Please check homework3. This analysis is based on the dataset contains leetcode.csv. I found the dataset to be intuitive due to the fact that practing leetcode questions would normally help with the interview process in the future.
knitr::opts_chunk$set(echo = TRUE)
# Import readr and assign csv dataset to a variable
library(readr)
library(tidyverse)
library(tidyr)
library(rstudioapi)
leetcode <- read_csv("C:\\Umass\\leetcode.csv")
#newData <- data.frame(leetcode)
view(leetcode)
# Getting the path of your current open file
#setwd(dirname(rstudioapi::getActiveDocumentContext()$path))
# 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",))
# Replace the numercal value in difficulty
leetcode<-mutate(leetcode, trueDifficulty = recode(difficulty, `1` = "easy", `2` = "medium", `3` = "difficult"))
# 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 × 11
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 5 more variables: difficulty <dbl>,
# isPaid <lgl>, solution <chr>, trueDifficulty <chr>, rate <df[,1]># 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>, trueDifficulty <chr>, rate <df[,1]>colnames(leetcode)
[1] "question_id" "video" "title"
[4] "link" "total Accepted" "total Submitted"
[7] "difficulty" "isPaid" "solution"
[10] "trueDifficulty" "rate" 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
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
library(dplyr)
#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>, trueDifficulty <chr>, rate <df[,1]>#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