Final_Proj_APA_Man.Rmd

---
title             : "EDLD 610 Final Project"
shorttitle        : "Cool running headder here"
author:
  - name          : "Akhila Nekkanti"
    affiliation   : "1"
    corresponding : yes    # Define only one corresponding author
    address       : "Center for Translational Neuroscience University of Oregon 1585 E 13th Ave. Eugene, OR 97403"
    email         : "akhilan@uoregon.edu"
  - name         : "Kyle Reardon"
    affiliation   : "1"
    corresponding : no
  - name          : "Brock Rowley"
    affiliation   : "1"
    corresponding : no
  - name          : "Jeff Gau"
    affiliation   : "1"
    corresponding : no
affiliation:
  - id            : "1"
    institution   : "University of Oregon"

authornote: |
  Akhila Nekkanti  

  Kyle Reardon (M.A. Special Education) is a doctoral candidate at the
  University of Oregon and a Graduate Employee in the Special Education
  department. His research interests include Universal Design in post-secondary
  educational settings to support students with disabilities, support for
  students on the autism spectrum attending post-secondary education, and the
  role of self-determination in secondary transition for individuals with
  disabilities.

  Brock Rowley D.Ed, is a Senior Research Associate I at Behavioral Research and
  Teaching (BRT) and Co-Director of the Admin Licensure Program in the Education
  Methodolgy, Policy, and Leadership (EMPL) in the Coledge of Education at the
  University of Oregon.

  Jeff Gau

abstract: |
  Not only does a career promote financial independence, it can also provide an
  individual with confidence, self-determination, supportive relationships, and
  engagement within the community. A primary focus of the employment literature
  to date has been on strategies to improve employment readiness and the
  identification of factors that influence employment outcomes for students with
  disabilities.Thus, a gap has been identified between the preparedness of
  individuals with disabilities for competitive integrated employment and the
  willingness of employers to hire them. Therefore, it is necessary to better
  understand the perspectives of employers on individuals with disabilities in
  the workforce. Better understanding of this gap in knowledge will led to
  better outcomes for individuals with disabilities and more appropriate job
  matches, as well as leading to more positive perspectives on hiring
  individuals with disabilities on the part of employers.

keywords          : "employment, employers, individuals, disabilities, barriers, hiring"

wordcount         : "X"

bibliography      : ["r-references.bib"]

floatsintext      : no
figurelist        : no
tablelist         : no
footnotelist      : no
linenumbers       : no
mask              : no
draft             : no

documentclass     : "apa6"

classoption       : "man"

output            : papaja::apa6_pdf
---

```{r setup, include = FALSE}
library("papaja")
library(rio)
library(here)
library(tidyverse)
library(janitor)
```

```{r analysis-preferences}
# Seed for random number generation
set.seed(42) #Super cool, I don't think we've even talked about this yet
knitr::opts_chunk$set(cache.extra = knitr::rand_seed)
```

```{r load_data}
d <- import(here("data", "employer.sav")) %>%  
            characterize() %>% 
            janitor::clean_names() 

d_trim <- d %>% 
  select(location_latitude, location_longitude, q3:q6, q7, q8, q25_1:q28_7) %>% 
  filter(complete.cases(.)) %>% 
  rename(loc_lat = location_latitude, loc_long = location_longitude, role = q3, company_size = q5, industry = q6, yrs_company = q7, yrs_role = q8, basic_1 = q25_1, basic_2 = q25_2, basic_3 = q25_3, basic_4 = q25_4, basic_5 = q25_5, basic_6 = q25_6, basic_7 = q25_7, basic_8 = q25_8, higherorder_1 = q24_1, higherorder_2 = q24_2, higherorder_3 = q24_3, higherorder_4 = q24_4, higherorder_5 = q24_5, higherorder_6 = q24_6, persmanage_1 = q26_1, persmanage_2 = q26_2, persmanage_3 = q26_3, persmanage_4 = q26_4, persmanage_5 = q26_5, persmanage_6 = q26_6, persmanage_7 = q26_7, persmanage_8 = q26_8, persmanage_9 = q26_9, persmanage_10 = q26_10, persmanage_11 = q26_11, persmanage_12 = q26_12, interpers_1 = q27_1, interpers_2 = q27_2, interpers_3 = q27_3, interpers_4 = q27_4, interpers_5 = q27_5, interpers_6 = q27_6, interpers_7 = q27_7, attribute_1 = q28_1, attribute_2 = q28_2, attribute_3 = q28_3, attribute_4 = q28_4, attribute_5 = q28_5, attribute_6 = q28_6, attribute_7 = q28_7)

#I wonder if there is an easier way to do this, I found myself also just listing my selected variables. 

```

```{r lat_long}

if(!require(maps)){install.packages("maps")}
if(!require(RColorBrewer)){install.packages("RColorBrewer")}
if(!require(mapproj)){install.packages("mapproj")}
# I have zero clue what this does, looks fancy though, may label what your code is doing on for these unfamiliar chunks?

library(ggplot2)
library(maps)
library(RColorBrewer)
library(mapproj)
library(viridis)

counts_state <- count(d_trim, region = tolower(q4)) #this is super cool, will definitely have to remember this 

states <- map_data("state")
head(states)
md <- left_join(states, counts_state)

ggplot(md, aes(long, lat, group = group)) +
  geom_polygon(aes(fill = n)) +
  coord_map() +
    labs(x = NULL, 
         y = NULL, 
         title = "Survey Regional Responses", 
         subtitle = "Number of respondents by state", 
         caption = "Data collected from companies by state") +
  scale_fill_viridis(
    name = "Number of survey responses\n",
    direction = 1) +
  theme_void() +
  theme(legend.position = "bottom",
        legend.key.width = unit(2, "cm")) 

#very cool graph! so clean and straight forward

# theme_map <- function(...) {
# theme_minimal() +
#   theme(
#     axis.line = element_blank(),
#     axis.text.x = element_blank(),
#     axis.text.y = element_blank(),
#     axis.ticks = element_blank(),
#     axis.title.x = element_blank(),
#     axis.title.y = element_blank(),
#     # panel.grid.minor = element_line(color = "#ebebe5", size = 0.2),
#     panel.grid.major = element_line(color = "#ebebe5", size = 0.2),
#     panel.grid.minor = element_blank(),
#     plot.background = element_rect(fill = "#ebebe5", color = NA), 
#     panel.background = element_rect(fill = "#ebebe5", color = NA), 
#     legend.background = element_rect(fill = "#ebebe5", color = NA),
#     panel.border = element_blank(),
#     ...
#   )
# }
```

```{r}

#Q24= higherorder, 1= solve probs; 2= seek help, 3= critical thinking, 4=creative thinking, 5=impact of decisions, 6= employ research

if(!require(psych)){install.packages("psych")}
if(!require(likert)){install.packages("likert")}
#Oh I see what this is now, very cool!

View(d_trim)

higherorder <- d_trim %>% 
  drop_na() %>% 
  select(starts_with("higherorder")) %>% 
  mutate(higherorder_1= factor(higherorder_1,
                               levels = c(
                                   "Not at all",
                              "Somewhat",
                              "Very",
                              "Extremely"),
                              ordered = TRUE),
         higherorder_2 = factor(higherorder_2,
                                 levels = c(
                                   "Not at all",
                              "Somewhat",
                              "Very",
                              "Extremely"),
                              ordered = TRUE),
         higherorder_3 = factor(higherorder_3,
                                 levels = c(
                                   "Not at all",
                              "Somewhat",
                              "Very",
                              "Extremely"),
                              ordered = TRUE),
         higherorder_4= factor(higherorder_4,
                                levels = c(
                                   "Not at all",
                              "Somewhat",
                              "Very",
                              "Extremely"),
                              ordered = TRUE),
         higherorder_5 = factor(higherorder_5,
                                 levels = c(
                                   "Not at all",
                              "Somewhat",
                              "Very",
                              "Extremely"),
                              ordered = TRUE),
         higherorder_6 = factor(higherorder_6,
                                 levels = c(
                                   "Not at all",
                              "Somewhat",
                              "Very",
                              "Extremely"),
                              ordered = TRUE))

summary(higherorder)
str(higherorder$higherorder_1)

likert(higherorder) # oh so cool! I'll have to remeber this as well

#q24_plot <- d_trim %>% 
#  group_by(industry) %>% 
#  summarize()


```



# Methods
I just copied and pasted info directly into the sections from what Kyle sent, including the two questions Klye said would be worth looking at:

a) What are employers experiences hiring individuals with disabilities?
b) What industries are most open to hiring individuals with disabilities?

Survey participants will be contacted via email and asked to respond to an
online survey (attachment A). Recruitment emails will include a recruitment
statement and a link to the survey, which will be developed using online survey
software called Qualtrics. The survey is being developed by the researchers
conducting the current using previous employer surveys described in prior
studies (Ju et al., 2014; ODEP, 2008) as an impetus for the survey and will
include additional questions and necessary modifications in order to address the
specific research questions described above. Though the survey is still
currently under development by the research team, a draft survey is attached to
this application (attachment B). The research team will submit an IRB amendment
once the final draft of the survey is complete.

## Participants
Our intent is to include relevant employers from across the country, generating
a sample representative of employers across all industries. We will target
individuals who are responsible for or knowledgeable of the hiring practices at
their respective places of employment, though the specifics of this will be
unable to be confirmed. They may be managers, Human Resources representatives,
or others in charge of hiring.
## Material
A researcher-developed survey comprised of questions targeting the hiring
practices and willingness of employers to hire individuals with disabilities
will be used in this study (see attachment A). The survey is anticipated to take
approximately 20 minutes to complete.
## Procedure

## Data analysis
We used `r cite_r("r-references.bib")` for all our analyses.
Given the exploratory purpose of the study (i.e., to learn more about the
perspectives of employers toward hiring individuals with disabilities), analyses
will be primarily descriptive in nature. Initially, however, we will conduct a
confirmatory factor analysis to validate factor structures of employability
skills, basing the factor structures on a previous study (Ju et al., 2012; Ju et
al., 2014). Next, we will run descriptive statistics for survey items to explore
patterns among survey respondents. Logistic regression analyses will also be
computed to explore whether certain variables are predictive of employers’
responses to individual survey items. Structural equation modeling (e.g.,
exploratory factor analysis) may be used to identify the factor structure of
perceived characteristics of employers willing to hire individuals with
disabilities.

# Results

# Discussion


\newpage

# References
```{r create_r-references}
r_refs(file = "r-references.bib")
```

\begingroup
\setlength{\parindent}{-0.5in}
\setlength{\leftskip}{0.5in}

<div id = "refs"></div>
\endgroup

\newpage

# Final Paper

## The final project must:

(a) be a reproducible and dynamic APA manuscript produced with R Markdown, via
the {papaja} package and include references to the extant literature; 
(b) be housed on GitHub, with contributions from all authors obvious;
(c) demonstrate moving data from its raw “messy” format to a tidy data format
through the R Markdown file, but not in the final document;
(d) include at least two exploratory data visualizations, and
(e) include at least summary statistics of the data in tables, although fitted
models of any sort are an added bonus (not literally, there are not extra points
for fitting a model).

## The points for the final project are broken down as follows: 
### Writing (abstract, intro, methods, results, discussion, references)

* 30 points(25%) Document is fully reproducible and housed on GitHub 
* 25 points (21%) Demonstrate use of inline code 
* 5 points (4%) Demonstrate tidying messy data 
* 30 points (25%) Two data visualizations 
* 20 points(10 points each) (17%) Production of at least one table (of summary
  statistics or model results)
* 10 points (8%)