lpr_mover: LAPOP "Multiple-Over" Breakdown Graphs

In lapop: Processing, Visualizing, and Labeling Americas Barometer Data

LAPOP "Multiple-Over" Breakdown Graphs

Description

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(s)

Luke Plutowski, [e-mail] && Robert Vidigal, [e-mail]

Examples

require(lapop); data(ym23)

# Set SUrvey Context
ym23lpr<-lpr_data(ym23)

# Single DV
lpr_mover(data = ym23lpr,
 outcome = "ing4",
 grouping_vars = c("q1tc_r", "edre"),
 rec = c(5, 7), ttest = FALSE)

# Multiple DV
lpr_mover(data = ym23lpr,
outcome = c("ing4", "pn4"),
grouping_vars = c("q1tc_r", "edre"),
rec = c(5, 7), rec2 = c(1, 2),
ttest = FALSE)

# Single DV X Single IV
lpr_mover(data = ym23lpr,
outcome="ing4",
grouping_vars="pn4",
rec=c(5,7),
ttest = FALSE)

# Multiple DV X Single IV
lpr_mover(data = ym23lpr,
outcome=c("ing4", "pn4"),
grouping_vars="edre",
rec=c(5,7), rec2=c(1,2),
ttest = FALSE)

# Multiple DV X Multiple IV
lpr_mover(data = ym23lpr,
outcome=c("ing4", "pn4"),
grouping_vars=c("edre", "q1tc_r"),
rec=c(5,7), rec2=c(1,2),
ttest = FALSE)

lapop documentation built on April 30, 2026, 1:07 a.m.