vivaglint: Analysis Tools for 'Viva Glint' Survey Data

Description

Provides functions for importing, validating, and analyzing 'Viva Glint' survey data exports, with optional API-based import via the 'Microsoft Graph' API. Includes tools for data reshaping, question-level analysis, multi-cycle comparisons, organizational hierarchy analysis, factor analysis, and correlation analysis. Harman (1960, ISBN: 0226316513); Husser (2017) doi:10.1002/9781118901731.iecrm0048.

Author(s)

Maintainer: Eric Knudsen ericknudsen@microsoft.com

Other contributors:

• Microsoft Corporation [copyright holder]

See Also

Useful links:

• https://github.com/microsoft/vivaglint

• Report bugs at https://github.com/microsoft/vivaglint/issues

Aggregate Responses by Manager

Description

Rolls up survey responses to the manager level, calculating the same metrics as summarize_survey() for each manager's team.

Usage

aggregate_by_manager(
  survey,
  scale_points,
  emp_id_col = NULL,
  manager_id_col,
  full_tree = FALSE,
  plot = FALSE
)

Arguments

Value

A tibble with one row per manager-question combination containing:

manager_id

The manager's employee ID

manager_name

The manager's full name (First Name + Last Name)

question

The question text

team_size

Number of employees in the team (direct reports only, or full subtree when full_tree = TRUE)

mean, sd, glint_score, n_responses, n_skips, n_total

Descriptive statistics for this manager's team on this question

pct_favorable, pct_neutral, pct_unfavorable

Favorability percentages for this manager's team on this question

When plot = TRUE, the same tibble is returned invisibly after printing the plot.

Examples

survey_path <- system.file("extdata", "survey_export.csv", package = "vivaglint")
survey <- read_glint_survey(survey_path, emp_id_col = "EMP ID")

# Direct reports only
manager_summary <- aggregate_by_manager(survey, scale_points = 5,
                                        emp_id_col = "EMP ID",
                                        manager_id_col = "Manager ID")

# Full organizational tree
manager_summary_full <- aggregate_by_manager(survey, scale_points = 5,
                                             emp_id_col = "EMP ID",
                                             manager_id_col = "Manager ID",
                                             full_tree = TRUE)

# With ranked dot plot
if (requireNamespace("ggplot2", quietly = TRUE)) {
  aggregate_by_manager(survey, scale_points = 5, emp_id_col = "EMP ID",
                       manager_id_col = "Manager ID", plot = TRUE)
}

Analyze Employee Attrition

Description

Analyzes the relationship between survey responses and employee attrition, calculating how much more (or less) likely employees are to leave within specified time periods if they respond unfavorably vs favorably to survey questions.

Usage

analyze_attrition(
  survey,
  attrition_file,
  emp_id_col = NULL,
  term_date_col,
  scale_points,
  time_periods = c(90, 180, 365),
  attribute_cols = NULL,
  min_group_size = 5,
  plot = FALSE
)

Arguments

Value

A tibble with one row per (attribute group)-question-time period combination containing:

attribute columns

One column per entry in attribute_cols (only present when attribute_cols is supplied)

group_size

Number of employees in the attribute group (only present when attribute_cols is supplied)

question

The question text

days

The time period in days

favorable_n

Number of employees who responded favorably

favorable_attrition

Proportion who left within time period (favorable)

unfavorable_n

Number of employees who responded unfavorably

unfavorable_attrition

Proportion who left within time period (unfavorable)

attrition_ratio

Ratio of unfavorable to favorable attrition rates

When plot = TRUE, the same tibble is returned invisibly after printing the plot.

Examples

survey_path <- system.file("extdata", "survey_export.csv", package = "vivaglint")
attrition_file <- system.file("extdata", "attrition.csv", package = "vivaglint")
attr_path <- system.file("extdata", "employee_attributes.csv", package = "vivaglint")
survey <- read_glint_survey(survey_path, emp_id_col = "EMP ID")

# Overall attrition analysis
attrition <- analyze_attrition(
  survey,
  attrition_file = attrition_file,
  emp_id_col = "EMP ID",
  term_date_col = "Termination Date",
  scale_points = 5
)

# Attrition segmented by Department and Gender
survey_enriched <- join_attributes(survey, attr_path, emp_id_col = "EMP ID")
attrition_by_dept <- analyze_attrition(
  survey_enriched,
  attrition_file = attrition_file,
  emp_id_col = "EMP ID",
  term_date_col = "Termination Date",
  scale_points = 5,
  attribute_cols = c("Department", "Gender"),
  min_group_size = 2
)

# With attrition chart
if (requireNamespace("ggplot2", quietly = TRUE)) {
  analyze_attrition(survey, attrition_file = attrition_file,
                    emp_id_col = "EMP ID", term_date_col = "Termination Date",
                    scale_points = 5, plot = TRUE)
}

Analyze Survey Responses by Attributes

Description

Aggregates survey responses by employee attributes (e.g., Department, Gender, Tenure) and calculates the same metrics as summarize_survey() for each attribute group combination. Only groups meeting the minimum size threshold are included in the results.

Usage

analyze_by_attributes(
  survey,
  attribute_file = NULL,
  scale_points,
  attribute_cols,
  emp_id_col = NULL,
  min_group_size = 5,
  plot = FALSE
)

Arguments

Details

Attributes can be supplied in two ways:

1. Pass attribute_file (a file path or data frame) and the join is performed internally on each call.

2. Pre-join attributes once with join_attributes() and pass the enriched survey directly — omit attribute_file entirely. This is more efficient when calling analyze_by_attributes() multiple times or when you want to filter the data before analysis.

Value

A tibble with one row per attribute-group-question combination containing:

attribute columns

Values for each attribute grouping variable

group_size

Number of employees in this attribute group

question, mean, sd, n_responses, n_skips, n_total

Descriptive statistics for this group on this question

pct_favorable, pct_neutral, pct_unfavorable

Favorability percentages for this group on this question

When plot = TRUE, the same tibble is returned invisibly after printing the plot.

Examples

survey_path <- system.file("extdata", "survey_export.csv", package = "vivaglint")
attr_file <- system.file("extdata", "employee_attributes.csv", package = "vivaglint")
survey <- read_glint_survey(survey_path, emp_id_col = "EMP ID")

# Option 1: provide attribute_file directly
results <- analyze_by_attributes(
  survey,
  attribute_file = attr_file,
  scale_points = 5,
  attribute_cols = "Department",
  emp_id_col = "EMP ID",
  min_group_size = 2
)

# Option 2: pre-join with join_attributes(), then omit attribute_file
survey_enriched <- join_attributes(survey, attr_file, emp_id_col = "EMP ID")
results <- analyze_by_attributes(survey_enriched, scale_points = 5,
                                 attribute_cols = "Department",
                                 emp_id_col = "EMP ID",
                                 min_group_size = 2)

# With dot plot
if (requireNamespace("ggplot2", quietly = TRUE)) {
  analyze_by_attributes(survey_enriched, scale_points = 5,
                        attribute_cols = "Department",
                        emp_id_col = "EMP ID", plot = TRUE,
                        min_group_size = 2)
}

Build a glint_survey Object

Description

Internal helper to validate, parse, and attach metadata.

Usage

build_glint_survey(
  data,
  emp_id_col,
  first_name_col = "First Name",
  last_name_col = "Last Name",
  email_col = "Email",
  status_col = "Status",
  completion_date_col = "Survey Cycle Completion Date",
  sent_date_col = "Survey Cycle Sent Date",
  file_path = NA_character_
)

Arguments

Value

A glint_survey object

Compare Survey Cycles

Description

Compares question-level metrics across multiple survey cycles, calculating change scores and trends over time.

Usage

compare_cycles(..., scale_points, cycle_names = NULL, plot = FALSE)

Arguments

Value

A tibble with one row per question-cycle combination containing:

cycle

The cycle name or number

question

The question text

mean, sd, n_responses, n_skips, n_total

Descriptive statistics for this question in this cycle (see summarize_survey())

pct_favorable, pct_neutral, pct_unfavorable

Favorability percentages for this question in this cycle

change_from_previous

Change in raw mean score from the previous cycle (NA for the first cycle)

pct_change_from_previous

Percentage change in raw mean score from the previous cycle (NA for the first cycle)

glint_score_change_from_previous

Change in Glint Score (0-100 scale) from the previous cycle (NA for the first cycle)

When plot = TRUE, the same tibble is returned invisibly after printing the plot.

Examples

survey_path <- system.file("extdata", "survey_export.csv", package = "vivaglint")
survey1 <- read_glint_survey(survey_path, emp_id_col = "EMP ID")
survey2 <- read_glint_survey(survey_path, emp_id_col = "EMP ID")
survey3 <- read_glint_survey(survey_path, emp_id_col = "EMP ID")

comparison <- compare_cycles(survey1, survey2, survey3,
                              scale_points = 5,
                              cycle_names = c("Q1 2024", "Q2 2024", "Q3 2024"))
print(comparison)

# With trend chart
if (requireNamespace("ggplot2", quietly = TRUE)) {
  compare_cycles(survey1, survey2, survey3, scale_points = 5,
                 cycle_names = c("Q1 2024", "Q2 2024", "Q3 2024"),
                 plot = TRUE)
}

Expand Value Distributions

Description

Internal function to expand value_counts and value_percents list columns into separate columns for each response value.

Usage

expand_value_distributions(analysis_df)

Arguments

Value

A tibble with expanded columns for each response value

Extract Questions from Glint Survey

Description

Parses column names to extract unique questions and their associated column names.

Usage

extract_questions(data, emp_id_col = NULL)

Arguments

Value

A tibble with one row per question containing:

question

The question text

response_col

Column name for numeric responses

comment_col

Column name for comments

topics_col

Column name for comment topics

flag_col

Column name for sensitive comment flags

Examples

survey_path <- system.file("extdata", "survey_export.csv", package = "vivaglint")
survey <- read_glint_survey(survey_path, emp_id_col = "EMP ID")
questions <- extract_questions(survey)
print(questions)

Extract Survey Factors

Description

Performs factor analysis on survey question responses to identify underlying latent factors. Supports multiple rotation methods with oblique rotation as default.

Usage

extract_survey_factors(
  survey,
  n_factors = NULL,
  rotation = "oblimin",
  min_loading = 0.3,
  fm = "minres",
  plot = FALSE
)

Arguments

Value

A list containing:

factor_summary

A tibble with one row per question-factor combination (filtered to abs(loading) >= min_loading), containing: question (item text), factor (factor name), loading (loading coefficient), loading_label ("Strong" >= 0.75 / "Medium" 0.60-0.74 / "Weak" < 0.60), communality (proportion of item variance explained by all factors), and factor_variance_pct (percentage of total variance explained by this factor). Sorted by factor then descending loading strength.

fa_object

Original fa object from psych package for further analysis

When plot = TRUE, the same list is returned invisibly after printing the plot.

Examples

survey_path <- system.file("extdata", "survey_export.csv", package = "vivaglint")
survey <- read_glint_survey(survey_path, emp_id_col = "EMP ID")

if (requireNamespace("psych", quietly = TRUE)) {
  # Extract factors with fixed count to keep runtime small
  factors <- extract_survey_factors(survey, n_factors = 1)
  print(factors$factor_summary)

  # Filter to strong loaders only
  strong <- dplyr::filter(factors$factor_summary, loading_label == "Strong")

  # With factor loading heatmap
  if (requireNamespace("ggplot2", quietly = TRUE)) {
    extract_survey_factors(survey, n_factors = 1, plot = TRUE)
  }
}

Get All Reports for a Manager

Description

Internal function to get all direct and indirect reports for a manager.

Usage

get_all_reports(manager_id, data, emp_id_col, manager_id_col)

Arguments

Value

Character vector of employee IDs for all reports

Calculate Question Correlations

Description

Calculates correlations between all question response columns in the survey. Supports multiple correlation methods and output formats.

Usage

get_correlations(
  survey,
  method = "pearson",
  format = "long",
  use = "pairwise.complete.obs",
  plot = FALSE
)

Arguments

Value

If format = "long", a tibble with columns:

question1

First question text

question2

Second question text

correlation

Correlation coefficient

p_value

P-value for test of correlation significance

n

Number of complete pairs used in calculation

If format = "matrix", a matrix with questions as rows and columns. When plot = TRUE and format = "long", the tibble is returned invisibly after printing the plot.

Examples

survey_path <- system.file("extdata", "survey_export.csv", package = "vivaglint")
survey <- read_glint_survey(survey_path, emp_id_col = "EMP ID")

# Get Pearson correlations in long format (default)
correlations <- get_correlations(survey)

# Get Spearman correlations
correlations_spearman <- get_correlations(survey, method = "spearman")

# Get correlation matrix
cor_matrix <- get_correlations(survey, format = "matrix")

# With correlation heatmap
if (requireNamespace("ggplot2", quietly = TRUE)) {
  get_correlations(survey, plot = TRUE)
}

Get Favorability Map for Scale Points

Description

Returns the favorability classification for a given scale point count, based on Glint standards.

Usage

get_favorability_map(scale_points)

Arguments

Value

A list with three elements: favorable, neutral, and unfavorable, each containing a vector of scale points classified into that category

Get Question Stem from Column Name

Description

Extracts the base question text from a column name by removing standard suffixes (_COMMENT, _COMMENT_TOPICS, _SENSITIVE_COMMENT_FLAG).

Usage

get_question_stem(col_name)

Arguments

Value

Character string with the base question text

Get Response Distribution

Description

Calculates the distribution of response values for survey questions, returning counts and percentages for each response value in tidy format.

Usage

get_response_dist(survey, questions = "all", plot = FALSE)

Arguments

Value

A tibble with one row per question containing:

question

The question text

count_X

Count of responses with value X (for each unique response value)

pct_X

Percentage of responses with value X (for each unique response value)

When plot = TRUE, the same tibble is returned invisibly after printing the plot.

Examples

survey_path <- system.file("extdata", "survey_export.csv", package = "vivaglint")
survey <- read_glint_survey(survey_path, emp_id_col = "EMP ID")

# Get response distribution for all questions
dist <- get_response_dist(survey)

# Get response distribution for specific questions
dist_subset <- get_response_dist(survey,
  questions = c("My work is meaningful", "I feel valued"))

# With distribution chart
if (requireNamespace("ggplot2", quietly = TRUE)) {
  get_response_dist(survey, plot = TRUE)
}

Get Standard Column Names

Description

Returns the vector of standard column names expected in Viva Glint exports. The employee ID column name is configurable because customers use different names. "Manager ID" is included for question-detection exclusion but is not required to be present.

Usage

get_standard_columns(
  emp_id_col,
  first_name_col = "First Name",
  last_name_col = "Last Name",
  email_col = "Email",
  status_col = "Status",
  completion_date_col = "Survey Cycle Completion Date",
  sent_date_col = "Survey Cycle Sent Date"
)

Arguments

Value

Character vector of standard column names

Read a required Glint env var, stopping with a helpful message if missing.

Description

Read a required Glint env var, stopping with a helpful message if missing.

Usage

glint_env(var_name, label)

Configure Viva Glint API credentials

Description

Stores Viva Glint API credentials in environment variables for the current R session. Optionally writes the values to ~/.Renviron for persistence.

Usage

glint_setup(
  tenant_id,
  client_id,
  client_secret,
  experience_name,
  save_to_renviron = FALSE
)

Arguments

Details

Required environment variables:

• GLINT_TENANT_ID

• GLINT_CLIENT_ID

• GLINT_CLIENT_SECRET

• GLINT_EXPERIENCE_NAME

Value

Invisibly returns TRUE after saving credentials

Examples

## Not run: 
glint_setup(
  tenant_id = "your-tenant-id",
  client_id = "your-client-id",
  client_secret = "your-client-secret",
  experience_name = "your-experience-name",
  save_to_renviron = TRUE
)

## End(Not run)

Join Employee Attributes to Survey Data

Description

Reads employee attribute data from a CSV file or data frame and joins it to a survey object by employee ID. Returns an enriched glint_survey object that can be passed directly to analyze_by_attributes() or any other package function, eliminating the need to re-read and re-join the attribute file on every call.

Usage

join_attributes(survey, attribute_source, emp_id_col = NULL)

Arguments

Value

If survey is a glint_survey object, returns an enriched glint_survey with the attribute columns appended to $data and the names of all joined attribute columns stored in $metadata$attribute_cols. If survey is a plain data frame, returns the joined data frame.

Respondents with no match in the attribute data will have NA for all attribute columns; a message is emitted indicating how many were affected.

Examples

survey_path <- system.file("extdata", "survey_export.csv", package = "vivaglint")
attr_path <- system.file("extdata", "employee_attributes.csv", package = "vivaglint")
survey <- read_glint_survey(survey_path, emp_id_col = "EMP ID")

survey_enriched <- join_attributes(survey, attr_path, emp_id_col = "EMP ID")
results <- analyze_by_attributes(survey_enriched, scale_points = 5,
                                 attribute_cols = "Department")

Convert Raw Mean to Glint Score

Description

Transforms a raw mean response on a rating scale to the 100-point Glint Score as displayed in the Viva Glint UI. Rounded to the nearest whole number, consistent with Glint's rounding convention.

Usage

mean_to_glint_score(mean_val, scale_points)

Arguments

Value

Numeric Glint Score on a 0-100 scale

Parse Comment Topics

Description

Splits comma-separated topic strings into vectors, handling empty and NA values.

Usage

parse_comment_topics(topics, return_format = "list")

Arguments

Value

If return_format is "list", returns a list of character vectors. If return_format is "tidy", returns a tibble with columns for original index and individual topics.

Pivot Survey Data to Long Format

Description

Transforms survey data from wide format (one row per respondent) to long format (one row per respondent-question combination). Can return all responses, only comments, or both depending on the data_type parameter.

Usage

pivot_long(
  survey,
  data_type = "all",
  include_empty = FALSE,
  include_standard_cols = TRUE
)

Arguments

Value

When data_type = "all" or "comments", a single tibble in long format with columns:

Standard columns

EMP ID, First Name, Last Name, etc. (if include_standard_cols = TRUE)

question

The question text

response

Numeric response value

comment

Comment text (NA if not provided)

comment_topics

Comma-separated topic tags (NA if not provided)

sensitive_flag

Sensitivity flag value

When data_type = "both", a named list with two elements:

all

Long-format tibble of all responses (same structure as above)

comments

Long-format tibble filtered to rows with non-empty comments

Examples

survey_path <- system.file("extdata", "survey_export.csv", package = "vivaglint")
survey <- read_glint_survey(survey_path, emp_id_col = "EMP ID")

# Get all responses (with and without comments)
all_data <- pivot_long(survey)

# Get only responses with comments
comments_only <- pivot_long(survey, data_type = "comments")

# Get both separately
both <- pivot_long(survey, data_type = "both")
all_responses <- both$all
comments <- both$comments

Read Viva Glint Survey Data

Description

Reads a Viva Glint survey export CSV file with automatic validation and parsing. This function validates the file structure, parses dates, and organizes the data into a structured format ready for analysis. To import directly from the Viva Glint API, use read_glint_survey_api().

Usage

read_glint_survey(
  file_path,
  emp_id_col = NULL,
  first_name_col = "First Name",
  last_name_col = "Last Name",
  email_col = "Email",
  status_col = "Status",
  completion_date_col = "Survey Cycle Completion Date",
  sent_date_col = "Survey Cycle Sent Date",
  encoding = "UTF-8"
)

Arguments

Value

A glint_survey object (an S3 class extending list) with two elements:

data

A tibble containing all survey responses. Date columns are parsed automatically from DD-MM-YYYY HH:MM format to POSIXct.

metadata

A list with five elements: standard_columns (the eight standard Glint column names), questions (a tibble from extract_questions() with one row per question), n_respondents (total row count), n_questions (number of questions), and file_path (the path originally supplied).

Examples

survey_path <- system.file("extdata", "survey_export.csv", package = "vivaglint")
survey <- read_glint_survey(survey_path, emp_id_col = "EMP ID")
head(survey$data)
survey$metadata$questions

Read Viva Glint Survey Data via API

Description

Exports a survey cycle through the Microsoft Graph beta API, downloads the resulting ZIP archive, and returns a glint_survey object. This is an alternative to read_glint_survey() when you want to pull data directly from Viva Glint instead of importing a local CSV export.

Usage

read_glint_survey_api(
  survey_uuid,
  cycle_id,
  emp_id_col = NULL,
  first_name_col = "First Name",
  last_name_col = "Last Name",
  email_col = "Email",
  status_col = "Status",
  completion_date_col = "Survey Cycle Completion Date",
  sent_date_col = "Survey Cycle Sent Date",
  start_date = NULL,
  end_date = NULL,
  encoding = "UTF-8",
  poll_interval = 10,
  max_attempts = 60,
  experience_name = NULL
)

Arguments

Value

A glint_survey object (same structure as read_glint_survey). The metadata$file_path field is set to NA.

Examples

## Not run: 
glint_setup(
  tenant_id = "your-tenant-id",
  client_id = "your-client-id",
  client_secret = "your-client-secret",
  experience_name = "your-experience-name"
)

survey <- read_glint_survey_api(
  survey_uuid = "your-survey-uuid",
  cycle_id = "your-cycle-id",
  emp_id_col = "EMP ID"
)

## End(Not run)

Search Survey Comments

Description

Searches through all survey comment text and returns matching responses with their associated question text, numeric response values, comments, and topics. Supports both exact substring matching and fuzzy (approximate) matching that tolerates minor spelling differences.

Usage

search_comments(survey, query, exact = FALSE, max_distance = 0.2)

Arguments

Value

A tibble with one row per matching comment containing:

question

The survey question text

response

The numeric survey response value

comment

The comment text that matched the query

topics

The comment topic(s) associated with the comment

Returns an empty tibble with the same columns if no matches are found.

Examples

survey_path <- system.file("extdata", "survey_export.csv", package = "vivaglint")
survey <- read_glint_survey(survey_path, emp_id_col = "EMP ID")

# Fuzzy search (default) - finds "manager", "Manager", "managers", etc.
results <- search_comments(survey, "manager")

# Exact search - finds only the literal string "Manager" (case-sensitive)
results_exact <- search_comments(survey, "Manager", exact = TRUE)

# Wider fuzzy tolerance to catch more spelling variations
results_wide <- search_comments(survey, "management", max_distance = 0.3)

Split Survey Data into Quantitative and Qualitative Components

Description

Separates a survey dataset into two data frames: one containing only numeric response data (for statistical analysis) and one containing only comment and topic data (for qualitative analysis). Both outputs retain the employee ID column so they can be rejoined at any time.

Usage

split_survey_data(survey, emp_id_col = NULL)

Arguments

Value

A named list with two elements:

quantitative

A tibble with all standard respondent columns plus one numeric response column per question. Comment, topic, and sensitive flag columns are excluded.

qualitative

A tibble with the employee ID column plus the comment, topic, and sensitive flag columns for every question. Numeric response columns are excluded.

Examples

survey_path <- system.file("extdata", "survey_export.csv", package = "vivaglint")
survey <- read_glint_survey(survey_path, emp_id_col = "EMP ID")
parts  <- split_survey_data(survey)

# Analyze numeric responses
summary <- summarize_survey(parts$quantitative, scale_points = 5,
                            emp_id_col = "EMP ID")

# Work with comments separately
comments <- parts$qualitative

Summarize Survey Questions

Description

Calculates comprehensive metrics for survey questions, returning a summary analysis in tidy format. This includes mean, standard deviation, Glint Score, response counts, skip counts, and favorability percentages.

Usage

summarize_survey(
  survey,
  scale_points,
  questions = "all",
  emp_id_col = NULL,
  plot = FALSE
)

Arguments

Value

A tibble with one row per question containing:

question

The question text

mean

Mean of numeric responses (raw scale)

sd

Standard deviation of numeric responses

glint_score

Mean transformed to a 0-100 scale, matching the score displayed in the Viva Glint UI: round(((mean - 1) / (scale_points - 1)) * 100)

n_responses

Count of non-blank, non-null responses

n_skips

Count of blank or null responses

n_total

Total number of respondents

pct_favorable

Percentage of responses classified as favorable

pct_neutral

Percentage of responses classified as neutral

pct_unfavorable

Percentage of responses classified as unfavorable

When plot = TRUE, the same tibble is returned invisibly after printing the plot.

Examples

survey_path <- system.file("extdata", "survey_export.csv", package = "vivaglint")
survey <- read_glint_survey(survey_path, emp_id_col = "EMP ID")

# Summarize all questions (5-point scale)
summary <- summarize_survey(survey, scale_points = 5)

# Summarize specific questions
summary_subset <- summarize_survey(survey, scale_points = 5,
  questions = c("My work is meaningful", "I feel valued"))

# With favorability chart
if (requireNamespace("ggplot2", quietly = TRUE)) {
  summarize_survey(survey, scale_points = 5, plot = TRUE)
}

Validate Glint Data Structure

Description

Internal function to validate that a data frame conforms to the expected Viva Glint export structure. Throws detailed, human-readable errors if validation fails.

Usage

validate_glint_structure(
  data,
  emp_id_col,
  first_name_col = "First Name",
  last_name_col = "Last Name",
  email_col = "Email",
  status_col = "Status",
  completion_date_col = "Survey Cycle Completion Date",
  sent_date_col = "Survey Cycle Sent Date"
)

Arguments

Value

NULL (invisibly) if validation passes, otherwise throws an error