knitr::opts_chunk$set(echo = TRUE)
My Hw4 code lines and content,
Read in/clean the dataset leetcode.csv,
Compute descriptive statistics for each of variable including mean, median, and standard deviation for numerical variables, and frequencies for categorical variables,
Visualize using ggplot2 with both univariate and bivariate; Explain these graphs with variables, questions and conclusion,
Identify limitations of the visualization if any.
# Import readr and assign csv dataset to a variable
library(readr)
library(tidyverse)
library(tidyr)
library(rstudioapi)
library(ggplot2)
library(dplyr)
library(plotrix)
# Getting the path of your current open file automatically
leetcode <- read_csv("leetcode.csv")
#newData <- data.frame(leetcode)
view(leetcode)
# Preview the whole dataset before any operations
dim(leetcode)
[1] 1650 8head(leetcode, n = 1650)
# A tibble: 1,650 × 8
question_id video title link `total Accepted` `total Submitt…`
<dbl> <lgl> <chr> <chr> <dbl> <dbl>
1 1959 NA Minimum … http… 262 434
2 1949 NA Implemen… http… 691 1126
3 1947 NA Number o… http… 1976 7740
4 1946 NA Minimum … http… 5411 13595
5 1945 NA Finding … http… 5890 7460
6 1944 NA Truncate… http… 7056 8979
7 1943 NA Count Pa… http… 584 850
8 1937 NA Maximize… http… 571 819
9 1936 NA Maximize… http… 2958 10959
10 1935 NA Minimum … http… 7134 10125
# … with 1,640 more rows, and 2 more variables: difficulty <dbl>,
# isPaid <lgl> question_id video title link
0 701 0 0
total Accepted total Submitted difficulty isPaid
0 0 0 0 # Replace the NA value in videos as "No solution" since FALSE and TRUE have been written down, rename TRUE as "Video" and FALSE as "Written"
#replace_na(leetcode, list(video = "No solution"))
# Calling str() function to confirm
str(leetcode)
spec_tbl_df [1,650 × 8] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
$ question_id : num [1:1650] 1959 1949 1947 1946 1945 ...
$ video : logi [1:1650] NA NA NA NA NA NA ...
$ title : chr [1:1650] "Minimum Path Cost in a Hidden Grid" "Implement Trie II (Prefix Tree)" "Number of Different Subsequences GCDs" "Minimum Absolute Sum Difference" ...
$ link : chr [1:1650] "https://leetcode.com/problems/minimum-path-cost-in-a-hidden-grid" "https://leetcode.com/problems/implement-trie-ii-prefix-tree" "https://leetcode.com/problems/number-of-different-subsequences-gcds" "https://leetcode.com/problems/minimum-absolute-sum-difference" ...
$ total Accepted : num [1:1650] 262 691 1976 5411 5890 ...
$ total Submitted: num [1:1650] 434 1126 7740 13595 7460 ...
$ difficulty : num [1:1650] 2 2 3 2 2 1 2 3 3 2 ...
$ isPaid : logi [1:1650] TRUE TRUE FALSE FALSE FALSE FALSE ...
- attr(*, "spec")=
.. cols(
.. question_id = col_double(),
.. video = col_logical(),
.. title = col_character(),
.. link = col_character(),
.. `total Accepted` = col_double(),
.. `total Submitted` = col_double(),
.. difficulty = col_double(),
.. isPaid = col_logical()
.. )
- attr(*, "problems")=<externalptr> leetcode <- leetcode %>%
mutate(solution = case_when(
video == TRUE ~ "Video",
video == FALSE ~ "Written",
video == NA ~ "No solution",))
# Define the column rate which could be calculated by division between totalAccepted andd totalSubmitted
acceptedRate <- select(leetcode, `total Accepted`)
totalSubmitted <- select(leetcode, `total Submitted`)
# Add new column named acceptedRate to leetcode dataset
leetcode <- mutate(leetcode, rate = (acceptedRate / totalSubmitted))
#rate <- transform(rate, ar = accepetedRate / totalSubmitted)
# Preview the rate to check whether it is right
head(leetcode, n = 1650)
# A tibble: 1,650 × 10
question_id video title link `total Accepted` `total Submitt…`
<dbl> <lgl> <chr> <chr> <dbl> <dbl>
1 1959 NA Minimum … http… 262 434
2 1949 NA Implemen… http… 691 1126
3 1947 NA Number o… http… 1976 7740
4 1946 NA Minimum … http… 5411 13595
5 1945 NA Finding … http… 5890 7460
6 1944 NA Truncate… http… 7056 8979
7 1943 NA Count Pa… http… 584 850
8 1937 NA Maximize… http… 571 819
9 1936 NA Maximize… http… 2958 10959
10 1935 NA Minimum … http… 7134 10125
# … with 1,640 more rows, and 4 more variables: difficulty <dbl>,
# isPaid <lgl>, solution <chr>, rate <df[,1]># Compute the median for the accepted solutions and submitted solutions
summarize(leetcode, mean.totalAccepted = mean(`total Accepted`, na.rm = TRUE))
# A tibble: 1 × 1
mean.totalAccepted
<dbl>
1 128905.# A tibble: 1 × 1
mean.totalSubmitted
<dbl>
1 280704.# Compute the average for the difficulty, 1.978 which means a bit below medium
summarize(leetcode, mean.difficulty = mean(difficulty, na.rm = TRUE))
# A tibble: 1 × 1
mean.difficulty
<dbl>
1 1.98# Compute the sd for the accepted solutions
summarize(leetcode, sd.totalAccepted = sd(`total Accepted`, na.rm = TRUE))
# A tibble: 1 × 1
sd.totalAccepted
<dbl>
1 233157.# Group by the dataset by the number level of difficulty and calculate the average for submitted count for each difficulty
leetcode %>%
group_by(difficulty) %>%
summarize(mean.totoalSubmittedDifficult=mean(`total Submitted`))
# A tibble: 3 × 2
difficulty mean.totoalSubmittedDifficult
<dbl> <dbl>
1 1 418797.
2 2 269992.
3 3 155624.# Compute freq table with leetcode$difficulty
frequencyDifficulty <- leetcode %>%
select(difficulty) %>%
table()
frequencyDifficulty
difficulty
1 2 3
397 891 362 frequencyDifficulty %>%
prop.table()
difficulty
1 2 3
0.2406061 0.5400000 0.2193939 # Visualize the pie chart graph showing the total submitted attempts over the 3 levels of difficulty
pie(frequencyDifficulty,labels = frequencyDifficulty, edges=10, main = "Total count for each difficulty")
# The graph has variables including frequency for each difficulty level;
# It tries to answer the question "How is the difficulty of the questions correlated with the total submitted attempts?";
# It could not be concluded that the higher the difficulty as the questions become more challenging , the fewer the total submitted attempts will be
# More work is needed to add the mean.totalSubmitted to the graph
# Make the plot leetcode access values from question_id
# Visualize the histogram graph showing the question id as x-axis and the count for each question as y-axis
ggplot(leetcode, aes(`question_id`)) + geom_histogram(binwidth = 5, color="black", fill="white") +
labs(title = "question_id") +
theme_bw()
# The graph has variables including question id as x-axis and the count for each question as y-axis;
# It tries to answer the question "Could the number id of the question affect the submitted attempts for each question?";
# It could be not concluded the question_id might affect the submitted attempts for each question. Additional work is needed
# Replace the numercal value in difficulty, recode leetcode to make a boxplot
#The result should be three separate box plots for different values of year_postgrad
leetcode<-mutate(leetcode, trueDifficulty = recode(difficulty, `1` = "easy", `2` = "medium", `3` = "difficult"))
# Visualize the boxplot graph showing the relationship between question difficulty and the count for total submitted each question as y-axis
ggplot(leetcode, aes(`trueDifficulty`, `total Submitted`)) + geom_boxplot()
# The graph has variables including question difficulty and the count for total submitted;
# It tries to answer the question "How is the difficulty of the questions correlated with the total submitted attempts";
# It could not be concluded that the higher the difficulty as the questions become more challenging , the fewer the total submitted attempts will be
Identify the variables in the dataset and the dataset
# Preview and get the column names of the dataset
head(leetcode)
# A tibble: 6 × 11
question_id video title link `total Accepted` `total Submitt…`
<dbl> <lgl> <chr> <chr> <dbl> <dbl>
1 1959 NA Minimum P… http… 262 434
2 1949 NA Implement… http… 691 1126
3 1947 NA Number of… http… 1976 7740
4 1946 NA Minimum A… http… 5411 13595
5 1945 NA Finding t… http… 5890 7460
6 1944 NA Truncate … http… 7056 8979
# … with 5 more variables: difficulty <dbl>, isPaid <lgl>,
# solution <chr>, rate <df[,1]>, trueDifficulty <chr>colnames(leetcode)
[1] "question_id" "video" "title"
[4] "link" "total Accepted" "total Submitted"
[7] "difficulty" "isPaid" "solution"
[10] "rate" "trueDifficulty" As you may see with colnames(), we have list all variables
Varible type classification:
String/char type
title definition: the leetcode question description titles on the official website
link definition: offcial link to the specific question
trueDifficulty definition: here as you may know, we tranform 1 to easy difficulty, 2 to medium difficulty, 3 to hard difficulty. Usuallly, higher the difficulty, the more likely the user might spend time
Numeric type
question_id definition: the number which corresponds to each question total Accepted definition: number of submitted solutions which can compile and run well
total Submitted definition: number of solutions/coding attempt submitted to the website, could be wrong
difficulty definition: in general, this represents the learning curve and how challenging a typical question could be
rate definition: we use the division between totalAccepted andd totalSubmitted to get the percentage which the submitted solution have been approved by the server
Logical type
video definition: TRUE means the solution has video format, FALSE means written, NA means none
isPaid definition: TRUE means the question requires premium membership, FALSE means not requiring
Research questions
The questions are identified as below so far with this dataset:
-How is the difficulty of the questions correlated with the total submitted attempts?
-How is the acceptance rate which we have added as a new column correlate to the difficulty of the questions?
-Could the number id of the question affect the submitted attempts for each question?
-How is the video/written solution correlate to the submitted attempts and acceptance rate?
-Are the paid questions guaranteed to provide at least one form of written solution?
Hopefully, I could answer the above questions soon
#Use filter() to list all the questions without solutions
solvedQuestions <- filter(leetcode, is.na(`video`))
head(solvedQuestions)
# A tibble: 6 × 11
question_id video title link `total Accepted` `total Submitt…`
<dbl> <lgl> <chr> <chr> <dbl> <dbl>
1 1959 NA Minimum P… http… 262 434
2 1949 NA Implement… http… 691 1126
3 1947 NA Number of… http… 1976 7740
4 1946 NA Minimum A… http… 5411 13595
5 1945 NA Finding t… http… 5890 7460
6 1944 NA Truncate … http… 7056 8979
# … with 5 more variables: difficulty <dbl>, isPaid <lgl>,
# solution <chr>, rate <df[,1]>, trueDifficulty <chr>#descend to find the questions with highest accepted rate
rateDescending <- arrange(leetcode, desc(`rate`))
head(select(leetcode, `rate`))
# A tibble: 6 × 1
rate$`total Accepted`
<dbl>
1 0.604
2 0.614
3 0.255
4 0.398
5 0.790
6 0.786#Hence we know could rank the questions in another order