if (!require(devtools)) install.packages("devtools")

devtools::install_github("mnpopcenter/ripums")

devtools::install_github("mnpopcenter/ripums/ripumsexamples")
devtools::install_github("mnpopcenter/ripums", build_vignettes = TRUE)

suppressPackageStartupMessages({
  library(ripums)
  library(haven)
  library(ggplot2) # ggplot2 version > 2.2.1 (development version as of 8/15/2017)
  library(dplyr)
  library(sf)
})

# Use example file included with package:
cps_hier_file <- ripums_example("cps_00010.xml")
ddi <- read_ipums_ddi(cps_hier_file)
data <- read_ipums_micro(ddi)
#> Users of IPUMS-CPS data must agree to abide by the conditions of use. A user's
#> license is valid for one year and may be renewed. Users must agree to the
#> following conditions:
#> 
#> (1) No fees may be charged for use or distribution of the data. All persons are
#> granted a limited license to use these data, but you may not charge a fee for
#> the data if you distribute it to others.
#> 
#> (2) Cite IPUMS appropriately. For information on proper citation, refer to the
#> citation requirement section of this DDI document.
#> 
#> (3) Tell us about any work you do using the IPUMS. Publications, research
#> reports, or presentations making use of IPUMS-CPS should be added to our
#> Bibliography. Continued funding for the IPUMS depends on our ability to show our
#> sponsor agencies that researchers are using the data for productive purposes.
#> 
#> (4) Use it for GOOD -- never for EVIL.
#> 
#> Publications and research reports based on the IPUMS-CPS database must cite it
#> appropriately. The citation should include the following:
#> 
#> Sarah Flood, Miriam King, Steven Ruggles, and J. Robert Warren. Integrated
#> Public Use Microdata Series, Current Population Survey: Version 5.0 [dataset].
#> Minneapolis, MN: University of Minnesota, 2017. https://doi.org/10.18128/
#> D030.V5.0
#> 
#> The licensing agreement for use of IPUMS-CPS data requires that users supply
#> us with the title and full citation for any publications, research reports, or
#> educational materials making use of the data or documentation. Please add your
#> citation to the IPUMS bibliography: http://bibliography.ipums.org/
#> 
#> Reading data...
#> Parsing data...

# Variable description for the month variable
cat(ipums_var_desc(ddi, MONTH))
#> MONTH indicates the calendar month of the CPS interview.

# Hierarachical data loaded as a data frame


# Value labels loaded as haven::labelled class
# Convert to factors with `as_factor`
table(as_factor(data$MONTH, levels = "both"))
#> 
#>   [1] January  [2] February     [3] March     [4] April       [5] May 
#>             0             0          3385             0             0 
#>      [6] June      [7] July    [8] August [9] September  [10] October 
#>             0             0             0             0             0 
#> [11] November [12] December 
#>             0             0

# Can also load as a list by rectype
data <- read_ipums_micro_list(ddi, verbose = FALSE)
# Household data
data$HOUSEHOLD
#> # A tibble: 3,385 x 6
#>      RECTYPE  YEAR SERIAL HWTSUPP  STATEFIP     MONTH
#>    <chr+lbl> <dbl>  <dbl>   <dbl> <int+lbl> <int+lbl>
#>  1         H  1962     80 1475.59        55         3
#>  2         H  1962     82 1597.61        27         3
#>  3         H  1962     83 1706.65        27         3
#>  4         H  1962     84 1790.25        27         3
#>  5         H  1962    107 4355.40        19         3
#>  6         H  1962    108 1479.05        19         3
#>  7         H  1962    122 3602.75        27         3
#>  8         H  1962    124 4104.41        55         3
#>  9         H  1962    125 2182.17        55         3
#> 10         H  1962    126 1826.38        55         3
#> # ... with 3,375 more rows
# Person data
data$PERSON
#> # A tibble: 7,668 x 6
#>      RECTYPE  YEAR SERIAL PERNUM  WTSUPP    INCTOT
#>    <chr+lbl> <dbl>  <dbl>  <dbl>   <dbl> <dbl+lbl>
#>  1         P  1962     80      1 1475.59      4883
#>  2         P  1962     80      2 1470.72      5800
#>  3         P  1962     80      3 1578.75  99999998
#>  4         P  1962     82      1 1597.61     14015
#>  5         P  1962     83      1 1706.65     16552
#>  6         P  1962     84      1 1790.25      6375
#>  7         P  1962    107      1 4355.40  99999999
#>  8         P  1962    107      2 1385.81         0
#>  9         P  1962    107      3 1629.10       600
#> 10         P  1962    107      4 1432.24  99999999
#> # ... with 7,658 more rows

# Use example file included with package
cps_rect_file <- ripums_example("cps_00006.xml")
data <- read_ipums_micro(cps_rect_file, verbose = FALSE)

# While working interactively, can get convenient display of variable information
# in RStudio's viewer
ipums_view(data)

data <- read_nhgis_sf(
  ripums_example("nhgis0008_csv.zip"),
  shape_file = ripums_example("nhgis0008_shape_small.zip"),
  verbose = FALSE
)

ipums_var_info(data, starts_with("D6Z"))
#> # A tibble: 8 x 4
#>   var_name          var_label                   var_desc       val_labels
#>      <chr>              <chr>                      <chr>           <list>
#> 1   D6Z001 1989 to March 1990 Year Structure Built (D6Z) <tibble [0 x 2]>
#> 2   D6Z002       1985 to 1988 Year Structure Built (D6Z) <tibble [0 x 2]>
#> 3   D6Z003       1980 to 1984 Year Structure Built (D6Z) <tibble [0 x 2]>
#> 4   D6Z004       1970 to 1979 Year Structure Built (D6Z) <tibble [0 x 2]>
#> 5   D6Z005       1960 to 1969 Year Structure Built (D6Z) <tibble [0 x 2]>
#> 6   D6Z006       1950 to 1959 Year Structure Built (D6Z) <tibble [0 x 2]>
#> 7   D6Z007       1940 to 1949 Year Structure Built (D6Z) <tibble [0 x 2]>
#> 8   D6Z008    1939 or earlier Year Structure Built (D6Z) <tibble [0 x 2]>

data <- data %>%
  mutate(
    pct_before_1950 = (D6Z007 + D6Z008) / 
           (D6Z001 + D6Z002 + D6Z003 + D6Z004 + D6Z005 + D6Z006 + D6Z007 + D6Z008)
  )

# Note the function `geom_sf()` is currently only in the development version, 
# so you may need to update ggplot2 to run using 
#   devtools::install_github("tidyverse/ggplot2")
if ("geom_sf" %in% getNamespaceExports("ggplot2")) {
  ggplot(data = data) + 
    geom_sf(aes(fill = pct_before_1950)) + 
    labs(
      title = "Percent of homes built before 1950", 
      subtitle = "By Primary Metropolitan Statistical Area in 1990 Census", 
      caption = "Simplified PMSA boundaries (1% of polygon points retained)"
    )
}

# Raster data
data <- ripums:::read_terra_raster(
  "2552_bundle.zip",
  "CROPLAND2000ZM2013.tiff",
  verbose = FALSE
)

# Area data
data <- ripums:::read_terra_area(
  "2553_bundle.zip",
  verbose = FALSE
)

# Microdata
data <- ripums:::read_terra_micro(
  "2554_bundle.zip",
  verbose = FALSE
)