Unit 2: Rtutorial

Author

Alfred Machingambi_

Week 6: Working with Data

In the previous R tutorial, we started to work on science classroom dataset. We applied the data intensive research steps to explore our data and investigate the relationship between students’ grades and time-spent.

Let’s remember which libraries and functions we used!

Your Turn:

Please write down one or two sentence to explain why and how we use the following libraries and functions.

  1. tidyverse: Theses are R packages which assists to manipulate and analyze data efficiently. It is utilized to transform, clean and visualize data.

  2. skimr: Is a package we use to summarize data sets quickly with detailed descriptive statistics. It assists us to explore learning data sets by providing insights into variable types, missing values, and distributions.

  3. ggplot: A data visualization package and part of tidyverse used to create clear and customization of graphs. It helps us to visualize trends such as grades distribution of engagement patterns.

  4. read_csv(): Used to import CSV files into RStudio as data frames. It allows us to load data for example on student records, assessment data, or LMS exports of data for analysis.

  5. view(): Is for opening spreadsheets-style for easy visualizing of the data set inside RStudio. It assists with inspection of data to be presented in a readable table format.

  6. glimpse(): For the provision of quick overview of data set’s structure, showing column names, types, and sample of values. Useful for understanding the format of learning analytics data.

  7. head(): Displays the first six rows of a data set as a quick preview of data after importing it.

  8. tail(): Displays the last six rows of a data set for checking the end of large learning data sets.

  9. select(): Used to choose specific columns from a dataset. In learning analytics, it allows us to focus on variables like grades, attendance, or quiz scores.

  10. filter(): Used to extract rows that meet certain conditions. For example, filtering students who scored below 50% for a specific course.

  11. arrange(): Sorts the rows of a data set in ascending order. It helps rank students by scores or attendance.

  12. desc(): Used inside arrange() to sort data in descending order. For example, ranking students from highest to lowest grade.

  13. geom_histogram(): Used in ggplot to create a histogram that shows the distribution of a numeric variable. In learning analytics, it helps visualize grade distributions or time spent on tasks.

  14. geom_point(): Used in ggplot to create scatter plots. It helps analyze relationships, such as time spent studying versus exam scores.

Load the Tidyverse Package

Let’s start our R code along by loading the tidyverse package.

library(tidyverse)
── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
✔ dplyr     1.1.4     ✔ readr     2.1.6
✔ forcats   1.0.1     ✔ stringr   1.6.0
✔ ggplot2   4.0.2     ✔ tibble    3.3.1
✔ lubridate 1.9.5     ✔ tidyr     1.3.2
✔ purrr     1.2.1     
── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag()    masks stats::lag()
ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors

Load the online science class data

Now, load the online science class data from the data folder and assign your data to a new object.

library(readr)
sci_online<- read_csv("Data/sci-online-classes.csv")
Rows: 603 Columns: 30
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr  (6): course_id, subject, semester, section, Gradebook_Item, Gender
dbl (23): student_id, total_points_possible, total_points_earned, percentage...
lgl  (1): Grade_Category

ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

You loaded the data, now what should we do?

view(sci_online)

Your Turn:

Examine the contents of sci_classes in your console. You should type the object name to the console and check that.

Question: Is your object a tibble? How do you know?

Your response here: Yes, it is a tibble but not for the header of the columns

I know it as the column has the <dbl> naming convention as reflected in the console.**

Hint:** Check the output in the console.

Check your data with different functions

You can check your data with different functions. Let’s remember how we use different functions to check our data.

glimpse(sci_online)
Rows: 603
Columns: 30
$ student_id            <dbl> 43146, 44638, 47448, 47979, 48797, 51943, 52326,…
$ course_id             <chr> "FrScA-S216-02", "OcnA-S116-01", "FrScA-S216-01"…
$ total_points_possible <dbl> 3280, 3531, 2870, 4562, 2207, 4208, 4325, 2086, …
$ total_points_earned   <dbl> 2220, 2672, 1897, 3090, 1910, 3596, 2255, 1719, …
$ percentage_earned     <dbl> 0.6768293, 0.7567261, 0.6609756, 0.6773345, 0.86…
$ subject               <chr> "FrScA", "OcnA", "FrScA", "OcnA", "PhysA", "FrSc…
$ semester              <chr> "S216", "S116", "S216", "S216", "S116", "S216", …
$ section               <chr> "02", "01", "01", "01", "01", "03", "01", "01", …
$ Gradebook_Item        <chr> "POINTS EARNED & TOTAL COURSE POINTS", "ATTEMPTE…
$ Grade_Category        <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
$ FinalGradeCEMS        <dbl> 93.45372, 81.70184, 88.48758, 81.85260, 84.00000…
$ Points_Possible       <dbl> 5, 10, 10, 5, 438, 5, 10, 10, 443, 5, 12, 10, 5,…
$ Points_Earned         <dbl> NA, 10.00, NA, 4.00, 399.00, NA, NA, 10.00, 425.…
$ Gender                <chr> "M", "F", "M", "M", "F", "F", "M", "F", "F", "M"…
$ q1                    <dbl> 5, 4, 5, 5, 4, NA, 5, 3, 4, NA, NA, 4, 3, 5, NA,…
$ q2                    <dbl> 4, 4, 4, 5, 3, NA, 5, 3, 3, NA, NA, 5, 3, 3, NA,…
$ q3                    <dbl> 4, 3, 4, 3, 3, NA, 3, 3, 3, NA, NA, 3, 3, 5, NA,…
$ q4                    <dbl> 5, 4, 5, 5, 4, NA, 5, 3, 4, NA, NA, 5, 3, 5, NA,…
$ q5                    <dbl> 5, 4, 5, 5, 4, NA, 5, 3, 4, NA, NA, 5, 4, 5, NA,…
$ q6                    <dbl> 5, 4, 4, 5, 4, NA, 5, 4, 3, NA, NA, 5, 3, 5, NA,…
$ q7                    <dbl> 5, 4, 4, 4, 4, NA, 4, 3, 3, NA, NA, 5, 3, 5, NA,…
$ q8                    <dbl> 5, 5, 5, 5, 4, NA, 5, 3, 4, NA, NA, 4, 3, 5, NA,…
$ q9                    <dbl> 4, 4, 3, 5, NA, NA, 5, 3, 2, NA, NA, 5, 2, 2, NA…
$ q10                   <dbl> 5, 4, 5, 5, 3, NA, 5, 3, 5, NA, NA, 4, 4, 5, NA,…
$ TimeSpent             <dbl> 1555.1667, 1382.7001, 860.4335, 1598.6166, 1481.…
$ TimeSpent_hours       <dbl> 25.91944500, 23.04500167, 14.34055833, 26.643610…
$ TimeSpent_std         <dbl> -0.18051496, -0.30780313, -0.69325954, -0.148446…
$ int                   <dbl> 5.0, 4.2, 5.0, 5.0, 3.8, 4.6, 5.0, 3.0, 4.2, NA,…
$ pc                    <dbl> 4.50, 3.50, 4.00, 3.50, 3.50, 4.00, 3.50, 3.00, …
$ uv                    <dbl> 4.333333, 4.000000, 3.666667, 5.000000, 3.500000…

Isolating Data with dplyr

We will use select() function to select the following columns from our data.

  • student_id

  • subject

  • semester

  • FinalGradeCEMS

  • After selecting these columns, assign that to a new object with a name of “student_grade”.

student_grade <-sci_online |> 
  select(student_id, subject, semester, FinalGradeCEMS )
student_grade
# A tibble: 603 × 4
   student_id subject semester FinalGradeCEMS
        <dbl> <chr>   <chr>             <dbl>
 1      43146 FrScA   S216               93.5
 2      44638 OcnA    S116               81.7
 3      47448 FrScA   S216               88.5
 4      47979 OcnA    S216               81.9
 5      48797 PhysA   S116               84  
 6      51943 FrScA   S216               NA  
 7      52326 AnPhA   S216               83.6
 8      52446 PhysA   S116               97.8
 9      53447 FrScA   S116               96.1
10      53475 FrScA   S116               NA  
# ℹ 593 more rows

Your Turn:

Examine students’ grades, what did you realize about it?

Your response here: There are 2 missing final grades and majority of the students scored 70-95%

Hint: Check the missing data.

Specific select

Now, we will make a specific selection.

  • Select all columns except subject and semester.

  • Assign to a new object with a different name.

  • Examine your data frame.

sci_dataframe<- select(sci_online, -c(subject, semester))
sci_dataframe
# A tibble: 603 × 28
   student_id course_id     total_points_possible total_points_earned
        <dbl> <chr>                         <dbl>               <dbl>
 1      43146 FrScA-S216-02                  3280                2220
 2      44638 OcnA-S116-01                   3531                2672
 3      47448 FrScA-S216-01                  2870                1897
 4      47979 OcnA-S216-01                   4562                3090
 5      48797 PhysA-S116-01                  2207                1910
 6      51943 FrScA-S216-03                  4208                3596
 7      52326 AnPhA-S216-01                  4325                2255
 8      52446 PhysA-S116-01                  2086                1719
 9      53447 FrScA-S116-01                  4655                3149
10      53475 FrScA-S116-02                  1710                1402
# ℹ 593 more rows
# ℹ 24 more variables: percentage_earned <dbl>, section <chr>,
#   Gradebook_Item <chr>, Grade_Category <lgl>, FinalGradeCEMS <dbl>,
#   Points_Possible <dbl>, Points_Earned <dbl>, Gender <chr>, q1 <dbl>,
#   q2 <dbl>, q3 <dbl>, q4 <dbl>, q5 <dbl>, q6 <dbl>, q7 <dbl>, q8 <dbl>,
#   q9 <dbl>, q10 <dbl>, TimeSpent <dbl>, TimeSpent_hours <dbl>,
#   TimeSpent_std <dbl>, int <dbl>, pc <dbl>, uv <dbl>

Checking the data frame:

Your Turn:

  • Select all columns except student_id and FinalGradeCEMS.

  • Assign to a new object with a different name.

  • Examine your data frame.

library(dplyr)
sci_newdata <- sci_online |>
  select(-c(student_id, FinalGradeCEMS))

Specific select

  • Select only the columns that start with Time

  • Assign to a new object with a different name.

  • Use view() function to examine your data frame.

time_object<-select(sci_online,starts_with("Time"))
view(time_object)

Your Turn:

  • Select only the columns that ends with “r”

  • Assign to a new object with a different name.

  • Use view() function to examine your data frame.

sci_r_columns <- sci_online |>
  select(ends_with("r"))

View(sci_r_columns)

Filter Function

  • Filter the sci_classes data frame for just males.

  • Assign to a new object.

  • Use the head() function to examine your data frame.

male_students<-sci_online |> 
  filter(Gender=="M")
head(male_students)
# A tibble: 6 × 30
  student_id course_id     total_points_possible total_points_earned
       <dbl> <chr>                         <dbl>               <dbl>
1      43146 FrScA-S216-02                  3280                2220
2      47448 FrScA-S216-01                  2870                1897
3      47979 OcnA-S216-01                   4562                3090
4      52326 AnPhA-S216-01                  4325                2255
5      53475 FrScA-S116-02                  1710                1402
6      53475 FrScA-S216-01                  1209                 977
# ℹ 26 more variables: percentage_earned <dbl>, subject <chr>, semester <chr>,
#   section <chr>, Gradebook_Item <chr>, Grade_Category <lgl>,
#   FinalGradeCEMS <dbl>, Points_Possible <dbl>, Points_Earned <dbl>,
#   Gender <chr>, q1 <dbl>, q2 <dbl>, q3 <dbl>, q4 <dbl>, q5 <dbl>, q6 <dbl>,
#   q7 <dbl>, q8 <dbl>, q9 <dbl>, q10 <dbl>, TimeSpent <dbl>,
#   TimeSpent_hours <dbl>, TimeSpent_std <dbl>, int <dbl>, pc <dbl>, uv <dbl>

Your Turn:

  • Filter the sci_classes data frame for just females.

  • Assign to a new object.

  • Use the tail() function to examine your data frame.

female_students<-sci_online |> 
  filter(Gender=="F")
head(female_students)
# A tibble: 6 × 30
  student_id course_id     total_points_possible total_points_earned
       <dbl> <chr>                         <dbl>               <dbl>
1      44638 OcnA-S116-01                   3531                2672
2      48797 PhysA-S116-01                  2207                1910
3      51943 FrScA-S216-03                  4208                3596
4      52446 PhysA-S116-01                  2086                1719
5      53447 FrScA-S116-01                  4655                3149
6      54282 OcnA-S116-02                   3581                2777
# ℹ 26 more variables: percentage_earned <dbl>, subject <chr>, semester <chr>,
#   section <chr>, Gradebook_Item <chr>, Grade_Category <lgl>,
#   FinalGradeCEMS <dbl>, Points_Possible <dbl>, Points_Earned <dbl>,
#   Gender <chr>, q1 <dbl>, q2 <dbl>, q3 <dbl>, q4 <dbl>, q5 <dbl>, q6 <dbl>,
#   q7 <dbl>, q8 <dbl>, q9 <dbl>, q10 <dbl>, TimeSpent <dbl>,
#   TimeSpent_hours <dbl>, TimeSpent_std <dbl>, int <dbl>, pc <dbl>, uv <dbl>

Let’s try filter function with two arguments now.

  • Filter the sci_classes data frame for students whose

  • percentage_earned is greater than 0.8

  • in class “BioA”

  • Assign to a new object.

  • Use the tail() function to examine your data frame. 

bio_students<-filter(sci_online, percentage_earned >0.8 & subject=="BioA")
tail(bio_students)
# A tibble: 6 × 30
  student_id course_id    total_points_possible total_points_earned
       <dbl> <chr>                        <dbl>               <dbl>
1      91066 BioA-S116-01                  5766                4820
2      91067 BioA-S116-01                  2672                2249
3      92633 BioA-S116-01                  2954                2495
4      95658 BioA-S216-01                  3362                2775
5      96950 BioA-S216-01                  6190                4970
6      97374 BioA-S216-01                  8586                6978
# ℹ 26 more variables: percentage_earned <dbl>, subject <chr>, semester <chr>,
#   section <chr>, Gradebook_Item <chr>, Grade_Category <lgl>,
#   FinalGradeCEMS <dbl>, Points_Possible <dbl>, Points_Earned <dbl>,
#   Gender <chr>, q1 <dbl>, q2 <dbl>, q3 <dbl>, q4 <dbl>, q5 <dbl>, q6 <dbl>,
#   q7 <dbl>, q8 <dbl>, q9 <dbl>, q10 <dbl>, TimeSpent <dbl>,
#   TimeSpent_hours <dbl>, TimeSpent_std <dbl>, int <dbl>, pc <dbl>, uv <dbl>

Your Turn:

Filter the sci_classes data frame for students whose

  • percentage_earned is smaller or equal to 0.6

  • Assign to a new object.

  • Use the head() function to examine your data frame. 

low_percentage <- sci_online |>
  filter(percentage_earned <= 0.6)

head(low_percentage)
# A tibble: 6 × 30
  student_id course_id     total_points_possible total_points_earned
       <dbl> <chr>                         <dbl>               <dbl>
1      52326 AnPhA-S216-01                  4325                2255
2      65116 FrScA-S216-01                  4320                2326
3      77067 FrScA-S216-02                  3272                1896
4      78153 PhysA-S216-01                  6530                3702
5      84749 AnPhA-T116-01                  4543                2646
6      84794 BioA-T116-01                   2787                1439
# ℹ 26 more variables: percentage_earned <dbl>, subject <chr>, semester <chr>,
#   section <chr>, Gradebook_Item <chr>, Grade_Category <lgl>,
#   FinalGradeCEMS <dbl>, Points_Possible <dbl>, Points_Earned <dbl>,
#   Gender <chr>, q1 <dbl>, q2 <dbl>, q3 <dbl>, q4 <dbl>, q5 <dbl>, q6 <dbl>,
#   q7 <dbl>, q8 <dbl>, q9 <dbl>, q10 <dbl>, TimeSpent <dbl>,
#   TimeSpent_hours <dbl>, TimeSpent_std <dbl>, int <dbl>, pc <dbl>, uv <dbl>

Let’s use filter () function for the missing data.

  • Filter the sci_classes data frame so rows with 

  • NA for points earned are removed.

  • Assign to a new object.

  • Use the glimpse() function to examine your data frame.

clean_data<-sci_online|> 
  filter(!is.na(Points_Earned))
glimpse(clean_data)
Rows: 511
Columns: 30
$ student_id            <dbl> 44638, 47979, 48797, 52446, 53447, 53475, 53475,…
$ course_id             <chr> "OcnA-S116-01", "OcnA-S216-01", "PhysA-S116-01",…
$ total_points_possible <dbl> 3531, 4562, 2207, 2086, 4655, 1710, 1209, 4641, …
$ total_points_earned   <dbl> 2672, 3090, 1910, 1719, 3149, 1402, 977, 3429, 2…
$ percentage_earned     <dbl> 0.7567261, 0.6773345, 0.8654282, 0.8240652, 0.67…
$ subject               <chr> "OcnA", "OcnA", "PhysA", "PhysA", "FrScA", "FrSc…
$ semester              <chr> "S116", "S216", "S116", "S116", "S116", "S116", …
$ section               <chr> "01", "01", "01", "01", "01", "02", "01", "01", …
$ Gradebook_Item        <chr> "ATTEMPTED", "POINTS EARNED & TOTAL COURSE POINT…
$ Grade_Category        <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
$ FinalGradeCEMS        <dbl> 81.70184, 81.85260, 84.00000, 97.77778, 96.11872…
$ Points_Possible       <dbl> 10, 5, 438, 10, 443, 5, 12, 10, 5, 10, 220, 30, …
$ Points_Earned         <dbl> 10.00, 4.00, 399.00, 10.00, 425.00, 2.50, 12.00,…
$ Gender                <chr> "F", "M", "F", "F", "F", "M", "M", "M", "F", "F"…
$ q1                    <dbl> 4, 5, 4, 3, 4, NA, NA, 4, 3, 5, NA, 4, 4, NA, 4,…
$ q2                    <dbl> 4, 5, 3, 3, 3, NA, NA, 5, 3, 3, NA, 2, 4, NA, 3,…
$ q3                    <dbl> 3, 3, 3, 3, 3, NA, NA, 3, 3, 5, NA, 2, 3, NA, 3,…
$ q4                    <dbl> 4, 5, 4, 3, 4, NA, NA, 5, 3, 5, NA, 4, 5, NA, 4,…
$ q5                    <dbl> 4, 5, 4, 3, 4, NA, NA, 5, 4, 5, NA, 4, 4, NA, 4,…
$ q6                    <dbl> 4, 5, 4, 4, 3, NA, NA, 5, 3, 5, NA, 4, 4, NA, 3,…
$ q7                    <dbl> 4, 4, 4, 3, 3, NA, NA, 5, 3, 5, NA, 4, 5, NA, 3,…
$ q8                    <dbl> 5, 5, 4, 3, 4, NA, NA, 4, 3, 5, NA, 4, 4, NA, 4,…
$ q9                    <dbl> 4, 5, NA, 3, 2, NA, NA, 5, 2, 2, NA, 2, 4, NA, 2…
$ q10                   <dbl> 4, 5, 3, 3, 5, NA, NA, 4, 4, 5, NA, 4, 4, NA, 3,…
$ TimeSpent             <dbl> 1382.7001, 1598.6166, 1481.8000, 1390.2167, 1479…
$ TimeSpent_hours       <dbl> 23.04500167, 26.64361000, 24.69666667, 23.170278…
$ TimeSpent_std         <dbl> -0.30780313, -0.14844697, -0.23466291, -0.302255…
$ int                   <dbl> 4.2, 5.0, 3.8, 3.0, 4.2, NA, NA, 4.4, 3.4, 4.7, …
$ pc                    <dbl> 3.50, 3.50, 3.50, 3.00, 3.00, NA, NA, 4.00, 3.00…
$ uv                    <dbl> 4.000000, 5.000000, 3.500000, 3.333333, 2.666667…

Filter the sci_classes data for the following subjects:

  • BioA

  • PhysA

  • OcnA

  • Assign to a new object with a different name.

  • Use the summary() function to examine your data frame.

my_classes<-filter(sci_online, subject %in% c("BioA", "PhysA", "OnsA"))

Arrange () Function

Let’s recall how we were using the arrange () function for our dataset.

  • Arrange sci_classes by subject subject then 

  • percentage_earned in descending order.

  • Assign to a new object.

  • Use the str() function to examine your data frame.

order_classses<-sci_online |> 
  arrange(subject, desc(percentage_earned))

str(order_classses)
spc_tbl_ [603 × 30] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
 $ student_id           : num [1:603] 70192 86488 96690 91175 86267 ...
 $ course_id            : chr [1:603] "AnPhA-S116-02" "AnPhA-S116-01" "AnPhA-S216-01" "AnPhA-S116-02" ...
 $ total_points_possible: num [1:603] 1936 3342 4804 3199 3045 ...
 $ total_points_earned  : num [1:603] 1763 3033 4309 2867 2705 ...
 $ percentage_earned    : num [1:603] 0.911 0.908 0.897 0.896 0.888 ...
 $ subject              : chr [1:603] "AnPhA" "AnPhA" "AnPhA" "AnPhA" ...
 $ semester             : chr [1:603] "S116" "S116" "S216" "S116" ...
 $ section              : chr [1:603] "02" "01" "01" "02" ...
 $ Gradebook_Item       : chr [1:603] "POINTS EARNED & TOTAL COURSE POINTS" "POINTS EARNED & TOTAL COURSE POINTS" "POINTS EARNED & TOTAL COURSE POINTS" "POINTS EARNED & TOTAL COURSE POINTS" ...
 $ Grade_Category       : logi [1:603] NA NA NA NA NA NA ...
 $ FinalGradeCEMS       : num [1:603] 96 87.4 64.8 82.2 35.1 ...
 $ Points_Possible      : num [1:603] 10 28 10 5 50 15 10 10 353 460 ...
 $ Points_Earned        : num [1:603] 7 26 3 5 50 11 8 10 330 452 ...
 $ Gender               : chr [1:603] "F" "M" "F" "F" ...
 $ q1                   : num [1:603] 4 4 4 5 5 4 5 4 NA NA ...
 $ q2                   : num [1:603] 3 4 3 3 5 2 4 4 NA NA ...
 $ q3                   : num [1:603] 3 2 2 3 3 3 4 3 NA NA ...
 $ q4                   : num [1:603] 4 3 5 5 5 4 5 4 NA NA ...
 $ q5                   : num [1:603] 4 3 4 5 5 4 5 4 NA NA ...
 $ q6                   : num [1:603] 3 3 4 4 5 3 5 4 NA NA ...
 $ q7                   : num [1:603] 3 3 3 3 4 4 5 4 NA NA ...
 $ q8                   : num [1:603] 5 2 4 5 5 4 4 4 NA NA ...
 $ q9                   : num [1:603] 2 3 3 3 5 1 4 4 NA NA ...
 $ q10                  : num [1:603] 5 3 2 5 5 2 5 4 NA NA ...
 $ TimeSpent            : num [1:603] 1537 3600 1970 1315 406 ...
 $ TimeSpent_hours      : num [1:603] 25.62 60 32.83 21.92 6.77 ...
 $ TimeSpent_std        : num [1:603] -0.194 1.328 0.125 -0.358 -1.029 ...
 $ int                  : num [1:603] 4.4 3 3.8 5 5 3.9 4.6 4 4.8 4.6 ...
 $ pc                   : num [1:603] 3 2.5 2.5 3 3.5 3.5 3.75 3.5 3.5 4.5 ...
 $ uv                   : num [1:603] 2.67 3.33 3.33 3.33 5 ...
 - attr(*, "spec")=
  .. cols(
  ..   student_id = col_double(),
  ..   course_id = col_character(),
  ..   total_points_possible = col_double(),
  ..   total_points_earned = col_double(),
  ..   percentage_earned = col_double(),
  ..   subject = col_character(),
  ..   semester = col_character(),
  ..   section = col_character(),
  ..   Gradebook_Item = col_character(),
  ..   Grade_Category = col_logical(),
  ..   FinalGradeCEMS = col_double(),
  ..   Points_Possible = col_double(),
  ..   Points_Earned = col_double(),
  ..   Gender = col_character(),
  ..   q1 = col_double(),
  ..   q2 = col_double(),
  ..   q3 = col_double(),
  ..   q4 = col_double(),
  ..   q5 = col_double(),
  ..   q6 = col_double(),
  ..   q7 = col_double(),
  ..   q8 = col_double(),
  ..   q9 = col_double(),
  ..   q10 = col_double(),
  ..   TimeSpent = col_double(),
  ..   TimeSpent_hours = col_double(),
  ..   TimeSpent_std = col_double(),
  ..   int = col_double(),
  ..   pc = col_double(),
  ..   uv = col_double()
  .. )
 - attr(*, "problems")=<externalptr>

%>% Pipe Operator

Using sci_classes data and the %>% pipe operator:

  • Select subject, section, time spent in hours and final course grade.

  • Filter for students in OcnA courses with grades greater than or equal to 60.

  • Arrange grades by section in descending order.

  • Assign to a new object.

Examine the contents using a method of your choosing.

specific_course<-sci_online %>%
  select(subject, section, TimeSpent_hours, FinalGradeCEMS) %>%
  filter(subject=="OcnA" & FinalGradeCEMS >=60) %>%
  arrange(desc(section))

Deriving info with dplyr

We will practice summarise () and group_by () functions now.

Summarise () Function

Using sci_classes data and the summarise() function:

  • Get a distinct count of course ids.

  • Use the %>% operator

sci_online %>%
  summarise(course<-n_distinct(course_id))
# A tibble: 1 × 1
  `course <- n_distinct(course_id)`
                              <int>
1                                26

  • Get a distinct count of course ids.

  • Use the |> operator (for this l used the =instead of the < and l got the same answer) as < gave me an error

sci_online |>
  summarise(courses=n_distinct(course_id))
# A tibble: 1 × 1
  courses
    <int>
1      26

Group_by () Function

Using the sci_classes data and the pipe operator.

  • Filter final grades to remove NAs.

  • Group your data by subject and gender.

  • Summarise your data to calculate the following stats:

  • total number of students

  • mean final grade

  • mean time spent in the course

  • Assign to a new object

  • Examine the contents using a method of your choosing.

finals<-sci_online %>%
  filter(!is.na(FinalGradeCEMS)) %>%
  group_by(subject, Gender) %>%
  summarise(total=sum(student_id),
            grade=mean(FinalGradeCEMS),
            time=mean(TimeSpent_hours))
`summarise()` has grouped output by 'subject'. You can override using the
`.groups` argument.

Mutate () Function

Replace the dashed lines in the following code to;

  • Create a new variable called score that is the product of percentage earned and 100

  • Create a faceted scatter plot with hours spent in the course on the x-axis, score on the y-axis, and point colored by gender.

  • Include an alpha value to your graph.

sci_online %>%
  mutate(score = percentage_earned * 100) %>%
  ggplot() +
  geom_point(mapping = aes(x = TimeSpent_hours,
                           y =score,
                           col = Gender))
   alpha = 0.6) +
  facet_wrap(~subject)

Final Step:

You are almost done, all you need to is to render your file and publish it in one of the following platform.

Your Turn:

Render File: For now, we will wrap up this work by converting our work into a webpage that can be used to communicate your learning and demonstrate some of your new R skills. To do so, you will need to “render” your document by clicking the Render button in the menu bar at that the top of this file. This will do two things; it will:

  1. check through all your code for any errors; and,

  2. create a file in your directory that you can use to share you work through Posit Cloud, RPubs , GitHub Pages, Quarto Pub, or other methods.

  3. Submit your link to the Blackboard!

Now that you’ve finished your Rtutorial study, scroll back to the very top of this Quarto Document and change the author: “YOUR NAME HERE” to your actual name surrounded by quotation marks like so: author: “Dr. Cansu Tatar”.