---
title: "Content Dashboard"
author: "Kenna Reagan"
date: "`r Sys.Date()`"
output:
flexdashboard::flex_dashboard:
orientation: rows
social: menu
source_code: embed
---
---
```{r include=FALSE}
# NOTE 1: I would make this available on RStudio Connect where it could automatically update the results as often as I tell it to and anyone with access can view
# NOTE 2: The submission link doesn't accept html files and you can only view the raw version of the file on Github and Google Drive. I am emailing the html file to sarina.lim@instructionpartners.org
# load libraries
library(gsheet) # to load data from the google sheet
library(tidyverse)
library(dplyr)
library(lubridate) # format date from chr to date
library(ggplot2) # create graphics
library(wesanderson)
library(flexdashboard)
```
```{r include=FALSE}
# import data from google sheets, convert to tibble, save to a variable
url1 <- 'https://docs.google.com/spreadsheets/d/1Jwj-H3T28nySYGttAwA_9bUE_kKRR2jq/edit#gid=1872698168'
url2 <-'https://docs.google.com/spreadsheets/d/1Jwj-H3T28nySYGttAwA_9bUE_kKRR2jq/edit#gid=771323801'
survey <- gsheet2tbl(url1)
org <- gsheet2tbl(url2)
# print the tibbles
survey
org
# check the dimensions of each tibble for comparison after joining them
dim(survey)
dim(org)
```
```{r include=FALSE}
# join org data on 'Primary Facilitator' coloumn so that we have the org data for the primary facilitator
data <- left_join(survey, org, by = c("Primary Facilitator" = "Faciliator ID"))
# check the dimensions to make sure we didn't lose data
dim(data)
data
data$Date <- as.Date(data$Date, format = "%m/%d/%Y")
# replace null values with 0 or 'N/A'
data$`Likely to recommend Instruction Partners`[is.na(data$`Likely to recommend Instruction Partners`)]<- 0
data[is.na(data)] <- "N/A"
```
```{r include=FALSE}
# get counts of each answer, grouped by date
Q1 <- data %>% group_by(Date,`Clear about change we seek`) %>% summarise(var_counts = n())
Q2 <- data %>% group_by(Date,`Clear about roles and responsibilities`) %>% summarise(var_counts = n())
Q3 <- data %>% group_by(Date,`Confident plan will achieve goals`) %>% summarise(var_counts = n())
Q4 <- data %>% group_by(Date,`Feel more equipped`) %>% summarise(var_counts = n())
Q5 <- data %>% group_by(Date,`Equipped and supported`) %>% summarise(var_counts = n())
Q6 <- data %>% group_by(Date,`Understand state of instruction`) %>% summarise(var_counts = n())
Q7 <- data %>% group_by(Date,`Valuable use of my time:`) %>% summarise(var_counts = n())
# get monthly averages for how likely people are to recommend Instruction Partners
Q8 <- filter(data, `Likely to recommend Instruction Partners` > 0) %>%
mutate(month = floor_date(Date, "month")) %>%
group_by(month) %>%
summarize(avg = mean(`Likely to recommend Instruction Partners`))
```
Monthly Averages
=======================================================================
Column {data-width=600}
-----------------------------------------------------------------------
### Overall Performance
```{r}
# create time series plot
ggplot(Q8 ,aes(x=month,y=avg)) + geom_line(color="#1D549E",size=1) + geom_point(color="#1D549E",size=2) + theme(panel.background = element_rect(fill="#F0EDED",color="#F0EDED"))+ ggtitle("Monthly 'Likely to recommend Instruction Partners' Average") +xlab("Date") + ylab("'Likely to recommend Instruction Partners' Average")
```
Column {.tabset}
-------------------------------------
### Team 1
```{r include=FALSE}
# remove all 0's (non-answers), sort by month, get the mean for each month
Q8 <- filter(data, `Likely to recommend Instruction Partners` > 0) %>%
mutate(month = floor_date(Date, "month")) %>%
group_by(month, Team) %>%
summarize(avg = mean(`Likely to recommend Instruction Partners`))
Q8
```
```{r}
# plot the monthly mean for Team 1
ggplot(Q8, aes(month, avg)) + geom_line(data=subset(Q8,Team=="Team 1"),color="#E69F00", size=1) + geom_point(data=subset(Q8,Team=="Team 1"),size=2,color="#E69F00")+ theme(panel.background = element_rect(fill="#F0EDED",color="#F0EDED")) + ggtitle("Monthly 'Likely to recommend Instruction Partners' Average") +xlab("Date") + ylab("'Likely to recommend Instruction Partners' Average")
```
### Team 2
```{r}
# plot the montly mean for Team 2
ggplot(Q8, aes(month, avg)) + geom_line(data=subset(Q8,Team=="Team 2"),size=1,color="#56B4E9") + geom_point(data=subset(Q8,Team=="Team 2"),size=2,color="#56B4E9")+ theme(panel.background = element_rect(fill="#F0EDED",color="#F0EDED")) + ggtitle("Monthly 'Likely to recommend Instruction Partners' Average") +xlab("Date") + ylab("'Likely to recommend Instruction Partners' Average")
```
### Team 3
```{r}
# plot the monthly mean for Team 3
ggplot(Q8, aes(month, avg)) + geom_line(data=subset(Q8,Team=="Team 3"),size=1,color="#009E73")+ geom_point(data=subset(Q8,Team=="Team 3"),size=2,color="#009E73")+ theme(panel.background = element_rect(fill="#F0EDED",color="#F0EDED")) + ggtitle("Monthly 'Likely to recommend Instruction Partners' Average") +xlab("Date") + ylab("'Likely to recommend Instruction Partners' Average")
```
### Team 4
```{r}
# plot the monthly mean for Team 4
ggplot(Q8, aes(month, avg)) + geom_line(data=subset(Q8,Team=="Team 4"),size=1,color="#F0E442")+ geom_point(data=subset(Q8,Team=="Team 4"),size=2,color="#F0E442")+ theme(panel.background = element_rect(fill="#F0EDED",color="#F0EDED")) + ggtitle("Monthly 'Likely to recommend Instruction Partners' Average") +xlab("Date") + ylab("'Likely to recommend Instruction Partners' Average")
```
### Team 5
```{r}
# plot the montly means for Team 5
ggplot(Q8, aes(month, avg)) + geom_line(data=subset(Q8,Team=="Team 5"),size=1,color="#0072B2")+ geom_point(data=subset(Q8,Team=="Team 5"),size=2,color="#0072B2")+ theme(panel.background = element_rect(fill="#F0EDED",color="#F0EDED")) + ggtitle("Monthly 'Likely to recommend Instruction Partners' Average") +xlab("Date") + ylab("'Likely to recommend Instruction Partners' Average")
```
### Team 6
```{r}
# plot the montly mean for Team 6
ggplot(Q8, aes(month, avg)) + geom_line(data=subset(Q8,Team=="Team 6"),size=1,color="#D55E00")+ geom_point(data=subset(Q8,Team=="Team 6"),size=2,color="#D55E00")+ theme(panel.background = element_rect(fill="#F0EDED",color="#F0EDED")) + ggtitle("Monthly 'Likely to recommend Instruction Partners' Average") +xlab("Date") + ylab("'Likely to recommend Instruction Partners' Average")
```
### Team 7
```{r}
# plot the montly mean for Team 7
ggplot(Q8, aes(month, avg)) + geom_line(data=subset(Q8,Team=="Team 7"),size=1,color="#CC79A7")+ geom_point(data=subset(Q8,Team=="Team 7"),size=2,color="#CC79A7")+ theme(panel.background = element_rect(fill="#F0EDED",color="#F0EDED")) + ggtitle("Monthly 'Likely to recommend Instruction Partners' Average") +xlab("Date") + ylab("'Likely to recommend Instruction Partners' Average")
```
### Team 8
```{r}
# plot the montly mean for Team 8
ggplot(Q8, aes(month, avg)) + geom_line(data=subset(Q8,Team=="Team 8"),size=1,color="#336633")+ geom_point(data=subset(Q8,Team=="Team 8"),size=2,color="#336633")+ theme(panel.background = element_rect(fill="#F0EDED",color="#F0EDED")) + ggtitle("Monthly 'Likely to recommend Instruction Partners' Average") +xlab("Date") + ylab("'Likely to recommend Instruction Partners' Average")
```
### Team 9
```{r}
# plot the montly mean for Team 9
ggplot(Q8, aes(month, avg)) + geom_line(data=subset(Q8,Team=="Team 9"),size=1) + geom_point(data=subset(Q8,Team=="Team 9"),size=2)+ theme(panel.background = element_rect(fill="#F0EDED",color="#F0EDED")) + ggtitle("Monthly 'Likely to recommend Instruction Partners' Average") +xlab("Date") + ylab("'Likely to recommend Instruction Partners' Average")
```
Survey Questions
=======================================================================
Column {.tabset data-width=400}
-----------------------------------------------------------------------
### Clear about change we seek
```{r include=FALSE}
# filter out non-answers, get counts of each answer
Q1 <- filter(data, `Clear about change we seek` != "N/A") %>% group_by(`Submitter Role`, `Clear about change we seek`) %>% summarise(var_counts = n())
# get percentage of each answer for each submitter role
asst_prin <- filter(Q1, `Submitter Role` == "Assistant Principal")
asst_prin$var_per <- with(asst_prin, var_counts/sum(var_counts))
dist_lead <- filter(Q1, `Submitter Role` == "District Leader / CMO Leader")
dist_lead$var_per <- with(dist_lead, var_counts/sum(var_counts))
inst_coach <- filter(Q1, `Submitter Role` == "Instructional Coach")
inst_coach$var_per <- with(inst_coach, var_counts/sum(var_counts))
other <- filter(Q1, `Submitter Role` == "Other")
other$var_per <- with(other, var_counts/sum(var_counts))
principal <- filter(Q1, `Submitter Role` == "Principal")
principal$var_per <- with(principal, var_counts/sum(var_counts))
super <- filter(Q1, `Submitter Role` == "Superintendent / CMO Executive Director")
super$var_per <- with(super, var_counts/sum(var_counts))
teacher <- filter(Q1, `Submitter Role` == "Teacher")
teacher$var_per <- with(teacher, var_counts/sum(var_counts))
teach_lead <- filter(Q1, `Submitter Role` == "Teacher Leader")
teach_lead$var_per <- with(teach_lead, var_counts/sum(var_counts))
Q1 = rbind(asst_prin,dist_lead,inst_coach,other,principal,super,teacher,teach_lead)
Q1
```
```{r echo=FALSE, warning=FALSE}
# get color palette
color_palette <- c("#999999", "#E69F00", "#56B4E9", "#009E73", "#F0E442", "#0072B2", "#D55E00", "#CC79A7")
# reorder answers
new_order1 <-factor(Q1$`Clear about change we seek`, levels=c("Strongly Disagree", "Disagree","Somewhat Disagree", "Neutral","Somewhat Agree","Agree","Strongly Agree"))
# plot results
ggplot(Q1, aes(`Submitter Role`, var_per), ) +
geom_bar(aes(fill = new_order1),position = position_dodge2(width = 5, preserve = "single"), stat="identity") + theme(plot.title = element_text(size = 12, face = "bold"), axis.text.x = element_text(angle = 60, hjust = 1),panel.background = element_rect(fill="#F0EDED",color="#F0EDED")) + ggtitle("Clear about change we seek") +xlab("Submitter Roles") + ylab("Percentage") + guides(fill=guide_legend(title="Agreement")) + scale_fill_manual(values=color_palette)
```
### Clear about roles and responsibilities
```{r include=FALSE}
# filter out non-answers, get counts of each answer
Q2 <- filter(data, `Clear about roles and responsibilities` != "N/A") %>% group_by(`Submitter Role`, `Clear about roles and responsibilities`) %>% summarise(var_counts = n())
# get percentage of each answer for each submitter role
asst_prin <- filter(Q2, `Submitter Role` == "Assistant Principal")
asst_prin$var_per <- with(asst_prin, var_counts/sum(var_counts))
dist_lead <- filter(Q2, `Submitter Role` == "District Leader / CMO Leader")
dist_lead$var_per <- with(dist_lead, var_counts/sum(var_counts))
inst_coach <- filter(Q2, `Submitter Role` == "Instructional Coach")
inst_coach$var_per <- with(inst_coach, var_counts/sum(var_counts))
other <- filter(Q2, `Submitter Role` == "Other")
other$var_per <- with(other, var_counts/sum(var_counts))
principal <- filter(Q2, `Submitter Role` == "Principal")
principal$var_per <- with(principal, var_counts/sum(var_counts))
super <- filter(Q2, `Submitter Role` == "Superintendent / CMO Executive Director")
super$var_per <- with(super, var_counts/sum(var_counts))
teacher <- filter(Q2, `Submitter Role` == "Teacher")
teacher$var_per <- with(teacher, var_counts/sum(var_counts))
teach_lead <- filter(Q2, `Submitter Role` == "Teacher Leader")
teach_lead$var_per <- with(teach_lead, var_counts/sum(var_counts))
Q2 = rbind(asst_prin,dist_lead,inst_coach,other,principal,super,teacher,teach_lead)
Q2
```
```{r echo=FALSE, warning=FALSE}
new_order2 <-factor(Q2$`Clear about roles and responsibilities`, levels=c("Strongly Disagree", "Disagree","Somewhat Disagree", "Neutral","Somewhat Agree","Agree","Strongly Agree"))
ggplot(Q2, aes(`Submitter Role`, var_per), ) +
geom_bar(aes(fill = new_order2),position = position_dodge2(width = 5, preserve = "single"), stat="identity") + theme(plot.title = element_text(size = 12, face = "bold"), axis.text.x = element_text(angle = 60, hjust = 1),panel.background = element_rect(fill="#F0EDED",color="#F0EDED")) + ggtitle("Clear about roles and responsibilities") +xlab("Submitter Roles") + ylab("Percentage") + guides(fill=guide_legend(title="Agreement")) + scale_fill_manual(values=color_palette)
```
### Confident plan will achieve goals
```{r include=FALSE}
# filter out non-answers, get counts of each answer
Q3 <- filter(data, `Confident plan will achieve goals` != "N/A") %>% group_by(`Submitter Role`, `Confident plan will achieve goals`) %>% summarise(var_counts = n())
# get percentage of each answer for each submitter role
asst_prin <- filter(Q3, `Submitter Role` == "Assistant Principal")
asst_prin$var_per <- with(asst_prin, var_counts/sum(var_counts))
dist_lead <- filter(Q3, `Submitter Role` == "District Leader / CMO Leader")
dist_lead$var_per <- with(dist_lead, var_counts/sum(var_counts))
inst_coach <- filter(Q3, `Submitter Role` == "Instructional Coach")
inst_coach$var_per <- with(inst_coach, var_counts/sum(var_counts))
other <- filter(Q3, `Submitter Role` == "Other")
other$var_per <- with(other, var_counts/sum(var_counts))
principal <- filter(Q3, `Submitter Role` == "Principal")
principal$var_per <- with(principal, var_counts/sum(var_counts))
super <- filter(Q3, `Submitter Role` == "Superintendent / CMO Executive Director")
super$var_per <- with(super, var_counts/sum(var_counts))
teacher <- filter(Q3, `Submitter Role` == "Teacher")
teacher$var_per <- with(teacher, var_counts/sum(var_counts))
teach_lead <- filter(Q3, `Submitter Role` == "Teacher Leader")
teach_lead$var_per <- with(teach_lead, var_counts/sum(var_counts))
Q3 = rbind(asst_prin,dist_lead,inst_coach,other,principal,super,teacher,teach_lead)
Q3
```
```{r echo=FALSE, warning=FALSE}
new_order3 <-factor(Q3$`Confident plan will achieve goals`, levels=c("Strongly Disagree", "Disagree","Somewhat Disagree", "Neutral","Somewhat Agree","Agree","Strongly Agree"))
ggplot(Q3, aes(`Submitter Role`, var_per), ) +
geom_bar(aes(fill = new_order3),position = position_dodge2(width = 5, preserve = "single"), stat="identity") + theme(plot.title = element_text(size = 12, face = "bold"), axis.text.x = element_text(angle = 60, hjust = 1),panel.background = element_rect(fill="#F0EDED",color="#F0EDED")) + ggtitle("Confident plan will achieve goals") +xlab("Submitter Roles") + ylab("Percentage") + guides(fill=guide_legend(title="Agreement")) + scale_fill_manual(values=color_palette)
```
### Feel more equipped
```{r include=FALSE}
# filter out non-answers, get counts of each answer
Q4 <- filter(data, `Feel more equipped` != "N/A") %>% group_by(`Submitter Role`, `Feel more equipped`) %>% summarise(var_counts = n())
# get percentage of each answer for each submitter role
asst_prin <- filter(Q4, `Submitter Role` == "Assistant Principal")
asst_prin$var_per <- with(asst_prin, var_counts/sum(var_counts))
dist_lead <- filter(Q4, `Submitter Role` == "District Leader / CMO Leader")
dist_lead$var_per <- with(dist_lead, var_counts/sum(var_counts))
inst_coach <- filter(Q4, `Submitter Role` == "Instructional Coach")
inst_coach$var_per <- with(inst_coach, var_counts/sum(var_counts))
other <- filter(Q4, `Submitter Role` == "Other")
other$var_per <- with(other, var_counts/sum(var_counts))
principal <- filter(Q4, `Submitter Role` == "Principal")
principal$var_per <- with(principal, var_counts/sum(var_counts))
super <- filter(Q4, `Submitter Role` == "Superintendent / CMO Executive Director")
super$var_per <- with(super, var_counts/sum(var_counts))
teacher <- filter(Q4, `Submitter Role` == "Teacher")
teacher$var_per <- with(teacher, var_counts/sum(var_counts))
teach_lead <- filter(Q4, `Submitter Role` == "Teacher Leader")
teach_lead$var_per <- with(teach_lead, var_counts/sum(var_counts))
Q4 = rbind(asst_prin,dist_lead,inst_coach,other,principal,super,teacher,teach_lead)
Q4
```
```{r echo=FALSE, warning=FALSE}
new_order4 <-factor(Q4$`Feel more equipped`, levels=c("Strongly Disagree", "Disagree","Somewhat Disagree", "Neutral","Somewhat Agree","Agree","Strongly Agree"))
ggplot(Q4, aes(`Submitter Role`, var_per), ) +
geom_bar(aes(fill = new_order4),position = position_dodge2(width = 5, preserve = "single"), stat="identity") + theme(plot.title = element_text(size = 12, face = "bold"), axis.text.x = element_text(angle = 60, hjust = 1),panel.background = element_rect(fill="#F0EDED",color="#F0EDED")) + ggtitle("Feel more equipped") +xlab("Submitter Roles") + ylab("Percentage") + guides(fill=guide_legend(title="Agreement")) + scale_fill_manual(values=color_palette)
```
### Equipped and supported
```{r include=FALSE}
# filter out non-answers, get counts of each answer
Q5 <- filter(data, `Equipped and supported` != "N/A") %>% group_by(`Submitter Role`, `Equipped and supported`) %>% summarise(var_counts = n())
# get percentage of each answer for each submitter role
asst_prin <- filter(Q5, `Submitter Role` == "Assistant Principal")
asst_prin$var_per <- with(asst_prin, var_counts/sum(var_counts))
dist_lead <- filter(Q5, `Submitter Role` == "District Leader / CMO Leader")
dist_lead$var_per <- with(dist_lead, var_counts/sum(var_counts))
inst_coach <- filter(Q5, `Submitter Role` == "Instructional Coach")
inst_coach$var_per <- with(inst_coach, var_counts/sum(var_counts))
other <- filter(Q5, `Submitter Role` == "Other")
other$var_per <- with(other, var_counts/sum(var_counts))
principal <- filter(Q5, `Submitter Role` == "Principal")
principal$var_per <- with(principal, var_counts/sum(var_counts))
super <- filter(Q5, `Submitter Role` == "Superintendent / CMO Executive Director")
super$var_per <- with(super, var_counts/sum(var_counts))
teacher <- filter(Q5, `Submitter Role` == "Teacher")
teacher$var_per <- with(teacher, var_counts/sum(var_counts))
teach_lead <- filter(Q5, `Submitter Role` == "Teacher Leader")
teach_lead$var_per <- with(teach_lead, var_counts/sum(var_counts))
Q5 = rbind(asst_prin,dist_lead,inst_coach,other,principal,super,teacher,teach_lead)
Q5
```
```{r echo=FALSE, warning=FALSE}
new_order5 <-factor(Q5$`Equipped and supported`, levels=c("Strongly Disagree", "Disagree","Somewhat Disagree", "Neutral","Somewhat Agree","Agree","Strongly Agree"))
ggplot(Q5, aes(`Submitter Role`, var_per), ) +
geom_bar(aes(fill = new_order5),position = position_dodge2(width = 5, preserve = "single"), stat="identity") + theme(plot.title = element_text(size = 12, face = "bold"), axis.text.x = element_text(angle = 60, hjust = 1),panel.background = element_rect(fill="#F0EDED",color="#F0EDED")) + ggtitle("Equipped and supported") +xlab("Submitter Roles") + ylab("Percentage") + guides(fill=guide_legend(title="Agreement")) + scale_fill_manual(values=color_palette)
```
### Understand state of instruction
```{r include=FALSE}
# filter out non-answers, get counts of each answer
Q6 <- filter(data, `Understand state of instruction` != "N/A") %>% group_by(`Submitter Role`, `Understand state of instruction`) %>% summarise(var_counts = n())
# get percentage of each answer for each submitter role
asst_prin <- filter(Q6, `Submitter Role` == "Assistant Principal")
asst_prin$var_per <- with(asst_prin, var_counts/sum(var_counts))
dist_lead <- filter(Q6, `Submitter Role` == "District Leader / CMO Leader")
dist_lead$var_per <- with(dist_lead, var_counts/sum(var_counts))
inst_coach <- filter(Q6, `Submitter Role` == "Instructional Coach")
inst_coach$var_per <- with(inst_coach, var_counts/sum(var_counts))
other <- filter(Q6, `Submitter Role` == "Other")
other$var_per <- with(other, var_counts/sum(var_counts))
principal <- filter(Q6, `Submitter Role` == "Principal")
principal$var_per <- with(principal, var_counts/sum(var_counts))
super <- filter(Q6, `Submitter Role` == "Superintendent / CMO Executive Director")
super$var_per <- with(super, var_counts/sum(var_counts))
teacher <- filter(Q6, `Submitter Role` == "Teacher")
teacher$var_per <- with(teacher, var_counts/sum(var_counts))
teach_lead <- filter(Q6, `Submitter Role` == "Teacher Leader")
teach_lead$var_per <- with(teach_lead, var_counts/sum(var_counts))
Q6 = rbind(asst_prin,dist_lead,inst_coach,other,principal,super,teacher,teach_lead)
Q6
```
```{r echo=FALSE, warning=FALSE}
new_order6 <-factor(Q6$`Understand state of instruction`, levels=c("Strongly Disagree", "Disagree","Somewhat Disagree", "Neutral","Somewhat Agree","Agree","Strongly Agree"))
ggplot(Q6, aes(`Submitter Role`, var_per), ) +
geom_bar(aes(fill = new_order6),position = position_dodge2(width = 5, preserve = "single"), stat="identity") + theme(plot.title = element_text(size = 12, face = "bold"), axis.text.x = element_text(angle = 60, hjust = 1),panel.background = element_rect(fill="#F0EDED",color="#F0EDED")) + ggtitle("Understand state of instruction") +xlab("Submitter Roles") + ylab("Percentage") + guides(fill=guide_legend(title="Agreement")) + scale_fill_manual(values=color_palette)
```
### Valuable use of my time
```{r include=FALSE}
# filter out non-answers, get counts of each answer
Q7 <- filter(data, `Valuable use of my time:` != "N/A") %>% group_by(`Submitter Role`, `Valuable use of my time:`) %>% summarise(var_counts = n())
# get percentage of each answer for each submitter role
asst_prin <- filter(Q7, `Submitter Role` == "Assistant Principal")
asst_prin$var_per <- with(asst_prin, var_counts/sum(var_counts))
dist_lead <- filter(Q7, `Submitter Role` == "District Leader / CMO Leader")
dist_lead$var_per <- with(dist_lead, var_counts/sum(var_counts))
inst_coach <- filter(Q7, `Submitter Role` == "Instructional Coach")
inst_coach$var_per <- with(inst_coach, var_counts/sum(var_counts))
other <- filter(Q7, `Submitter Role` == "Other")
other$var_per <- with(other, var_counts/sum(var_counts))
principal <- filter(Q7, `Submitter Role` == "Principal")
principal$var_per <- with(principal, var_counts/sum(var_counts))
super <- filter(Q7, `Submitter Role` == "Superintendent / CMO Executive Director")
super$var_per <- with(super, var_counts/sum(var_counts))
teacher <- filter(Q7, `Submitter Role` == "Teacher")
teacher$var_per <- with(teacher, var_counts/sum(var_counts))
teach_lead <- filter(Q7, `Submitter Role` == "Teacher Leader")
teach_lead$var_per <- with(teach_lead, var_counts/sum(var_counts))
Q7 = rbind(asst_prin,dist_lead,inst_coach,other,principal,super,teacher,teach_lead)
Q7
```
```{r echo=FALSE, warning=FALSE}
new_order7 <-factor(Q7$`Valuable use of my time:`, levels=c("Strongly Disagree", "Disagree","Somewhat Disagree", "Neutral","Somewhat Agree","Agree","Strongly Agree"))
ggplot(Q7, aes(`Submitter Role`, var_per), ) +
geom_bar(aes(fill = new_order7),position = position_dodge2(width = 5, preserve = "single"), stat="identity") + theme(plot.title = element_text(size = 12, face = "bold"), axis.text.x = element_text(angle = 60, hjust = 1),panel.background = element_rect(fill="#F0EDED",color="#F0EDED")) + ggtitle("Valuable use of my time") +xlab("Submitter Roles") + ylab("Percentage") + guides(fill=guide_legend(title="Agreement")) + scale_fill_manual(values=color_palette)
```
