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LAPOP Time-Series Line Graph Pre-Processing

lpr_ts.Rd

This function creates dataframes which can then be input in lapop_ts for comparing values across time with a line graph using LAPOP formatting.

Usage

lpr_ts(
  data,
  outcome,
  rec = c(1, 1),
  use_wave = FALSE,
  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 of interest to be plotted across time.

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

use_wave

Logical. If TRUE, will use "wave" for the x-axis; otherwise, will use "year". Default: FALSE.

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

Character. 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 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_ts()

Author

Berta Diaz, berta.diaz.martinez@vanderbilt.edu & Luke Plutowski, luke.plutowski@vanderbilt.edu

Examples


require(lapop); data(ym23)

# Set Survey Context
ym23lpr<-lpr_data(ym23)

# Run lpr_ts
lpr_ts(ym23lpr,
outcome = "ing4",
use_wave = TRUE,
mean = TRUE,
ttest = TRUE)
#>       wave     prop       lb       ub proplabel         se
#> 8  2018/19 4.753684 4.731888 4.775480       4.8 0.01112058
#> 9     2021       NA       NA       NA      <NA>         NA
#> 10    2023 4.756581 4.734816 4.778347       4.8 0.01110472