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library(tidyverse)
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library(lubridate)
library(nycflights13)
library(tidyquant)
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library(tidyr)
library(forcats)
data <- read.csv("multipleChoiceResponses1.csv")
# Select relevant columns and perform the required operations
result <- data %>%
select(starts_with("LearningPlatformUsefulness")) %>%
gather(key = "learning_platform", value = "usefulness", na.rm = TRUE) %>%
mutate(learning_platform = gsub('LearningPlatformUsefulness', '', learning_platform)) %>%
count(learning_platform, usefulness)
print(result)
## learning_platform usefulness n
## 1 Arxiv Not Useful 37
## 2 Arxiv Somewhat useful 1038
## 3 Arxiv Very useful 1316
## 4 Blogs Not Useful 45
## 5 Blogs Somewhat useful 2406
## 6 Blogs Very useful 2314
## 7 College Not Useful 101
## 8 College Somewhat useful 1405
## 9 College Very useful 1853
## 10 Communities Not Useful 16
## 11 Communities Somewhat useful 567
## 12 Communities Very useful 559
## 13 Company Not Useful 41
## 14 Company Somewhat useful 502
## 15 Company Very useful 438
## 16 Conferences Not Useful 119
## 17 Conferences Somewhat useful 1305
## 18 Conferences Very useful 758
## 19 Courses Not Useful 47
## 20 Courses Somewhat useful 1750
## 21 Courses Very useful 4195
## 22 Documentation Not Useful 42
## 23 Documentation Somewhat useful 1067
## 24 Documentation Very useful 1212
## 25 Friends Not Useful 51
## 26 Friends Somewhat useful 774
## 27 Friends Very useful 756
## 28 Kaggle Not Useful 56
## 29 Kaggle Somewhat useful 2451
## 30 Kaggle Very useful 4076
## 31 Newsletters Not Useful 56
## 32 Newsletters Somewhat useful 686
## 33 Newsletters Very useful 347
## 34 Podcasts Not Useful 124
## 35 Podcasts Somewhat useful 818
## 36 Podcasts Very useful 272
## 37 Projects Not Useful 39
## 38 Projects Somewhat useful 1185
## 39 Projects Very useful 3570
## 40 SO Not Useful 64
## 41 SO Somewhat useful 2044
## 42 SO Very useful 3532
## 43 Textbook Not Useful 69
## 44 Textbook Somewhat useful 1796
## 45 Textbook Very useful 2316
## 46 TradeBook Not Useful 9
## 47 TradeBook Somewhat useful 162
## 48 TradeBook Very useful 162
## 49 Tutoring Not Useful 32
## 50 Tutoring Somewhat useful 569
## 51 Tutoring Very useful 825
## 52 YouTube Not Useful 104
## 53 YouTube Somewhat useful 2605
## 54 YouTube Very useful 2520
result <- data %>%
gather(key = "learning_platform", value = "usefulness", na.rm = TRUE) %>%
mutate(learning_platform = gsub('LearningPlatformUsefulness', '', learning_platform),
perc_usefulness = as.numeric(usefulness == "Very useful" | usefulness == "Somewhat useful")) %>%
group_by(learning_platform) %>%
summarise(count = n(),
tot = sum(perc_usefulness),
perc_usefulness = mean(perc_usefulness))
print(result)
## # A tibble: 47 × 4
## learning_platform count tot perc_usefulness
## <chr> <int> <dbl> <dbl>
## 1 Age 16385 0 0
## 2 Arxiv 2391 2354 0.985
## 3 Blogs 4765 4720 0.991
## 4 College 3359 3258 0.970
## 5 Communities 1142 1126 0.986
## 6 Company 981 940 0.958
## 7 Conferences 2182 2063 0.945
## 8 Courses 5992 5945 0.992
## 9 CurrentJobTitleSelect 11830 0 0
## 10 DataScienceIdentitySelect 12671 0 0
## # ℹ 37 more rows
learning_data <- data.frame(
learning_platform = c("Kaggle", "Courses", "SO", "YouTube", "Projects",
"Blogs", "Textbook", "College", "Arxiv", "Documentation"),
count1 = c(6527, 5945, 5576, 5125, 4755, 4720, 4112, 3258, 2354, 2279)
)
# Calculate cumulative percentage
learning_data <- learning_data %>%
mutate(cum_pct = cumsum(count1) / sum(count1))
# Reorder levels based on cumulative percentage
learning_data <- learning_data %>%
mutate(learning_platform = fct_reorder(learning_platform, cum_pct))
# Set levels for 'Other' category
learning_data <- learning_data %>%
mutate(learning_platform = fct_relevel(learning_platform, "Other", after = Inf))
## Warning: There was 1 warning in `mutate()`.
## ℹ In argument: `learning_platform = fct_relevel(learning_platform, "Other",
## after = Inf)`.
## Caused by warning:
## ! 1 unknown level in `f`: Other
# Print the result
print(learning_data)
## learning_platform count1 cum_pct
## 1 Kaggle 6527 0.1461781
## 2 Courses 5945 0.2793219
## 3 SO 5576 0.4042015
## 4 YouTube 5125 0.5189805
## 5 Projects 4755 0.6254731
## 6 Blogs 4720 0.7311818
## 7 Textbook 4112 0.8232738
## 8 College 3258 0.8962397
## 9 Arxiv 2354 0.9489597
## 10 Documentation 2279 1.0000000
result <- result %>%
mutate(learning_platform = fct_reorder(learning_platform, perc_usefulness))
# Convert 'tot' to numeric for continuous fill scale
result$tot <- as.numeric(result$tot)
# Create the plot
ggplot(result, aes(x = learning_platform, y = perc_usefulness, fill = tot)) +
geom_bar(stat = "identity", position = "stack") +
scale_fill_gradient(low = "pink", high = "darkred") +
coord_flip() +
labs(title = "Percentage of Useful Responses by Learning Platform",
x = "Learning Platform",
y = "Percentage of Usefulness") +
theme_minimal()
