student_survey.Rmd

---
title: "Taskforce 3 Data Analysis Subgroup Report - Review 2"
author: "Laetitia, Giulia, John, Winfred"
output:
  html_document:
    code_folding: hide
    toc: true
    toc_float: true
  pdf_document:
    toc: true
---

# Data analysis approach

This subgroup of ERC Taskforce 3 (Training) analysed and interpreted results of the student and programme staff surveys. When data was made available, analysts downloaded anonymised data to their local computers (students survey) from the OneDrive folder it was shared through, and kept identifiable data in the folder (programme staff survey).

All four analysts were assigned a number of questions to analyse. Analyses were then collated. All analyses were conducted using R version 4.1.0 and RStudio Build 554.

For quantitative questions, where responses were multiple choice, data was tallied and plotted with bar charts, subsetting the results by the year of study of the respondent, using package 'ggplot2' version 3.3.6. Open questions were read by the assigned analyst. Key themes were extracted, and data was reassigned to newly defined categories and plotted with bar charts. The code used is shared below for transparency (toggle 'code').

# Student survey

```{r, message=FALSE, warning=FALSE}
# Loading necessary packages
library(readxl)
library(ggplot2)
library(dplyr)
library(tidyr)
library(formatR)
library(gt)
```

```{r, echo = FALSE, warning=FALSE}
library(knitr)
opts_chunk$set(tidy.opts=list(width.cutoff=30),tidy=TRUE)
```

```{r, message = FALSE, warning=FALSE}
# Loading the data
student_survey_responses <- as.data.frame(read_excel(
    "/Users/giuliapiazza/Desktop/OneDrive - University College London/PhD/TC3_Wellcome/Student survey-task-force-3.xlsx"
  ))
y4 = which(student_survey_responses[6] == "Year 4") # remove a year 4 from dataset as suggested by reviewer
student_survey_responses = student_survey_responses[-35,] 
```

```{r, message = FALSE, warning=FALSE}
student_survey_responses_q1_clean <- na.omit(data.frame("Year" = as.factor(student_survey_responses[, 6])))

n_q1 <- length(student_survey_responses_q1_clean[, 1]) #get the sample size for Q1


plot_q1 <- ggplot(data = student_survey_responses_q1_clean,
                  mapping = aes (x = Year, fill = Year)) +
  geom_bar(color = "black") + #barcharts
  labs(
    title = " Which year of your PhD are you in (n = 35) ?",
    x = "Answer",
    y = "Count",
    color = "Answer"
  ) + 
  theme_classic() + 
  theme(legend.position = "none") + 
  scale_fill_brewer(palette = "Accent") + 
  geom_text(stat='count', aes(label=..count..), vjust=-1) + ylim(0,18)

q1_table = as.data.frame(table(student_survey_responses_q1_clean))
names(q1_table) = c('Year', 'Count')

plot_q1
ggsave("Figure3_r2.eps", plot = plot_q1, width = 15.93, height = 11.38, units = "cm")
```

## Awareness of training

Key points:

-   Most people reported being informed of training possibilities across all years

-   Most students reported knowing where to find information about training

-   Most students said registering for training was easy

-   Students reported Wellcome made them aware of possible training in comparable numbers (although more indicated they Wellcome did not); a difference can be seen in the Year 3 group, who indicated largely being informed of Wellcome training.

```{r, message=FALSE}
student_survey_responses_q3_clean <-
  na.omit(data.frame(
    "Year" = as.factor(student_survey_responses[, 6]),
    "Q3" = as.factor(student_survey_responses[, 8])
  )) #create a dataframe with Q1 and Q3 and remove NA values in answers to Q3

n_q3 <-
  length(student_survey_responses_q3_clean[, 1]) #get the sample size for Q3

student_survey_responses_q3_clean$Q3 <-
  factor(
    student_survey_responses_q3_clean$Q3,
    levels = c("Strongly Agree", "Agree", "Neutral")
  ) #reorder the levels of Q3 in the way we want to present them

plot_q3_full <- ggplot(data = student_survey_responses_q3_clean,
                  mapping = aes (x = Q3, fill = Q3)) +
  geom_bar(color = "black") +
  labs(
       x = "Answer",
       y = "Count",
       fill = "Answer") +
  scale_fill_brewer(palette = "Accent") + theme_classic() +
  theme(axis.text.x = element_text(angle = 90), 
        legend.position = "none", axis.title.x = element_blank())+ 
  geom_text(stat='count', aes(label=..count..), vjust=-1) + ylim(0,20)

plot_q3 <- ggplot(data = student_survey_responses_q3_clean,
                  mapping = aes (x = Q3, fill = Q3)) +
  geom_bar(color = "black") + #barcharts
  facet_wrap( ~ Year) + # create a different plot for each Year
  labs(
       x = "Answer",
       y = "Count",
       fill = "Answer") +
  scale_fill_brewer(palette = "Paired") + theme_classic() +
  theme(axis.text.x = element_text(angle = 90), 
        legend.position = "none", 
        axis.title.x = element_blank())+ 
  geom_text(stat='count', aes(label=..count..), vjust=-1) + ylim(0,10)

q3_table = student_survey_responses_q3_clean %>% group_by(Year, Q3) %>% summarise(count =
                                                                                    n())

gridExtra::grid.arrange(plot_q3_full,
                        plot_q3,
                        ncol = 2,
                        top = "I was informed about upcoming training possibilities (n = 32)")
```

```{r, message=FALSE}
student_survey_responses_q2_clean <-
  na.omit(data.frame(
    "Year" = as.factor(student_survey_responses[, 6]),
    "Q2" = as.factor(student_survey_responses[, 7])
  )) #create a dataframe with Q1 and Q2 and remove NA values in answers to Q2

n_q2 <-
  length(student_survey_responses_q2_clean[, 1]) #get the sample size for Q2

student_survey_responses_q2_clean$Q2 <-
  factor(student_survey_responses_q2_clean$Q2, levels = c("Yes", "No")) 
#reorder the levels of Q2 in the way we want to present them "

plot_q2_full <- ggplot(data = student_survey_responses_q2_clean,
                  mapping = aes (x = Q2, fill = Q2)) +
  geom_bar(color = "black") + 
  labs(
       x = "Answer",
       y = "Count",
       fill = "Answer") +
  scale_fill_brewer(palette = "Accent") + theme_classic() +
  theme(axis.text.x = element_text(angle = 90), 
        legend.position = "none",
        axis.title.x = element_blank())+ 
  geom_text(stat='count', aes(label=..count..), vjust=-1) + ylim(0,32)

plot_q2 <- ggplot(data = student_survey_responses_q2_clean,
                  mapping = aes (x = Q2, fill = Q2)) +
  geom_bar(color = "black") + #barcharts
  facet_wrap( ~ Year) + # create a different plot for each Year
  labs(
       x = "Answer",
       y = "Count",
       fill = "Answer") +
  scale_fill_brewer(palette = "Paired") + theme_classic() +
  theme(axis.text.x = element_text(angle = 90), 
        legend.position = "none",
        axis.title.x = element_blank())+ 
  geom_text(stat='count', aes(label=..count..), vjust=-1) + ylim(0,15)

q2_table = student_survey_responses_q2_clean %>% group_by(Year, Q2) %>% summarise(count =
                                                                                    n())


Fig4 = gridExtra::grid.arrange(plot_q2_full,
                        plot_q2,
                        ncol = 2,
                        top = "I know where to find information about the training offered (n = 35)")

ggsave("Figure4_r2.eps", plot = Fig4, width = 15.93, height = 11.28 , units = "cm")
```

```{r, message=FALSE}
student_survey_responses_q4_clean <-
  na.omit(data.frame(
    "Year" = as.factor(student_survey_responses[, 6]),
    "Q4" = as.factor(student_survey_responses[, 9])
  )) #create a dataframe with Q1 and Q4 and remove NA values in answers to Q4

n_q4 <-
  length(student_survey_responses_q4_clean[, 1]) #get the sample size for Q4

student_survey_responses_q4_clean$Q4 <-
  factor(student_survey_responses_q4_clean$Q4, levels = c("Yes", "No")) 
#reorder the levels of Q4 in the way we want to present them "

plot_q4_full <- ggplot(data = student_survey_responses_q4_clean,
                  mapping = aes (x = Q4, fill = Q4)) +
  geom_bar(color = "black") + 
  labs(
       x = "Answer",
       y = "Count",
       fill = "Answer") +
  scale_fill_brewer(palette = "Accent") + theme_classic() +
  theme(axis.text.x = element_text(angle = 90), 
        legend.position = "none",
        axis.title.x = element_blank())+ 
  geom_text(stat='count', aes(label=..count..), vjust=-1) + ylim(0,32)

plot_q4 <- ggplot(data = student_survey_responses_q4_clean,
                  mapping = aes (x = Q4, fill = Q4)) +
  geom_bar(color = "black") + #barcharts
  facet_wrap( ~ Year) + # create a different plot for each Year
  labs(
       x = "Answer",
       y = "Count",
       fill = "Answer") +
  scale_fill_brewer(palette = "Paired") + theme_classic() +
  theme(axis.text.x = element_text(angle = 90), 
        legend.position = "none",
        axis.title.x = element_blank())+ 
  geom_text(stat='count', aes(label=..count..), vjust=-1) + ylim(0,13)

q4_table = student_survey_responses_q4_clean %>% group_by(Year, Q4) %>% summarise(count =
                                                                                    n())
gridExtra::grid.arrange(plot_q4_full,
                        plot_q4,
                        ncol = 2,
                        top = "It is easy to register to attend training (n = 34)")

```

```{r, message = FALSE}
student_survey_responses_q5_clean <-
  na.omit(data.frame(
    "Year" = as.factor(student_survey_responses[, 6]),
    "Q5" = as.factor(student_survey_responses[, 10])
  )) #create a dataframe with Q1 and Q5 and remove NA values in answers to Q5

n_q5 <-
  length(student_survey_responses_q5_clean[, 1]) #get the sample size for Q5

student_survey_responses_q5_clean$Q5 <-
  factor(student_survey_responses_q5_clean$Q5, levels = c("Yes", "No")) 
#reorder the levels of Q5 in the way we want to present them "

plot_q5_full <- ggplot(data = student_survey_responses_q5_clean,
                  mapping = aes (x = Q5, fill = Q5)) +
  geom_bar(color = "black") + 
  labs(
       x = "Answer",
       y = "Count",
       fill = "Answer")  +
  scale_fill_brewer(palette = "Accent") + theme_classic() +
  theme(axis.text.x = element_text(angle = 90), 
        legend.position = "none",
        axis.title.x = element_blank())+ 
  geom_text(stat='count', aes(label=..count..), vjust=-1) + ylim(0,20)


plot_q5 <- ggplot(data = student_survey_responses_q5_clean,
                  mapping = aes (x = Q5, fill = Q5)) +
  geom_bar(color = "black") + #barcharts
  facet_wrap( ~ Year) + # create a different plot for each Year
  labs(
       x = "Answer",
       y = "Count",
       fill = "Answer")  +
  scale_fill_brewer(palette = "Paired") + theme_classic() +
  theme(axis.text.x = element_text(angle = 90), 
        legend.position = "none",
        axis.title.x = element_blank())+ 
  geom_text(stat='count', aes(label=..count..), vjust=-1) + ylim(0,11)

q5_table = student_survey_responses_q5_clean %>% group_by(Year, Q5) %>% summarise(count =
                                                                                    n())
gridExtra::grid.arrange(plot_q5_full,
                        plot_q5,
                        ncol = 2,
                        top = "My Wellcome trust programme made me aware of training opportunities (n = 35)")

```


## Scheduling of training

Key points:

-   Most people across all years agreed that training was provided at an appropriate time

-   Students reported that training was not generally repeated to account for scheduling conflicts

```{r, message = FALSE}
student_survey_responses_q6_clean <-
  na.omit(data.frame(
    "Year" = as.factor(student_survey_responses[, 6]),
    "Q6" = as.factor(student_survey_responses[, 11])
  )) #create a dataframe with Q1 and Q6 and remove NA values in answers to Q6

n_q6 <-
  length(student_survey_responses_q6_clean[, 1]) #get the sample size for Q6

student_survey_responses_q6_clean$Q6 <-
  factor(
    student_survey_responses_q6_clean$Q6,
    levels = c(
      "Strongly agree",
      "Agree",
      "Disagree",
      "Strongly disagree",
      "Not applicable"
    )
  ) #reorder the levels of Q6 in the way we want to present them

plot_q6_full <- ggplot(data = student_survey_responses_q6_clean,
                  mapping = aes (x = Q6, fill = Q6)) +
  geom_bar(color = "black") + 
  labs(x = "Answer", y = "Count", fill = "Answer")  +
  scale_fill_brewer(palette = "Accent") + theme_classic() +
  theme(axis.text.x = element_text(angle = 90), 
        legend.position = "none",
        axis.title.x = element_blank())+ 
  geom_text(stat='count', aes(label=..count..), vjust=-1) + ylim(0,22)

plot_q6 <- ggplot(data = student_survey_responses_q6_clean,
                  mapping = aes (x = Q6, fill = Q6)) +
  geom_bar(color = "black") + #barcharts
  facet_wrap( ~ Year) + 
  labs(x = "Answer", y = "Count", fill = "Answer")  +
  scale_fill_brewer(palette = "Paired") + theme_classic() +
  theme(axis.text.x = element_text(angle = 90), 
        legend.position = "none",
        axis.title.x = element_blank())+ 
  geom_text(stat='count', aes(label=..count..), vjust=-1) + ylim(0,10)

q6_table = student_survey_responses_q6_clean %>% group_by(Year, Q6) %>% summarise(count =
                                                                                    n())

Fig5 = gridExtra::grid.arrange(plot_q6_full,
                        plot_q6,
                        ncol = 2,
  top = "The training is delivered at an appropriate time to provide me \n with the skills needed for my PhD project (n = 35)")

ggsave("Figure5_r2.eps", plot = Fig5, width = 15.93, height = 11.28 , units = "cm")
```

```{r, message = FALSE}
student_survey_responses_q7_clean <-
  na.omit(data.frame(
    "Year" = as.factor(student_survey_responses[, 6]),
    "Q7" = as.factor(student_survey_responses[, 12])
  )) #create a dataframe with Q1 and Q7 and remove NA values in answers to Q7

n_q7 <-
  length(student_survey_responses_q7_clean[, 1]) #get the sample size for Q7

student_survey_responses_q7_clean$Q7 <-
  factor(student_survey_responses_q7_clean$Q7, levels = c("Yes", "No")) 
#reorder the levels of Q7 in the way we want to present them "

plot_q7_full <- ggplot(data = student_survey_responses_q7_clean,
                  mapping = aes (x = Q7, fill = Q7)) +
  geom_bar(color= "black") + 
  labs(
       x = "Answer",
       y = "Count",
       fill = "Answer") +
  scale_fill_brewer(palette = "Accent") + theme_classic() +
  theme(axis.text.x = element_text(angle = 90), 
        legend.position = "none",
        axis.title.x = element_blank())+ 
  geom_text(stat='count', aes(label=..count..), vjust=-1) + ylim(0,22)

plot_q7 <- ggplot(data = student_survey_responses_q7_clean,
                  mapping = aes (x = Q7, fill = Q7)) +
  geom_bar(color= "black") + #barcharts
  facet_wrap( ~ Year) + # create a different plot for each Year
  labs(
       x = "Answer",
       y = "Count",
       fill = "Answer") +
  scale_fill_brewer(palette = "Paired") + theme_classic() +
  theme(axis.text.x = element_text(angle = 90), 
        legend.position = "none",
        axis.title.x = element_blank())+ 
  geom_text(stat='count', aes(label=..count..), vjust=-1) + ylim(0,12)

q7_table = student_survey_responses_q7_clean %>% group_by(Year, Q7) %>% summarise(count =
                                                                                    n())
gridExtra::grid.arrange(plot_q7_full,
                        plot_q7,
                        ncol = 2,
       top = "Training was repeated to account for scheduling conflict (n = 33)")

```

## Content of training

Key points:

-   Most students did not receive training on equality and diversity; Those who did found it somewhat useful across years.

-   Most students found training in professional, personal, wellbeing, engagement and career development useful, though many did not receive training in these areas.

-   Technical training and cohort-specific training were considered useful or extremely useful across years.

```{r, message=FALSE}
dat_studentupdated <- student_survey_responses %>% rename('yearofstudy'=6,'trainingareas'=13,'equal_diversity'=14,'professional_dev'=15,'personal_dev'=16,'wellbeing'=17,'career_dev'=18,'tech_training'=19,'cohort_training'=20,'engage_outreach'=21,'usefulnessoftraining'=22,'trainingformat'=25) # reframe to make things  easier

```

```{r, warning=FALSE, message=FALSE}
student_survey_responses_q8b_clean <-
  na.omit(data.frame(
    "Year" = as.factor(student_survey_responses[, 6]),
    "Q8b" = as.factor(student_survey_responses[, 13])
  ))

mylist = unlist(sapply(
  as.character(student_survey_responses_q8b_clean[, 2]),
  strsplit,
  split = ';',
  USE.NAMES = F
)) # get all responses
Val = c(levels(as.factor(mylist))) # get unique responses
Q8b = student_survey_responses_q8b_clean %>% 
  separate(Q8b, into = paste0("V", 1:8), sep = ";") 
# separate column of responses

Q8b[Q8b == ""] <- NA # set empty values as NA


# wide to long
qwide = gather(Q8b, V, Answer, V1:V8)
qwide$Answer[qwide$Answer ==  "participatory research "] <- NA
qfinal = subset(qwide,!is.na(qwide$Answer))


p1 = ggplot(qfinal, mapping = aes(x = Answer, fill = Answer)) + geom_bar(color = "black") + 
  # create a different plot for each Year
  labs(title = "Overall", y = "Count", fill = "Answer") + scale_fill_brewer(palette = "Set3")+
  theme_classic() +   theme(
    legend.position = "none",
    axis.title.x = element_blank(),
    axis.text.x = element_blank(),
    title = element_text(size = 8)
  ) +
  geom_text(
    stat = 'count',
    aes(label = ..count..),
    position = position_stack(vjust = 0.5),
    size = 2
  )

p2 = ggplot(qfinal, mapping = aes(x = as.factor(Answer), 
                                  fill = as.factor(Answer))) + 
  geom_bar(color =                                                                                  "black") +  facet_wrap( ~ Year) + # create a different plot for each Year
  labs(y = "Count", fill = "Answer") + 
  theme_classic() + 
  theme(
    legend.position = "right",
    axis.title.x = element_blank(),
    axis.text.x = element_blank(),
    legend.text = element_text(size = 7)
  ) +
  geom_text(
    stat = 'count',
    aes(label = ..count..),
    position = position_stack(vjust = 0.5),
    size = 2
  ) +  scale_fill_brewer(
    palette = "Set3",
    labels = c(
      "Career Development",
      "Cohort Specifc Training",
      "Engagement and Outreach",
      "Equality and Diversity",
      "No training in these areas",
      "Personal Development",
      "Professional Development",
      "Technical Training",
      "Wellbeing and Support"
    )
  )

gridExtra::grid.arrange(p1,
                        p2,
                        ncol = 2,
                        top = "I received training in these areas",
                        
                        widths = c(1, 3))
```


## Update figure 6 after reviewer comments

```{r, warning=FALSE, message=FALSE}
student_survey_responses_q8a_clean <-
  na.omit(data.frame(
    "Year" = as.factor(student_survey_responses[, 6]),
    "Q8a" = as.factor(student_survey_responses[, 22])
  ))

Q8a = student_survey_responses_q8a_clean %>% 
  separate(Q8a, into = paste0(1:8), sep = ";") # separate column of responses

Q8b[Q8b == ""] <- NA # set empty values as NA


# wide to long
qawide = gather(Q8a, Rank, Answer, 2:9)
category_names = c(
  `Career Development` = "Career Development",
  `Cohort Specific Training` = "Cohort Specific Training",
  `Engagement and Outreach (e.g., science communication)` = "Engagement and Outreach",
   `Equality and Diversity` = "Equality and Diversity",
  `Personal Development/ Management (e.g., time management, applying for funding, research integrity)` = "Personal Development",
  `Professional Development (e.g., presenting or writing workshops)` = "Professional Development",
  `Technical Training (e.g., using specific computer programmes)` = "Technical Training",
  `Wellbeing and Support (e.g., mental health support)` = "Wellbeing and Support"
)
Fig6_r2 = ggplot(qawide, mapping = aes(x = as.factor(Rank), fill = Answer)) +
  geom_bar(color ="black") +  
  facet_wrap(~ Answer, labeller = as_labeller(category_names)) + # create a different plot for each Year
  labs(y = "Count", fill = "Answer", x = "Most to least preferred mode of training",
       title = "I found training in these areas most helpful") + theme_classic() + theme(
    legend.position = "none",
    legend.text = element_text(size = 7)
  ) +
  scale_fill_brewer(
    palette = "Set3"
  )
Fig6_r2
ggsave("Figure6_r2.eps", plot = Fig6_r2, width = 14, height = 18 , units = "cm")

```

```{r, message=FALSE}
plot_q8_full <-
  ggplot(subset(dat_studentupdated,!is.na(equal_diversity)),
         aes(x = equal_diversity, fill = equal_diversity)) +
  geom_bar(color = "black") +
  scale_fill_brewer(palette = "Accent") + theme_classic() +
  theme(
    axis.text.x = element_text(angle = 90),
    legend.position = "none",
    axis.title.x = element_blank()
  ) +
  labs(y = "Count") +
  geom_text(stat = 'count', aes(label = ..count..), vjust = -1) + ylim(0, 18)


plot_q8 <-
  ggplot(subset(dat_studentupdated,!is.na(equal_diversity)),
         aes(x = equal_diversity, fill = equal_diversity)) +
  geom_bar(color = "black") +
  facet_wrap( ~ yearofstudy) +
  scale_fill_brewer(palette = "Paired") + theme_classic() +
  theme(
    axis.text.x = element_text(angle = 90),
    legend.position = "none",
    axis.title.x = element_blank()
  ) +
  labs(y = "Count") +
  geom_text(stat = 'count', aes(label = ..count..), vjust = -1) + ylim(0, 7)



p_table <- subset(dat_studentupdated,!is.na(equal_diversity)) %>%
  group_by(equal_diversity, yearofstudy) %>%
  summarise(count = n()) %>%
  gridExtra::tableGrob(.,
                       cols  = c("Usefulness", "Year", "Count"),
                       rows = NULL)


gridExtra::grid.arrange(plot_q8_full,
                        plot_q8,
                        ncol = 2,
                        top = "Usefulness of equality and diversity training (n=32)")

```

```{r, message=FALSE}
plot_q9_full <-
  ggplot(
    subset(dat_studentupdated,!is.na(professional_dev)),
    aes(x = professional_dev, fill = professional_dev)
  ) +
  geom_bar(color = "black") +
  scale_fill_brewer(palette = "Accent") + theme_classic() +
  theme(
    axis.text.x = element_text(angle = 90),
    legend.position = "none",
    axis.title.x = element_blank()
  ) +
  labs(y = "Count") +
  geom_text(stat = 'count', aes(label = ..count..), vjust = -1) + ylim(0, 15)


plot_q9 <-
  ggplot(
    subset(dat_studentupdated,!is.na(professional_dev)),
    aes(x = professional_dev, fill = professional_dev)
  ) +
  geom_bar(color = "black") +
  facet_wrap( ~ yearofstudy) +
  scale_fill_brewer(palette = "Paired") + theme_classic() +
  theme(
    axis.text.x = element_text(angle = 90),
    legend.position = "none",
    axis.title.x = element_blank()
  ) +
  labs(y = "Count") +
  geom_text(stat = 'count', aes(label = ..count..), vjust = -1) + ylim(0, 9)

gridExtra::grid.arrange(plot_q9_full,
                        plot_q9,
                        ncol = 2,
                        top = "Usefulness of professional development training (n=32)")


```

```{r, message=FALSE}
plot_q10_full <-
  ggplot(subset(dat_studentupdated,!is.na(personal_dev)),
         aes(x = personal_dev, fill = personal_dev),
  ) +
  geom_bar(color = "black") +
  scale_fill_brewer(palette = "Accent") + theme_classic() +
  theme(
    axis.text.x = element_text(angle = 90),
    legend.position = "none",
    axis.title.x = element_blank()
  ) +
  labs(y = "Count") +
  geom_text(stat = 'count', aes(label = ..count..), vjust = -1) + ylim(0, 13)


plot_q10 <- ggplot(subset(dat_studentupdated,!is.na(personal_dev)),
                   aes(x = personal_dev, fill = personal_dev)) +
  geom_bar(color = "black") +
  facet_wrap( ~ yearofstudy) +
  scale_fill_brewer(palette = "Paired") + theme_classic() +
  theme(
    axis.text.x = element_text(angle = 90),
    legend.position = "none",
    axis.title.x = element_blank()
  ) +
  labs(y = "Count") +
  geom_text(stat = 'count', aes(label = ..count..), vjust = -1) + ylim(0, 6)

gridExtra::grid.arrange(plot_q10_full,
                        plot_q10,
                        ncol = 2,
                        top = "Usefulness of personal development training (n=30)")

```

```{r, message=FALSE}
plot_q11_full <-
  ggplot(subset(dat_studentupdated,!is.na(wellbeing)),
         aes(x = wellbeing, fill = wellbeing)) +
  geom_bar(position = 'dodge', color = "black") +
  scale_fill_brewer(palette = "Accent") + theme_classic() +
  theme(
    axis.text.x = element_text(angle = 90),
    legend.position = "none",
    axis.title.x = element_blank()
  ) +
  labs(y = "Count") +
  geom_text(stat = 'count', aes(label = ..count..), vjust = -1) + ylim(0, 15)


plot_q11 <- ggplot(subset(dat_studentupdated,!is.na(wellbeing)),
                   aes(x = wellbeing, fill = wellbeing)) +
  geom_bar(position = 'dodge', color = "black") +
  facet_wrap( ~ yearofstudy) +
  scale_fill_brewer(palette = "Paired") + theme_classic() +
  theme(
    axis.text.x = element_text(angle = 90),
    legend.position = "none",
    axis.title.x = element_blank()
  ) +
  labs(y = "Count") +
  geom_text(stat = 'count', aes(label = ..count..), vjust = -1) + ylim(0, 8)

gridExtra::grid.arrange(plot_q11_full,
                        plot_q11,
                        ncol = 2,
                        top = "Usefulness of wellbeing training (n=32)")
```

```{r, message=FALSE}
plot_q12_full <-
  ggplot(subset(dat_studentupdated,!is.na(career_dev)),
         aes(x = career_dev, fill = career_dev)) +
  geom_bar(color = "black") +
  scale_fill_brewer(palette = "Accent") + theme_classic() +
  theme(
    axis.text.x = element_text(angle = 90),
    legend.position = "none",
    axis.title.x = element_blank()
  ) +
  labs(y = "Count") +
  geom_text(stat = 'count', aes(label = ..count..), vjust = -1) + ylim(0, 18)


plot_q12 <- ggplot(subset(dat_studentupdated,!is.na(career_dev)),
                   aes(x = career_dev, fill = career_dev)) +
  geom_bar(position = 'dodge', color = "black") +
  facet_wrap( ~ yearofstudy) +
  scale_fill_brewer(palette = "Paired") + theme_classic() +
  theme(
    axis.text.x = element_text(angle = 90),
    legend.position = "none",
    axis.title.x = element_blank()
  ) +
  labs(y = "Count") +
  geom_text(stat = 'count', aes(label = ..count..), vjust = -1) + ylim(0, 8)

gridExtra::grid.arrange(plot_q12_full,
                        plot_q12,
                        ncol = 2,
                        top = "Usefulness of career development training (n=30)")
```

```{r, message=FALSE}
plot_q13_full <-
  ggplot(subset(dat_studentupdated,!is.na(tech_training)),
         aes(x = tech_training, fill = tech_training)) +
  geom_bar(position = 'dodge', color = "black") +
  scale_fill_brewer(palette = "Accent") + theme_classic() +
  theme(
    axis.text.x = element_text(angle = 90),
    legend.position = "none",
    axis.title.x = element_blank()
  ) +
  labs(y = "Count") +
  geom_text(stat = 'count', aes(label = ..count..), vjust = -1) + ylim(0, 17)


plot_q13 <-
  ggplot(subset(dat_studentupdated,!is.na(tech_training)),
         aes(x = tech_training, fill = tech_training)) +
  geom_bar(position = 'dodge', color = "black") +
  facet_wrap( ~ yearofstudy) +
  scale_fill_brewer(palette = "Paired") + theme_classic() +
  theme(
    axis.text.x = element_text(angle = 90),
    legend.position = "none",
    axis.title.x = element_blank()
  ) +
  labs(y = "Count") +
  geom_text(stat = 'count', aes(label = ..count..), vjust = -1) + ylim(0, 8)

gridExtra::grid.arrange(plot_q13_full,
                        plot_q13,
                        ncol = 2,
                        top = "Usefulness of technical training (n=32)")
```

```{r, message=FALSE}
plot_q14_full <-
  ggplot(subset(dat_studentupdated,!is.na(cohort_training)),
         aes(x = cohort_training, fill = cohort_training)) +
  geom_bar(color = "black") +
  scale_fill_brewer(palette = "Accent") + theme_classic() +
  theme(
    axis.text.x = element_text(angle = 90),
    legend.position = "none",
    axis.title.x = element_blank()
  ) +
  labs(y = "Count") +
  geom_text(stat = 'count', aes(label = ..count..), vjust = -1) + ylim(0, 15)


plot_q14 <-
  ggplot(subset(dat_studentupdated,!is.na(cohort_training)),
         aes(x = cohort_training, fill = cohort_training)) +
  geom_bar(color = "black") +
  facet_wrap( ~ yearofstudy) +
  scale_fill_brewer(palette = "Paired") + theme_classic() +
  theme(
    axis.text.x = element_text(angle = 90),
    legend.position = "none",
    axis.title.x = element_blank()
  ) +
  labs(y = "Count") +
  geom_text(stat = 'count', aes(label = ..count..), vjust = -1) + ylim(0, 7)

gridExtra::grid.arrange(plot_q14_full,
                        plot_q14,
                        ncol = 2,
                        top = "Usefulness of cohort-specific training (n=32)")
```

```{r, message=FALSE}
plot_q15_full <-
  ggplot(subset(dat_studentupdated,!is.na(engage_outreach)),
         aes(x = engage_outreach, fill = engage_outreach)) +
  geom_bar(color = "black") +
  scale_fill_brewer(palette = "Accent") + theme_classic() +
  theme(
    axis.text.x = element_text(angle = 90),
    legend.position = "none",
    axis.title.x = element_blank()
  ) +
  labs(y = "Count") +
  geom_text(stat = 'count', aes(label = ..count..), vjust = -1) + ylim(0, 14)


plot_q15 <-
  ggplot(subset(dat_studentupdated,!is.na(engage_outreach)),
         aes(x = engage_outreach, fill = engage_outreach)) +
  geom_bar(position = 'dodge', color = "black") +
  facet_wrap( ~ yearofstudy) +
  scale_fill_brewer(palette = "Paired") + theme_classic() +
  theme(
    axis.text.x = element_text(angle = 90),
    legend.position = "none",
    axis.title.x = element_blank()
  ) +
  labs(y = "Count") +
  geom_text(stat = 'count', aes(label = ..count..), vjust = -1) + ylim(0, 7)

gridExtra::grid.arrange(plot_q15_full,
                        plot_q15,
                        ncol = 2,
                        top = "Usefulness of engagement and outreach training (n=32)")
```

Data for the following question was looked at and grouped into the following variable manually due to its free-text nature (Distribution of answers : NA / Yes (including detailed comments) / No)

Out of the "Yes" answers, n=2 were just "Yes" and n=11 gave detailed answers. Out of these 11 answers : - 3 mentioned issues related to their well being, including struggles with "loneliness" and "self-doubt" - 6 mentioned issues related to academia / PhD-specific, including struggles with selecting a supervisor, project management and grant applications - 4 mentioned issues related to their career beyond research skills, including struggles with networking, planning post their PhD and managing their finances (2) (Some answers included multiple different points)

```{r, message = FALSE}
student_survey_responses_q18 <-
  data.frame(
    "Year" = as.factor(student_survey_responses[, 6]),
    "Q18" = as.factor(student_survey_responses[, 23])
  )

student_survey_responses_q18_clean <-
  data.frame(
    "Q18" = c(
      rep("Not answered", 19),
      rep("Yes", 2),
      rep("No", 3),
      rep("Yes", 11)
    ),
    "Year" = c(
      rep("Year 1", 4),
      rep("Year 2", 9),
      rep("Year 3", 6),
      rep("Year 1", 1),
      rep("Year 2", 1),
      rep("Year 2", 2),
      rep("Year 3", 1),
      rep("Year 1", 4),
      rep("Year 2", 3),
      rep("Year 3", 4)
    )
  )

student_survey_responses_q18_clean$Q18 <-
  factor(student_survey_responses_q18_clean$Q18,
         levels = c("Yes", "No", "Not answered")) 
#reorder the levels of Q7 in the way we want to present them "


plot_q18_full <- ggplot(data = student_survey_responses_q18_clean,
                        mapping = aes (x = Q18, fill = Q18)) +
  geom_bar(color = "black") +
  labs(x = "Answer",
       y = "Count",
       fill = "Answer") +
  scale_fill_brewer(palette = "Accent") + theme_classic() +
  theme(
    axis.text.x = element_text(angle = 90),
    legend.position = "none",
    axis.title.x = element_blank()
  ) +
  geom_text(stat = 'count', aes(label = ..count..), vjust = -1) + ylim(0, 22)

plot_q18 <- ggplot(data = student_survey_responses_q18_clean,
                   mapping = aes (x = Q18, fill = Q18)) +
  geom_bar(color = "black") + #barcharts
  facet_wrap(~ Year) + # create a different plot for each Year
  labs(x = "Answer",
       y = "Count",
       fill = "Answer") +
  scale_fill_brewer(palette = "Paired") + theme_classic() +
  theme(
    axis.text.x = element_text(angle = 90),
    legend.position = "none",
    axis.title.x = element_blank()
  ) +
  geom_text(stat = 'count', aes(label = ..count..), vjust = -1) + ylim(0, 11)

q18_table = student_survey_responses_q18_clean %>% group_by(Year, Q18) %>% 
  summarise(count =n())
gridExtra::grid.arrange(plot_q18_full,
                        plot_q18,
                        ncol = 2,
                        top = "Are there any aspects of the PhD project or \nlife as a PhD student you do not feel equipped to manage? (n = 35)")
```

## Format of training

Key points:

-   Subject specific training was most useful for people in Year 2

-   Project management was most useful to students in Year 3

-   The format of training was judged appropriate by most

-   Training was mostly reported to be compulsory

-   Training was considered a good use of time and resources


```{r, message = FALSE}
student_open <-
  as.data.frame(read_xlsx("/Users/giuliapiazza/Desktop/OneDrive - University College London/PhD/TC3_Wellcome/student_open.xlsx"))
student_open$Year = student_open[, 2]

plot_q19 <-
  ggplot(
    data = subset(student_open,!is.na(training_like)),
    mapping = aes (x = training_like, fill = training_like)
  ) +
  geom_bar(color = "black") + #barcharts
  facet_wrap( ~ Year) + # create a different plot for each Year
  labs(x = "Answer", y = "Count", fill = "Answer") +
  theme(axis.text.x = element_blank()) +
  scale_fill_brewer(palette = "Accent") + theme_classic() +
  theme(
    axis.text.x = element_text(angle = 90),
    legend.position = "none",
    axis.title.x = element_blank()
  ) +
  geom_text(stat = 'count', aes(label = ..count..), vjust = -1) + ylim(0, 6)

plot_q19_full <-
  ggplot(
    data = subset(student_open,!is.na(training_like)),
    mapping = aes (x = training_like, fill = training_like)
  ) +
  geom_bar(color = "black") +
  labs(x = "Answer", y = "Count", fill = "Answer") +
  theme(axis.text.x = element_blank()) +
  scale_fill_brewer(palette = "Paired") + theme_classic() +
  theme(
    axis.text.x = element_text(angle = 90),
    legend.position = "none",
    axis.title.x = element_blank()
  ) +
  geom_text(stat = 'count', aes(label = ..count..), vjust = -1) + ylim(0, 9)

gridExtra::grid.arrange(plot_q19_full,
                        plot_q19,
                        ncol = 2,
                        top = "What training would you like to be offered? (n=35)")
```

## Update figure 8 after review
```{r, message=FALSE}
student_survey_responses_q21_clean <-
  na.omit(data.frame(
    "Year" = as.factor(student_survey_responses[, 6]),
    "Q22" = as.factor(student_survey_responses[, 26])
  ))
n_q21 <-
  length(student_survey_responses_q21_clean[, 1]) #get the sample size for Q21


mylist = unlist(sapply(
  as.character(student_survey_responses_q21_clean[, 2]),
  strsplit,
  split = ';',
  USE.NAMES = F
)) # get all responses
Val = c(levels(as.factor(mylist))) # get unique responses

df1 = student_survey_responses_q21_clean %>% filter(Year == 'Year 1')
df2 = student_survey_responses_q21_clean %>% filter(Year == 'Year 2')
df3 = student_survey_responses_q21_clean %>% filter(Year == 'Year 3')

Var = list(df1, df2, df3) # create list of dfs.
names(Var) <-
  c('Year1', 'Year2', 'Year3') # name the data frames for each year responses

cat("pref",
    "mode",
    "Year",
    file = "Testfile.txt",
    sep = ",",
    fill = TRUE) ##create  a log file to append indices for each response
Ind = function(x) {
  for (i in 1:length(Var)) {
    year = (names(Var)[i])
    LIST = sapply(as.character(Var[[i]][[2]]),
                  strsplit,
                  split = ';',
                  USE.NAMES = F)
    for (l in LIST) {
      cat(
        which(l == x),
        x,
        year,
        file = "Testfile.txt",
        sep = ",",
        fill = TRUE,
        append = TRUE
      ) # write data to log fie
    }
  }
  
  
}
Val = sapply(Val, Ind, USE.NAMES = F)
plot_q22 = read.table('Testfile.txt', header = T, sep = ',')

Fig8_r2 = ggplot(plot_q22, mapping = aes(fill = as.factor(mode), x = as.factor(pref))) +
  geom_bar(color ="black") +
  facet_wrap(~ mode) + # create a different plot for each mode
  labs(title = "I most enjoy training delivered as (n=31)", y = "Count", fill = "Answer",
       x = "Most to least preferred mode of delivery") + 
  scale_fill_brewer(palette = "Set3") + theme_classic() + theme(
    legend.position = "none",
    axis.title.x = element_text(size=12),
    title = element_text(size = 10)
  ) 


Fig8_r2
ggsave("Figure8_r2.eps", plot = Fig8_r2, width = 16, height = 18 , units = "cm")

```

```{r, message = FALSE}
student_survey_responses_q22_clean <-
  na.omit(data.frame(
    "Year" = as.factor(student_survey_responses[, 6]),
    "Q22" = as.factor(student_survey_responses[, 27])
  )) #create a dataframe with Q1 and Q22 and remove NA values in answers to Q22

n_q22 <-
  length(student_survey_responses_q22_clean[, 1]) #get the sample size for Q22

student_survey_responses_q22_clean$Q22 <-
  factor(
    student_survey_responses_q22_clean$Q22,
    levels = c(
      "Strongly agree",
      "Agree",
      "Disagree",
      "Strongly disagree",
      "Not applicable"
    )
  ) #reorder the levels of Q22 in the way we want to present them



plot_q22_full <- ggplot(data = student_survey_responses_q22_clean,
                        mapping = aes (x = Q22, fill = Q22)) +
  geom_bar(color = "black") +
  labs(x = "Answer",
       y = "Count",
       fill = "Answer") +
  scale_fill_brewer(palette = "Accent") + theme_classic() +
  theme(
    axis.text.x = element_text(angle = 90),
    legend.position = "none",
    axis.title.x = element_blank()
  ) +
  geom_text(stat = 'count', aes(label = ..count..), vjust = -1) + ylim(0, 25)

plot_q22 <- ggplot(data = student_survey_responses_q22_clean,
                   mapping = aes (x = Q22, fill = Q22)) +
  geom_bar(color = "black") + #barcharts
  facet_wrap(~ Year) + # create a different plot for each Year
  labs(x = "Answer",
       y = "Count",
       fill = "Answer") +
  scale_fill_brewer(palette = "Paired") + theme_classic() +
  theme(
    axis.text.x = element_text(angle = 90),
    legend.position = "none",
    axis.title.x = element_blank()
  ) +
  geom_text(stat = 'count', aes(label = ..count..), vjust = -1) + ylim(0, 11)

q22_table = student_survey_responses_q22_clean %>% group_by(Year, Q22) %>% summarise(count =
                                                                                       n())
gridExtra::grid.arrange(plot_q22_full,
                        plot_q22,
                        ncol = 2,
  top = "I feel the format of the training is generally appropriate to \nthe subject matter (n = 33)")

```

```{r, message= FALSE}
student_survey_responses_q23_clean <-
  na.omit(data.frame(
    "Year" = as.factor(student_survey_responses[, 6]),
    "Q23" = as.factor(student_survey_responses[, 28])
  )) #create a dataframe with Q1 and Q23 and remove NA values in answers to Q23

n_q23 <-
  length(student_survey_responses_q23_clean[, 1]) #get the sample size for Q23

student_survey_responses_q23_clean$Q23 <-
  factor(
    student_survey_responses_q23_clean$Q23,
    levels = c(
      "Strongly agree",
      "Agree",
      "Disagree",
      "Strongly disagree",
      "Not applicable"
    )
  ) #reorder the levels of Q23 in the way we want to present them


plot_q23_full <- ggplot(data = student_survey_responses_q23_clean,
                        mapping = aes (x = Q23, fill = Q23)) +
  geom_bar(color = "black") +
  labs(x = "Answer",
       y = "Count",
       fill = "Answer") +
  scale_fill_brewer(palette = "Accent") + theme_classic() +
  theme(
    axis.text.x = element_text(angle = 90),
    legend.position = "none",
    axis.title.x = element_blank()
  ) +
  geom_text(stat = 'count', aes(label = ..count..), vjust = -1) + ylim(0, 25)

plot_q23 <- ggplot(data = student_survey_responses_q23_clean,
                   mapping = aes (x = Q23, fill = Q23)) +
  geom_bar(color = "black") + #barcharts
  facet_wrap(~ Year) + # create a different plot for each Year
  labs(x = "Answer",
       y = "Count",
       fill = "Answer") +
  scale_fill_brewer(palette = "Paired") + theme_classic() +
  theme(
    axis.text.x = element_text(angle = 90),
    legend.position = "none",
    axis.title.x = element_blank()
  ) +
  geom_text(stat = 'count', aes(label = ..count..), vjust = -1) + ylim(0, 8)

q23_table = student_survey_responses_q23_clean %>% group_by(Year, Q23) %>% 
  summarise(count =
                                                                                       n())
gridExtra::grid.arrange(plot_q23_full,
                        plot_q23,
                        ncol = 2,
  top = "Training offered by my programme is compulsory (n = 33)")

```

```{r, message=FALSE}
student_survey_responses_q24_clean <-
  na.omit(data.frame(
    "Year" = as.factor(student_survey_responses[, 6]),
    "Q24" = as.factor(student_survey_responses[, 29])
  )) #create a dataframe with Q1 and Q24 and remove NA values in answers to Q24

n_q24 <-
  length(student_survey_responses_q24_clean[, 1]) #get the sample size for Q24

student_survey_responses_q24_clean$Q24 <-
  factor(
    student_survey_responses_q24_clean$Q24,
    levels = c(
      "Strongly agree",
      "Agree",
      "Disagree",
      "Strongly disagree",
      "Not applicable"
    )
  ) #reorder the levels of Q24 in the way we want to present them


plot_q24_full <- ggplot(data = student_survey_responses_q24_clean,
                        mapping = aes (x = Q24, fill = Q24)) +
  geom_bar(color = "black") +
  labs(x = "Answer",
       y = "Count",
       fill = "Answer") +
  scale_fill_brewer(palette = "Accent") + theme_classic() +
  theme(
    axis.text.x = element_text(angle = 90),
    legend.position = "none",
    axis.title.x = element_blank()
  ) +
  geom_text(stat = 'count', aes(label = ..count..), vjust = -1) + ylim(0, 25)

plot_q24 <- ggplot(data = student_survey_responses_q24_clean,
                   mapping = aes (x = Q24, fill = Q24)) +
  geom_bar(color = "black") + #barcharts
  facet_wrap(~ Year) + # create a different plot for each Year
  labs(x = "Answer",
       y = "Count",
       fill = "Answer") +
  scale_fill_brewer(palette = "Paired") + theme_classic() +
  theme(
    axis.text.x = element_text(angle = 90),
    legend.position = "none",
    axis.title.x = element_blank()
  ) +
  geom_text(stat = 'count', aes(label = ..count..), vjust = -1) + ylim(0, 10)

q24_table = student_survey_responses_q24_clean %>% group_by(Year, Q24) %>% summarise(count =
                                                                                       n())
gridExtra::grid.arrange(plot_q24_full,
                        plot_q24,
                        ncol = 2,
                        top = "Compulsory training was a good use of my time and resources (n = 33)")

```

## Usefulness of training

-   Subject specific training was reported to be the most useful

-   Most students reported using skills acquired through personal development training during the course of their PhD, although some did not

```{r}


plot_q25b <-
  ggplot(
    data = subset(student_open,!is.na(training_useful)),
    mapping = aes (x = training_useful, fill = training_useful)
  ) +
  geom_bar(color = "black") + #barcharts
  facet_wrap( ~ Year) + # create a different plot for each Year
  labs(x = "Answer", y = "Count", fill = "Answer") +
  theme(axis.text.x = element_blank()) +
  scale_fill_brewer(palette = "Paired") + theme_classic() +
  theme(
    axis.text.x = element_text(angle = 90),
    legend.position = "none",
    axis.title.x = element_blank()
  ) +
  geom_text(stat = 'count', aes(label = ..count..), vjust = -1) + ylim(0, 7)

plot_q25b_full <-
  ggplot(
    data = subset(student_open,!is.na(training_useful)),
    mapping = aes (x = training_useful, fill = training_useful)
  ) +
  geom_bar(color = "black") +
  labs(x = "Answer", y = "Count", fill = "Answer") +
  theme(axis.text.x = element_blank()) +
  scale_fill_brewer(palette = "Paired") + theme_classic() +
  theme(
    axis.text.x = element_text(angle = 90),
    legend.position = "none",
    axis.title.x = element_blank()
  ) +
  geom_text(stat = 'count', aes(label = ..count..), vjust = -1) + ylim(0, 25)

Fig7 = gridExtra::grid.arrange(plot_q25b_full,
                        plot_q25b,
                        ncol = 2,
                        top = "What training modules did you find most useful (n = 30)?")

ggsave("Figure7.eps", plot = Fig7, width = 15.93, height = 11.28 , units = "cm")
```

```{r, message= FALSE}
student_survey_responses_q26_clean <-
  na.omit(data.frame(
    "Year" = as.factor(student_survey_responses[, 6]),
    "Q26" = as.factor(student_survey_responses[, 31])
  )) #create a dataframe with Q1 and Q26 and remove NA values in answers to Q26

n_q26 <-
  length(student_survey_responses_q26_clean[, 1]) #get the sample size for Q26

student_survey_responses_q26_clean$Q26 <-
  factor(student_survey_responses_q26_clean$Q26, levels = c("Yes", "No")) 
#reorder the levels of Q26 in the way we want to present them "


plot_q26_full <- ggplot(data = student_survey_responses_q26_clean,
                        mapping = aes (x = Q26, fill = Q26)) +
  geom_bar(color = "black") +
  labs(x = "Answer",
       y = "Count",
       fill = "Answer") +
  scale_fill_brewer(palette = "Accent") + theme_classic() +
  theme(
    axis.text.x = element_text(angle = 90),
    legend.position = "none",
    axis.title.x = element_blank()
  ) +
  geom_text(stat = 'count', aes(label = ..count..), vjust = -1) + ylim(0, 25)

plot_q26 <- ggplot(data = student_survey_responses_q26_clean,
                   mapping = aes (x = Q26, fill = Q26)) +
  geom_bar(color = "black") + #barcharts
  facet_wrap(~ Year) + # create a different plot for each Year
  labs(x = "Answer",
       y = "Count",
       fill = "Answer") +
  scale_fill_brewer(palette = "Paired") + theme_classic() +
  theme(
    axis.text.x = element_text(angle = 90),
    legend.position = "none",
    axis.title.x = element_blank()
  ) +
  geom_text(stat = 'count', aes(label = ..count..), vjust = -1) + ylim(0, 9)

q26_table = student_survey_responses_q26_clean %>% group_by(Year, Q26) %>% summarise(count =
                                                                                       n())
gridExtra::grid.arrange(plot_q26_full,
                        plot_q26,
                        ncol = 2,
    top = "I have used skills learned in personal development training during \nthe course of my PhD so far (n = 32)")

```

## Process of recording training

Key points:

-   Universities kept records of training

-   Students know how to access the records

```{r, message  = FALSE}
student_survey_responses_q27_clean <-
  na.omit(data.frame(
    "Year" = as.factor(student_survey_responses[, 6]),
    "Q27" = as.factor(student_survey_responses[, 32])
  )) #create a dataframe with Q1 and Q27 and remove NA values in answers to Q27

n_q27 <-
  length(student_survey_responses_q27_clean[, 1]) #get the sample size for Q27

student_survey_responses_q27_clean$Q27 <-
  factor(student_survey_responses_q27_clean$Q27, levels = c("Yes", "No")) 
#reorder the levels of Q27 in the way we want to present them "


plot_q27_full <- ggplot(data = student_survey_responses_q27_clean,
                        mapping = aes (x = Q27, fill = Q27)) +
  geom_bar(color = "black") +
  labs(x = "Answer",
       y = "Count",
       fill = "Answer") +
  scale_fill_brewer(palette = "Accent") + theme_classic() +
  theme(
    axis.text.x = element_text(angle = 90),
    legend.position = "none",
    axis.title.x = element_blank()
  ) +
  geom_text(stat = 'count', aes(label = ..count..), vjust = -1) + ylim(0, 28)

plot_q27 <- ggplot(data = student_survey_responses_q27_clean,
                   mapping = aes (x = Q27, fill = Q27)) +
  geom_bar(color = "black") + #barcharts
  facet_wrap(~ Year) + # create a different plot for each Year
  labs(x = "Answer",
       y = "Count",
       fill = "Answer") +
  scale_fill_brewer(palette = "Paired") + theme_classic() +
  theme(
    axis.text.x = element_text(angle = 90),
    legend.position = "none",
    axis.title.x = element_blank()
  ) +
  geom_text(stat = 'count', aes(label = ..count..), vjust = -1) + ylim(0, 10)

q27_table = student_survey_responses_q27_clean %>% group_by(Year, Q27) %>% summarise(count =
                                                                                       n())
gridExtra::grid.arrange(plot_q27_full,
                        plot_q27,
                        ncol = 2,
                        top = "A record of my training is kept by my university (n = 32)")
```

```{r, message= FALSE}
student_survey_responses_q28_clean <-
  na.omit(data.frame(
    "Year" = as.factor(student_survey_responses[, 6]),
    "Q28" = as.factor(student_survey_responses[, 33])
  )) #create a dataframe with Q1 and Q28 and remove NA values in answers to Q28

n_q28 <-
  length(student_survey_responses_q28_clean[, 1]) #get the sample size for Q28

student_survey_responses_q28_clean$Q28 <-
  factor(
    student_survey_responses_q28_clean$Q28,
    levels = c(
      "Yes",
      "No",
      "N/A - my university does not provide a way to record my training."
    )
  ) #reorder the levels of Q28 in the way we want to present them "

plot_q28_full <- ggplot(data = student_survey_responses_q28_clean,
                        mapping = aes (x = Q28, fill = Q28)) +
  geom_bar(color = "black") +
  labs(x = "Answer",
       y = "Count",
       fill = "Answer") +
  scale_fill_brewer(palette = "Accent") + theme_classic() +
  theme(
    axis.text.x = element_text(angle = 90),
    legend.position = "none",
    axis.title.x = element_blank()
  ) +
  scale_x_discrete(
    labels = c(
      "Yes" = "Yes",
      "No" = "No",
      "N/A - my university does not provide a way to record my training." = "Record not provided"
    )
  ) +
  geom_text(stat = 'count', aes(label = ..count..), vjust = -1) + ylim(0, 25)

plot_q28 <- ggplot(data = student_survey_responses_q28_clean,
                   mapping = aes (x = Q28, fill = Q28)) +
  geom_bar(color = "black") + #barcharts
  facet_wrap(~ Year) + # create a different plot for each Year
  labs(x = "Answer",
       y = "Count",
       fill = "Answer") +
  scale_fill_brewer(palette = "Paired") + theme_classic() +
  theme(
    axis.text.x = element_text(angle = 90),
    legend.position = "none",
    axis.title.x = element_blank()
  ) +
  scale_x_discrete(
    labels = c(
      "Yes" = "Yes",
      "No" = "No",
      "N/A - my university does not provide a way to record my training." = "Record not provided"
    )
  ) +
  geom_text(stat = 'count', aes(label = ..count..), vjust = -1) + ylim(0, 10)

q28_table = student_survey_responses_q28_clean %>% group_by(Year, Q28) %>% summarise(count =
                                                                                       n())
gridExtra::grid.arrange(plot_q28_full,
                        plot_q28,
                        ncol = 2,
 top = "I know how to access and record training \nin the training log provided by my university (n = 32)")

```

## Overall satisfaction

Key points:

-   Most students are satisfied with training and think it will prepare them to become good researchers

-   Not all students in their second year think skills will be transferable to outside of academia

-   Most students wanted to learn new skills relevant to their PhDs when engaging in training

```{r, message = FALSE}
student_survey_responses_q29_clean <-
  na.omit(data.frame(
    "Year" = as.factor(student_survey_responses[, 6]),
    "Q29" = as.factor(student_survey_responses[, 34])
  )) #create a dataframe with Q1 and Q29 and remove NA values in answers to Q29

n_q29 <-
  length(student_survey_responses_q29_clean[, 1]) #get the sample size for Q29

student_survey_responses_q29_clean$Q29 <-
  factor(
    student_survey_responses_q29_clean$Q29,
    levels = c(
      "Strongly agree",
      "Agree",
      "Disagree",
      "Strongly disagree",
      "Not applicable"
    )
  ) #reorder the levels of Q29 in the way we want to present them


plot_q29_full <- ggplot(data = student_survey_responses_q29_clean,
                        mapping = aes (x = Q29, fill = Q29)) +
  geom_bar(color = "black") +
  labs(x = "Answer",
       y = "Count",
       fill = "Answer") +
  scale_fill_brewer(palette = "Accent") + theme_classic() +
  theme(
    axis.text.x = element_text(angle = 90),
    legend.position = "none",
    axis.title.x = element_blank()
  ) +
  geom_text(stat = 'count', aes(label = ..count..), vjust = -1) + ylim(0, 25)

plot_q29 <- ggplot(data = student_survey_responses_q29_clean,
                   mapping = aes (x = Q29, fill = Q29)) +
  geom_bar(color = "black") + #barcharts
  facet_wrap(~ Year) + # create a different plot for each Year
  labs(x = "Answer",
       y = "Count",
       fill = "Answer") +
  scale_fill_brewer(palette = "Paired") + theme_classic() +
  theme(
    axis.text.x = element_text(angle = 90),
    legend.position = "none",
    axis.title.x = element_blank()
  ) +
  geom_text(stat = 'count', aes(label = ..count..), vjust = -1) + ylim(0, 12)

q29_table = student_survey_responses_q29_clean %>% group_by(Year, Q29) %>% summarise(count =
                                                                                       n())
gridExtra::grid.arrange(plot_q29_full,
                        plot_q29,
                        ncol = 2,
top = "Training provided as part of my programme prepares me \nto become a good researcher (n = 32)")

```

```{r, message = FALSE}
student_survey_responses_q30_clean <-
  na.omit(data.frame(
    "Year" = as.factor(student_survey_responses[, 6]),
    "Q30" = as.factor(student_survey_responses[, 35])
  )) #create a dataframe with Q1 and Q30 and remove NA values in answers to Q30

n_q30 <-
  length(student_survey_responses_q30_clean[, 1]) #get the sample size for Q30

student_survey_responses_q30_clean$Q30 <-
  factor(
    student_survey_responses_q30_clean$Q30,
    levels = c("Strongly agree", "Agree", "Disagree", "Strongly disagree")
  ) #reorder the levels of Q30 in the way we want to present them


plot_q30_full <- ggplot(data = student_survey_responses_q30_clean,
                        mapping = aes (x = Q30, fill = Q30)) +
  geom_bar(color = "black") +
  labs(x = "Answer",
       y = "Count",
       fill = "Answer") +
  scale_fill_brewer(palette = "Accent") + theme_classic() +
  theme(
    axis.text.x = element_text(angle = 90),
    legend.position = "none",
    axis.title.x = element_blank()
  ) +
  geom_text(stat = 'count', aes(label = ..count..), vjust = -1) + ylim(0, 20)

plot_q30 <- ggplot(data = student_survey_responses_q30_clean,
                   mapping = aes (x = Q30, fill = Q30)) +
  geom_bar(color = "black") + #barcharts
  facet_wrap(~ Year) + # create a different plot for each Year
  labs(x = "Answer",
       y = "Count",
       fill = "Answer") +
  scale_fill_brewer(palette = "Paired") + theme_classic() +
  theme(
    axis.text.x = element_text(angle = 90),
    legend.position = "none",
    axis.title.x = element_blank()
  ) +
  geom_text(stat = 'count', aes(label = ..count..), vjust = -1) + ylim(0, 9)

q30_table = student_survey_responses_q30_clean %>% group_by(Year, Q30) %>% summarise(count =
                                                                                       n())
gridExtra::grid.arrange(plot_q30_full,
                        plot_q30,
                        ncol = 2,
 top = "Training provided as part of my programme gives me valuable \ntransferable skills for working outside of academia (n = 32)")

```

For these last two open question, comments were read, common themes were detected and comments were assigned to categories.

```{r}


plot_q31b <-
  ggplot(
    data = subset(student_open,!is.na(goals_training)),
    mapping = aes (x = goals_training, fill = goals_training)
  ) +
  geom_bar(color = "black") + #barcharts
  facet_wrap( ~ Year) + # create a different plot for each Year
  labs(x = "Answer", y = "Count", fill = "Answer") +
  theme(axis.text.x = element_blank()) +
  scale_fill_brewer(palette = "Paired") + theme_classic() +
  theme(
    axis.text.x = element_text(angle = 90),
    legend.position = "none",
    axis.title.x = element_blank()
  ) +
  geom_text(stat = 'count', aes(label = ..count..), vjust = -1) + ylim(0, 9)

plot_q31b_full <-
  ggplot(
    data = subset(student_open,!is.na(goals_training)),
    mapping = aes (x = goals_training, fill = goals_training)
  ) +
  geom_bar(color = "black") +
  labs(x = "Answer", y = "Count", fill = "Answer") +
  theme(axis.text.x = element_blank()) +
  scale_fill_brewer(palette = "Paired") + theme_classic() +
  theme(
    axis.text.x = element_text(angle = 90),
    legend.position = "none",
    axis.title.x = element_blank()
  ) +
  geom_text(stat = 'count', aes(label = ..count..), vjust = -1) + ylim(0, 25)

gridExtra::grid.arrange(plot_q31b_full,
                        plot_q31b,
                        ncol = 2,
top = "What was your main objective when engaging with training?")

```


-   "in person" = prefferred training to be delivered in person in the future

-   "share courses" = course information should be shared across Wellcome programmes

-   "good but missing" = training was good but lacked courses on some topics

```{r}
plot_q32 <- ggplot(
  data = subset(student_open,!is.na(comments)),
  mapping = aes (x = comments, fill = comments)
) +
  geom_bar(color = "black") + #barcharts + # create a different plot for each Year
  labs(title = "General comments on training?" ,
       x = "Answer",
       y = "Count",
       fill = "Answer") +
  scale_fill_brewer(palette = "Paired") + theme_classic() +
  theme(
    axis.text.x = element_text(angle = 90),
    legend.position = "Accent",
    axis.title.x = element_blank()
  ) +
  geom_text(stat = 'count', aes(label = ..count..), vjust = -1) + ylim(0, 5)

plot_q32

```

## Summarise and save data

```{r}


summary = lapply(student_survey_responses[-c(1:5)], table)

save(summary, file = "summary_tally.RData")
```


## Addition after review: table with response rates

```{r}
resp_rates = gt(data.frame("Survey" = c("Programmes", "Students"),
           "Respondents" = c("10", "35"),
           "Year1" = c("", "9"),
           "Year2" = c("", "15"),
           "Year3" = c("", "11")),
   rowname_col = "Survey")

gtsave(resp_rates, "resp_rates.docx")
```