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Public vs Private Sector Employment in Canada

Graphs of public vs private sector employment in Canada using STATCAN data

Data

STATCAN Table: 14-10-0288-01 (Employment by class of worker)

Prepare Data

# devtools::install_github("derekmichaelwright/agData")
library(agData)
# Prep data
myCaption = "www.dblogr.com/ or derekmichaelwright.github.io/dblogr/ | Data: STATCAN"
myColors_Ind <- c("darkgreen", "darkslategray", "darkred", "steelblue")
myColors_Sex <- c("palevioletred3","steelblue")
myMeasures <- c("Self-employed", "Private sector", "Public sector", "Total")
myDates <- as.Date(c("2010-01-01","2015-01-01","2020-01-01"))
#
d1 <- read.csv("1410028801_databaseLoadingData.csv") %>%
  select(Area=GEO, Date=REF_DATE, Measurement=Class.of.worker, 
         Sex, Unit=UOM, Scale=SCALAR_FACTOR, Value=VALUE) %>%
  mutate(Value = Value * 1000,
         Area = factor(Area, levels = agData_STATCAN_Region_Table$Area),
         Date = as.Date(paste0(Date, "-15"), format = "%Y-%m-%d"),
         Measurement = plyr::mapvalues(Measurement, "Total employed, all classes of workers", "Total"),
         Measurement = gsub(" employees", "", Measurement),
         Measurement = factor(Measurement, levels = myMeasures))
d2 <- d1 %>% 
  mutate(Year = as.numeric(substr(Date, 1, 4))) %>%
  group_by(Area, Year, Measurement, Sex) %>%
  summarise(Value = sum(Value)) %>% ungroup() %>%
  group_by(Area, Year, Sex) %>% mutate(Total = sum(Value)) %>% ungroup() %>%
  mutate(Percent = 100 * Value / Total)
unique(d1$Measurement)
## [1] Total          Public sector  Private sector Self-employed 
## Levels: Self-employed Private sector Public sector Total

Private vs Government

Canada

# Prep data
xx <- d1 %>% 
  filter(Area == "Canada", Sex == "Both sexes", Measurement != "Total")
# Plot
mp <- ggplot(xx, aes(x = Date, y = Value / 1000000, color = Measurement)) +
  geom_vline(xintercept = as.Date("2020-03-01"), lty = 2, alpha = 0.5) +
  geom_line(size = 1, alpha = 0.7) +
  scale_color_manual(name = NULL, values = myColors_Ind) +
  scale_y_continuous(breaks = 0:13, minor_breaks = 0:13, limits = c(0,13)) +
  theme_agData(legend.position = "bottom") +
  labs(title = "Employment in Canada", x = NULL,
       y = "Million Jobs", caption = myCaption)
ggsave("canada_public_private_1_01.png", mp, width = 6, height = 4)

Provinces

# Prep data
xx <- d1 %>% 
  filter(Area != "Canada", Sex == "Both sexes", Measurement != "Total")
# Plot
mp <- ggplot(xx, aes(x = Date, y = Value / 1000000, color = Measurement)) +
  geom_vline(xintercept = as.Date("2020-03-01"), lty = 2, alpha = 0.5) +
  geom_line(alpha = 0.7) +
  scale_color_manual(name = NULL, values = myColors_Ind) +
  facet_wrap(Area ~ ., scales = "free_y", ncol = 5) +
  theme_agData(legend.position = "bottom") +
  labs(title = "Employment in Canada", x = NULL,
       y = "Million Jobs", caption = myCaption)
ggsave("canada_public_private_1_02.png", mp, width = 12, height = 5)

Provinces > 2010

# Prep data
xx <- xx %>% filter(Date > "2010-01-01")
# Plot
mp <- ggplot(xx, aes(x = Date, y = Value / 1000000, color = Measurement)) +
  geom_vline(xintercept = as.Date("2020-03-01"), lty = 2, alpha = 0.5) +
  geom_line(alpha = 0.7, size = 1) +
  scale_color_manual(name = NULL, values = myColors_Ind) +
  scale_x_date(breaks = myDates, minor_breaks = "1 year", date_labels = "%Y") +
  facet_wrap(Area ~ ., scales = "free_y", ncol = 5) +
  theme_agData(legend.position = "bottom") +
  labs(title = "Employment in Canada", x = NULL,
       y = "Million Jobs", caption = myCaption)
ggsave("canada_public_private_1_03.png", mp, width = 12, height = 5)

Scaled

Create plotting function

# Create plotting function
gg_Priv_Gov <- function(myArea = "Canada") {
  # Prep data
  xx <- d1 %>% 
    filter(Sex == "Both sexes", Date > "2010-01-01",
           Area == myArea, Measurement != "Total")
  # Plot
  ggplot(xx, aes(x = Date, y = Value / 1000000, color = Measurement)) +
    geom_vline(xintercept = as.Date("2020-03-01"), lty = 2, alpha = 0.5) +
    geom_line(size = 1, alpha = 0.7) +
    scale_color_manual(name = NULL, values = myColors_Ind) +
    scale_x_date(breaks = "1 year", minor_breaks = "1 year", date_labels = "%Y") +
    facet_wrap(. ~ Measurement, ncol = 3, scale = "free_y") +
    theme_agData(legend.position = "bottom",
                 axis.text.x = element_text(angle = 45, hjust = 1)) +
    labs(title = paste("Employment in", myArea), 
         y = "Million Jobs", x = NULL, caption = myCaption)
}

Canada

mp <- gg_Priv_Gov(myArea = "Canada")
ggsave("canada_public_private_2_01.png", mp, width = 10, height = 4)

Ontario

mp <- gg_Priv_Gov(myArea = "Ontario")
ggsave("canada_public_private_2_02.png", mp, width = 10, height = 4)

Alberta

mp <- gg_Priv_Gov(myArea = "Alberta")
ggsave("canada_public_private_2_03.png", mp, width = 10, height = 4)

Saskatchewan

mp <- gg_Priv_Gov(myArea = "Saskatchewan")
ggsave("canada_public_private_2_04.png", mp, width = 10, height = 4)

Change

Since Feburary

# Prep data
xx <- d1 %>% 
  filter(Area == "Canada", Sex == "Both sexes", 
         Date >= "2020-02-15", Measurement != "Total") %>%
  select(Date, Measurement, Value) %>%
  spread(Date, Value) %>%
  mutate(Start = `2020-02-15`) %>%
  select(Measurement, Start, everything()) %>%
  gather(Date, Value, 3:ncol(.)) %>%
  mutate(Date = as.Date(Date),
         PercentChange = 100 * (Value - Start) / Start)
# Plot
mp <- ggplot(xx, aes(x = Date, y = PercentChange, 
                     color = Measurement, group = Measurement)) +
  geom_hline(yintercept = 0, alpha = 0.7) + 
  geom_line(size = 1.5, alpha = 0.7) +
  scale_color_manual(name = NULL, values = myColors_Ind) +
  scale_x_date(breaks = unique(xx$Date), minor_breaks = unique(xx$Date),
               date_labels = "%Y-%m") +
  theme_agData(legend.position = "bottom",
               axis.text.x = element_text(angle = 45, hjust = 1)) +
  labs(title = "Percent change since Feburary 2020",
       x = NULL, y = NULL, caption = myCaption)
ggsave("canada_public_private_3_01.png", mp, width = 7, height = 4)

Since 2019-09-15

# Prep data
xx <- d1 %>% 
  filter(Area == "Canada", Sex == "Both sexes", 
         Date >= "2019-09-15", Measurement != "Total") %>%
  select(Date, Measurement, Value) %>%
  spread(Date, Value) %>%
  mutate(Start = `2019-09-15`) %>%
  select(Measurement, Start, everything()) %>%
  gather(Date, Value, 3:ncol(.)) %>%
  mutate(Date = as.Date(Date),
         PercentChange = 100 * (Value - Start) / Start)
# Plot
mp <- ggplot(xx, aes(x = Date, y = PercentChange, 
                     color = Measurement, group = Measurement)) +
  geom_hline(yintercept = 0, alpha = 0.7) + 
  geom_line(size = 1.5, alpha = 0.7) +
  scale_color_manual(name = NULL, values = myColors_Ind) +
  scale_x_date(breaks = unique(xx$Date), minor_breaks = unique(xx$Date),
               date_labels = "%Y-%m") +
  theme_agData(legend.position = "bottom",
               axis.text.x = element_text(angle = 45, hjust = 1)) +
  labs(title = "Percent change since 2019-09-15",
       x = NULL, y = NULL, caption = myCaption)
ggsave("canada_public_private_3_02.png", mp, width = 7, height = 4)

Percentages

Canada

# Prep data
xx <- d2 %>% filter(Area == "Canada", Sex == "Both sexes", Measurement != "Total")
# Plot
mp <- ggplot(xx, aes(x = Year, y = Percent, fill = Measurement)) +
  geom_col(color = "black", alpha = 0.7) +
  scale_fill_manual(name = NULL, values = myColors_Ind) +
  theme_agData(legend.position = "bottom") +
  labs(title = "Employment in Canada", x = NULL, caption = myCaption)
ggsave("canada_public_private_4_01.png", mp, width = 6, height = 4)

Sex

# Prep data
xx <- d2 %>% filter(Area == "Canada", Sex != "Both sexes", Measurement != "Total")
# Plot
mp <- ggplot(xx, aes(x = Year, y = Percent, fill = Measurement)) +
  geom_col(color = "black", alpha = 0.7) +
  facet_grid(. ~ Sex) +
  scale_fill_manual(name = NULL, values = myColors_Ind) +
  theme_agData(legend.position = "bottom") +
  labs(title = "Employment in Canada", x = NULL, caption = myCaption)
ggsave("canada_public_private_4_02.png", mp, width = 8, height = 4)

Provinces

# Prep data
xx <- d2 %>% filter(Area != "Canada", Sex == "Both sexes", Measurement != "Total")
# Plot
mp <- ggplot(xx, aes(x = Year, y = Percent, fill = Measurement)) +
  geom_col(color = "black", alpha = 0.7, lwd = 0.1) +
  facet_wrap(Area ~ ., ncol = 5) +
  scale_fill_manual(name = NULL, values = myColors_Ind) +
  theme_agData(legend.position = "bottom",
               axis.text.x = element_text(angle = 45, hjust = 1)) +
  labs(title = "Employment in Canada", x = NULL, caption = myCaption)
ggsave("canada_public_private_4_03.png", mp, width = 12, height = 5)

Males vs. Females

Canada

All Data

# Prep data
xx <- d1 %>% 
  filter(Area == "Canada", Sex != "Both sexes", Measurement != "Total")
# Plot
mp <- ggplot(xx, aes(x = Date, y = Value / 1000000, color = Sex)) +
  geom_vline(xintercept = as.Date("2020-03-01"), lty = 2, alpha = 0.5) +
  geom_line(size = 1, alpha = 0.8) +
  scale_color_manual(name = NULL, values = myColors_Sex) +
  facet_wrap(. ~ Measurement, ncol = 3, scales = "free_y") +
  theme_agData(legend.position = "bottom") +
  labs(title = "Employment in Canada", x = NULL,
       y = "Million Jobs", caption = myCaption)
ggsave("canada_public_private_5_01.png", mp, width = 10, height = 4)

> 2010

# Prep data
xx <- d1 %>% 
  filter(Area == "Canada", Sex != "Both sexes", 
         Date > "2010-01-01", Measurement != "Total")
# Plot
mp <- ggplot(xx, aes(x = Date, y = Value / 1000000, color = Sex)) +
  geom_vline(xintercept = as.Date("2020-03-01"), lty = 2, alpha = 0.5) +
  geom_line(size = 1, alpha = 0.8) +
  scale_color_manual(name = NULL, values = myColors_Sex) +
  scale_x_date(breaks = myDates, minor_breaks = "1 year", date_labels = "%Y") +
  facet_wrap(. ~ Measurement, ncol = 3, scales = "free_y") +
  theme_agData(legend.position = "bottom") +
  labs(title = "Employment in Canada", x = NULL,
       y = "Million Jobs", caption = myCaption)
ggsave("canada_public_private_5_02.png", mp, width = 10, height = 4)

Change

Since Februrary

# Prep data
xx <- d1 %>% 
  filter(Area == "Canada", Sex != "Both sexes", 
         Date %in% as.Date(c("2020-02-15","2022-05-15"))) %>%
  select(Date, Measurement, Sex, Value) %>%
  spread(Date, Value) %>%
  mutate(Change = `2022-05-15` - `2020-02-15`)
# Plot
mp <- ggplot(xx, aes(x = Sex, y = Change, fill = Sex)) +
  geom_col(position = "dodge", color = "black", alpha = 0.7) +
  facet_grid(. ~ Measurement) +
  scale_fill_manual(values = myColors_Sex) +
  theme_agData(legend.position = "none") +
  labs(title = "Change since Feburary 2020", x = NULL,
       y = "Thousand", caption = myCaption)
ggsave("canada_public_private_5_03.png", mp, width = 6, height = 4)

Since Februrary

# Prep data
xx <- d1 %>% 
  filter(Area == "Canada", Sex != "Both sexes", 
         Date %in% as.Date(c("2019-09-15","2022-05-15"))) %>%
  select(Date, Measurement, Sex, Value) %>%
  spread(Date, Value) %>%
  mutate(Change = `2022-05-15` - `2019-09-15`)
# Plot
mp <- ggplot(xx, aes(x = Sex, y = Change, fill = Sex)) +
  geom_col(position = "dodge", color = "black", alpha = 0.7) +
  facet_grid(. ~ Measurement) +
  scale_fill_manual(values = myColors_Sex) +
  theme_agData(legend.position = "none") +
  labs(title = "Change since 2019-09-15", x = NULL,
       y = "Thousand", caption = myCaption)
ggsave("canada_public_private_5_04.png", mp, width = 6, height = 4)

Public Sector

Provinces

# Prep data
xx <- d1 %>% 
  filter(Area != "Canada", Sex != "Both sexes",
         Measurement == "Public sector")
# Plot
mp <- ggplot(xx, aes(x = Date, y = Value / 1000000, color = Sex)) +
  geom_vline(xintercept = as.Date("2020-03-01"), lty = 2, alpha = 0.5) +
  geom_line(size = 0.8, alpha = 0.8) +
  scale_color_manual(name = NULL, values = myColors_Sex) +
  facet_wrap(Area ~ ., scales = "free_y", ncol = 5) +
  theme_agData(legend.position = "bottom") +
  labs(title = "Public Sector Employees", x = NULL,
       y = "Million Jobs", caption = myCaption)
ggsave("canada_public_private_6_01.png", mp, width = 12, height = 5)

# Prep data
xx <- xx %>% filter(Date > "2010-01-01")
# Plot
mp <- ggplot(xx, aes(x = Date, y = Value / 1000000, color = Sex)) +
  geom_vline(xintercept = as.Date("2020-03-01"), lty = 2, alpha = 0.5) +
  geom_line(size = 1, alpha = 0.8) +
  scale_color_manual(name = NULL, values = myColors_Sex) +
  scale_x_date(breaks = myDates, minor_breaks = "1 year", date_labels = "%Y") +
  facet_wrap(Area ~ ., scales = "free_y", ncol = 5) +
  theme_agData(legend.position = "bottom") +
  labs(title = "Public Sector Employees", x = NULL,
       y = "Million Jobs", caption = myCaption)
ggsave("canada_public_private_6_02.png", mp, width = 12, height = 5)

Self Employed

Canada

All Data

# Prep data
xx <- d1 %>% 
  filter(Area == "Canada", Sex == "Both sexes",
         Measurement == "Self-employed")
# Plot
mp <- ggplot(xx, aes(x = Date, y = Value / 1000000)) +
  geom_vline(xintercept = as.Date("2020-03-01"), lty = 2, alpha = 0.5) +
  geom_line(color = "darkgreen", size = 1.25, alpha = 0.7) +
  expand_limits(y = 0) +
  #scale_y_continuous(breaks = 0:3, minor_breaks = 0:3, limits = c(0,3)) +
  theme_agData() +
  labs(title = "Self-Employment in Canada", x = NULL,
       y = "Million Jobs", caption = myCaption)
ggsave("canada_public_private_7_01.png", mp, width = 6, height = 4)

> 2010

# Prep data
xx <- xx %>% filter(Date > "2010-01-01")
# Plot
mp <- ggplot(xx, aes(x = Date, y = Value / 1000000)) +
  geom_vline(xintercept = as.Date("2020-03-01"), lty = 2, alpha = 0.5) +
  geom_line(color = "darkgreen", size = 1.25, alpha = 0.7) +
  scale_x_date(breaks = myDates, minor_breaks = "1 year", date_labels = "%Y") +
  expand_limits(y = 0) +
  theme_agData() +
  labs(title = "Self-Employment in Canada", x = NULL,
       y = "Million Jobs", caption = myCaption)
ggsave("canada_public_private_7_02.png", mp, width = 6, height = 4)

Provinces

All Data

# Prep data
xx <- d1 %>% 
  filter(Area != "Canada", Sex == "Both sexes",
         Measurement == "Self-employed")
# Plot
mp <- ggplot(xx, aes(x = Date, y = Value / 1000000)) +
  geom_vline(xintercept = as.Date("2020-03-01"), lty = 2, alpha = 0.5) +
  geom_line(color = "darkgreen", alpha = 0.7) +
  facet_wrap(Area ~ ., scales = "free_y", ncol = 5) +
  expand_limits(y = 0) +
  theme_agData() +
  labs(title = "Self-Employment in Canada", x = NULL,
       y = "Million Jobs", caption = myCaption)
ggsave("canada_public_private_7_03.png", mp, width = 12, height = 5)

> 2010

# Prep data
xx <- xx %>% filter(Date > "2010-01-01")
# Plot
mp <- ggplot(xx, aes(x = Date, y = Value / 1000000)) +
  geom_vline(xintercept = as.Date("2020-03-01"), lty = 2, alpha = 0.5) +
  geom_line(color = "darkgreen", size = 1, alpha = 0.7) +
  facet_wrap(Area ~ ., scales = "free_y", ncol = 5) +
  scale_x_date(breaks = myDates, minor_breaks = "1 year", date_labels = "%Y") +
  expand_limits(y = 0) +
  theme_agData() +
  labs(title = "Self-Employment in Canada", x = NULL,
       y = "Million Jobs", caption = myCaption)
ggsave("canada_public_private_7_04.png", mp, width = 12, height = 5)

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