---
title: "Geographic comparison between Paranoia Study recruitment and CloudResearch recruitment"
author: "Praveen Suthaharan"
date: "1/14/2021"
output: html_document
---

## Load libraries

```{r setup, include=FALSE}
# install.packages("DT")
# install.packages("rpivotTable")
# install.packages("ggplot2")
# install.packages("plotly")
# install.packages("openintro")
# install.packages("highcharter")
# install.packages("ggvis")
# install.packages("lessR")
# install.packages("rstatix")

library(flexdashboard)
library(knitr)
library(DT)
library(rpivotTable)
library(ggplot2)
library(plotly)
library(dplyr)
library(openintro)
library(highcharter)
library(ggvis)
require(gridExtra)
library(grid)
library(png)
library(lessR)
library(ggpubr)
library(plyr)
```

## Load data


```{r}
pandemic <- read.csv('C:/Users/ps967/Desktop/dashboard/pandemic.csv')
pandemic.CR <- read.csv('C:/Users/ps967/Desktop/dashboard/pandemic_cloudresearch.csv')
```

## Geographic map analysis of Paranoia Study


```{r}
## Paranoia study
# summarize data into proportions of individuals recruited per pandemic period per state
df.states <- ddply(df, .(period), summarise,
                   perc=prop.table(table(state))*100,
                   State=names(table(state)))
# convert state abbreviations into full names
df.states$State <- abbr2state(df.states$State)

# subset data into pandemic group
df.states.pre <- df.states[which(df.states$period == "Pre-lockdown"),]
df.states.lock <- df.states[which(df.states$period == "Lockdown"),]
df.states.reopen <- df.states[which(df.states$period == "Reopening"),]


# Map 1: US distribution of MTurkers recruited pre-pandemic
highchart() %>%
   hc_title(text = "Percentage of US MTurk completers") %>%
   hc_subtitle(text = "Period: pre-pandemic") %>%
   hc_add_series_map(usgeojson, df.states.pre,
                     name = "State",
                     value = "perc",
                     joinBy = c("woename","State")) %>%
   hc_colorAxis(minColor = "#B7FFBF", maxColor = "#00AB08") %>%
   hc_mapNavigation(enabled=T)

# Map 2: US distribution of MTurkers recruited during lockdown
highchart() %>%
   hc_title(text = "Percentage of US MTurk completers") %>%
   hc_subtitle(text = "Period: lockdown") %>%
   hc_add_series_map(usgeojson, df.states.lock,
                     name = "State",
                     value = "perc",
                     joinBy = c("woename","State")) %>%
   hc_colorAxis(minColor = "#B7FFBF", maxColor = "#00AB08") %>%
   hc_mapNavigation(enabled=T)

# Map 3: US distribution of MTurkers recruited during reopening
highchart() %>%
   hc_title(text = "Percentage of US MTurk completers") %>%
   hc_subtitle(text = "Period: reopening") %>%
   hc_add_series_map(usgeojson, df.states.reopen,
                     name = "State",
                     value = "perc",
                     joinBy = c("woename","State")) %>%
   hc_colorAxis(minColor = "#B7FFBF", maxColor = "#00AB08") %>%
   hc_mapNavigation(enabled=T)

```

## Geographic map analysis of CloudResearch Study

```{r}
# convert state abbreviations into full names
pandemic.CR$State <- abbr2state(pandemic.CR$state)

# subset data into pandemic group
pandemic.CR.pre <- pandemic.CR[which(pandemic.CR$period == "Pre-lockdown"),]
pandemic.CR.lock <- pandemic.CR[which(pandemic.CR$period == "Lockdown"),]
pandemic.CR.reopen <- pandemic.CR[which(pandemic.CR$period == "Reopening"),]

# Map 1: US distribution of MTurkers recruited by CloudResearch during the pre-pandemic period
highchart() %>%
   hc_title(text = "Mean US MTurk completers") %>%
   hc_subtitle(text = "Period: pre-pandemic") %>%
   hc_add_series_map(usgeojson, pandemic.CR.pre,
                     name = "State",
                     value = "mean",
                     joinBy = c("woename","State")) %>%
   hc_mapNavigation(enabled=T)

# Map 2: US distribution of MTurkers recruited by CloudResearch during the lockdown period
highchart() %>%
   hc_title(text = "Mean US MTurk completers") %>%
   hc_subtitle(text = "Period: lockdown") %>%
   hc_add_series_map(usgeojson, pandemic.CR.lock,
                     name = "State",
                     value = "mean",
                     joinBy = c("woename","State")) %>%
   hc_mapNavigation(enabled=T)

# Map 3: US distribution of MTurkers recruited by CloudResearch during the reopening period
highchart() %>%
   hc_title(text = "Mean US MTurk completers") %>%
   hc_subtitle(text = "Period: reopening") %>%
   hc_add_series_map(usgeojson, pandemic.CR.reopen,
                     name = "State",
                     value = "mean",
                     joinBy = c("woename","State")) %>%
   hc_mapNavigation(enabled=T)

```



```{r}
# Regional recruitment did not differ across our study periods
df.region <- ddply(df, .(period), summarise,
                   count=table(region),
                   region=names(table(region)))

# Count of US MTurkers per region; G1 = pre-lockdown
G1_Mid=43
G1_North=51
G1_South=77
G1_West=31

# Count of US MTurkers per region; G2 = lockdown
G2_Mid=48
G2_North=51
G2_South=87
G2_West=45

# Count of US MTurkers per region; G3 = reopening
G3_Mid=32
G3_North=32
G3_South=65
G3_West=43

regionG = data.frame(G1 = c(G1_Mid,G1_North,G1_South,G1_West),
                     G2 = c(G2_Mid,G2_North,G2_South,G2_West),
                     G3 = c(G3_Mid,G3_North,G3_South,G3_West))

rownames(regionG) <- c("Midwest","Northeast","South","West")
colnames(regionG) <- c("Pre-lockdown","Lockdown","Reopening")
regionG

chisq.test(regionG, correct = F)
```

