README.md

In hadrilec2/insee: Tools to Easily Download Data from INSEE BDM Database

insee R package

Overview

The insee package contains tools to easily download data and metadata from INSEE BDM database. Using embedded SDMX queries, get the data of more than 140 000 INSEE series from BDM database (Banque de données macroéconomiques).

Installation

# Get the development version from GitHub
# install.packages("devtools")
# devtools::install_github("hadrilec/insee")

# Get the CRAN version
install.packages("insee")

Library

# examples below use tidyverse packages 
library(tidyverse)
library(insee)

get INSEE datasets list

dataset = get_dataset_list()

get INSEE series key (idbank) list

idbank_list = get_idbank_list()

select idbanks

idbank_list_selected = 
  idbank_list %>% 
  filter(nomflow == "ENQ-CONJ-ACT-IND") %>% #industry activity survey 
  filter(dim4 == "A88-29") %>% # 29 : automotive industry, A88 : aggregation level - 88 sectors
  filter(dim8  == "CVS") %>% #seasonally adjusted
  filter(dim5 == "SOLDE_PROPORTION") %>% #balance of opinion
  filter(dim2 == "ECAI_TPE")  #expected trend in employment

get idbank title

idbank_list_selected = 
  idbank_list_selected %>% 
  mutate(title = get_insee_title(idbank, lang = "en")) 

extract selected idbanks list

list_idbank = idbank_list_selected %>% pull(idbank)

get selected idbanks data

data = get_insee_idbank(list_idbank)

avoid proxy issues

Sys.setenv(http_proxy = "my_proxy_server")
Sys.setenv(https_proxy = "my_proxy_server")

Examples

GDP growth rate

library(tidyverse)

idbank_list = get_idbank_list()

df_idbank_list_selected =
  idbank_list %>%
  filter(nomflow == "CNT-2014-PIB-EQB-RF") %>% # Gross domestic product balance
  filter(dim1 == "T") %>% #quarter
  filter(dim4 == "PIB") %>% #GDP
  filter(dim6 == "TAUX") %>% #rate
  filter(dim10 == "CVS-CJO") #SA-WDA, seasonally adjusted, working day adjusted

idbank = df_idbank_list_selected %>% pull(idbank)

data = get_insee_idbank(idbank)

#plot
ggplot(data, aes(x = DATE, y = OBS_VALUE)) +
  geom_col() +
  ggtitle("French GDP growth rate, quarter-on-quarter, sa-wda") +
  labs(subtitle = sprintf("Last updated : %s", data$TIME_PERIOD[1]))

Inflation

library(tidyverse)
library(lubridate)

idbank_list = get_idbank_list()

df_idbank_list_selected =
  idbank_list %>%
  filter(nomflow == "IPC-2015") %>% #Inflation dataset
  filter(dim1 == "M") %>% # monthly
  filter(str_detect(dim4, "^[0-9]{2}$")) %>% # coicop aggregation level
  filter(dim6 == "INDICE") %>% # index
  filter(dim7 == "ENSEMBLE") %>% # all kinds of household
  filter(dim8 == "FE") %>% # all France including overseas departements
  mutate(title = get_insee_title(idbank))

list_idbank = df_idbank_list_selected %>% pull(idbank)

data = get_insee_idbank(list_idbank, startPeriod = "2015-01")

n_sep = str_count(data$TITLE_FR[1], " - ") + 1

data_plot = data %>%
  separate(TITLE_EN, into = paste0("title", 1:n_sep),
           sep = " - ", remove = FALSE, fill = "right") %>%
  mutate(title6 = case_when(is.na(title6) ~ title5,
                            TRUE ~ as.character(title6))) %>%
  mutate(title6 = substr(title6, 1 , 22)) %>%
  mutate(month = month(DATE)) %>%
  arrange(DATE) %>%
  group_by(title6, month) %>%
  mutate(growth = 100 * (OBS_VALUE / dplyr::lag(OBS_VALUE) - 1))

ggplot(data_plot, aes(x = DATE, y = growth)) +
  geom_col() +
  facet_wrap(~title6, scales = "free") +
  ggtitle("French inflation, by product categories, year-on-year") +
  labs(subtitle = sprintf("Last updated : %s", data_plot$TIME_PERIOD[nrow(data_plot)])) 

Unemployment rate

library(tidyverse)

dataset_list = get_dataset_list()

idbank_list = get_idbank_list()

df_idbank_list_selected =
  idbank_list %>%
  filter(nomflow == "CHOMAGE-TRIM-NATIONAL") %>% #Unemployment dataset
  mutate(title = get_insee_title(idbank)) %>%
  filter(dim2 == "CTTXC") %>% #unemployment rate based on ILO standards
  filter(dim4 == "FE") %>%  # all France including overseas departements
  filter(dim5 == 0) # men and women

list_idbank = df_idbank_list_selected %>% pull(idbank)

data = get_insee_idbank(list_idbank, startPeriod = "2000-01")

n_sep = str_count(data$TITLE_FR[1], " - ") + 1

data_plot = data %>%
  separate(TITLE_EN, into = paste0("title", 1:n_sep),
           sep = " - ", remove = FALSE, fill = "right")

ggplot(data_plot, aes(x = DATE, y = OBS_VALUE, colour = title2)) +
  geom_line() +
  geom_point() +
  ggtitle("French unemployment rate, by age") +
  labs(subtitle = sprintf("Last updated : %s", data_plot$TIME_PERIOD[1]))

Population

Population by age

library(tidyverse)

dataset_list = get_dataset_list()

idbank_list = get_idbank_list()

df_idbank_list_selected =
  idbank_list %>%
  filter(nomflow == "POPULATION-STRUCTURE") %>% #population dataset
  mutate(title = get_insee_title(idbank)) %>%
  filter(dim2 == "POPULATION_1ER_JANVIER") %>% #population at the beginning of the year
  filter(dim5 == "FE") %>%  # all France including overseas departements
  filter(dim6 == 0) %>%  # men and women
  filter(dim7 %in% c("00-19", "20-59", "60-")) #age ranges

list_idbank = df_idbank_list_selected %>% pull(idbank)

data = get_insee_idbank(list_idbank)

n_sep = str_count(data$TITLE_FR[1], " - ") + 1

data_plot = data %>%
  separate(TITLE_EN, into = paste0("title", 1:n_sep),
           sep = " - ", remove = FALSE, fill = "right") %>%
  mutate(OBS_VALUE = OBS_VALUE / 10^6)

ggplot(data_plot, aes(x = DATE, y = OBS_VALUE, fill = title3)) +
  geom_area() +
  ggtitle("French population in millions, by age") +
  labs(subtitle = sprintf("Last updated : %s", data_plot$TIME_PERIOD[1]))

Population by departement

library(insee)
library(tidyverse)

library(raster)
library(rgdal)
library(broom)
library(viridis)

idbank_list = get_idbank_list()

dataset_list = get_dataset_list()

list_idbank = idbank_list %>%
  filter(nomflow == "TCRED-ESTIMATIONS-POPULATION") %>%
  filter(dim6 == "00-") %>% #all ages
  filter(dim5 == 0) %>% #men and women
  filter(str_detect(dim4, "^D")) %>% #select only departements
  mutate(title = get_insee_title(idbank))

list_idbank_selected = list_idbank %>% pull(idbank)

# get population data by departement
pop = get_insee_idbank(list_idbank_selected)

#get departements' geographical limits
FranceMap <- raster::getData(name = "GADM", country = "FRA", level = 2)

# extract the population by departement in 2020
pop_plot = pop %>%
  group_by(TITLE_EN) %>%
  filter(DATE == "2020-01-01") %>%
  mutate(dptm = gsub("D", "", REF_AREA)) %>%
  filter(dptm %in% FranceMap@data$CC_2) %>%
  mutate(dptm = factor(dptm, levels = FranceMap@data$CC_2)) %>%
  arrange(dptm) %>%
  mutate(id = dptm)

vec_pop = pop_plot %>% pull(OBS_VALUE)

# add population data to the departement object map
FranceMap@data$pop = vec_pop

# extract the departements' limits from the spatial object
FranceMap_tidy <- broom::tidy(FranceMap)

# mapping table
dptm_df = data.frame(dptm = FranceMap@data$CC_2,
                     dptm_name = FranceMap@data$NAME_2,
                     pop = FranceMap@data$pop,
                     id = rownames(FranceMap@data))

# add population data to departement dataframe
FranceMap_tidy_final =
  FranceMap_tidy %>%
  left_join(dptm_df, by = "id") %>%
  dplyr::select(long, lat, pop, group, id)

ggplot() +
  geom_polygon(data = FranceMap_tidy_final,
               aes(fill = pop, x = long, y = lat, group = group) ,
               size = 0, alpha = 0.9) +
  coord_map() +
  theme_void() +
  scale_fill_viridis() +
  ggtitle("Distribution of the population within French territory in 2020")

Support

Feel free to contact me with any question about this package using this e-mail address.

Disclaimer

This package is in no way officially related to or endorsed by INSEE.

hadrilec2/insee documentation built on Aug. 10, 2020, 1:53 a.m.