For Q35 and Q36, survey respondents were allowed to choose more than one response. Therefore, it is challenging to analyze the data as it is. Therefore, I created a new object for each question in order to make it easier to understand the survey responses. Since this is not within the scope of what you learned, feel free to include the following in your presentation.

Import data

# Load dplyr package
library(dplyr) #for use of dplyr functions such as glimpse(), mutate(), and filter()
library(ggplot2) #for use of ggplot2 functions such ggplot()

# Import data
hunger <- read.csv("hunger.csv")

Create a new object for Q35

items <- c("Amount of food", "Hours", "Location", "Staff", "Quality of food",
                 "Nutrition", "Other")
freq1 <- c(14, 10,  16, 18, 10, 5,  2)

items_liked <- data.frame(items, freq1)
items_liked
##             items freq1
## 1  Amount of food    14
## 2           Hours    10
## 3        Location    16
## 4           Staff    18
## 5 Quality of food    10
## 6       Nutrition     5
## 7           Other     2

# Relevel the items_liked by freq1
items_liked$items <-
  factor(items_liked$items, levels=items_liked[order(desc(items_liked$freq1)), "items"])


items_liked %>%
  arrange(freq1) %>%
  ggplot(aes(x = items, y = freq1)) +
  geom_col()

Create a new object for Q36

freq2 <- c(5,   7,  5,  4,  13, 14, 5)

items_tobeImproved <- data.frame(items, freq2)
items_tobeImproved
##             items freq2
## 1  Amount of food     5
## 2           Hours     7
## 3        Location     5
## 4           Staff     4
## 5 Quality of food    13
## 6       Nutrition    14
## 7           Other     5

# Relevel the items_tobeImproved by freq2
items_tobeImproved$items <-
  factor(items_tobeImproved$items, levels=items_tobeImproved[order(desc(items_tobeImproved$freq2)), "items"])


items_tobeImproved %>%
  arrange(freq2) %>%
  ggplot(aes(x = items, y = freq2)) +
  geom_col()