LAPOP Cross-Country Bar Graph Pre-Processing

This function creates dataframes which can then be input in lapop_cc for comparing values across countries with a bar graph using LAPOP formatting.

Usage

lpr_cc(
  data,
  outcome,
  xvar = "pais_lab",
  rec = list(c(1, 1)),
  rec2 = list(c(1, 1)),
  rec3 = list(c(1, 1)),
  rec4 = list(c(1, 1)),
  ci_level = 0.95,
  mean = FALSE,
  filesave = "",
  cfmt = "",
  sort = "y",
  order = "hi-lo",
  ttest = FALSE,
  keep_nr = FALSE
)

Arguments

data: A survey object. The data that should be analyzed.
outcome: Outcome variable(s) of interest to be plotted across countries. It can handle a single variable across countries, or multiple variables instead of multiple countries. See examples below.
xvar: Grouping variable. Default: pais_lab. It can handle other variables grouping like year/wave.
rec: Numeric. The minimum and maximum values of the outcome variable that should be included in the numerator of the percentage. For example, if the variable is on a 1-7 scale and rec is c(5, 7), the function will show the percentage who chose an answer of 5, 6, 7 out of all valid answers. Default: c(1, 1).
rec2: Numeric. Same as rec(). Default: c(1, 1).
rec3: Numeric. Same as rec(). Default: c(1, 1).
rec4: Numeric. Same as rec(). Default: c(1, 1).
ci_level: Numeric. Confidence interval level for estimates. Default: 0.95
mean: Logical. If TRUE, will produce the mean of the variable rather than rescaling to percentage. Default: FALSE.
filesave: Character. Path and file name to save the dataframe as csv.
cfmt: changes the format of the numbers displayed above the bars. Uses sprintf string formatting syntax. Default is whole numbers for percentages and tenths place for means.
sort: Character. On what value the bars are sorted: the x or the y. Options are "y" (default; for the value of the outcome variable), "xv" (for the underlying values of the x variable), "xl" (for the labels of the x variable, i.e., alphabetical).
order: Character. How the bars should be sorted. Options are "hi-lo" (default) or "lo-hi".
ttest: Logical. If TRUE, will conduct pairwise t-tests for difference of means between all individual x levels and save them in attr(x, "t_test_results"). Default: FALSE.
keep_nr: Logical. If TRUE, will convert "don't know" (missing code .a) and "no response" (missing code .b) into valid data (value = 99) and use them in the denominator when calculating percentages. The default is to examine valid responses only. Default: FALSE.

Value

Returns a data frame, with data formatted for visualization by lapop_cc

Author

Luke Plutowski, luke.plutowski@vanderbilt.edu & Robert Vidigal, robert.vidigal@vanderbilt.edu

Examples


require(lapop); data(ym23); data(bra23)

# Set Survey Context
bra23lpr <- lpr_data(bra23, wt = TRUE)
ym23lpr <- lpr_data(ym23)

# Multiple variables in Single Country
lpr_cc(data = bra23lpr,
outcome = c("b12", "b13"),
rec = c(5, 7))
#> # A tibble: 2 × 5
#>   vallabel  prop proplabel    lb    ub
#>   <chr>    <dbl> <chr>     <dbl> <dbl>
#> 1 b12       57.4 57%        54.6  60.2
#> 2 b13       35.3 35%        32.2  38.3

# Single variable in Multiple Countries
lpr_cc(data = ym23lpr,
      outcome = "ing4",
      rec = c(5, 7),
      xvar = "pais")
#> # A tibble: 23 × 5
#>    vallabel              prop proplabel    lb    ub
#>    <fct>                <dbl> <chr>     <dbl> <dbl>
#>  1 Uruguay               75.8 76%        73.8  77.9
#>  2 Costa Rica            72.2 72%        70.3  74.1
#>  3 Argentina             69.4 69%        67.5  71.4
#>  4 Chile                 66.7 67%        64.9  68.6
#>  5 Bahamas               64.9 65%        62.3  67.5
#>  6 El Salvador           62.9 63%        61.1  64.7
#>  7 México                62.2 62%        60.5  63.9
#>  8 Brasil                61.9 62%        59.8  64.0
#>  9 República Dominicana  61.5 62%        59.9  63.1
#> 10 Grenada               58.7 59%        56.1  61.3
#> # ℹ 13 more rows