 10 21-Jan 57.4 67.4   47.7
## 11 21-Feb 58.6 68.4   49.1
## 12 21-Mar 59.8 69.6   50.4
## 13 21-Apr 60.4 70.0   51.2
# commit x-bar to prevent alphabetical reordering
employed_perc$Month <- factor(employed_perc$Month, levels = employed_perc$Month)
# econ_active plot
employed_perc %>%
  gather("Gender", "Percentage", -Month) %>%
  ggplot(aes(x= Month, y = Percentage, fill = Gender)) +
  geom_bar(width = 0.7, position = "dodge", stat = "identity") +
  theme_minimal() +
  labs(title = "Percentage Employment By Gender | Korea 2020 - 2021", 
       x = "Month", y = "Percentage") + 
  theme(plot.title = element_text(face = "bold"),
        axis.title.x = element_text(face = "bold"),
        axis.title.y = element_text(face = "bold"),
        legend.title = element_text(face = "bold")
  ) +
  coord_cartesian(ylim = c(48, 71)) +
  scale_y_continuous(breaks = seq(48, 71, 5), labels = scales::percent_format(scale = 1)) +
  scale_fill_manual(values = c("Female" = "#ff69b4", 
                               "Male" = "#00bfff",
                               "All" = "#90ee90"))