README.rmd

---
output: github_document
---

```{r setup, include = FALSE}
library(dplyr)
library(doubleheadr)
library(gt)

```

# doubleheadr

[![lifecycle](https://img.shields.io/badge/lifecycle-experimental-orange.svg)](https://www.tidyverse.org/lifecycle/#experimental)

This package provides helper functions for data exports from SurveyMonkey. Most useful will likely be the `clean_headr()` function, which will concatenate the column names + first row values from a SurveyMonkey data export that is `.csv` or `.xlsx`.

### The Main Issue

Exported SurveyMonkey data contains a column and a second row containing column-name data, which typically requires data cleaning before starting analysis.

### Why `doubleheadr`?

-   Adopting `clean_headr` + `trim_headr` will (hopefully) make your workflow more efficient.

-   Quick and simple approach when working with downloaded or inherited `.csv` or `.xlsx` files, or when there are too many responses to pull via API. (Another highly recommended solution for Advantage and Premier-level users is pulling data via the SurveyMonkey API with [the surveymonkey package.](https://github.com/tntp/surveymonkey))

### Lifecycle

This package is in the early stages of development. Any and all issues are welcome, please report them and I will be happy to troubleshoot anything that comes up.

### Overview

-   `clean_headr` concatenates values from column names and the first row so that it makes sense.

-   `trim_headr` trims long strings from column names.

-   `flag_mins` flags respondents not meeting a minimum duration in minutes to complete the survey.

-   `flag_text` flags rows where a certain string value is found. This can be used to look for keywords, PII, or depending on your audience, profanity and other vulgarities.

### Install `doubleheadr`

```{r eval=FALSE}
devtools::install_github('mattroumaya/doubleheadr')
```

## Usage

Your downloaded or inherited `.csv`/`.xlsx` file will look something like the demo included in `doubleheadr`.

No worries though! Cleaning your column names is as easy as 1,2... that's it! Two steps.

Start with unhelpful column names:

```{r}
colnames(doubleheadr::demo)
```

##### `1. clean_headr()`

-   Now you have some really long column names

-   You can make them easier to read by setting `clean_names == FALSE`

```{r}

demo %>% 
  clean_headr(., "...") %>% 
  colnames(.)

```

##### 2. trim_headr()

```{r}
demo %>% 
  clean_headr(., "...") %>% 
  trim_headr(., c("please_provide_your_contact_information_",
                  "i_wish_it_would_have_",
                  "_response")) %>% 
  colnames(.)
```

##### flag_mins()

-   Coming to the `demo` soon...