NA Main Aggregates - NA_MAIN

Data - UN

Author

François Geerolf

Info

  • Missing data - COICOP (Classification of Individual Consumption According to Purpose)

FREQ

Code 
NA_MAIN %>%
  left_join(FREQ, by = "FREQ") %>%
  group_by(FREQ, Freq) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  print_table_conditional()
FREQ Freq Nobs
Q Quarterly 793156
A Annual 247223

obsTime

Code 
NA_MAIN %>%
  group_by(obsTime) %>%
  summarise(Nobs = n()) %>%
  print_table_conditional()

STO

Code 
NA_MAIN %>%
  left_join(STO, by = "STO") %>%
  group_by(STO, Sto) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  print_table_conditional()

ACTIVITY

Code 
NA_MAIN %>%
  left_join(ACTIVITY, by = "ACTIVITY") %>%
  group_by(ACTIVITY, Activity) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  print_table_conditional()
ACTIVITY Activity Nobs
_Z Not applicable 490105
_T Total - All activities 207852
A Agriculture, forestry and fishing 30801
BTE Industry (except construction) 30585
F Construction 30481
GTI Wholesale and retail trade; repair of motor vehicles and motorcycles; transportation and storage; accommodation and food service activities 30468
C Manufacturing 30363
OTQ Public administration, defence, education, human health and social work activities 30339
L Real estate activities 29996
J Information and communication 29966
K Financial and insurance activities 29868
RTU Arts, entertainment and recreation; other service activities; activities of household and extra-territorial organizations and bodies 29394
M_N Professional, scientific and technical activities; administrative and support service activities 29148
G Wholesale and retail trade; repair of motor vehicles and motorcycles 825
H Transportation and storage 825
I Accommodation and food service activities 825
D Electricity, gas, steam and air conditioning supply 817
B Mining and quarrying 760
O Public administration and defence; compulsory social security 739
P Education 739
Q Human health and social work activities 709
A01 Crop and animal production, hunting and related service activities 636
M Professional, scientific and technical activities 601
A03 Fishing and aquaculture 592
R Arts, entertainment and recreation 586
S Other service activities 560
E Water supply; sewerage, waste management and remediation activities 533
N Administrative and support service activities 520
A02 Forestry and logging 415
T Activities of households as employers; undifferentiated goods- and services-producing activities of households for own use 331

PRICES

Code 
NA_MAIN %>%
  left_join(PRICES, by = "PRICES") %>%
  group_by(PRICES, Prices) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  print_table_conditional()
PRICES Prices Nobs
V Current prices 502846
LR Chain linked volume (rebased) 195740
Y Previous year prices 149297
L Chain linked volume 85512
_Z Not applicable 67987
Q Constant prices 38997

INSTR_ASSET

Code 
NA_MAIN %>%
  left_join(INSTR_ASSET, by = "INSTR_ASSET") %>%
  group_by(INSTR_ASSET, Instr_asset) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  print_table_conditional()
INSTR_ASSET Instr_asset Nobs
_Z Not applicable 951685
N1G Produced non-financial assets (gross) 46607
N11G Fixed assets by type of asset (gross) 42087

ADJUSTMENT

Code 
NA_MAIN %>%
  left_join(ADJUSTMENT, by = "ADJUSTMENT") %>%
  group_by(ADJUSTMENT, Adjustment) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  print_table_conditional()
ADJUSTMENT Adjustment Nobs
N Neither seasonally adjusted nor calendar adjusted data 696931
Y Calendar and seasonally adjusted data 343448

REF_AREA

Code 
NA_MAIN %>%
  left_join(REF_AREA, by = "REF_AREA") %>%
  group_by(REF_AREA, Ref_area) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  mutate(Flag = gsub(" ", "-", str_to_lower(gsub(" ", "-", Ref_area))),
         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 .}

COUNTERPART_AREA

Code 
NA_MAIN %>%
  left_join(COUNTERPART_AREA, by = "COUNTERPART_AREA") %>%
  group_by(COUNTERPART_AREA, Counterpart_area) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  print_table_conditional()
COUNTERPART_AREA Counterpart_area Nobs
W2 NA 602304
W0 NA 296001
W1 NA 142074

B1G - Value added, gross

All Countries

Code 
NA_MAIN %>%
  filter(STO == "B1G") %>%
  left_join(REF_AREA, by = "REF_AREA") %>%
  group_by(REF_AREA, Ref_area, ACTIVITY, FREQ, ADJUSTMENT, PRICES) %>%
  summarise(Nobs = n(),
            min = first(obsTime),
            max = last(obsTime)) %>%
  arrange(-Nobs) %>%
  mutate(Flag = gsub(" ", "-", str_to_lower(gsub(" ", "-", Ref_area))),
         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 .}

France: Structure

Code 
NA_MAIN %>%
  filter(STO == "B1G",
         REF_AREA == "FR") %>%
  select(-obsValue, -obsTime, -LAST_UPDATE) %>%
  distinct %>%
  select_if(~n_distinct(.) > 1) %>%
  print_table_conditional()

C - Manufacturing

Variables

Code 
NA_MAIN %>%
  filter(ACTIVITY == "C") %>%
  select(-obsValue, -obsTime, -LAST_UPDATE, -REF_AREA) %>%
  distinct %>%
  select_if(~n_distinct(.) > 1) %>%
  print_table_conditional()