In pttry/ficomp: Short Run Price Competitiveness of Finland
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.height = 6,
fig.width = 8,
fig.path = "Report/",
echo = FALSE, message = FALSE, warning = FALSE
)
# library(ficomp)
devtools::load_all()
library(dplyr)
library(forcats)
library(ggplot2)
library(eurostat)
library(tidyr)
library(ggptt)
library(glue)
set_proj_theme()
data("data_main_total_a", "data_main_groups_a", "data_ameco")
base_year <- 2010
base_years <- 2000:2018
geos <- c(Finland = "FI", Sweden = "SE", Germany = "DE", France = "FR")
start_time <- "1995-01-01"
geo_trans <- geo_fi
nace0_trans <- nace0_fi
h_geos <- c("FI", "SE", "DE", "US")
Data
TODO
Yhdistelmä Eurostatin ja OECD:n National Accounts datasta
Maat ja vuodet.
data_main_groups_a %>%
filter(nace0 == "total") %>%
select(geo, time, values = nulc_aper_va) %>%
group_by(geo) %>%
mutate(tmin = min(time[!is.na(values)]),
tmax = max(time[!is.na(values)])) %>%
ungroup() %>%
mutate(selected = geo %in% c(eurostat_geos, oecd_geos_ulcq)) %>%
translate(geo, geo_trans) %>%
ggplot(aes(geo, ymin = tmin, ymax = tmax, colour = selected)) +
geom_linerange(size = 2, colour = geo_col(2)[1]) +
coord_flip() +
scale_colour_manual(values = rev(geo_col(2)), guide = "none") +
the_title_blank("y")
save_fig("A1_Eurostat_OECD_na")
data_ameco %>%
select(geo, time, values = nulc_aper) %>%
group_by(geo) %>%
mutate(is_value = !is.na(values)) %>%
ungroup() %>%
mutate(selected = geo %in% c(eurostat_geos, oecd_geos_ulcq)) %>%
translate(geo, geo_trans) %>%
ggplot(aes(geo, time, alpha = is_value)) +
geom_line(size = 2) +
coord_flip() +
scale_alpha_manual(values = c(0,1), guide = "none") +
# scale_colour_manual(values = rev(geo_col(2)), guide = "none") +
the_title_blank("y")
save_fig("A1_ameco_na")
Nimellinen yksikkötyökustannus koko talous
Tunnit, taso (2018) ja suhteellinen (2017, koska Yhdysvallat)
data_main_groups_a %>%
filter(nace0 == "total") %>%
select(geo, time, nulc_hw_va, nulc_hw_va_rel_imf) %>%
gather(vars, values, nulc_hw_va, nulc_hw_va_rel_imf) %>%
mutate(h_geo = fct_other(geo, keep = h_geos,other_level = "muut"),
h_geo = fct_relevel(h_geo, h_geos),
geo = fct_relevel(geo, rev(h_geos), after = Inf),
size = !(geo %in% h_geo)) %>%
translate(h_geo, geo_trans) %>%
translate(vars, c(nulc_aper = "Indeksi",
nulc_aper_rel_imf = "Suhteellinen indeksi")) %>%
ggplot(aes(time, values, group = geo, colour = h_geo, size = size)) +
facet_wrap(~ vars) +
geom_line() +
scale_size_manual(values = c(2,1), guide = "none") +
scale_colour_manual(values = geo_col(length(h_geos) + 1)) +
the_title_blank(c("x", "l")) +
scale_y_continuous(limits = c(70,130), expand = expansion(mult = 0, add = 0)) +
labs(y = "indeksi, 2010 = 100")
save_fig("A2_nulc")
data_main_annual %>%
select(geo, time, nulc_aper) %>%
gather(vars, values, nulc_aper) %>%
mutate(h_geo = fct_other(geo, keep = tuku_geos,other_level = "muut"),
h_geo = fct_relevel(h_geo, h_geos),
geo = fct_relevel(geo, rev(h_geos), after = Inf),
size = !(geo %in% h_geo)) %>%
translate(h_geo, geo_trans) %>%
translate(vars, c(nulc_aper = "Indeksi",
nulc_aper_rel_imf = "Suhteellinen indeksi")) %>%
ggplot(aes(time, values, group = geo, colour = h_geo, size = size)) +
geom_line() +
scale_size_manual(values = c(2,1), guide = "none") +
scale_colour_manual(values = c(geo_col(length(tuku_geos)), "grey75")) +
the_title_blank(c("x", "l")) +
scale_y_continuous(limits = c(70,130), expand = expansion(mult = 0, add = 0)) +
labs(y = "indeksi, 2010 = 100")
save_fig("A_nulc_levels")
vars_rel <- c(nulc_aper_rel_ecfin20 = "omassa valuutassa", nulc_aper_eur_rel_ecfin20 = "samassa valuutassa")
data_main_annual %>%
filter(geo == "FI") %>%
select(geo, time, names(vars_rel)) %>%
gather(vars, values, -geo, -time) %>%
group_by(geo, vars) %>%
mutate(values = rebase(values, time, base_year)) %>%
ungroup() %>%
translate(vars, vars_rel) %>%
ggplot(aes(time, values, color = vars)) +
geom_line() +
the_title_blank(c("x", "l")) +
labs(y = glue("Indeksi, {base_year} = 100")) +
the_legend_bot()
save_fig("a_nulc_aper_rel")
data_long_annual %>%
filter(geo == "FI") %>%
filter(time >= "1995-01-01") %>%
select(geo, time, nulc_var = nulc_aper_rel_ecfin15,
# rulc_var = rulc_aper_rel_ameco15,
out_var = gdp_ind_rel_ecfin15) %>%
mutate(nulc_var = rebase(1/nulc_var, time, base_year)) %>%
# mutate(rulc_var = rebase(1/rulc_var, time, base_year)) %>%
# mutate(out_var = 100* (out_var / lag(out_var) -1) + 100) %>%
gather(vars, values, -geo, -time) %>%
# mutate(h_geo = fct_other(geo, keep = h_geos,other_level = "muut"),
# geo = fct_relevel(geo, h_geos, after = Inf)) %>%
translate(geo, geo_fi) %>%
# translate(vars, c(nulc_aper_rel_imf = "ULCy, käänteinen", gdp_ind_rel_imf = "BKT",
# exp_ind_rel_imf = "Vienti", XPERF = "Vientimenestys")) %>%
ggplot(aes(time, values, colour = vars)) +
geom_line() +
facet_wrap(~ geo, scales = "free") +
scale_size_manual(values = c(2,1)) +
the_title_blank()
data_ameco_long %>%
select(geo, time, nulc_aper, nulc_aper_usd_rel_ameco15) %>%
gather(vars, values, nulc_aper, nulc_aper_usd_rel_ameco15) %>%
mutate(h_geo = fct_other(geo, keep = h_geos,other_level = "muut"),
h_geo = fct_relevel(h_geo, h_geos),
geo = fct_relevel(geo, rev(h_geos), after = Inf),
size = !(geo %in% h_geo)) %>%
translate(h_geo, geo_trans) %>%
translate(vars, c(nulc_aper = "Indeksi",
nulc_aper_rel_imf = "Suhteellinen indeksi")) %>%
ggplot(aes(time, values, group = geo, colour = h_geo, size = size)) +
facet_wrap(~ vars, scales = "free_y") +
geom_line() +
scale_size_manual(values = c(2,1), guide = "none") +
scale_colour_manual(values = geo_col(length(h_geos) + 1)) +
the_title_blank(c("x", "l")) +
# scale_y_continuous(limits = c(70,130), expand = expansion(mult = 0, add = 0)) +
labs(y = "indeksi, 2010 = 100")
save_fig("A2_nulc_ameco")
Vaihtosuhdekorjattu
data_main_annual %>%
filter(geo == "FI") %>%
select(geo, time,
"Vaihtosuhdekorjattu" = nulc_aper_eur_atot_rel_ecfin20,
"Normaali" = nulc_aper_eur_rel_ecfin20) %>%
gather(vars, values, -geo, -time) %>%
group_by(geo, vars) %>%
mutate(values = rebase(values, time, 2010),
change = 100 * (values / lag(values) -1)) %>%
ungroup() %>%
gather(type, values, values, change) %>%
ggplot(aes(time, values, color = vars)) +
facet_wrap(~type, scales = "free_y",
labeller = labeller(type = c(change = "muutos, %", values = "indeksi, 2010 = 100"))) +
geom_line()+
the_title_blank(c("x", "l", "y")) +
geom_hline(aes(yintercept = yint),
data = data.frame(yint = 0, type = "change")) +
labs(y = glue("Indeksi, 2010 = 100")) +
the_legend_bot()
save_fig("A_nulc_a_tot")
Nimellinen yksikkötyökustannus toimialaryhmittäin
Koko talous, yksityinen sektori (pl. vaikeasti mitattavat), teollisuus ja palvelut. Yksityinen ja teollisuus myös ilman eletroniikkateollisuutta.
data_main_groups_a %>%
select(geo, time, nace0, nulc_hw_va, nulc_hw_va_rel_imf) %>%
gather(vars, values, nulc_hw_va, nulc_hw_va_rel_imf) %>%
mutate(h_geo = fct_other(geo, keep = h_geos,other_level = "muut"),
h_geo = fct_relevel(h_geo, h_geos),
geo = fct_relevel(geo, rev(h_geos), after = Inf),
size = !(geo %in% h_geo)) %>%
translate(h_geo, geo_trans) %>%
translate(vars, c(nulc_hw_va = "Indeksi",
nulc_hw_va_rel_imf = "Suhteellinen indeksi")) %>%
translate(nace0, nace0_trans) %>%
ggplot(aes(time, values, group = geo, colour = h_geo, size = size)) +
facet_grid(nace0 ~ vars) +
geom_line() +
scale_size_manual(values = c(2,1), guide = "none") +
scale_colour_manual(values = geo_col(length(h_geos) + 1)) +
the_title_blank(c("x", "l")) +
scale_y_continuous(limits = c(70,130), expand = expansion(mult = 0, add = 0)) +
labs(y = "indeksi, 2010 = 100")
save_fig("A3_nulc_nace0", height = 9)
Toimialaryhmät Suomelle
data_main_groups_a %>%
filter(geo == "FI") %>%
select(geo, time, nace0, nulc_hw_va, nulc_hw_va_rel_imf) %>%
gather(vars, values, nulc_hw_va, nulc_hw_va_rel_imf) %>%
# mutate(h_geo = fct_other(geo, keep = h_geos,other_level = "muut"),
# h_geo = fct_relevel(h_geo, h_geos),
# geo = fct_relevel(geo, rev(h_geos), after = Inf),
# size = !(geo %in% h_geo)) %>%
# translate(h_geo, geo_trans) %>%
translate(vars, c(nulc_hw_va = "Indeksi",
nulc_hw_va_rel_imf = "Suhteellinen indeksi")) %>%
translate(nace0, nace0_trans) %>%
ggplot(aes(time, values, colour = nace0)) +
facet_wrap( ~ vars) +
geom_line() +
scale_size_manual(values = c(2,1), guide = "none") +
# scale_colour_manual(values = geo_col(length(h_geos) + 1)) +
the_title_blank(c("x", "l")) +
scale_y_continuous(limits = c(70,130), expand = expansion(mult = 0, add = 0)) +
labs(y = "indeksi, 2010 = 100")
save_fig("A4_nulc_nace0_fi")
Koko talous ja yrityssektori (pl. vaikeasti mitattavat) Suomelle
data_main_groups_a %>%
filter(geo == "FI", nace0 %in% c("total", "private")) %>%
select(geo, time, nace0, nulc_hw_va, nulc_hw_va_rel_imf) %>%
gather(vars, values, nulc_hw_va, nulc_hw_va_rel_imf) %>%
# mutate(h_geo = fct_other(geo, keep = h_geos,other_level = "muut"),
# h_geo = fct_relevel(h_geo, h_geos),
# geo = fct_relevel(geo, rev(h_geos), after = Inf),
# size = !(geo %in% h_geo)) %>%
# translate(h_geo, geo_trans) %>%
translate(vars, c(nulc_aper = "Indeksi",
nulc_aper_rel_imf = "Suhteellinen indeksi")) %>%
ggplot(aes(time, values, colour = nace0)) +
facet_wrap( ~ vars) +
geom_line() +
scale_size_manual(values = c(2,1), guide = "none") +
# scale_colour_manual(values = geo_col(length(h_geos) + 1)) +
the_title_blank(c("x", "l")) +
scale_y_continuous(limits = c(70,130), expand = expansion(mult = 0, add = 0)) +
labs(y = "indeksi, 2010 = 100")
Nimellinen ja reaalinen yksikkötyökustannus Suomelle
data_main_groups_a %>%
filter(geo == "FI") %>%
select(geo, time, nace0, nulc_hw_va, nulc_hw_va_rel_imf, rulc_hw_va, rulc_hw_va_rel_imf) %>%
gather(vars, values, nulc_hw_va, nulc_hw_va_rel_imf, rulc_hw_va, rulc_hw_va_rel_imf) %>%
mutate(vars2 = fct_recode(vars, taso = "nulc_hw_va", taso = "rulc_hw_va",
suhteellinen = "nulc_hw_va_rel_imf", suhteellinen = "rulc_hw_va_rel_imf"),
vars3 = fct_recode(vars, nimellinen = "nulc_hw_va", reaalinen = "rulc_hw_va",
nimellinen = "nulc_hw_va_rel_imf", reaalinen = "rulc_hw_va_rel_imf")) %>%
# mutate(h_geo = fct_other(geo, keep = h_geos,other_level = "muut"),
# h_geo = fct_relevel(h_geo, h_geos),
# geo = fct_relevel(geo, rev(h_geos), after = Inf),
# size = !(geo %in% h_geo)) %>%
# translate(h_geo, geo_trans) %>%
translate(vars, c(nulc_aper = "Indeksi",
nulc_aper_rel_imf = "Suhteellinen indeksi")) %>%
translate(nace0, nace0_trans) %>%
ggplot(aes(time, values, colour = vars3)) +
facet_grid(nace0 ~ vars2) +
geom_line() +
scale_size_manual(values = c(2,1), guide = "none") +
# scale_colour_manual(values = geo_col(length(h_geos) + 1)) +
the_title_blank(c("x", "l")) +
scale_y_continuous(limits = c(70,130), expand = expansion(mult = 0, add = 0)) +
labs(y = "indeksi, 2010 = 100")
save_fig("A_nulc_rulc_fi", height = 9)
Samassa valuutassa volatiliteetti paljon suurempi.
data_main_groups_a %>%
filter(geo %in% c("SE"),
time >= start_time,
nace0 == "total") %>%
select(geo, time, nulc_va_rel_imf, nulc_va_eur_rel_imf) %>%
gather(ind, values, -geo, -time, factor_key = TRUE) %>%
ggplot(aes(time, values, colour = ind)) +
geom_line() +
the_title_blank()
data_main_groups_a %>%
filter(nace0 == "total") %>%
select(geo, time, nulc_va_rel_imf, nulc_va_eur_rel_imf) %>%
left_join(select(ameco_lc, geo, time, nulc_aper_rel_ameco, nulc_aper_rel_ameco_usd,
nulc_aper_rel37_ameco, nulc_aper_rel37_ameco_usd,
nulc_aper_relEU_ameco, nulc_aper_relEU_ameco_usd), by = c("geo", "time")) %>%
filter(geo %in% c("US", "FI", "SE", "DE"),
time >= start_time) %>%
gather(ind, values, -geo, -time, factor_key = TRUE) %>%
ggplot(aes(time, values, colour = ind)) +
geom_line() +
facet_wrap(~geo) +
the_title_blank()
data_main_groups_a %>%
filter(nace0 == "total") %>%
select(geo, time, nulc_va, nulc_va_eur) %>%
left_join(select(ameco_lc, geo, time, nulc_aper, nulc_aper_eur), by = c("geo", "time")) %>%
filter(geo %in% c("US", "FI", "SE", "DE"),
time >= start_time) %>%
gather(ind, values, -geo, -time, factor_key = TRUE) %>%
ggplot(aes(time, values, colour = ind)) +
geom_line() +
facet_wrap(~geo) +
the_title_blank()
data_main_groups_a %>%
filter(nace0 == "total") %>%
select(geo, time, nulc_va_rel_imf, nulc_va_eur_rel_imf) %>%
left_join(select(ameco_lc, geo, time, nulc_aper_rel_ameco, nulc_aper_rel_ameco_usd), by = c("geo", "time")) %>%
filter(geo %in% c("US", "FI", "SE", "DE"),
time >= start_time) %>%
# filter(geo == "SE") %>%
gather(ind, values, -geo, -time, factor_key = TRUE) %>%
ggplot(aes(time, values, colour = ind)) +
geom_line() +
facet_wrap(~geo) +
the_title_blank()
data_main_groups_a %>%
select(-contains("__")) %>%
filter(nace0 == "total") %>%
select(-nace0) %>%
gather(vars, values, -geo, -time) %>%
mutate(h_geo = fct_other(geo, keep = h_geos,other_level = "muut"),
geo = fct_relevel(geo, h_geos, after = Inf)) %>%
ggplot(aes(time, values, group = geo, colour = h_geo)) +
geom_line() +
facet_wrap(~ vars, scales = "free") +
# scale_size_manual(values = c(2,1)) +
the_title_blank()
data_main_groups_a %>%
filter(nace0 %in% c("total", "total_exK"),
geo == "FI") %>%
select(geo, time, nace0, nulc_aper_va_eur_rel_ecfinnace, rulc_aper_va_rel_ecfinnace) %>%
gather(vars, values, -geo, -time, -nace0) %>%
ggplot(aes(time, values, colour = nace0)) +
geom_line() +
facet_wrap(~vars, nrow = 2)
data_main_groups_a %>%
filter(
# nace0 %in% c("total", "total_exK", "private", "private_ex26"),
geo == "FI") %>%
select(geo, time, nace0, nulc_aper_va_eur_rel_ecfinnace, rulc_aper_va_rel_ecfinnace) %>%
gather(vars, values, -geo, -time, -nace0) %>%
ggplot(aes(time, values, colour = vars)) +
geom_line() +
facet_wrap(~nace0)
ggsave_twitter("nace")
data_main_groups_a %>%
filter(nace0 == "total") %>%
select(geo, time, nulc_aper_va) %>%
spread(geo, nulc_aper_va) %>%
View()
data_main_groups_a %>%
filter(
# nace0 %in% c("total", "total_exK", "private", "private_ex26"),
geo == "FI") %>%
select(geo, time, nace0, rulc_aper_va, rulc_aper_va_rel_ecfinnace) %>%
gather(vars, values, -geo, -time, -nace0) %>%
ggplot(aes(time, values, colour = vars)) +
geom_line() +
facet_wrap(~nace0)
pttry/ficomp documentation built on June 11, 2024, 9:53 p.m.
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knitr::opts_chunk$set( collapse = TRUE, comment = "#>", fig.height = 6, fig.width = 8, fig.path = "Report/", echo = FALSE, message = FALSE, warning = FALSE ) # library(ficomp) devtools::load_all() library(dplyr) library(forcats) library(ggplot2) library(eurostat) library(tidyr) library(ggptt) library(glue) set_proj_theme() data("data_main_total_a", "data_main_groups_a", "data_ameco") base_year <- 2010 base_years <- 2000:2018 geos <- c(Finland = "FI", Sweden = "SE", Germany = "DE", France = "FR") start_time <- "1995-01-01" geo_trans <- geo_fi nace0_trans <- nace0_fi h_geos <- c("FI", "SE", "DE", "US")
Data
TODO
Yhdistelmä Eurostatin ja OECD:n National Accounts datasta
Maat ja vuodet.
data_main_groups_a %>% filter(nace0 == "total") %>% select(geo, time, values = nulc_aper_va) %>% group_by(geo) %>% mutate(tmin = min(time[!is.na(values)]), tmax = max(time[!is.na(values)])) %>% ungroup() %>% mutate(selected = geo %in% c(eurostat_geos, oecd_geos_ulcq)) %>% translate(geo, geo_trans) %>% ggplot(aes(geo, ymin = tmin, ymax = tmax, colour = selected)) + geom_linerange(size = 2, colour = geo_col(2)[1]) + coord_flip() + scale_colour_manual(values = rev(geo_col(2)), guide = "none") + the_title_blank("y") save_fig("A1_Eurostat_OECD_na")
data_ameco %>% select(geo, time, values = nulc_aper) %>% group_by(geo) %>% mutate(is_value = !is.na(values)) %>% ungroup() %>% mutate(selected = geo %in% c(eurostat_geos, oecd_geos_ulcq)) %>% translate(geo, geo_trans) %>% ggplot(aes(geo, time, alpha = is_value)) + geom_line(size = 2) + coord_flip() + scale_alpha_manual(values = c(0,1), guide = "none") + # scale_colour_manual(values = rev(geo_col(2)), guide = "none") + the_title_blank("y") save_fig("A1_ameco_na")
Nimellinen yksikkötyökustannus koko talous
Tunnit, taso (2018) ja suhteellinen (2017, koska Yhdysvallat)
data_main_groups_a %>% filter(nace0 == "total") %>% select(geo, time, nulc_hw_va, nulc_hw_va_rel_imf) %>% gather(vars, values, nulc_hw_va, nulc_hw_va_rel_imf) %>% mutate(h_geo = fct_other(geo, keep = h_geos,other_level = "muut"), h_geo = fct_relevel(h_geo, h_geos), geo = fct_relevel(geo, rev(h_geos), after = Inf), size = !(geo %in% h_geo)) %>% translate(h_geo, geo_trans) %>% translate(vars, c(nulc_aper = "Indeksi", nulc_aper_rel_imf = "Suhteellinen indeksi")) %>% ggplot(aes(time, values, group = geo, colour = h_geo, size = size)) + facet_wrap(~ vars) + geom_line() + scale_size_manual(values = c(2,1), guide = "none") + scale_colour_manual(values = geo_col(length(h_geos) + 1)) + the_title_blank(c("x", "l")) + scale_y_continuous(limits = c(70,130), expand = expansion(mult = 0, add = 0)) + labs(y = "indeksi, 2010 = 100") save_fig("A2_nulc")
data_main_annual %>% select(geo, time, nulc_aper) %>% gather(vars, values, nulc_aper) %>% mutate(h_geo = fct_other(geo, keep = tuku_geos,other_level = "muut"), h_geo = fct_relevel(h_geo, h_geos), geo = fct_relevel(geo, rev(h_geos), after = Inf), size = !(geo %in% h_geo)) %>% translate(h_geo, geo_trans) %>% translate(vars, c(nulc_aper = "Indeksi", nulc_aper_rel_imf = "Suhteellinen indeksi")) %>% ggplot(aes(time, values, group = geo, colour = h_geo, size = size)) + geom_line() + scale_size_manual(values = c(2,1), guide = "none") + scale_colour_manual(values = c(geo_col(length(tuku_geos)), "grey75")) + the_title_blank(c("x", "l")) + scale_y_continuous(limits = c(70,130), expand = expansion(mult = 0, add = 0)) + labs(y = "indeksi, 2010 = 100") save_fig("A_nulc_levels")
vars_rel <- c(nulc_aper_rel_ecfin20 = "omassa valuutassa", nulc_aper_eur_rel_ecfin20 = "samassa valuutassa") data_main_annual %>% filter(geo == "FI") %>% select(geo, time, names(vars_rel)) %>% gather(vars, values, -geo, -time) %>% group_by(geo, vars) %>% mutate(values = rebase(values, time, base_year)) %>% ungroup() %>% translate(vars, vars_rel) %>% ggplot(aes(time, values, color = vars)) + geom_line() + the_title_blank(c("x", "l")) + labs(y = glue("Indeksi, {base_year} = 100")) + the_legend_bot() save_fig("a_nulc_aper_rel")
data_long_annual %>% filter(geo == "FI") %>% filter(time >= "1995-01-01") %>% select(geo, time, nulc_var = nulc_aper_rel_ecfin15, # rulc_var = rulc_aper_rel_ameco15, out_var = gdp_ind_rel_ecfin15) %>% mutate(nulc_var = rebase(1/nulc_var, time, base_year)) %>% # mutate(rulc_var = rebase(1/rulc_var, time, base_year)) %>% # mutate(out_var = 100* (out_var / lag(out_var) -1) + 100) %>% gather(vars, values, -geo, -time) %>% # mutate(h_geo = fct_other(geo, keep = h_geos,other_level = "muut"), # geo = fct_relevel(geo, h_geos, after = Inf)) %>% translate(geo, geo_fi) %>% # translate(vars, c(nulc_aper_rel_imf = "ULCy, käänteinen", gdp_ind_rel_imf = "BKT", # exp_ind_rel_imf = "Vienti", XPERF = "Vientimenestys")) %>% ggplot(aes(time, values, colour = vars)) + geom_line() + facet_wrap(~ geo, scales = "free") + scale_size_manual(values = c(2,1)) + the_title_blank()
data_ameco_long %>% select(geo, time, nulc_aper, nulc_aper_usd_rel_ameco15) %>% gather(vars, values, nulc_aper, nulc_aper_usd_rel_ameco15) %>% mutate(h_geo = fct_other(geo, keep = h_geos,other_level = "muut"), h_geo = fct_relevel(h_geo, h_geos), geo = fct_relevel(geo, rev(h_geos), after = Inf), size = !(geo %in% h_geo)) %>% translate(h_geo, geo_trans) %>% translate(vars, c(nulc_aper = "Indeksi", nulc_aper_rel_imf = "Suhteellinen indeksi")) %>% ggplot(aes(time, values, group = geo, colour = h_geo, size = size)) + facet_wrap(~ vars, scales = "free_y") + geom_line() + scale_size_manual(values = c(2,1), guide = "none") + scale_colour_manual(values = geo_col(length(h_geos) + 1)) + the_title_blank(c("x", "l")) + # scale_y_continuous(limits = c(70,130), expand = expansion(mult = 0, add = 0)) + labs(y = "indeksi, 2010 = 100") save_fig("A2_nulc_ameco")
Vaihtosuhdekorjattu
data_main_annual %>% filter(geo == "FI") %>% select(geo, time, "Vaihtosuhdekorjattu" = nulc_aper_eur_atot_rel_ecfin20, "Normaali" = nulc_aper_eur_rel_ecfin20) %>% gather(vars, values, -geo, -time) %>% group_by(geo, vars) %>% mutate(values = rebase(values, time, 2010), change = 100 * (values / lag(values) -1)) %>% ungroup() %>% gather(type, values, values, change) %>% ggplot(aes(time, values, color = vars)) + facet_wrap(~type, scales = "free_y", labeller = labeller(type = c(change = "muutos, %", values = "indeksi, 2010 = 100"))) + geom_line()+ the_title_blank(c("x", "l", "y")) + geom_hline(aes(yintercept = yint), data = data.frame(yint = 0, type = "change")) + labs(y = glue("Indeksi, 2010 = 100")) + the_legend_bot() save_fig("A_nulc_a_tot")
Nimellinen yksikkötyökustannus toimialaryhmittäin
Koko talous, yksityinen sektori (pl. vaikeasti mitattavat), teollisuus ja palvelut. Yksityinen ja teollisuus myös ilman eletroniikkateollisuutta.
data_main_groups_a %>% select(geo, time, nace0, nulc_hw_va, nulc_hw_va_rel_imf) %>% gather(vars, values, nulc_hw_va, nulc_hw_va_rel_imf) %>% mutate(h_geo = fct_other(geo, keep = h_geos,other_level = "muut"), h_geo = fct_relevel(h_geo, h_geos), geo = fct_relevel(geo, rev(h_geos), after = Inf), size = !(geo %in% h_geo)) %>% translate(h_geo, geo_trans) %>% translate(vars, c(nulc_hw_va = "Indeksi", nulc_hw_va_rel_imf = "Suhteellinen indeksi")) %>% translate(nace0, nace0_trans) %>% ggplot(aes(time, values, group = geo, colour = h_geo, size = size)) + facet_grid(nace0 ~ vars) + geom_line() + scale_size_manual(values = c(2,1), guide = "none") + scale_colour_manual(values = geo_col(length(h_geos) + 1)) + the_title_blank(c("x", "l")) + scale_y_continuous(limits = c(70,130), expand = expansion(mult = 0, add = 0)) + labs(y = "indeksi, 2010 = 100") save_fig("A3_nulc_nace0", height = 9)
Toimialaryhmät Suomelle
data_main_groups_a %>% filter(geo == "FI") %>% select(geo, time, nace0, nulc_hw_va, nulc_hw_va_rel_imf) %>% gather(vars, values, nulc_hw_va, nulc_hw_va_rel_imf) %>% # mutate(h_geo = fct_other(geo, keep = h_geos,other_level = "muut"), # h_geo = fct_relevel(h_geo, h_geos), # geo = fct_relevel(geo, rev(h_geos), after = Inf), # size = !(geo %in% h_geo)) %>% # translate(h_geo, geo_trans) %>% translate(vars, c(nulc_hw_va = "Indeksi", nulc_hw_va_rel_imf = "Suhteellinen indeksi")) %>% translate(nace0, nace0_trans) %>% ggplot(aes(time, values, colour = nace0)) + facet_wrap( ~ vars) + geom_line() + scale_size_manual(values = c(2,1), guide = "none") + # scale_colour_manual(values = geo_col(length(h_geos) + 1)) + the_title_blank(c("x", "l")) + scale_y_continuous(limits = c(70,130), expand = expansion(mult = 0, add = 0)) + labs(y = "indeksi, 2010 = 100") save_fig("A4_nulc_nace0_fi")
Koko talous ja yrityssektori (pl. vaikeasti mitattavat) Suomelle
data_main_groups_a %>% filter(geo == "FI", nace0 %in% c("total", "private")) %>% select(geo, time, nace0, nulc_hw_va, nulc_hw_va_rel_imf) %>% gather(vars, values, nulc_hw_va, nulc_hw_va_rel_imf) %>% # mutate(h_geo = fct_other(geo, keep = h_geos,other_level = "muut"), # h_geo = fct_relevel(h_geo, h_geos), # geo = fct_relevel(geo, rev(h_geos), after = Inf), # size = !(geo %in% h_geo)) %>% # translate(h_geo, geo_trans) %>% translate(vars, c(nulc_aper = "Indeksi", nulc_aper_rel_imf = "Suhteellinen indeksi")) %>% ggplot(aes(time, values, colour = nace0)) + facet_wrap( ~ vars) + geom_line() + scale_size_manual(values = c(2,1), guide = "none") + # scale_colour_manual(values = geo_col(length(h_geos) + 1)) + the_title_blank(c("x", "l")) + scale_y_continuous(limits = c(70,130), expand = expansion(mult = 0, add = 0)) + labs(y = "indeksi, 2010 = 100")
Nimellinen ja reaalinen yksikkötyökustannus Suomelle
data_main_groups_a %>% filter(geo == "FI") %>% select(geo, time, nace0, nulc_hw_va, nulc_hw_va_rel_imf, rulc_hw_va, rulc_hw_va_rel_imf) %>% gather(vars, values, nulc_hw_va, nulc_hw_va_rel_imf, rulc_hw_va, rulc_hw_va_rel_imf) %>% mutate(vars2 = fct_recode(vars, taso = "nulc_hw_va", taso = "rulc_hw_va", suhteellinen = "nulc_hw_va_rel_imf", suhteellinen = "rulc_hw_va_rel_imf"), vars3 = fct_recode(vars, nimellinen = "nulc_hw_va", reaalinen = "rulc_hw_va", nimellinen = "nulc_hw_va_rel_imf", reaalinen = "rulc_hw_va_rel_imf")) %>% # mutate(h_geo = fct_other(geo, keep = h_geos,other_level = "muut"), # h_geo = fct_relevel(h_geo, h_geos), # geo = fct_relevel(geo, rev(h_geos), after = Inf), # size = !(geo %in% h_geo)) %>% # translate(h_geo, geo_trans) %>% translate(vars, c(nulc_aper = "Indeksi", nulc_aper_rel_imf = "Suhteellinen indeksi")) %>% translate(nace0, nace0_trans) %>% ggplot(aes(time, values, colour = vars3)) + facet_grid(nace0 ~ vars2) + geom_line() + scale_size_manual(values = c(2,1), guide = "none") + # scale_colour_manual(values = geo_col(length(h_geos) + 1)) + the_title_blank(c("x", "l")) + scale_y_continuous(limits = c(70,130), expand = expansion(mult = 0, add = 0)) + labs(y = "indeksi, 2010 = 100") save_fig("A_nulc_rulc_fi", height = 9)
Samassa valuutassa volatiliteetti paljon suurempi.
data_main_groups_a %>% filter(geo %in% c("SE"), time >= start_time, nace0 == "total") %>% select(geo, time, nulc_va_rel_imf, nulc_va_eur_rel_imf) %>% gather(ind, values, -geo, -time, factor_key = TRUE) %>% ggplot(aes(time, values, colour = ind)) + geom_line() + the_title_blank()
data_main_groups_a %>% filter(nace0 == "total") %>% select(geo, time, nulc_va_rel_imf, nulc_va_eur_rel_imf) %>% left_join(select(ameco_lc, geo, time, nulc_aper_rel_ameco, nulc_aper_rel_ameco_usd, nulc_aper_rel37_ameco, nulc_aper_rel37_ameco_usd, nulc_aper_relEU_ameco, nulc_aper_relEU_ameco_usd), by = c("geo", "time")) %>% filter(geo %in% c("US", "FI", "SE", "DE"), time >= start_time) %>% gather(ind, values, -geo, -time, factor_key = TRUE) %>% ggplot(aes(time, values, colour = ind)) + geom_line() + facet_wrap(~geo) + the_title_blank()
data_main_groups_a %>% filter(nace0 == "total") %>% select(geo, time, nulc_va, nulc_va_eur) %>% left_join(select(ameco_lc, geo, time, nulc_aper, nulc_aper_eur), by = c("geo", "time")) %>% filter(geo %in% c("US", "FI", "SE", "DE"), time >= start_time) %>% gather(ind, values, -geo, -time, factor_key = TRUE) %>% ggplot(aes(time, values, colour = ind)) + geom_line() + facet_wrap(~geo) + the_title_blank()
data_main_groups_a %>% filter(nace0 == "total") %>% select(geo, time, nulc_va_rel_imf, nulc_va_eur_rel_imf) %>% left_join(select(ameco_lc, geo, time, nulc_aper_rel_ameco, nulc_aper_rel_ameco_usd), by = c("geo", "time")) %>% filter(geo %in% c("US", "FI", "SE", "DE"), time >= start_time) %>% # filter(geo == "SE") %>% gather(ind, values, -geo, -time, factor_key = TRUE) %>% ggplot(aes(time, values, colour = ind)) + geom_line() + facet_wrap(~geo) + the_title_blank()
data_main_groups_a %>% select(-contains("__")) %>% filter(nace0 == "total") %>% select(-nace0) %>% gather(vars, values, -geo, -time) %>% mutate(h_geo = fct_other(geo, keep = h_geos,other_level = "muut"), geo = fct_relevel(geo, h_geos, after = Inf)) %>% ggplot(aes(time, values, group = geo, colour = h_geo)) + geom_line() + facet_wrap(~ vars, scales = "free") + # scale_size_manual(values = c(2,1)) + the_title_blank()
data_main_groups_a %>% filter(nace0 %in% c("total", "total_exK"), geo == "FI") %>% select(geo, time, nace0, nulc_aper_va_eur_rel_ecfinnace, rulc_aper_va_rel_ecfinnace) %>% gather(vars, values, -geo, -time, -nace0) %>% ggplot(aes(time, values, colour = nace0)) + geom_line() + facet_wrap(~vars, nrow = 2) data_main_groups_a %>% filter( # nace0 %in% c("total", "total_exK", "private", "private_ex26"), geo == "FI") %>% select(geo, time, nace0, nulc_aper_va_eur_rel_ecfinnace, rulc_aper_va_rel_ecfinnace) %>% gather(vars, values, -geo, -time, -nace0) %>% ggplot(aes(time, values, colour = vars)) + geom_line() + facet_wrap(~nace0) ggsave_twitter("nace")
data_main_groups_a %>% filter(nace0 == "total") %>% select(geo, time, nulc_aper_va) %>% spread(geo, nulc_aper_va) %>% View()
data_main_groups_a %>% filter( # nace0 %in% c("total", "total_exK", "private", "private_ex26"), geo == "FI") %>% select(geo, time, nace0, rulc_aper_va, rulc_aper_va_rel_ecfinnace) %>% gather(vars, values, -geo, -time, -nace0) %>% ggplot(aes(time, values, colour = vars)) + geom_line() + facet_wrap(~nace0)
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