A subset of PISA data, containing scores and other information from the triennial testing of 15 year olds around the globe. Original data available from https://www.oecd.org/pisa/data/. Data derived from https://github.com/kevinwang09/learningtower.

## Usage

pisa

## Format

A tibble of the following variables

• year the year of measurement

• country the three letter country code. This data contains Australia, New Zealand, and Indonesia. The full data from learningtower contains 99 countries.

• school_id The unique school identification number

• student_id The student identification number

• gender recorded gender - 1 female or 2 male or missing

• math Simulated score in mathematics

• science Simulated score in science

• stu_wgt The final survey weight score for the student score

Understanding a bit more about the PISA data, the school_id and student_id are not unique across time. This means the longitudinal element is the country within a given year.

We can cast pisa as a tsibble, but we need to aggregate the data to each year and country. In doing so, it is important that we provide some summary statistics of each of the scores - we want to include the mean, and minimum and maximum of the math, reading, and science scores, so that we do not lose the information of the individuals.

The example code below does this, first grouping by year and country, then calculating the weighted mean for math, reading, and science. This can be done using the student weight variable stu_wgt, to get the survey weighted mean. The minimum and maximum are then calculated.

## Examples

pisa
#> # A tibble: 433 × 11
#>    <fct>   <int>     <dbl>    <dbl>    <dbl>     <dbl>    <dbl>    <dbl>
#>  1 ALB      2000      395.     27.4     722.      354.  59.7        640.
#>  2 ALB      2009      377.     79.6     706.      385.  17.0        662.
#>  3 ALB      2012      395.     62.4     688.      394.   0.0834     742.
#>  4 ALB      2015      412.    122.      711.      405.  93.6        825.
#>  5 ALB      2018      437.     96.5     789.      405. 152.         693.
#>  6 ARE      2009      421.     57.8     768.      431.  48.1        772.
#>  7 ARE      2012      434.    138.      862.      442.  75.5        785.
#>  8 ARE      2015      427.     91.8     793.      432.  54.4        827.
#>  9 ARE      2018      437.     87.6     865.      431.  84.0        814.
#> 10 ARG      2000      385.     16.0     675.      417.  84.2        761.
#> # ℹ 423 more rows
#> # ℹ 3 more variables: science_mean <dbl>, science_min <dbl>, science_max <dbl>

library(dplyr)
# Let's identify

#1.  The **key**, the individual, who would have repeated measurements.
#2.  The **index**, the time component.
#3.  The **regularity** of the time interval (index).

# Here it looks like the key is the student_id, which is nested within
# school_id #' and country,

# And the index is year, so we would write the following

as_tsibble(pisa,
key = country,
index = year)
#> # A tsibble: 433 x 11 [3Y]
#> # Key:       country [100]
#>    <fct>   <int>     <dbl>    <dbl>    <dbl>     <dbl>    <dbl>    <dbl>
#>  1 ALB      2000      395.     27.4     722.      354.  59.7        640.
#>  2 ALB      2009      377.     79.6     706.      385.  17.0        662.
#>  3 ALB      2012      395.     62.4     688.      394.   0.0834     742.
#>  4 ALB      2015      412.    122.      711.      405.  93.6        825.
#>  5 ALB      2018      437.     96.5     789.      405. 152.         693.
#>  6 ARE      2009      421.     57.8     768.      431.  48.1        772.
#>  7 ARE      2012      434.    138.      862.      442.  75.5        785.
#>  8 ARE      2015      427.     91.8     793.      432.  54.4        827.
#>  9 ARE      2018      437.     87.6     865.      431.  84.0        814.
#> 10 ARG      2000      385.     16.0     675.      417.  84.2        761.
#> # ℹ 423 more rows
#> # ℹ 3 more variables: science_mean <dbl>, science_min <dbl>, science_max <dbl>

# We can assess the regularity of the year like so:

index_regular(pisa, year)
#> [1] TRUE
index_summary(pisa, year)
#>    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.
#>    2000    2004    2009    2009    2014    2018

# We can now convert this into a tsibble:

pisa_ts <- as_tsibble(pisa,
key = country,
index = year,
regular = TRUE)

pisa_ts
#> # A tsibble: 433 x 11 [3Y]
#> # Key:       country [100]
#>    <fct>   <int>     <dbl>    <dbl>    <dbl>     <dbl>    <dbl>    <dbl>
#>  1 ALB      2000      395.     27.4     722.      354.  59.7        640.
#>  2 ALB      2009      377.     79.6     706.      385.  17.0        662.
#>  3 ALB      2012      395.     62.4     688.      394.   0.0834     742.
#>  4 ALB      2015      412.    122.      711.      405.  93.6        825.
#>  5 ALB      2018      437.     96.5     789.      405. 152.         693.
#>  6 ARE      2009      421.     57.8     768.      431.  48.1        772.
#>  7 ARE      2012      434.    138.      862.      442.  75.5        785.
#>  8 ARE      2015      427.     91.8     793.      432.  54.4        827.
#>  9 ARE      2018      437.     87.6     865.      431.  84.0        814.
#> 10 ARG      2000      385.     16.0     675.      417.  84.2        761.
#> # ℹ 423 more rows
#> # ℹ 3 more variables: science_mean <dbl>, science_min <dbl>, science_max <dbl>
pisa_ts_au_nz <- pisa_ts %>% filter(country %in% c("AUS", "NZL", "QAT"))

library(ggplot2)
ggplot(pisa_ts_au_nz,
aes(x = year,
y = math_mean,
group = country,
colour = country)) +
geom_ribbon(aes(ymin = math_min,
ymax = math_max),
fill = "grey70") +
geom_line(size = 1) +
lims(y = c(0, 1000)) +
labs(y = "math") +
facet_wrap(~country)
#> Warning: Using size aesthetic for lines was deprecated in ggplot2 3.4.0.
#> ℹ Please use linewidth instead.