Employment by activities and status (ALFS)
Data - OECD
François Geerolf
Info
| source | dataset | .html | .RData |
|---|---|---|---|
| oecd | ALFS_EMP | 2024-04-15 | 2024-01-26 |
Data on employment
| source | dataset | .html | .RData |
|---|---|---|---|
| bls | jt | 2024-03-20 | NA |
| bls | la | 2024-01-06 | NA |
| bls | ln | 2024-01-06 | NA |
| eurostat | nama_10_a10_e | 2024-04-15 | 2024-04-09 |
| eurostat | nama_10_a64_e | 2024-04-15 | 2024-04-15 |
| eurostat | namq_10_a10_e | 2024-04-15 | 2024-04-15 |
| eurostat | une_rt_m | 2024-04-15 | 2024-04-09 |
| oecd | ALFS_EMP | 2024-04-15 | 2024-01-26 |
| oecd | EPL_T | 2024-04-15 | 2023-12-10 |
| oecd | LFS_SEXAGE_I_R | 2024-04-15 | 2024-04-15 |
| oecd | STLABOUR | 2024-04-15 | 2024-04-15 |
Data on industry
| source | dataset | .html | .RData |
|---|---|---|---|
| ec | INDUSTRY | 2023-10-01 | 2023-10-01 |
| eurostat | ei_isin_m | 2024-04-15 | 2024-04-09 |
| eurostat | htec_trd_group4 | 2024-04-15 | 2024-04-09 |
| eurostat | nama_10_a64 | 2024-04-01 | 2024-04-15 |
| eurostat | nama_10_a64_e | 2024-04-15 | 2024-04-15 |
| eurostat | namq_10_a10_e | 2024-04-15 | 2024-04-15 |
| eurostat | road_eqr_carmot | 2024-04-15 | 2024-04-09 |
| eurostat | sts_inpp_m | 2024-04-15 | 2024-04-15 |
| eurostat | sts_inppd_m | 2024-04-15 | 2024-04-15 |
| eurostat | sts_inpr_m | 2024-04-15 | 2024-04-09 |
| eurostat | sts_intvnd_m | 2024-04-15 | 2024-04-15 |
| fred | industry | 2024-04-15 | 2024-04-15 |
| oecd | ALFS_EMP | 2024-04-15 | 2024-01-26 |
| oecd | BERD_MA_SOF | 2024-04-15 | 2023-09-09 |
| oecd | GBARD_NABS2007 | 2024-04-15 | 2023-11-22 |
| oecd | MEI_REAL | 2024-04-15 | 2024-04-08 |
| oecd | MSTI_PUB | 2024-04-15 | 2023-10-04 |
| oecd | SNA_TABLE4 | 2024-04-15 | 2024-02-11 |
| wdi | NV.IND.EMPL.KD | 2024-01-06 | 2024-04-14 |
| wdi | NV.IND.MANF.CD | 2024-04-14 | 2024-04-14 |
| wdi | NV.IND.MANF.ZS | 2024-01-06 | 2024-04-14 |
| wdi | NV.IND.TOTL.KD | 2024-01-06 | 2024-04-14 |
| wdi | NV.IND.TOTL.ZS | 2024-01-06 | 2024-04-14 |
| wdi | SL.IND.EMPL.ZS | 2024-01-06 | 2024-04-14 |
| wdi | TX.VAL.MRCH.CD.WT | 2024-01-06 | 2024-04-14 |
LAST_COMPILE
| LAST_COMPILE |
|---|
| 2024-04-16 |
Last
| obsTime | Nobs |
|---|---|
| 2022 | 3160 |
SUBJECT
SEX
ALFS_EMP %>%
left_join(ALFS_EMP_var$SEX, by = "SEX") %>%
group_by(SEX, Sex) %>%
summarise(Nobs = n()) %>%
arrange(-Nobs) %>%
print_table_conditional()| SEX | Sex | Nobs |
|---|---|---|
| TT | All persons | 95634 |
| FE | Females | 63040 |
| MA | Males | 55914 |
LOCATION
ALFS_EMP %>%
left_join(ALFS_EMP_var$LOCATION, by = "LOCATION") %>%
group_by(LOCATION, Location) %>%
summarise(Nobs = n()) %>%
arrange(-Nobs) %>%
mutate(Flag = gsub(" ", "-", str_to_lower(gsub(" ", "-", Location))),
Flag = paste0('<img src="../../icon/flag/vsmall/', Flag, '.png" alt="Flag">')) %>%
select(Flag, everything()) %>%
{if (is_html_output()) datatable(., filter = 'top', rownames = F, escape = F) else .}Individual countries
United States
Table
Agriculture
ALFS_EMP %>%
left_join(ALFS_EMP_var$SUBJECT, by = "SUBJECT") %>%
filter(SEX == "TT",
LOCATION == "USA",
grepl("Agriculture", Subject)) %>%
year_to_date %>%
ggplot + theme_minimal() + xlab("") + ylab("Employment in Agriculture") +
geom_line(aes(x = date, y = obsValue, color = paste0(SUBJECT, " - ", Subject))) +
scale_x_date(breaks = seq(1950, 2025, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_continuous(breaks = seq(0, 80000, 500),
labels = dollar_format(accuracy = 1, prefix = "")) +
theme(legend.position = c(0.7, 0.80),
legend.title = element_blank())
Industry
ALFS_EMP %>%
left_join(ALFS_EMP_var$SUBJECT, by = "SUBJECT") %>%
filter(SEX == "TT",
LOCATION == "USA",
grepl("Industry", Subject)) %>%
year_to_date %>%
ggplot + theme_minimal() + xlab("") + ylab("Employment in Manufacturing") +
geom_line(aes(x = date, y = obsValue, color = paste0(SUBJECT, " - ", Subject))) +
scale_x_date(breaks = seq(1950, 2025, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_continuous(breaks = seq(0, 80000, 500),
labels = dollar_format(accuracy = 1, prefix = "")) +
theme(legend.position = c(0.7, 0.80),
legend.title = element_blank())
Manufacturing
ALFS_EMP %>%
left_join(ALFS_EMP_var$SUBJECT, by = "SUBJECT") %>%
filter(SEX == "TT",
LOCATION == "USA",
grepl("Manufacturing", Subject)) %>%
year_to_date %>%
ggplot + theme_minimal() + xlab("") + ylab("Employment in Manufacturing") +
geom_line(aes(x = date, y = obsValue, color = paste0(SUBJECT, " - ", Subject))) +
scale_x_date(breaks = seq(1950, 2025, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_continuous(breaks = seq(0, 80000, 500),
labels = dollar_format(accuracy = 1, prefix = "")) +
theme(legend.position = c(0.7, 0.80),
legend.title = element_blank())
Construction
ALFS_EMP %>%
left_join(ALFS_EMP_var$SUBJECT, by = "SUBJECT") %>%
filter(SEX == "TT",
LOCATION == "USA",
grepl("Construction", Subject)) %>%
year_to_date %>%
ggplot + theme_minimal() + xlab("") + ylab("Employment in Construction") +
geom_line(aes(x = date, y = obsValue, color = paste0(SUBJECT, " - ", Subject))) +
scale_x_date(breaks = seq(1940, 2030, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_continuous(breaks = seq(0, 8000, 100),
labels = dollar_format(accuracy = 1, prefix = "")) +
theme(legend.position = c(0.7, 0.80),
legend.title = element_blank())
All activities
ALFS_EMP %>%
left_join(ALFS_EMP_var$SUBJECT, by = "SUBJECT") %>%
filter(SEX == "TT",
LOCATION == "USA",
grepl("all activities", Subject),
!(SUBJECT %in% c("YS99TTL4_ST", "YS99TTL3_ST"))) %>%
year_to_date %>%
ggplot + theme_minimal() + xlab("") + ylab("Total Employment") +
geom_line(aes(x = date, y = obsValue, color = paste0(SUBJECT, " - ", Subject))) +
scale_x_date(breaks = seq(1940, 2020, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_continuous(breaks = seq(0, 100000, 2000),
labels = dollar_format(accuracy = 1, prefix = "")) +
theme(legend.position = c(0.4, 0.80),
legend.title = element_blank())
United Kingdom
Table
Agriculture
ALFS_EMP %>%
left_join(ALFS_EMP_var$SUBJECT, by = "SUBJECT") %>%
filter(SEX == "TT",
LOCATION == "GBR",
grepl("Agriculture", Subject)) %>%
year_to_date %>%
ggplot + theme_minimal() + xlab("") + ylab("Employment in Agriculture") +
geom_line(aes(x = date, y = obsValue, color = paste0(SUBJECT, " - ", Subject))) +
scale_x_date(breaks = seq(1950, 2025, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_continuous(breaks = seq(0, 80000, 500),
labels = dollar_format(accuracy = 1, prefix = "")) +
#
theme(legend.position = c(0.7, 0.80),
legend.title = element_blank())
Industry
ALFS_EMP %>%
left_join(ALFS_EMP_var$SUBJECT, by = "SUBJECT") %>%
filter(SEX == "TT",
LOCATION == "GBR",
grepl("Industry", Subject)) %>%
year_to_date %>%
ggplot + theme_minimal() + xlab("") + ylab("Employment in Manufacturing") +
geom_line(aes(x = date, y = obsValue, color = paste0(SUBJECT, " - ", Subject))) +
scale_x_date(breaks = seq(1950, 2025, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_continuous(breaks = seq(0, 80000, 500),
labels = dollar_format(accuracy = 1, prefix = "")) +
#
theme(legend.position = c(0.7, 0.80),
legend.title = element_blank())
Manufacturing
ALFS_EMP %>%
left_join(ALFS_EMP_var$SUBJECT, by = "SUBJECT") %>%
filter(SEX == "TT",
LOCATION == "GBR",
grepl("Manufacturing", Subject)) %>%
year_to_date %>%
ggplot + theme_minimal() + xlab("") + ylab("Employment in Manufacturing") +
geom_line(aes(x = date, y = obsValue, color = paste0(SUBJECT, " - ", Subject))) +
scale_x_date(breaks = seq(1950, 2025, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_continuous(breaks = seq(0, 80000, 500),
labels = dollar_format(accuracy = 1, prefix = "")) +
theme(legend.position = c(0.7, 0.80),
legend.title = element_blank())
Construction
ALFS_EMP %>%
left_join(ALFS_EMP_var$SUBJECT, by = "SUBJECT") %>%
filter(SEX == "TT",
LOCATION == "USA",
grepl("Construction", Subject)) %>%
year_to_date %>%
ggplot + theme_minimal() + xlab("") + ylab("Employment in Construction") +
geom_line(aes(x = date, y = obsValue, color = paste0(SUBJECT, " - ", Subject))) +
scale_x_date(breaks = seq(1940, 2030, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_continuous(breaks = seq(0, 8000, 100),
labels = dollar_format(accuracy = 1, prefix = "")) +
theme(legend.position = c(0.7, 0.80),
legend.title = element_blank())
All activities
ALFS_EMP %>%
left_join(ALFS_EMP_var$SUBJECT, by = "SUBJECT") %>%
filter(SEX == "TT",
LOCATION == "USA",
grepl("all activities", Subject),
!(SUBJECT %in% c("YS99TTL4_ST", "YS99TTL3_ST"))) %>%
year_to_date %>%
ggplot + theme_minimal() + xlab("") + ylab("Total Employment") +
geom_line(aes(x = date, y = obsValue, color = paste0(SUBJECT, " - ", Subject))) +
scale_x_date(breaks = seq(1940, 2020, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_continuous(breaks = seq(0, 500000, 2000),
labels = dollar_format(accuracy = 1, prefix = "")) +
theme(legend.position = c(0.4, 0.80),
legend.title = element_blank())
France
Table
Agriculture
ALFS_EMP %>%
left_join(ALFS_EMP_var$SUBJECT, by = "SUBJECT") %>%
filter(SEX == "TT",
LOCATION == "FRA",
grepl("Agriculture", Subject)) %>%
year_to_date %>%
ggplot + theme_minimal() + xlab("") + ylab("Employment in Agriculture") +
geom_line(aes(x = date, y = obsValue, color = paste0(SUBJECT, " - ", Subject))) +
scale_x_date(breaks = seq(1950, 2025, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_continuous(breaks = seq(0, 8000, 500),
labels = dollar_format(accuracy = 1, prefix = "")) +
theme(legend.position = c(0.7, 0.80),
legend.title = element_blank())
Manufacturing
ALFS_EMP %>%
left_join(ALFS_EMP_var$SUBJECT, by = "SUBJECT") %>%
filter(SEX == "TT",
LOCATION == "FRA",
grepl("Manufacturing", Subject)) %>%
year_to_date %>%
ggplot + theme_minimal() + xlab("") + ylab("Employment in Manufacturing") +
geom_line(aes(x = date, y = obsValue, color = paste0(SUBJECT, " - ", Subject))) +
scale_x_date(breaks = seq(1950, 2025, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_continuous(breaks = seq(0, 8000, 500),
labels = dollar_format(accuracy = 1, prefix = "")) +
theme(legend.position = c(0.7, 0.80),
legend.title = element_blank())
Construction
ALFS_EMP %>%
left_join(ALFS_EMP_var$SUBJECT, by = "SUBJECT") %>%
filter(SEX == "TT",
LOCATION == "FRA",
grepl("Construction", Subject)) %>%
year_to_date %>%
ggplot + theme_minimal() + xlab("") + ylab("Employment in Manufacturing") +
geom_line(aes(x = date, y = obsValue, color = paste0(SUBJECT, " - ", Subject))) +
scale_x_date(breaks = seq(1940, 2030, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_continuous(breaks = seq(0, 8000, 100),
labels = dollar_format(accuracy = 1, prefix = "")) +
theme(legend.position = c(0.7, 0.80),
legend.title = element_blank())
All activities
ALFS_EMP %>%
left_join(ALFS_EMP_var$SUBJECT, by = "SUBJECT") %>%
filter(SEX == "TT",
LOCATION == "FRA",
grepl("all activities", Subject),
!(SUBJECT %in% c("YS99TTL4_ST", "YS99TTL3_ST"))) %>%
year_to_date %>%
ggplot + theme_minimal() + xlab("") + ylab("Total Employment") +
geom_line(aes(x = date, y = obsValue, color = paste0(SUBJECT, " - ", Subject))) +
scale_x_date(breaks = seq(1940, 2020, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_continuous(breaks = seq(0, 100000, 2000),
labels = dollar_format(accuracy = 1, prefix = "")) +
theme(legend.position = c(0.4, 0.80),
legend.title = element_blank())
Germany
Table
Manufacturing
ALFS_EMP %>%
left_join(ALFS_EMP_var$SUBJECT, by = "SUBJECT") %>%
year_to_date %>%
filter(SEX == "TT",
LOCATION == "DEU",
grepl("Manufacturing", Subject),
date >= as.Date("1958-01-01")) %>%
ggplot + theme_minimal() + xlab("") + ylab("Employment in Manufacturing") +
geom_line(aes(x = date, y = obsValue, color = paste0(SUBJECT, " - ", Subject))) +
scale_x_date(breaks = seq(1940, 2030, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_continuous(breaks = seq(0, 20000, 500),
labels = dollar_format(accuracy = 1, prefix = "")) +
theme(legend.position = c(0.7, 0.80),
legend.title = element_blank())
Construction
ALFS_EMP %>%
year_to_date %>%
left_join(ALFS_EMP_var$SUBJECT, by = "SUBJECT") %>%
filter(SEX == "TT",
LOCATION == "DEU",
grepl("Construction", Subject),
date >= as.Date("1961-01-01")) %>%
ggplot + theme_minimal() + xlab("") + ylab("Employment in Construction") +
geom_line(aes(x = date, y = obsValue, color = paste0(SUBJECT, " - ", Subject))) +
scale_x_date(breaks = seq(1940, 2030, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_continuous(breaks = seq(0, 8000, 100),
labels = dollar_format(accuracy = 1, prefix = "")) +
theme(legend.position = c(0.3, 0.80),
legend.title = element_blank())
All activities
ALFS_EMP %>%
left_join(ALFS_EMP_var$SUBJECT, by = "SUBJECT") %>%
filter(SEX == "TT",
LOCATION == "DEU",
grepl("all activities", Subject),
!(SUBJECT %in% c("YS99TTL4_ST", "YS99TTL3_ST"))) %>%
year_to_date %>%
ggplot + theme_minimal() + xlab("") + ylab("Total Employment") +
geom_line(aes(x = date, y = obsValue, color = paste0(SUBJECT, " - ", Subject))) +
scale_x_date(breaks = seq(1940, 2020, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_continuous(breaks = seq(0, 100000, 2000),
labels = dollar_format(accuracy = 1, prefix = "")) +
theme(legend.position = c(0.4, 0.80),
legend.title = element_blank())
Italy
Table
Manufacturing
ALFS_EMP %>%
left_join(ALFS_EMP_var$SUBJECT, by = "SUBJECT") %>%
filter(SEX == "TT",
LOCATION == "ITA",
grepl("Manufacturing", Subject)) %>%
year_to_date %>%
ggplot + theme_minimal() + xlab("") + ylab("Employment in Manufacturing") +
geom_line(aes(x = date, y = obsValue, color = paste0(SUBJECT, " - ", Subject))) +
scale_x_date(breaks = seq(1940, 2030, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_continuous(breaks = seq(0, 8000, 500),
labels = dollar_format(accuracy = 1, prefix = "")) +
theme(legend.position = c(0.7, 0.80),
legend.title = element_blank())
Construction
ALFS_EMP %>%
year_to_date %>%
left_join(ALFS_EMP_var$SUBJECT, by = "SUBJECT") %>%
filter(SEX == "TT",
LOCATION == "ITA",
grepl("Construction", Subject),
date >= as.Date("1961-01-01")) %>%
ggplot + theme_minimal() + xlab("") + ylab("Employment in Construction") +
geom_line(aes(x = date, y = obsValue, color = paste0(SUBJECT, " - ", Subject))) +
scale_x_date(breaks = seq(1940, 2030, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_continuous(breaks = seq(0, 8000, 100),
labels = dollar_format(accuracy = 1, prefix = "")) +
theme(legend.position = c(0.3, 0.80),
legend.title = element_blank())
All activities
ALFS_EMP %>%
left_join(ALFS_EMP_var$SUBJECT, by = "SUBJECT") %>%
filter(SEX == "TT",
LOCATION == "ITA",
grepl("all activities", Subject),
!(SUBJECT %in% c("YS99TTL4_ST", "YS99TTL3_ST"))) %>%
year_to_date %>%
ggplot + theme_minimal() + xlab("") + ylab("Total Employment") +
geom_line(aes(x = date, y = obsValue, color = paste0(SUBJECT, " - ", Subject))) +
scale_x_date(breaks = seq(1940, 2020, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_continuous(breaks = seq(0, 100000, 2000),
labels = dollar_format(accuracy = 1, prefix = "")) +
theme(legend.position = c(0.4, 0.80),
legend.title = element_blank())
Spain
Table
Manufacturing
ALFS_EMP %>%
left_join(ALFS_EMP_var$SUBJECT, by = "SUBJECT") %>%
filter(SEX == "TT",
LOCATION == "ESP",
grepl("Manufacturing", Subject)) %>%
year_to_date %>%
ggplot + theme_minimal() + xlab("") + ylab("Employment in Manufacturing") +
geom_line(aes(x = date, y = obsValue, color = paste0(SUBJECT, " - ", Subject))) +
scale_x_date(breaks = seq(1940, 2030, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_continuous(breaks = seq(0, 8000, 500),
labels = dollar_format(accuracy = 1, prefix = "")) +
theme(legend.position = c(0.7, 0.80),
legend.title = element_blank())
Construction
ALFS_EMP %>%
year_to_date %>%
left_join(ALFS_EMP_var$SUBJECT, by = "SUBJECT") %>%
filter(SEX == "TT",
LOCATION == "ESP",
grepl("Construction", Subject),
date >= as.Date("1961-01-01")) %>%
ggplot + theme_minimal() + xlab("") + ylab("Employment in Construction") +
geom_line(aes(x = date, y = obsValue, color = paste0(SUBJECT, " - ", Subject))) +
scale_x_date(breaks = seq(1940, 2030, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_continuous(breaks = seq(0, 8000, 100),
labels = dollar_format(accuracy = 1, prefix = "")) +
theme(legend.position = c(0.3, 0.80),
legend.title = element_blank())
All activities
ALFS_EMP %>%
left_join(ALFS_EMP_var$SUBJECT, by = "SUBJECT") %>%
filter(SEX == "TT",
LOCATION == "ESP",
grepl("all activities", Subject),
!(SUBJECT %in% c("YS99TTL4_ST", "YS99TTL3_ST"))) %>%
year_to_date %>%
ggplot + theme_minimal() + xlab("") + ylab("Total Employment") +
geom_line(aes(x = date, y = obsValue, color = paste0(SUBJECT, " - ", Subject))) +
scale_x_date(breaks = seq(1940, 2020, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_continuous(breaks = seq(0, 100000, 2000),
labels = dollar_format(accuracy = 1, prefix = "")) +
theme(legend.position = c(0.4, 0.80),
legend.title = element_blank())
Japan
Table
Manufacturing
ALFS_EMP %>%
left_join(ALFS_EMP_var$SUBJECT, by = "SUBJECT") %>%
filter(SEX == "TT",
LOCATION == "JPN",
grepl("Manufacturing", Subject)) %>%
year_to_date %>%
ggplot + theme_minimal() + xlab("") + ylab("Employment in Manufacturing") +
geom_line(aes(x = date, y = obsValue, color = paste0(SUBJECT, " - ", Subject))) +
scale_x_date(breaks = seq(1940, 2030, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_continuous(breaks = seq(0, 20000, 1000),
labels = dollar_format(accuracy = 1, prefix = "")) +
theme(legend.position = c(0.5, 0.25),
legend.title = element_blank())
Construction
ALFS_EMP %>%
year_to_date %>%
left_join(ALFS_EMP_var$SUBJECT, by = "SUBJECT") %>%
filter(SEX == "TT",
LOCATION == "JPN",
grepl("Construction", Subject),
date >= as.Date("1961-01-01")) %>%
ggplot + theme_minimal() + xlab("") + ylab("Employment in Construction") +
geom_line(aes(x = date, y = obsValue, color = paste0(SUBJECT, " - ", Subject))) +
scale_x_date(breaks = seq(1940, 2030, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_continuous(breaks = seq(0, 8000, 100),
labels = dollar_format(accuracy = 1, prefix = "")) +
theme(legend.position = c(0.3, 0.80),
legend.title = element_blank())
All activities
ALFS_EMP %>%
left_join(ALFS_EMP_var$SUBJECT, by = "SUBJECT") %>%
filter(SEX == "TT",
LOCATION == "JPN",
grepl("all activities", Subject),
!(SUBJECT %in% c("YS99TTL4_ST", "YS99TTL3_ST"))) %>%
year_to_date %>%
ggplot + theme_minimal() + xlab("") + ylab("Total Employment") +
geom_line(aes(x = date, y = obsValue, color = paste0(SUBJECT, " - ", Subject))) +
scale_x_date(breaks = seq(1940, 2020, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_continuous(breaks = seq(0, 100000, 2000),
labels = dollar_format(accuracy = 1, prefix = "")) +
theme(legend.position = c(0.4, 0.80),
legend.title = element_blank())
Manufacturing
Table
ALFS_EMP %>%
# YA9903L1_ST: Employees in Industry (ISIC rev.2, 2-5)
# YS99TTL2_ST: Employees in all activities
filter(SUBJECT %in% c("YA9903L1_ST", "YS99TTL2_ST"),
SEX == "TT") %>%
select(LOCATION, SUBJECT, obsTime, obsValue) %>%
spread(SUBJECT, obsValue) %>%
mutate(share = (100*YA9903L1_ST/YS99TTL2_ST) %>% round(., digits = 1)) %>%
left_join(ALFS_EMP_var$LOCATION, by = "LOCATION") %>%
filter(!is.na(share)) %>%
mutate(share = paste0(share, " %")) %>%
group_by(Location) %>%
arrange(obsTime) %>%
summarise(`Year 1` = first(obsTime),
`Year 2` = last(obsTime),
`Share 1` = first(share),
`Share 2` = last(share)) %>%
mutate(Flag = gsub(" ", "-", str_to_lower(gsub(" ", "-", Location))),
Flag = paste0('<img src="../../icon/flag/vsmall/', Flag, '.png" alt="Flag">')) %>%
select(Flag, everything()) %>%
{if (is_html_output()) datatable(., filter = 'top', rownames = F, escape = F) else .}Industry
Table
ALFS_EMP %>%
# YW992IL1_ST: Employees in Industry (ISIC rev.2, 2-5)
# YS99TTL2_ST: Employees in all activities
filter(SUBJECT %in% c("YW992IL1_ST", "YS99TTL2_ST"),
SEX == "TT") %>%
select(LOCATION, SUBJECT, obsTime, obsValue) %>%
spread(SUBJECT, obsValue) %>%
mutate(share = (100*YW992IL1_ST/YS99TTL2_ST) %>% round(., digits = 1)) %>%
left_join(ALFS_EMP_var$LOCATION, by = "LOCATION") %>%
filter(!is.na(share)) %>%
mutate(share = paste0(share, " %")) %>%
group_by(Location) %>%
summarise(`Year 1` = first(obsTime),
`Year 2` = last(obsTime),
`Share 1` = first(share),
`Share 2` = last(share)) %>%
mutate(Flag = gsub(" ", "-", str_to_lower(gsub(" ", "-", Location))),
Flag = paste0('<img src="../../icon/flag/vsmall/', Flag, '.png" alt="Flag">')) %>%
select(Flag, everything()) %>%
{if (is_html_output()) datatable(., filter = 'top', rownames = F, escape = F) else .}