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LAPOP "Multiple-Over" Breakdown Graphs

lpr_mover.Rd

This function creates a dataframe which can then be input in lapop_mover() for comparing means across values of secondary variable(s) using LAPOP formatting.

Usage

lpr_mover(
  data,
  outcome,
  grouping_vars,
  rec = list(c(1, 1)),
  rec2 = c(1, 1),
  rec3 = c(1, 1),
  rec4 = c(1, 1),
  ci_level = 0.95,
  mean = FALSE,
  filesave = "",
  cfmt = "",
  ttest = FALSE,
  keep_nr = FALSE
)

Arguments

data

A survey object. The data that should be analyzed.

outcome

Character. Outcome variable(s) of interest to be plotted across secondary variable(s).

grouping_vars

A character vector specifying one or more grouping variables. For each variable, the function calculates the average of the outcome variable, broken down by the distinct values within the grouping variable(s).

rec

Numeric. The minimum and maximum values of the frst 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. Can also supply one value only, to produce the percentage that chose that value out of all other values. Default: c(1, 1).

rec2

Numeric. Similar to 'rec' for the second outcome. Default: c(1, 1).

rec3

Numeric. Similar to 'rec' for the third outcome. Default: c(1, 1).

rec4

Numeric. Similar to 'rec' for the fourth outcome. 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 recoding 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.

ttest

Logical. If TRUE, will conduct pairwise t-tests for difference of means between all individual year-xvar 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_mover

Author

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

Examples

# Single DV
if (FALSE) lpr_mover(data = ym23,
 outcome = "ing4",
 grouping_vars = c("q1tc_r", "edre", "wealth"),
 rec = c(5, 7)) # \dontrun{}

 # Multiple DV
 if (FALSE) lpr_mover(data = ym23,
 outcome = c("ing4", "pn4"),
 grouping_vars = c("q1tc_r", "edre", "wealth"),
 rec = c(5, 7), rec2 = c(1, 2)) # \dontrun{}

# Single DV X Single IV
if (FALSE) lpr_mover(data = ym23,
outcome="ing4",
grouping_vars="pn4",
rec=c(5,7), ttest=T) # \dontrun{}

# Multiple DV X Single IV
if (FALSE) lpr_mover(data = ym23,
outcome=c("ing4", "pn4"),
grouping_vars="edre",
rec=c(5,7), rec2=c(1,2), ttest=T) # \dontrun{}

# Multiple DV X Multiple IV
if (FALSE) lpr_mover(data = ym23,
outcome=c("ing4", "pn4"),
grouping_vars=c("edre", "q1tc_r"),
rec=c(5,7), rec2=c(1,2), ttest=T) # \dontrun{}