#Invoking Required Libraries
library(foreign)
library(tidyverse)
library(dplyr)
library(ggplot2)
library(pander)
library(readxl)
# Uploading the Given Data
D_data <- read.spss("Duplicate_data.sav", to.data.frame=TRUE)
#Changing the data into a data frame
D_data <- as.data.frame(D_data)
# Checking the variable types in the data D_data
#str(D_data)
# All other variables have accurate data types but the Student_Id. Thus, changing the data in this columns to a factor type
D_data$Std_ID <- as.factor(D_data$Std_ID)
#str(D_data)
#Checking the Last cases of the data
tail(D_data) Std_ID Gender Grade Teacher Attendance Wordsread
8 Student 10 Male Fourth Grade Oliver 18 21316
9 Student 11 Female Fourth Grade Oliver 18 19662
10 Student 13 Male Fifth Grade Turner 14 30453
11 Student 14 Female Fifth Grade Turner 17 31066
12 Student 15 Female Fifth Grade Turner 16 48149
13 Student 17 Female Fifth Grade Gunter 18 36191
MARSIGRSPre MARSIGRSPost MARSIPSSPre MARSIPSSPost MARSISRSPre MARSISRSPost
8 2.6 3.8 2.2 4.2 3.4 2.8
9 1.2 3.4 3.4 3.6 3.2 3.2
10 2.2 4.0 4.4 4.6 3.8 4.2
11 3.2 4.2 4.6 4.6 4.4 4.6
12 3.4 3.4 4.8 4.8 4.6 4.2
13 3.4 3.0 4.0 4.6 5.0 4.2
MARSITotalPre MARSITotalPost
8 2.73 3.60
9 2.60 3.40
10 3.46 4.26
11 4.06 4.46
12 4.26 4.13
13 4.13 3.93# Summarizing the Data (Descriptive Analysis)
summary(D_data[1:6]) Std_ID Gender Grade Teacher Attendance
Student 1 :1 Male :4 Third Grade :5 Luna :5 Min. :14.00
Student 10 :1 Female:9 Fourth Grade:4 Oliver:4 1st Qu.:15.00
Student 11 :1 Fifth Grade :4 Gunter:1 Median :17.00
Student 13 :1 Turner:3 Mean :16.38
Student 14 :1 3rd Qu.:18.00
Student 15 :1 Max. :18.00
(Other) :7
Wordsread
Min. : 16631
1st Qu.: 21316
Median : 31066
Mean : 37261
3rd Qu.: 38867
Max. :108885
# Changing Wide to Long data Creating a new Key-variable 'test_type' having 'pretest' and 'posttest' and collapsing data into a long format (for Global Reading Strategies)
GRS_data <- gather(D_data, key=test_type, value = average_GRS_scores, MARSIGRSPre:MARSIGRSPost, factor_key=TRUE)
#Truncating the data for Required Columns
GRS_data_final <- select(GRS_data, c(1:6, 13,14))
head(GRS_data_final) Std_ID Gender Grade Teacher Attendance Wordsread test_type
1 Student 1 Female Third Grade Luna 16 51858 MARSIGRSPre
2 Student 2 Female Third Grade Luna 18 28575 MARSIGRSPre
3 Student 4 Male Third Grade Luna 17 38867 MARSIGRSPre
4 Sudent 5 Female Third Grade Luna 14 33160 MARSIGRSPre
5 Student 6 Female Third Grade Luna 15 108885 MARSIGRSPre
6 Student 7 Female Fourth Grade Oliver 17 16631 MARSIGRSPre
average_GRS_scores
1 2.4
2 1.8
3 3.4
4 3.0
5 3.4
6 1.6 #str(GRS_data_final)
#Saving the GRS_data as a separate dataset in my local disc
write.csv(GRS_data_final, file = "GRS_data_final.csv", row.names = FALSE)PSS_data <- gather(D_data, key=test_type, value = average_PSS_scores, MARSIPSSPre:MARSIPSSPost, factor_key=TRUE)
#head(PSS_data)
#Truncating the data for Required Columns
PSS_data_final <- select(PSS_data, c(1:6, 13,14))
head(PSS_data_final) Std_ID Gender Grade Teacher Attendance Wordsread test_type
1 Student 1 Female Third Grade Luna 16 51858 MARSIPSSPre
2 Student 2 Female Third Grade Luna 18 28575 MARSIPSSPre
3 Student 4 Male Third Grade Luna 17 38867 MARSIPSSPre
4 Sudent 5 Female Third Grade Luna 14 33160 MARSIPSSPre
5 Student 6 Female Third Grade Luna 15 108885 MARSIPSSPre
6 Student 7 Female Fourth Grade Oliver 17 16631 MARSIPSSPre
average_PSS_scores
1 3.0
2 4.0
3 2.6
4 4.4
5 3.8
6 3.8#Saving the PSS_data as a separate dataset in my local disc
write.csv(PSS_data_final, file = "PSS_data_final.csv", row.names = FALSE)SRS_data <- gather(D_data, key=test_type, value = average_SRS_scores, MARSISRSPre:MARSISRSPost, factor_key=TRUE)
#head(SRS_data)
#Truncating the data for Required Columns
SRS_data_final <- select(SRS_data, c(1:6, 13,14))
head(SRS_data_final) Std_ID Gender Grade Teacher Attendance Wordsread test_type
1 Student 1 Female Third Grade Luna 16 51858 MARSISRSPre
2 Student 2 Female Third Grade Luna 18 28575 MARSISRSPre
3 Student 4 Male Third Grade Luna 17 38867 MARSISRSPre
4 Sudent 5 Female Third Grade Luna 14 33160 MARSISRSPre
5 Student 6 Female Third Grade Luna 15 108885 MARSISRSPre
6 Student 7 Female Fourth Grade Oliver 17 16631 MARSISRSPre
average_SRS_scores
1 2.8
2 2.0
3 3.0
4 4.0
5 3.6
6 3.6#Saving the SRS_data as a separate dataset in my local disc
write.csv(SRS_data_final, file = "SRS_data_final.csv", row.names = FALSE)total_data <- gather(D_data, key=test_type, value = average_total_scores, MARSITotalPre:MARSITotalPost, factor_key=TRUE)
head(total_data) Std_ID Gender Grade Teacher Attendance Wordsread MARSIGRSPre
1 Student 1 Female Third Grade Luna 16 51858 2.4
2 Student 2 Female Third Grade Luna 18 28575 1.8
3 Student 4 Male Third Grade Luna 17 38867 3.4
4 Sudent 5 Female Third Grade Luna 14 33160 3.0
5 Student 6 Female Third Grade Luna 15 108885 3.4
6 Student 7 Female Fourth Grade Oliver 17 16631 1.6
MARSIGRSPost MARSIPSSPre MARSIPSSPost MARSISRSPre MARSISRSPost test_type
1 3.2 3.0 3.0 2.8 2.8 MARSITotalPre
2 3.4 4.0 5.0 2.0 4.2 MARSITotalPre
3 4.0 2.6 2.6 3.0 3.0 MARSITotalPre
4 3.2 4.4 3.8 4.0 3.4 MARSITotalPre
5 3.2 3.8 3.8 3.6 4.2 MARSITotalPre
6 2.6 3.8 3.0 3.6 4.0 MARSITotalPre
average_total_scores
1 2.73
2 2.60
3 3.00
4 3.80
5 3.80
6 3.00 #Truncating the data for Required Columns
total_data_final <- select(total_data, c(1:6, 13,14))
head(total_data_final) Std_ID Gender Grade Teacher Attendance Wordsread
1 Student 1 Female Third Grade Luna 16 51858
2 Student 2 Female Third Grade Luna 18 28575
3 Student 4 Male Third Grade Luna 17 38867
4 Sudent 5 Female Third Grade Luna 14 33160
5 Student 6 Female Third Grade Luna 15 108885
6 Student 7 Female Fourth Grade Oliver 17 16631
test_type average_total_scores
1 MARSITotalPre 2.73
2 MARSITotalPre 2.60
3 MARSITotalPre 3.00
4 MARSITotalPre 3.80
5 MARSITotalPre 3.80
6 MARSITotalPre 3.00#Saving the total_data as a separate dataset in my local disc
write.csv(total_data_final, file = "total_data_final.csv", row.names = FALSE)I further manipulated the data. I merged all of the saved data into a single Excel file using the following syntax.
# Joining GRS table to PSS table
merged_data <- read_excel("merged_data.xlsx")
merged_data <- as.data.frame(merged_data)
head(merged_data) Std_ID Gender Grade Teacher Attendance Wordsread test_type
1 Student 1 Female Third Grade Luna 16 51858 MARSI_Pre
2 Student 2 Female Third Grade Luna 18 28575 MARSI_Pre
3 Student 4 Male Third Grade Luna 17 38867 MARSI_Pre
4 Sudent 5 Female Third Grade Luna 14 33160 MARSI_Pre
5 Student 6 Female Third Grade Luna 15 108885 MARSI_Pre
6 Student 7 Female Fourth Grade Oliver 17 16631 MARSI_Pre
GRS_scores PSS_scores SRS_scores total_scores
1 2.4 3.0 2.8 2.73
2 1.8 4.0 2.0 2.60
3 3.4 2.6 3.0 3.00
4 3.0 4.4 4.0 3.80
5 3.4 3.8 3.6 3.80
6 1.6 3.8 3.6 3.00stabda5d <- read.spss("Overton Summer 2021 School Data.sav")
stabda5d <- as.data.frame(stabda5d)
str(stabda5d)'data.frame': 20 obs. of 112 variables:
$ LastName : Factor w/ 20 levels "Bobbitt","Boney",..: 1 2 3 4 5 6 7 8 9 10 ...
$ FirstName : Factor w/ 20 levels "Makynna","Hattie",..: 1 2 3 4 5 6 7 8 9 10 ...
$ Gender : Factor w/ 2 levels "Male","Female": 2 2 2 1 2 2 2 1 1 1 ...
$ Ethnicity : num NA NA NA NA NA NA NA NA NA NA ...
$ Grade : Factor w/ 3 levels "Third Grade",..: 1 1 1 1 1 1 2 2 2 2 ...
$ Teacher : Factor w/ 4 levels "Maryanna Luna",..: 1 1 1 1 1 1 2 2 2 2 ...
$ Attendance : num 16 18 11 17 14 15 17 10 15 18 ...
$ Wordsread : num 51858 28575 49406 38867 33160 ...
$ MARSIGRSPre : num 2.4 1.8 3.2 3.4 3 3.4 1.6 2.2 3.4 2.6 ...
$ MARSIGRSPost : num 3.2 3.4 NA 4 3.2 3.2 2.6 NA 5 3.8 ...
$ MARSIPSSPre : num 3 4 3.4 2.6 4.4 3.8 3.8 2 4 2.2 ...
$ MARSIPSSPost : num 3 5 NA 2.6 3.8 3.8 3 NA 5 4.2 ...
$ MARSISRSPre : num 2.8 2 3 3 4 3.6 3.6 3.6 4.2 3.4 ...
$ MARSISRSPost : num 2.8 4.2 NA 3 3.4 4.2 4 NA 5 2.8 ...
$ MARSITotalPre : num 2.73 2.6 3.2 3 3.8 3.8 3 2.6 3.86 2.73 ...
$ MARSITotalPost : num 3 4.2 NA 3.2 3.46 3.73 3.2 NA 5 3.6 ...
$ GRADEWRRawPre : num 27 28 23 28 24 28 NA NA NA NA ...
$ GRADEWRRawPost : num 28 29 NA 30 24 26 NA NA NA NA ...
$ GRADEWRStaninePre : num 4 5 3 5 3 5 NA NA NA NA ...
$ GRADEWRStaninePost : num 5 6 NA 8 3 4 NA NA NA NA ...
$ GRADEWRGEPre : num NA NA NA NA NA NA NA NA NA NA ...
$ GRADEWRGEPost : num NA NA NA NA NA NA NA NA NA NA ...
$ GRADEWRSSPre : num NA NA NA NA NA NA NA NA NA NA ...
$ GRADEWRSSPost : num NA NA NA NA NA NA NA NA NA NA ...
$ GRADEWRNCEPre : num NA NA NA NA NA NA NA NA NA NA ...
$ GRADEWRNCEPost : num NA NA NA NA NA NA NA NA NA NA ...
$ GRADEWRGSVPre : num NA NA NA NA NA NA NA NA NA NA ...
$ GRADEWRGSVPost : num NA NA NA NA NA NA NA NA NA NA ...
$ GRADEVocRSPre : num 20 16 15 7 14 21 19 15 19 15 ...
$ GRADEVocRSPost : num 12 18 NA 6 15 NA 14 NA 17 13 ...
$ GRADEVocStaninePre : num 5 4 4 2 3 5 4 4 4 3 ...
$ GRADEVocStaninePost : num 3 4 NA 1 4 NA 4 NA 5 4 ...
$ GRADEVocGEPre : num NA NA NA NA NA NA 4.2 3.4 4.2 3.4 ...
$ GRADEVocGEPost : num NA NA NA NA NA NA 3.2 NA 3.8 3 ...
$ GRADEVocSSPre : num NA NA NA NA NA NA 95 93 95 87 ...
$ GRADEVocSSPost : num NA NA NA NA NA NA 91 NA 96 89 ...
$ GRADEVocNCEPre : num NA NA NA NA NA NA 43 40 43 32 ...
$ GRADEVocNCEPost : num NA NA NA NA NA NA 37 NA 44 35 ...
$ GRADEVocGSVPre : num NA NA NA NA NA NA NA NA NA NA ...
$ GRADEVocGSVPost : num NA NA NA NA NA NA NA NA NA NA ...
$ GRADEVocCompRSPre : num 47 44 38 35 38 49 NA NA NA NA ...
$ GRADEVocCompRSPost : num 40 47 NA 36 39 NA NA NA NA NA ...
$ GRADEVocCompStaninePre : num 5 4 3 2 3 5 NA NA NA NA ...
$ GRADEVocCompStaninePost : num 3 5 NA 3 3 NA NA NA NA NA ...
$ GRADEVocCompGEPre : num 2.5 2.3 2 1.9 2 2.7 NA NA NA NA ...
$ GRADEVocCompGEPost : num 2.1 2.5 NA 1.9 2 NA NA NA NA NA ...
$ GRADEVocCompSSPre : num 97 93 86 82 86 100 NA NA NA NA ...
$ GRADEVocCompSSPost : num 88 96 NA 83 86 NA NA NA NA NA ...
$ GRADEVocCompNCEPre : num 46 40 30 25 30 50 NA NA NA NA ...
$ GRADEVocCompNCEPost : num 33 44 NA 26 30 NA NA NA NA NA ...
$ GRADEVocCompGSVPre : num NA NA NA NA NA NA NA NA NA NA ...
$ GRADEVocCompGSVPost : num NA NA NA NA NA NA NA NA NA NA ...
$ GRADESenCompRSPre : num 14 14 10 8 13 15 13 8 15 13 ...
$ GRADESenCompRSPost : num 9 17 NA 8 12 NA 13 NA 12 10 ...
$ GRADESenCompStaninePre : num 4 4 3 2 4 4 5 3 5 5 ...
$ GRADESenCompStaninePost : num 3 4 NA 2 3 NA 5 NA 4 4 ...
$ GRADESenCompGEPre : num NA NA NA NA NA NA NA NA NA NA ...
$ GRADESenCompGEPost : num NA NA NA NA NA NA NA NA NA NA ...
$ GRADESenCompSSPre : num NA NA NA NA NA NA NA NA NA NA ...
$ GRADESenCompSSPost : num NA NA NA NA NA NA NA NA NA NA ...
$ GRADESenCompNCEPre : num NA NA NA NA NA NA NA NA NA NA ...
$ GRADESenCompNCEPost : num NA NA NA NA NA NA NA NA NA NA ...
$ GRADESenCompGSVPre : num NA NA NA NA NA NA NA NA NA NA ...
$ GRADESenCompGSVPost : num NA NA NA NA NA NA NA NA NA NA ...
$ GRADEPassCompRSPre : num 9 9 8 6 11 12 12 11 10 14 ...
$ GRADEPassCompRSPost : num 9 17 NA 4 14 NA 18 NA 11 11 ...
$ GRADEPassCompStaninePre : num 3 3 3 2 4 4 4 4 3 5 ...
$ GRADEPassCompStaninePost: num 3 5 NA 1 4 NA 5 NA 4 4 ...
$ GRADEPassCompGEPre : num NA NA NA NA NA NA NA NA NA NA ...
$ GRADEPassCompGEPost : num NA NA NA NA NA NA NA NA NA NA ...
$ GRADEPassCompSSPre : num NA NA NA NA NA NA NA NA NA NA ...
$ GRADEPassCompSSPost : num NA NA NA NA NA NA NA NA NA NA ...
$ GRADEPassCompNCEPre : num NA NA NA NA NA NA NA NA NA NA ...
$ GRADEPassCompNCEPost : num NA NA NA NA NA NA NA NA NA NA ...
$ GRADEPassCompGSVPre : num NA NA NA NA NA NA NA NA NA NA ...
$ GRADEPassCompGSVPost : num NA NA NA NA NA NA NA NA NA NA ...
$ GRADECompCompRSPre : num 23 23 18 14 24 27 25 19 25 27 ...
$ GRADECompCompRSPost : num 18 34 NA 12 26 NA 31 NA 23 21 ...
$ GRADECompCompStaninePre : num 3 3 3 2 4 4 4 3 4 4 ...
$ GRADECompCompStaninePost: num 3 5 NA 2 4 NA 5 NA 4 4 ...
$ GRADECompCompGEPre : num 2.1 2.1 1.8 1.5 2.2 2.5 3.4 2.6 3.4 3.7 ...
$ GRADECompCompGEPost : num 1.8 3.4 NA 1.3 2.4 NA 4.4 NA 3.1 2.9 ...
$ GRADECompCompSSPre : num 88 88 82 77 90 93 90 88 90 92 ...
$ GRADECompCompSSPost : num 82 102 NA 74 92 NA 102 NA 93 91 ...
$ GRADECompCompNCEPre : num 33 33 25 18 36 40 36 33 36 39 ...
$ GRADECompCompNCEPost : num 25 53 NA 13 39 NA 53 NA 40 37 ...
$ GRADECompCompGSVPre : num NA NA NA NA NA NA NA NA NA NA ...
$ GRADECompCompGSVPost : num NA NA NA NA NA NA NA NA NA NA ...
$ GRADETotalTRSPre : num 70 67 56 49 62 76 44 34 44 42 ...
$ GRADETotalTRSPost : num 58 81 NA 48 65 NA 45 NA 40 34 ...
$ GRADETotalTStaninePre : num 4 4 3 2 3 5 4 4 4 4 ...
$ GRADETotalTStaninePost : num 3 5 NA 2 4 NA 5 NA 4 4 ...
$ GRADETotalTGEPre : num 2.4 2.3 2 1.8 2.1 2.7 3.7 2.8 3.7 3.5 ...
$ GRADETotalTGEPost : num 2 3.1 NA 1.8 2.2 NA 3.8 NA 3.3 2.8 ...
$ GRADETotalTSSPre : num 92 90 84 79 87 96 91 90 91 90 ...
$ GRADETotalTSSPost : num 84 100 NA 78 89 NA 98 NA 95 90 ...
$ GRADETotalTNCEPre : num 39 36 28 21 32 44 37 36 37 36 ...
$ GRADETotalTNCEPost : num 28 50 NA 19 35 NA 47 NA 43 36 ...
$ GRADETotalTGSVPre : num 406 401 385 375 394 415 434 417 434 430 ...
[list output truncated]library(tidyverse)
library(dplyr)
stabda5d_truncated <- select(stabda5d,"Gender","Grade", "Attendance", "Wordsread","GRADETotalTSSPre","GRADETotalTSSPost","GRADETotalTNCEPre","GRADETotalTNCEPost","GRADETotalTGSVPre","GRADETotalTGSVPost")
#stabda5d_truncated
# Creating an ID Variable
stabda5d_truncated$Std_ID <- paste("Student", 1:20)
# Putting them in Order
stabda5d_truncated <- select(stabda5d_truncated,"Std_ID","Gender","Grade", "Attendance", "Wordsread","GRADETotalTSSPre","GRADETotalTSSPost","GRADETotalTNCEPre","GRADETotalTNCEPost","GRADETotalTGSVPre","GRADETotalTGSVPost")
# Checking the New Data set
#stabda5d_truncatedRows 3,6,8,12,18,19, & 20 have missing values in them. As this is a small sample size, we are going to get rid of these data points.
# Getting Rid of selected Rows
stabda5d_truncated <- stabda5d_truncated[-c(3,6,8,12,18,19,20),]
# Checking
#stabda5d_truncatedLooks like we got rid of the selected rows from our dataset. Now, We are changing the names of Columns, the dependent variables into more descriptive ones.
colnames(stabda5d_truncated) <-c("Std_ID","Gender","Grade", "Attendance", "Wordsread","Test_Standard_Score_Pre", "Test_Standard_Score_Post", "National_Curve_Equivalent_Pre", "National_Curve_Equivalent_Post","Growth_Scale_Pre","Growth_Scale_Post")
stabda5d_truncated Std_ID Gender Grade Attendance Wordsread Test_Standard_Score_Pre
1 Student 1 Female Third Grade 16 51858 92
2 Student 2 Female Third Grade 18 28575 90
4 Student 4 Male Third Grade 17 38867 79
5 Student 5 Female Third Grade 14 33160 87
7 Student 7 Female Fourth Grade 17 16631 91
9 Student 9 Male Fourth Grade 15 19578 91
10 Student 10 Male Fourth Grade 18 21316 90
11 Student 11 Female Fourth Grade 18 19662 82
13 Student 13 Male Fifth Grade 14 30453 84
14 Student 14 Female Fifth Grade 17 31066 79
15 Student 15 Female Fifth Grade 17 71353 84
16 Student 16 Female Fifth Grade 16 48149 104
17 Student 17 Female Fifth Grade 18 36191 97
Test_Standard_Score_Post National_Curve_Equivalent_Pre
1 84 39
2 100 36
4 78 21
5 89 32
7 98 37
9 95 37
10 90 36
11 91 25
13 88 28
14 92 21
15 92 28
16 105 56
17 90 46
National_Curve_Equivalent_Post Growth_Scale_Pre Growth_Scale_Post
1 28 406 387
2 50 401 423
4 19 375 372
5 35 394 398
7 47 434 436
9 43 434 428
10 36 430 418
11 37 413 420
13 33 422 428
14 39 412 438
15 39 422 438
16 57 466 467
17 36 448 433# Writing the Data in the Local File
write.csv(stabda5d_truncated, "stabda5d.csv", row.names = FALSE)library(reshape)
# Demo Data
stabd5d_demo <- select(stabda5d_truncated, 1:5)
# Pretest Data
stabd5d_pretest <- select(stabda5d_truncated,1,6,8,10)
stabda5d_pretest <- melt(stabd5d_pretest, id = c("Std_ID"), measured = c("Test_Standard_Score_Pre","National_Curve_Equivalent_Pre","Growth_Scale_Pre"))
# Changing the Column Names
colnames(stabda5d_pretest) <- c("Std_ID", "Test_Type", "Pretest_Scores")
# Changing the Description on Column Test_Type
stabda5d_pretest$Test_Type <- factor(stabda5d_pretest$Test_Type, labels = c("Test Standard Score", "National Curve Equivalent Score","Growth Scale Score"))
stabda5d_pretest Std_ID Test_Type Pretest_Scores
1 Student 1 Test Standard Score 92
2 Student 2 Test Standard Score 90
3 Student 4 Test Standard Score 79
4 Student 5 Test Standard Score 87
5 Student 7 Test Standard Score 91
6 Student 9 Test Standard Score 91
7 Student 10 Test Standard Score 90
8 Student 11 Test Standard Score 82
9 Student 13 Test Standard Score 84
10 Student 14 Test Standard Score 79
11 Student 15 Test Standard Score 84
12 Student 16 Test Standard Score 104
13 Student 17 Test Standard Score 97
14 Student 1 National Curve Equivalent Score 39
15 Student 2 National Curve Equivalent Score 36
16 Student 4 National Curve Equivalent Score 21
17 Student 5 National Curve Equivalent Score 32
18 Student 7 National Curve Equivalent Score 37
19 Student 9 National Curve Equivalent Score 37
20 Student 10 National Curve Equivalent Score 36
21 Student 11 National Curve Equivalent Score 25
22 Student 13 National Curve Equivalent Score 28
23 Student 14 National Curve Equivalent Score 21
24 Student 15 National Curve Equivalent Score 28
25 Student 16 National Curve Equivalent Score 56
26 Student 17 National Curve Equivalent Score 46
27 Student 1 Growth Scale Score 406
28 Student 2 Growth Scale Score 401
29 Student 4 Growth Scale Score 375
30 Student 5 Growth Scale Score 394
31 Student 7 Growth Scale Score 434
32 Student 9 Growth Scale Score 434
33 Student 10 Growth Scale Score 430
34 Student 11 Growth Scale Score 413
35 Student 13 Growth Scale Score 422
36 Student 14 Growth Scale Score 412
37 Student 15 Growth Scale Score 422
38 Student 16 Growth Scale Score 466
39 Student 17 Growth Scale Score 448# Posttest Data
stabd5d_posttest <- select(stabda5d_truncated,1,7,9,11)
stabd5d_posttest <- melt(stabd5d_posttest, id = c("Std_ID"), measured = c("Test_Standard_Score_Post","National_Curve_Equivalent_Post","Growth_Scale_Post"))
# Changing the Column Names
colnames(stabd5d_posttest) <- c("Std_ID", "Test_Type", "Posttest_Scores")
# Changing the Description on Column Test_Type
stabd5d_posttest$Test_Type <- factor(stabd5d_posttest$Test_Type, labels = c("Test Standard Score", "National Curve Equivalent Score","Growth Scale Score"))table_1 <- merge(stabd5d_demo,stabda5d_pretest, by = "Std_ID")
stabd5d_Long <- merge(table_1,stabd5d_posttest, by = c("Std_ID", "Test_Type"))
stabd5d_Long <- select(stabd5d_Long,"Std_ID","Gender","Grade","Attendance","Wordsread", "Test_Type","Pretest_Scores","Posttest_Scores")
stabd5d_Long Std_ID Gender Grade Attendance Wordsread
1 Student 1 Female Third Grade 16 51858
2 Student 1 Female Third Grade 16 51858
3 Student 1 Female Third Grade 16 51858
4 Student 10 Male Fourth Grade 18 21316
5 Student 10 Male Fourth Grade 18 21316
6 Student 10 Male Fourth Grade 18 21316
7 Student 11 Female Fourth Grade 18 19662
8 Student 11 Female Fourth Grade 18 19662
9 Student 11 Female Fourth Grade 18 19662
10 Student 13 Male Fifth Grade 14 30453
11 Student 13 Male Fifth Grade 14 30453
12 Student 13 Male Fifth Grade 14 30453
13 Student 14 Female Fifth Grade 17 31066
14 Student 14 Female Fifth Grade 17 31066
15 Student 14 Female Fifth Grade 17 31066
16 Student 15 Female Fifth Grade 17 71353
17 Student 15 Female Fifth Grade 17 71353
18 Student 15 Female Fifth Grade 17 71353
19 Student 16 Female Fifth Grade 16 48149
20 Student 16 Female Fifth Grade 16 48149
21 Student 16 Female Fifth Grade 16 48149
22 Student 17 Female Fifth Grade 18 36191
23 Student 17 Female Fifth Grade 18 36191
24 Student 17 Female Fifth Grade 18 36191
25 Student 2 Female Third Grade 18 28575
26 Student 2 Female Third Grade 18 28575
27 Student 2 Female Third Grade 18 28575
28 Student 4 Male Third Grade 17 38867
29 Student 4 Male Third Grade 17 38867
30 Student 4 Male Third Grade 17 38867
31 Student 5 Female Third Grade 14 33160
32 Student 5 Female Third Grade 14 33160
33 Student 5 Female Third Grade 14 33160
34 Student 7 Female Fourth Grade 17 16631
35 Student 7 Female Fourth Grade 17 16631
36 Student 7 Female Fourth Grade 17 16631
37 Student 9 Male Fourth Grade 15 19578
38 Student 9 Male Fourth Grade 15 19578
39 Student 9 Male Fourth Grade 15 19578
Test_Type Pretest_Scores Posttest_Scores
1 Growth Scale Score 406 387
2 National Curve Equivalent Score 39 28
3 Test Standard Score 92 84
4 Growth Scale Score 430 418
5 National Curve Equivalent Score 36 36
6 Test Standard Score 90 90
7 Growth Scale Score 413 420
8 National Curve Equivalent Score 25 37
9 Test Standard Score 82 91
10 Growth Scale Score 422 428
11 National Curve Equivalent Score 28 33
12 Test Standard Score 84 88
13 Growth Scale Score 412 438
14 National Curve Equivalent Score 21 39
15 Test Standard Score 79 92
16 Growth Scale Score 422 438
17 National Curve Equivalent Score 28 39
18 Test Standard Score 84 92
19 Growth Scale Score 466 467
20 National Curve Equivalent Score 56 57
21 Test Standard Score 104 105
22 Growth Scale Score 448 433
23 National Curve Equivalent Score 46 36
24 Test Standard Score 97 90
25 Growth Scale Score 401 423
26 National Curve Equivalent Score 36 50
27 Test Standard Score 90 100
28 Growth Scale Score 375 372
29 National Curve Equivalent Score 21 19
30 Test Standard Score 79 78
31 Growth Scale Score 394 398
32 National Curve Equivalent Score 32 35
33 Test Standard Score 87 89
34 Growth Scale Score 434 436
35 National Curve Equivalent Score 37 47
36 Test Standard Score 91 98
37 Growth Scale Score 434 428
38 National Curve Equivalent Score 37 43
39 Test Standard Score 91 95# Writing the Data in the Local File
write.csv(stabd5d_Long, "stabd5d_Long.csv", row.names = FALSE)