epidatatools contains functions we find useful at EPI that don’t have exact analogues elsewhere in the R package ecosystem.
One and two-way cross-tabulations
library(epidatatools)
crosstab(mtcars, cyl)
#> # A tibble: 3 × 4
#> cyl n percent cumul_percent
#> <dbl> <int> <dbl> <dbl>
#> 1 4 11 0.344 0.344
#> 2 6 7 0.219 0.562
#> 3 8 14 0.438 1
crosstab(mtcars, cyl, gear)
#> # A tibble: 3 × 4
#> cyl `3` `4` `5`
#> <dbl> <int> <int> <int>
#> 1 4 1 8 2
#> 2 6 2 4 1
#> 3 8 12 0 2
# weighted counts expressed as column percentages
crosstab(mtcars, cyl, gear, w = mpg, percent = "column")
#> # A tibble: 3 × 4
#> cyl `3` `4` `5`
#> <dbl> <dbl> <dbl> <dbl>
#> 1 4 0.0890 0.732 0.528
#> 2 6 0.163 0.268 0.184
#> 3 8 0.748 0 0.288Summarize across several distinct groups
summarize_groups(mtcars, cyl|gear|carb, p50 = median(mpg), avg = mean(hp))
#> # A tibble: 12 × 4
#> group_name group_value p50 avg
#> <chr> <dbl> <dbl> <dbl>
#> 1 cyl 4 26 82.6
#> 2 cyl 6 19.7 122.
#> 3 cyl 8 15.2 209.
#> 4 gear 3 15.5 176.
#> 5 gear 4 22.8 89.5
#> 6 gear 5 19.7 196.
#> 7 carb 1 22.8 86
#> 8 carb 2 22.1 117.
#> 9 carb 3 16.4 180
#> 10 carb 4 15.2 187
#> 11 carb 6 19.7 175
#> 12 carb 8 15 335Calculate averaged (smoothed) quantiles
averaged_median(mtcars$mpg)
#> [1] 19.2
p = c(10, 50, 90)
mtcars |>
dplyr::reframe(
p,
value = averaged_quantile(mpg, probs = p / 100)
)
#> p value
#> 1 10 13.832
#> 2 50 19.200
#> 3 90 30.108Download IPUMS microdata
Requires valid IPUMS API key stored in the .Renviron as IPUMS_API_KEY.
# First make sure you have a valid IPUMS API key stored in your .Renviron file as `IPUMS_API_KEY`.
# CPS ASEC microdata
dl_ipums_asec(2021:2022, c("YEAR", "OFFPOV", "ASECWT"))
#> DDI codebook file saved to /tmp/RtmpGaUUhf/cps_00161.xml
#> Data file saved to /tmp/RtmpGaUUhf/cps_00161.dat.gz
#> Use of data from IPUMS CPS is subject to conditions including that users should cite the data appropriately. Use command `ipums_conditions()` for more details.
#> # A tibble: 316,275 × 11
#> YEAR SERIAL MONTH CPSID ASECFLAG ASECWTH PERNUM CPSIDP CPSIDV ASECWT
#> <dbl> <dbl> <int+lbl> <dbl> <int+lb> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 2021 1 3 [March] 2.02e13 1 [ASEC] 706. 1 2.02e13 2.02e14 706.
#> 2 2021 1 3 [March] 2.02e13 1 [ASEC] 706. 2 2.02e13 2.02e14 706.
#> 3 2021 1 3 [March] 2.02e13 1 [ASEC] 706. 3 2.02e13 2.02e14 608.
#> 4 2021 2 3 [March] 2.02e13 1 [ASEC] 1537. 1 2.02e13 2.02e14 1537.
#> 5 2021 2 3 [March] 2.02e13 1 [ASEC] 1537. 2 2.02e13 2.02e14 1537.
#> 6 2021 2 3 [March] 2.02e13 1 [ASEC] 1537. 3 2.02e13 2.02e14 2119.
#> 7 2021 3 3 [March] 2.02e13 1 [ASEC] 1499. 1 2.02e13 2.02e14 1499.
#> 8 2021 8 3 [March] 2.02e13 1 [ASEC] 948. 1 2.02e13 2.02e14 948.
#> 9 2021 8 3 [March] 2.02e13 1 [ASEC] 948. 2 2.02e13 2.02e14 1225.
#> 10 2021 8 3 [March] 2.02e13 1 [ASEC] 948. 3 2.02e13 2.02e14 948.
#> # ℹ 316,265 more rows
#> # ℹ 1 more variable: OFFPOV <int+lbl>Get data from the BLS API
Requires valid BLS or FRED API keys stored in the .Renviron as IPUMS_API_KEY.
# To run this example you will need a BLS API key
# in your .Renviron file named BLS_API_KEY
bls_series_ids = c(
prime_age_epop = "LNS12300060",
injuries_annual = "ISU00000000031004"
)
get_bls(bls_series_ids, start = 2023, end = 2023)
#> # A tibble: 13 × 8
#> name series_id series_title date_frequency date year month value
#> <chr> <chr> <chr> <chr> <date> <dbl> <dbl> <dbl>
#> 1 prime_age… LNS12300… (Seas) Empl… month 2023-01-01 2023 1 80.3
#> 2 prime_age… LNS12300… (Seas) Empl… month 2023-02-01 2023 2 80.5
#> 3 prime_age… LNS12300… (Seas) Empl… month 2023-03-01 2023 3 80.7
#> 4 prime_age… LNS12300… (Seas) Empl… month 2023-04-01 2023 4 80.7
#> 5 prime_age… LNS12300… (Seas) Empl… month 2023-05-01 2023 5 80.7
#> 6 prime_age… LNS12300… (Seas) Empl… month 2023-06-01 2023 6 80.9
#> 7 prime_age… LNS12300… (Seas) Empl… month 2023-07-01 2023 7 80.9
#> 8 prime_age… LNS12300… (Seas) Empl… month 2023-08-01 2023 8 80.8
#> 9 prime_age… LNS12300… (Seas) Empl… month 2023-09-01 2023 9 80.8
#> 10 prime_age… LNS12300… (Seas) Empl… month 2023-10-01 2023 10 80.6
#> 11 prime_age… LNS12300… (Seas) Empl… month 2023-11-01 2023 11 80.7
#> 12 prime_age… LNS12300… (Seas) Empl… month 2023-12-01 2023 12 80.5
#> 13 injuries_… ISU00000… Industry-le… year 2023-01-01 2023 1 2.6Get data from the FRED API
# To run this example you will need a FRED API key
# in your .Renviron named FRED_API_KEY
fred_series_ids = c(
gdp = "GDP",
unemployment_rate = "UNRATE"
)
get_fred(fred_series_ids, start = as.Date("2024-06-01"), end = 2024)
#> # A tibble: 10 × 9
#> name series_id series_title date_frequency date year quarter month
#> <chr> <chr> <chr> <chr> <date> <dbl> <int> <dbl>
#> 1 gdp GDP Gross Domes… quarter 2024-04-01 2024 2 4
#> 2 gdp GDP Gross Domes… quarter 2024-07-01 2024 3 7
#> 3 gdp GDP Gross Domes… quarter 2024-10-01 2024 4 10
#> 4 unemplo… UNRATE Unemploymen… month 2024-06-01 2024 2 6
#> 5 unemplo… UNRATE Unemploymen… month 2024-07-01 2024 3 7
#> 6 unemplo… UNRATE Unemploymen… month 2024-08-01 2024 3 8
#> 7 unemplo… UNRATE Unemploymen… month 2024-09-01 2024 3 9
#> 8 unemplo… UNRATE Unemploymen… month 2024-10-01 2024 4 10
#> 9 unemplo… UNRATE Unemploymen… month 2024-11-01 2024 4 11
#> 10 unemplo… UNRATE Unemploymen… month 2024-12-01 2024 4 12
#> # ℹ 1 more variable: value <dbl>Installation
Install the latest version from R-Universe:
install.packages("epidatatools", repos = c("https://economic.r-universe.dev", "https://cloud.r-project.org"))