This course will teach you how to use the OECD package.
Retrieve data
# Loading OECD library
library(OECD)
# get_datasets()
dataset_list <- get_datasets() # this function will show you all the different datasets available
# search dataset()
search_dataset("unemployment", data = dataset_list) # this function will help you narrow your search of a specific dataset
id
92 DUR_I
93 DUR_D
157 AVD_DUR
669 AEO2012_CH6_FIG4
700 AEO2012_CH6_FIG29
746 AEO2012_CH6_FIG19
950 PTRUB
1305 NRR
1306 GRR
1405 PTRCCUB
title
92 Incidence of unemployment by duration
93 Unemployment by duration
157 Average duration of unemployment
669 Figure 4: Youth and adult unemployment
700 Figure 29: Youth employment and unemployment by education and country income groups
746 Figure 19: The trade off between vulnerable employment and unemployment
950 PTR for families claiming Unemployment Benefits
1305 Net replacement rate in unemployment
1306 Gross Replacement Rates in unemployment
1405 PTR for parents claiming Unemployment Benefits and using childcare services# choose your dataset and show the data in a data frame
dataset <- "DUR_D"
Extract data
# show the data in a data frame
dstruc <- get_data_structure(dataset)
str(dstruc, max.level = 1)
List of 12
$ VAR_DESC :'data.frame': 12 obs. of 2 variables:
$ COUNTRY :'data.frame': 53 obs. of 2 variables:
$ TIME :'data.frame': 52 obs. of 2 variables:
$ SEX :'data.frame': 3 obs. of 2 variables:
$ AGE :'data.frame': 7 obs. of 2 variables:
$ DURATION :'data.frame': 8 obs. of 2 variables:
$ FREQUENCY :'data.frame': 1 obs. of 2 variables:
$ OBS_STATUS :'data.frame': 16 obs. of 2 variables:
$ UNIT :'data.frame': 318 obs. of 2 variables:
$ POWERCODE :'data.frame': 32 obs. of 2 variables:
$ REFERENCEPERIOD:'data.frame': 96 obs. of 2 variables:
$ TIME_FORMAT :'data.frame': 5 obs. of 2 variables:dstruc$VAR_DESC # show this variable in a table
id description
1 COUNTRY Country
2 TIME Time
3 SEX Sex
4 AGE Age
5 DURATION Duration
6 FREQUENCY Frequency
7 OBS_VALUE Observation Value
8 TIME_FORMAT Time Format
9 OBS_STATUS Observation Status
10 UNIT Unit
11 POWERCODE Unit multiplier
12 REFERENCEPERIOD Reference perioddstruc$SEX # show this variable in a table
id label
1 MEN Men
2 WOMEN Women
3 MW All personsdstruc$AGE # show this variable in a table
id label
1 1519 15 to 19
2 1524 15 to 24
3 2024 20 to 24
4 2554 25 to 54
5 5564 55 to 64
6 6599 65+
7 900000 Total# filter your results
filter_list <- list(c("CAN", "FRA", "USA", "GBR"), "MW", "900000")
df <- get_dataset(dataset = dataset, filter = filter_list)
head(df)
COUNTRY SEX AGE DURATION FREQUENCY TIME_FORMAT obsTime obsValue
1 CAN MW 900000 UN1 A P1Y 1976 233.2
2 CAN MW 900000 UN1 A P1Y 1977 264.8
3 CAN MW 900000 UN1 A P1Y 1978 273.7
4 CAN MW 900000 UN1 A P1Y 1979 273.0
5 CAN MW 900000 UN1 A P1Y 1980 289.2
6 CAN MW 900000 UN1 A P1Y 1981 305.5# choose one time frame in the DURATION data frame
unique(df$DURATION)
[1] "UN1" "UN2" "UN3" "UN4" "UN5" "UN" "UND" "UNK"dstruc$DURATION # show this variable in a table
id label
1 UN1 < 1 month
2 UN2 > 1 month and < 3 months
3 UN3 > 3 month and < 6 months
4 UN4 > 6 month and < 1 year
5 UN5 1 year and over
6 UN Total
7 UND Total Declared
8 UNK UnknownVisualize data
# We will use the "UN" DURATION for this example
df_plot <- df[df$DURATION == "UN", ]
# Data wrangling
df_plot$obsTime <- as.numeric(df_plot$obsTime) # make sure the variable is in a numeric format
library(ggplot2)
palette <- c("black", "#f8c72d", "#db0a16", "#255293")
qplot(data = df_plot, x = obsTime, y = obsValue, geom = c("line","point"), color = COUNTRY) +
labs(x = NULL, y = "Persons, thousands", color = NULL, title = "Long-term unemployement") +
theme_minimal() +
scale_color_manual(values = palette)
The line chart above illustrates long term unemployement in Canada, France, the United Kingdom and the US since the 1970s. We can easily tell that around 2010, many people in the US were unemployed.