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Correspondence Analysis of Brands
Introduction
Correspondence Analysis helps us understand the relative relationships between and within two sets of brand attributes. For brand perceptions, these two groups are brands and the attributes that apply to these brands. Correspondence analysis helps in measuring similarities between different brands and the strength of brands in relation to their relationships with different attributes. Understanding these relationships allows brand owners to accurately identify the effects of previous marketing efforts on different brand-related attributes, and prescribe the next step of action/decision to make concerning the brand.
Importance of correspondence analysis in Brand Perceptions
1. Eliminate a misleading effect while evaluating the brand size
2. Gives an overall perspective of brand attributes and various relationships.
The dataset is obtained from a survey conducted and the average score of all the respondents are given in numeric format. The survey questions/aspects to measure various attributes for the brand is given below. Note that all the attributes and sub-attributes were measured on a 1 - 5 scale.
1. Competence
a. Reliable
b. Hardworking
c. Secured. Intelligent
e. Technical
f. Corporate
g. Successful
h. Leader
i. Confident
2. Involvement
a. Importance of decision
b. Though
c. Risk in case of wrong decision
3. Familiarity within the Category
4. Complexity
a. Getting used to (BRAND) requires a major learning effort
b. Getting used to (BRAND) takes a long time before one can fully understand the advantages
c. The product concept of (BRAND) is difficult to evaluate and understand
d. Overall, I believe that using (BRAND) is easy
e. Using (BRAND) requires a lot of mental effort
5. Visibility
a. I have seen how another person is using (BRAND)
b. In my environment, one sees (BRAND) quite often
c. (BRAND) is not very visible in my environment
d. I have had plenty of opportunities to see someone else using (BRAND)
e. It is rare that I see someone else using (BRAND)
6. Perceived Risk
a. When using (BRAND) I am sure I will get what I expected
b. Using (BRAND) may cause me additional unplanned expenses
c. Using ( BRAND) might cause me social embarrassment
7. Excitement
a. Daring
b. Trendy
c. Exciting
d. Spirited
e. Cool
f. Young
g. Imaginative
h. Unique
i. Up to date
j. Independent
k. Contemporary
The summary of the dataset is given below,
R Code block
#INSTALLING LIBRARIES
library(factoextra)
library(FactoMineR)
library(gplots)
#READ DATA
data.ca = readxl::read_xlsx("/Users/srirammuralikrishnan/Desktop/DARP - Project Files/Survey Characterisitcs/Main Brand Attributes.xlsx")
View(data.ca)
summary(data.ca)
colnames(data.ca)
# Select 5 predictor variables
data.ca = data.ca[,c(1,2,3,4,5)]
# Edit First Column as Row names of Data
data.ca.rownames = data.ca[,1]
data.ca = data.ca[,-1]
row.names(data.ca) = data.ca.rownames
# GRAPH OF CONTINGENCY TABLES
# Convert the data as a table
dt = as.table(as.matrix(data.ca))
# Graph
balloonplot(t(dt),
main = 'Brand Attributes for the Brands in study',
xlab = '',
ylab = '',
label = FALSE,
show.margins = FALSE)
# ROW PROFILES
data.ca.row = data.ca/rowSums(data.ca) # For each cell, divided by its total of rows
View(data.ca.row)
mass.row = colMeans(data.ca.row)
View(mass.row)
# COLUMN PROFILES
data.ca.col = t(data.ca)/colSums(data.ca) # For each cell, divided by its total of columns
View(data.ca.col)
mass.col = rowMeans(t(data.ca.col))
View(mass.col)
# CHIS-SQUARE TEST
# To evaluate whether there is a significant dependence between row and column categories
chisq = chisq.test(data.ca)
chisq
# CORRESPONDENCE ANALYSIS
res.ca = CA(data.ca, graph = TRUE)
print(res.ca)
fviz_ca_biplot(res.ca, repel = TRUE) # Make correspondence plot
# EIGENVALUE
eig.val = get_eigenvalue(res.ca)
eig.val
fviz_screeplot(res.ca, addlabels = TRUE, ylim = c(0, 50))
# GRAPH OF ROW VARIABLES
# Coordinates of rows
row = get_ca_row(res.ca)
row$coord
# Squared Cosine of rows
row$cos2
# Color by cos2 values: quality on the factor map
fviz_ca_row(res.ca, col.row = "cos2",
gradient.cols = c("#00AFBB", "#E7B800", "#FC4E07"),
repel = TRUE)
# Cos2 of rows on Dim.1 and Dim.2
fviz_cos2(res.ca, choice = "row", axes = 1:2)
# Contributions of individuals to dimension 1
row$contrib
fviz_contrib(res.ca, choice = "row", axes = 1, top = 10)
# Contributions of rows to dimension 2
fviz_contrib(res.ca, choice = "row", axes = 2, top = 10)
# Contribution of rows in Correpondence Plot
fviz_ca_row(res.ca, col.row = "contrib",
gradient.cols = c("#00AFBB", "#E7B800", "#FC4E07"),
repel = TRUE)
# GRAPH OF COLUMN VARIABLES
# Coordinates of columns
column = get_ca_col(res.ca)
column$coord
# Squared Cosine of columns
column$cos2
# Color by cos2 values: quality on the factor map
fviz_ca_col(res.ca, col.col = "cos2",
gradient.cols = c("#00AFBB", "#E7B800", "#FC4E07"),
repel = TRUE)
# Cos2 of columns on Dim.1 and Dim.2
fviz_cos2(res.ca, choice = "col", axes = 1:2)
# Contributions of columns to dimension 1
column$contrib
fviz_contrib(res.ca, choice = "col", axes = 1, top = 10)
# Contributions of columns to dimension 2
fviz_contrib(res.ca, choice = "col", axes = 2, top = 10)
# Contribution of columns in Correpondence Plot
fviz_ca_col(res.ca, col.col = "contrib",
gradient.cols = c("#00AFBB", "#E7B800", "#FC4E07"),
repel = TRUE)
Data Exploration
Data is explored to obtain initial information regarding Brands. The attributes explored are competence, involvement, the familiarity of the said brand within the category, complexity, Visibility, Perceived Risk and Excitement. The results are given in the form of a graph of the Contingency tables of various brands.
As mentioned earlier the values of the contingency tables are given in the form of a balloon plot. From the above plot, we can understand that,
24TVShowRespondents feel that they need to put the effort into understanding the brand, might take a longer time to understand the brand’s advantages and it requires larger mental effort to use it.
300 (the movie)The respondents are highly involved with the movie franchise.
ABC The Perceived risk of the brand is very low which says that they are a strong brand in the Entertainment Industry. amazing race respondents are excited while watching the show, but are less familiar when compared to other brands in the same category.
AMC TheatreThe brand is perceived to be less complex and customers are excited to be using the brand.
American IdolThe respondents feel that they aren’t sure of what they will get while using the brand, worried about the social perception and fear of unplanned expenses while using the brand.
America's Got TalentRespondents are excited to see the show and feel that it is less risky compared to American idols.
America's Next Top Model We can observe that it is less visible and less familiar compared to other brands in the same category.
BBC The respondents are less familiar, less visible and not excited to watch shows from BBC
YouTube Respondents feel that YouTube is the least complex brand out of the list of brands.
Profile Table
Row Profile
In reference to the above table, we can see that the largest mass value is 0.371 which is in the attribute Excitement. We can conclude that the respondents initially look for whether the brand is Daring, Trendy, Exciting, Spirited, Cool, Young, Imaginative, Unique, up to date, Independent or Contemporary. Further, the customers depend on the Visibility of the brand where the next largest mass value 0.344 is present.
Column Profile
24TVShow13.2% perceive the brand as complex
300 (the movie) 10% perceive that the brand is complex
ABC 11.4% perceive the brand is much more visible
Amazing Race 10.34% perceive that the brand is risky
AMC Theatre 10.5% perceive that the brand is visible
American Idol 11.1% perceive that the brand is risky to use. America's Got Talent 10.5% to perceive that the brand is exciting
America's Next Top Model 11.1% perceive that the brand is complex to understand
BBC 12.2% perceive that the brand is complex to understand
YouTube 12.1% perceive that the brand is much more visible.
Chi-Squared Characteristics
To interpret correspondence analysis, the first step is to evaluate whether there is a significant dependency between the rows and columns.
H0: Assumes that there is no association between the variables
H1: Assumes that there is an association between the variables
Pearson's Chi-squared test
data: data.ca
X-squared = 1.1408, df = 27, p-value = 1
We can see that the p-value is less than 5% (alpha) the decision is to reject the null hypothesis. So based on the Chi-Square test, it can be concluded that there is no association between the row and column with the significance level of 5%.
Correspondence Analysis
Correspondence analysis is a multivariate analysis using the concept of Principal Component Analysis. To look up the variance, it is necessary to calculate the eigenvalue. The eigenvalue of the correspondence analysis is
Dimension
eigenvalue
Variance per cent
Cumulative variance per cent
Dim1
0.0086111177
78.89963
78.89963
Dim2
0.0017706579
16.22371
95.12334
Dim3
0.0005322394
4.87666
100.00000
Based on the above table it is found that dimension 1 and dimension 2 explain the 95.12334% variability in the data. So we can conclude that the results obtained by correspondence analysis are quite good at explaining the data.
The scree plot and the symmetric plot of the correspondence analysis is given below.
Based on the Scree plot, it is observed that in dimension 2, the percentage of variability that can be explained drops steeply. It indicates that the addition of component 3 does not influence the data diversity that can be explained. So dimension 1 and dimension 2 are chosen. The total percentage of variability that can be explained through the two selected components is about 95.12334%.
Conclusion
The information obtained through correspondence Analysis are given below,
1. The brands 24TVShow, 300 (the movie) and BBC are seen to have high complexity.
2. The brands ABC, AMC theatre and YouTube are seen to have high visibility.
3. The brand's amazing race and America’s Next Top Model are seen to have higher perceived risks.
4. The brands American Idol and America’s Got Talent are seen to be the most exciting brands.
This blog is a part of the assignments done in the course Data Analysis using R and Python
Sriram M
Data Analytics using R and Python (MBA)
Amrita School of Business
Amrita Vishwa Vidyapeetham
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Auto Farm Ro Ghoul Script
Roblox (2006)
Apr 07, 2020 tabwin2:AddLabel (' Autofarm rep works with both ccg and ghoul b), also DONT enable autofarm when you have rep farm on, rep farm will break if you do that.' ) local crumy = true. Spawn (function while wait (1) do. If game.Players.LocalPlayer.PlayerGui:FindFirstChild ('HUD') then. LINK - HOW TO USE:1)Download the hack2)Run the setup3)Run the program on your desktop4)Run the game5)Press the inj.
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Probably one of the best autofarms out there for this game. With so many users you might never get detected.
Banana Hub
Created By Me with some simulator games that have most features right now.
Vehicle Simulator Autofarm Method
This is probably the best way to farm in this game. Might have a risk of ban idk.
SoulShatters GOD MODE AND MORE!
Ro Ghoul Gui
Pretty good script.
Lumber Tycoon 2 ITEM AUTO DUPE METHOD!
A cool little trick that someone calls auto duping with the auto money dupe.
Neon Hub
A really cool hub that is small but has a few simulator games.
Ragdoll Engine GUI | Crash Server | Bomb Server
Really good trolling script that you can use on the game.
Anime Fighting Simulator NEW PAID AUTOFARM
This is probably the best paid autofarm that works.
Build A Boat For Treasure NEW GUI AUGUST 2020
Really nice gui with autofarm.
Murder Mystery 2 ADMIN PANEL NEW GUI
Enjoy this gui.
FE BTools SCRIPT CRACK
Really cool to use on some games. (NOT ALL WORK)
Lumber Tycoon 2 LUMBER HAXX V1.0.2 GUI
Really nice Lumber haxx with cool features.
A pretty cool autofarm if you want to make a lot of money in a shitty game. This has server hope version and the non server hop version.
Demon Slayer RPG 2 UPDATED “SHITTY” GUI
A pretty simple gui with simple features. Let’s see if this can grow to a autofarm.
SYNAPSE DARK DEX V4 (HIDDEN PROPERTIES & MAJOR FIXES)
Finally we can have a script that fixes all those bugs.
Anti Ban! NEVER GET BANNED FROM EXPLOITING! V2
This is a really cool FE script that can make it where you will never get banned when you exploit.
The Wild West NEW OP GUI AUGUST 2020
A really cool gui for a game I use to enjoy playing a lot.
NEW Clicking Champions GUI
This is a really good GUI for a shitty game. It’s sad how good scripts get shitty games.
Jailbreak AUTO DRIVE
A good script if you want to add it to a FREE gui. Or just to use if you are too lazy to drive yourself.
Tower Heroes AUTO FARM UPDATED
A Really nice autofarm that you can use to get good at the game.
Demon Slayer RPG 2 AUTO MOB FARM (ANTI ATTACK)
This is the autofarm to use with the gui under this.
Has lot’s of features and is good to use with a auto farm on the game
RECOIL ZOMBIES NATCH MOD MENU (COD STYLE GUI)
This game is probably decent.
This script only works on some games.
Tapping Master NEW WORKING GUI!
A lot of people seem to like this gui and the game.
Anime Battle Arena AutoFarm GUI
This is a great autofarm and will help you out in the game a lot.
Island BERRY FARM AURA
This will let you grab the berrys if you are busy. You just have to walk by them and it’ll farm them. You can edit range if you want but I wouldn’t.
Battle Gods Simulator NEW BEST GUI
This gui has everything you need to become the best in the game super fast.
(FE LEAKED) GRABKNIFE, CLAIM ADMIN & KARAMBIT
This is something that is really cool since most FE scripts are no longer FE.
AkimboHub WITH 14 GAMES AND MORE SOON!
This is a hub that has potential if they update it a lot and unpatch a lot of scripts. Plus if they add games that they like they could get more members and more feed back.
Stellar Anime Fighting Simulator AUTOFARM UNPATCHED (By someone else)
Stellar made his own autofarm a few months ago. He decided to stop unpatching it because it would take too much time since it would get patched again.
(OPEN SOURCE) Anime Fighting Simulator GUI (Patched)
The game patched the script and he decided too make it open source.
WORKING FE FLING SCRIPT JULY 2020
WORKING FE FLING SCRIPT JULY 2020 Created By Diemiers#4209, improved version by hazelnut#2172 This is really cool. Enjoy. FE FLING DEV NOTES: THIS WON’T WORK ON GAMES THAT MANUALLY RESPAWN YOU. IT SHOULD WORK ON A LOT OF GAMES
RECOIL UPDATED FPS GUI
RECOIL UPDATED FPS GUI Created by wYn#5984 Enjoy. ESP AIMBOT AUTO REFILL AMMO AUTO REFILL GRENADES
Method To Remove HWID Ban On Roblox!
He discovered a way too remove the hwid so you can play roblox even if you get perm banned. Please note that you can get hwid banned on fortnite but that’s not a problem.
Blackhawk Rescue Mission 5 EASY MONEY & XP
A easy way to get tons of xp and money in this game.
The Wild West NEW GUI WITH LOTS OF FEATURES!
The new gui is full of features and is great if you like pissing people off in the game which is really ez.
Tower Of Hell CRUSHER GUI
A really cool gui for trolling and winning every round.
Murder Mystery 2 AUTO FARM COINS
Just a simple autofarm for this game. You can leave this running for a while while you’re asleep and this shouldn’t be detected.
Dragon Ball Final Stand AUTOFARM GUI UPDATED
A really nice NPC Autofarm for this game that a lot of people have been asking for. This might be detected but I don’t really know. DON’T BLAME ME IF YOU GET YOUR DATA WIPED!
A Bizarre Day A NEW AUTOFARM GUI
You can get banned if you use this is a fucking public server. If you think im wrong then use your FUCKING BRAIN! YOU FUCKING TP AROUND THE MAP.
Ninja Clicker Simulator NEW AUTOFARM GUI
This is probably one of his 1st yet really good releases for this game. Let’s hope too see more improvements in the future.
Stand Upright Autofarm GUI
I have not been able too add auto punch too the owners script because I don’t have his permission. So you will have too run the script I made with the GUI. It’s a simple script that I made. (It might only not work on Krnl because I tested it out. It works sometimes on protosmasher and it might work more on synapse but if it doesn’t use the script I made).
Lumber Tycoon 2 AUTO MONEY DUPE GUI
Ro Ghoul Auto Farm Script Download
Lumber Tycoon 2 AUTO MONEY DUPE GUI Created By magnol1a#1545 Consider lumber tycoon 2 finished after this release lol. AUTO DUPE HOW TO USE: 1. INPUT THE NUMBER OF TIMES YOU WANT TO DUPLICATE YOUR MONEY. (IF YOU HAVE LOTS OF CASH ALREADY THEN ONLY DO IT ONCE) 2. PRESS DUPLICATE AND DO NOTHING AS (…)
Battle Gods Simulator NEW GUI
Battle Gods Simulator NEW GUI Created By ItzHxS#1280 Enjoy. AUTO FARM AUTO SELL AUTO COLLECT GET ALL GAMEPASS
Build A Boat For Trease FINISH IN 20 SECONDS!
This script will let you finish the game in about 20 seconds. Still working as of 2020.
Jailbreak NEW AFK AUTOFARM
Jailbreak NEW AFK AUTO FARM , AUTO UPDATING! Created by Veernezus#5392 The best auto farm in Jailbreak. AUTO FARM
Saitama simulator NEW AUTO FARM
Saitama simulator NEW AUTO FARM Created by Cunning Enjoy. AUTO FARM
My Restaurant FAST NPC, TP TIP, & MORE!
My Restaurant FAST NPCS, TP TIP COLLECT & MORE! Created by NinjaOperator Haven’t had any recent releases for this. Enjoy. ANTI AFK FAST NPCS AUTO TP TO CASH PILES ADDED AUTO COLLECT WISHING WELL BLACKLIST ALL PLAYERS FROM YOUR RESTAURANT
Clould Hub REWRITTEN! NOW SUPPORTING 10 GAMES!
Cloud Hub was rewritten less then a month ago and it is better than it was before!
FluxHub NEW HUB SUPPORTING 3 GAMES! (RN)
A Fluxus hub is making his own hub and might be updating it too support more games. This hub could be the next best hub if he updates it even more too support a lot of games.
Welcome To Bloxburg WORKING AUTOFARM!
This is a detected autofarm but it works and it’s free. I suggest use it on a alt account.
Game Company Tycoon INFINITE MONEY SCRIPT
Game Company Tycoon INFINITE MONEY SCRIPT Created by wiird2012 Enjoy. INFINITE MONEY CHANGE “1000” TO A HUGE NUMBER FOR INFINITE CASH
Super Power Fighting Simulator AUTO FARM GUI JULY
Super Power Fighting Simulator AUTO FARM GUI JULY Created by Dk4Q Enjoy. AUTO FARM WITH TELEPORTS MANUAL AUTO FARM
Mega Noob Simulator NEW JULY GUI
Mega Noob Simulator NEW JULY GUI Created by eprx#9921 Enjoy. AUTO BUY AUTO KILL BOSS FARM HEADS TO COINS TELEPORT COINS Tally 7.2 software, free download.
Impulse Hub 9 GAMES
Impulse Hub 9 GAMES Created by Kunaii#8179 Enjoy. ARSENAL BIG PAINTBALL APOCALYPSE RISING 1 RESURRECTION MAGNET SIMULATOR PRISON LIFE LIFTING SIMULATOR MURDER MYSTERY 2 ZOMBIE RUSH
Spooder Space (Sound Space) NEW Auto Player
Spooder made his own script for Sound Space. Really good and can play on the hardest levels.
Stands Online v018 GUI
Stands Online v018 GUI Created by otakuOG Enjoy. AUTO FARM PUNCH SPAM ALL PUNCHES GO TO SPAWN AUTO STRENGTH INVISIBLE STAND NO STAND NAME NO STUN SPECIAL ATTACKS + OTHER STUFF!
Ro-Slayers EXP SCRIPT
Ro-Slayers EXP SCRIPT Created by DETRIX#2628 Enjoy. EXP SCRIPT EXECUTE MULTIPLE TIMES MAKE SURE TO HAVE THE SWORD EQUIP
Lucky Blocks Battlegrounds NEW SIMPLE GUI!
LUCKY BLOCKS Battlegrounds NEW SIMPLE GUI Created by iSadito Enjoy. SIMPLE GUI
Arsenal GUN MODS! (INCLUDE FAST FIRE!)
Arsenal GUN MOD, INCLUDES FAST FIRE RATE Created by Prestiged Enjoy. GUN MOD
Bad Business NEW BEST GUI!
This is a gui that showed up a few days ago and is really good in my opinion.
Sky Block/Islands SCHEMATICA (AUTO BUILD)
SKY BLOCK/ISLANDS SCHEMATICA (AUTO BUILD) Created by Jxl#0001 Really awesome. If you need any further help with this then make sure to message JXL or follow the video guide. AUTO BUILD SCHEMATICS
Frost Hub CRACKED!
Frost Hub was a $10 hub that was cracked recently. Has 17 games in it and can probably be the best cracked hub now.
Arsenal HITBOX EXPANDER (IMPROVED)
Arsenal hitboxes expander but improved.
Welcome To BloxBurg THE BEST UNDETECTED AUTOFARM
This is a PAID autofarm that is cheap and undetected. While if you want to play the shit game then you can buy it.
Jailbreak ANTI CAGE SCRIPT
This will help everyone who was banned to get out of the cage every time they want to play the shitty game.
Mad City AUTOFARM (30MINS=1MIL)
This is a really good autofarm that you can use over night to get a lot of money. I’ve seen something like it but it was patched a while back.
(350+ Games) MEGA SCRIPT PACK!
This has over 350 games in it and most of them work.
Roblox BOT FOLLOWER! (#1 BOT FOLLOWER 2020!)
This is probably a really good bot follower that can give you TONS of follows and you can brag to your friends about it.
Sky Block THE #1 JXNT FREE SKY BLOCK GUI!
Ro Ghoul Autofarm Script Pastebin 2021
This is one of the best FREE guis for Sky Block So far. TONS OF FEATURES AND MORE SHIT!
Tesla Hub DISCONTINUED (SOURCE CODE!)
It’s sad to see a cool script like this die because of a ban. Lets hope another can rise up and take it’s place.
RPG Simulator AUTOFARM MOBS, KILL AURA, NAME HIDE, AND MORE!
Probably a really cool release by a some what known dev in the exploiting community. Undetected by the games anti cheat.
Clicking Legends NEW AUTOFARM, BEST AUTOFARM!
This is probably going to be one of the best autofarms so far. It was made by the same creator of Boo Hub!
Swordburst ONE SHOT!
Really good if you can’t use autofarm or you just don’t want to get banned by a admin.
My Restaurant! Autofarm (08/07) Updated
Really cool script by a well known dev in the exploiting community.
Clicking Legends PET MULTI, AUTO LEVEL, AND MORE!
This is probably going to destroy the game because of how you can get on the board EASILY!
Auto Farmer Ro Ghoul
Execute Pastebin Script Without A VPN
This is a script that is going to help TONS of people out. Some country’s block pastebin links so you cant use them for loadstring scripts most of the time.
Duckie Simulator AUTO FARM, AUTO SWING, AND MORE!
Really nice script that can get you pretty OP very fast.
Battle Gods Simulator AUTO FARM, AUTO SELL, AUTO HIT BOSS!
Really cool Auto Farm for undetected exploiting
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CVE-2021-21828
A heap-based buffer overflow vulnerability exists in the XML Decompression DecodeTreeBlock functionality of AT&T Labs Xmill 0.7. In the default case of DecodeTreeBlock a label is created via CurPath::AddLabel in order to track the label for later reference. An attacker can provide a malicious file to trigger this vulnerability. source https://cve.report/CVE-2021-21828
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loop
foreach var of varlist * { local newname : subinstr local var "_" "", all if "`newname'" != "`var'" { rename `var' `newname' } }
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foreach var of varlist Age-Edu { generate t`var' = `var' * .10 }
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foreach var of varlist educat-Race2 {
replace `var’=. if `var’==666
}
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foreach var in `continuous'{ graph box `var’, saving(`var’,replace) }
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foreach reg in "probit" "logit" "regress" { foreach regressors in "lnprice" "lnprice rep78" "lnprice rep78 trunk" {
foreach stderr in "vce(robust)" "vce(cluster foreign)" "" { `reg’ foreign `regressors’, `stderr’
regsave using "`tmpfile’", p addlabel(vce,`stderr’,regressors,`regressors’,reg,`reg’) `replace’
local replace "append"
}
}
}
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foreach var of varlist inc1-inc12 { generate tax`var' = `var' * .10 }
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eQTL analysis for Candida Genome (R code)
gt = read.table("Desktop/Abhilash/study/Post-Doc/Lab_notebook/eQTL/Input_3/Differential_regulation_files/eQTL_input/MATRIX_eQTL/whole_genome/input/snps_Allchr.txt", sep="\t", header=TRUE, row.names = 1) gt <- as.data.frame(as.matrix(t(gt)))
head(gt) dim(gt)
# Calculate MAF and draw MAF histogram maf <- colMeans(gt)/2 maf maf <- pmin(maf, 1-maf) maf
sum(maf > 0.5)
truehist(maf, main = "Histogram of MAF values.", col = "steelblue") lines(density(maf), lty = 2, col = "darkorange", lwd = 3)
write.csv(maf, "Desktop/Abhilash/study/Post-Doc/Lab_notebook/eQTL/Input_3/Differential_regulation_files/eQTL_input/MATRIX_eQTL/whole_genome/input/snps_allchr_maf0.05.csv")
snp_values = read.table("Desktop/Abhilash/study/Post-Doc/Lab_notebook/eQTL/Input_3/Differential_regulation_files/eQTL_input/MATRIX_eQTL/whole_genome/input/snps_Allchr_maf0.05.txt", sep="\t", header=TRUE, row.names = 1)
#snp_values <- as.data.frame(as.matrix(t(snp_values)))
#Obtain the Covarties by PCA of the Genotypes
pca_snps = prcomp(t(snp_values), center=TRUE, scale = F) plot(pca_snps$x[,c(1,2)],pch=16)
head(pca_snps$x[,c(1,2)])
plot(pca_snps) fviz_eig(pca_snps, addlabels = TRUE)
pca_snps$sdev summary(pca_snps)
write.csv(pca_snps$x[,1:10],"Desktop/Abhilash/study/Post-Doc/Lab_notebook/eQTL/Input_3/Differential_regulation_files/eQTL_input/MATRIX_eQTL/input/pca_snps.csv")
# eQTL mapping, cis/trans, no pcs suppressMessages(library(MatrixEQTL))
SNP_file_name <- "Desktop/Abhilash/study/Post-Doc/Lab_notebook/eQTL/Input_3/Differential_regulation_files/eQTL_input/MATRIX_eQTL/whole_genome/input/snps_Allchr_maf0.05.txt";
snps_location_file_name <- "Desktop/Abhilash/study/Post-Doc/Lab_notebook/eQTL/Input_3/Differential_regulation_files/eQTL_input/MATRIX_eQTL/whole_genome/input/snploc_AllChr_maf0.05.txt"; expression_file_name <- "Desktop/Abhilash/study/Post-Doc/Lab_notebook/eQTL/Input_3/Differential_regulation_files/eQTL_input/MATRIX_eQTL/whole_genome/input/chrAll_FP_sum_GE.txt";
gene_location_file_name <- "Desktop/Abhilash/study/Post-Doc/Lab_notebook/eQTL/Input_3/Differential_regulation_files/eQTL_input/MATRIX_eQTL/whole_genome/input/chrAll_FP_sum_loc.txt";
covariates_file_name <- "Desktop/Abhilash/study/Post-Doc/Lab_notebook/eQTL/Input_3/Differential_regulation_files/eQTL_input/MATRIX_eQTL/whole_genome/input/pca_snps_10.txt";
cis_threshold <- 1e-5 trans_threshold <- 1e-5 cis_dist <- 2e4
# Output file name #output_file_name_cis = tempfile(); #output_file_name_tra = tempfile();
output_file_name_cis = "Desktop/Abhilash/study/Post-Doc/Lab_notebook/eQTL/Input_3/Differential_regulation_files/eQTL_input/MATRIX_eQTL/whole_genome/output_AllChr_cis_eqtls_PCA20kb.txt"; output_file_name_tra = "Desktop/Abhilash/study/Post-Doc/Lab_notebook/eQTL/Input_3/Differential_regulation_files/eQTL_input/MATRIX_eQTL/whole_genome/output_AllChr_trans_eqtls_PCA20kb.txt";
## Settings # Linear model to use, modelANOVA, modelLINEAR, or modelLINEAR_CROSS useModel = modelLINEAR; # modelANOVA, modelLINEAR, or modelLINEAR_CROSS
# Only associations significant at this level will be saved pvOutputThreshold_cis = cis_threshold; pvOutputThreshold_tra = trans_threshold;
# Set to character() for no covariates # covariates_file_name = character();
# Error covariance matrix # Set to numeric() for identity. errorCovariance = numeric();
# Distance for local gene-SNP pairs cisDist = cis_dist
## Load genotype data snps = SlicedData$new(); snps$fileDelimiter = "\t"; # the TAB character snps$fileOmitCharacters = "NA"; # denote missing values; snps$fileSkipRows = 1; # one row of column labels snps$fileSkipColumns = 1; # one column of row labels snps$fileSliceSize = 2000; # read file in slices of 2,000 rows snps$LoadFile(SNP_file_name);
## Load gene expression data gene = SlicedData$new(); gene$fileDelimiter = "\t"; # the TAB character gene$fileOmitCharacters = "NA"; # denote missing values; gene$fileSkipRows = 1; # one row of column labels gene$fileSkipColumns = 1; # one column of row labels gene$fileSliceSize = 2000; # read file in slices of 2,000 rows gene$LoadFile(expression_file_name);
## Load covariates cvrt = SlicedData$new(); cvrt$fileDelimiter = "\t"; # the TAB character cvrt$fileOmitCharacters = "NA"; # denote missing values; cvrt$fileSkipRows = 1; # one row of column labels cvrt$fileSkipColumns = 1; # one column of row labels if(length(covariates_file_name)>0) { cvrt$LoadFile(covariates_file_name); }
## Run the analysis snpspos = read.table(snps_location_file_name, header = TRUE, stringsAsFactors = FALSE); genepos = read.table(gene_location_file_name, header = TRUE, stringsAsFactors = FALSE);
me = Matrix_eQTL_main( snps = snps, gene = gene, cvrt = cvrt, output_file_name = output_file_name_tra, pvOutputThreshold = pvOutputThreshold_tra, useModel = useModel, errorCovariance = errorCovariance, verbose = FALSE, output_file_name.cis = output_file_name_cis, pvOutputThreshold.cis = pvOutputThreshold_cis, snpspos = snpspos, genepos = genepos, cisDist = cisDist, pvalue.hist = TRUE, min.pv.by.genesnp = FALSE, noFDRsaveMemory = FALSE);
me_qq = Matrix_eQTL_main( snps = snps, gene = gene, cvrt = cvrt, output_file_name = output_file_name_tra, pvOutputThreshold = pvOutputThreshold_tra, useModel = useModel, errorCovariance = errorCovariance, verbose = FALSE, output_file_name.cis = output_file_name_cis, pvOutputThreshold.cis = pvOutputThreshold_cis, snpspos = snpspos, genepos = genepos, cisDist = cisDist, pvalue.hist = "qqplot", min.pv.by.genesnp = FALSE, noFDRsaveMemory = FALSE)
unlink(output_file_name_tra); unlink(output_file_name_cis);
cat('Analysis done in: ', me$time.in.sec, ' seconds', '\n');
head(me$cis$eqtls) head(me$trans$eqtls)
## Make the histogram of local and distant p-values plot(me)
## Make the qq-plot of local and distant p-values plot(me_qq)
library(dplyr) snp_values = read.table(SNP_file_name, row.names=1, header=TRUE) snp_values = data.frame(snps = rownames(snp_values), snp_values, stringsAsFactors = FALSE) snp_values=snp_values[,-c(1)]
# For this we also need df with expression data gene_values = read.table(expression_file_name, row.names=1, header=TRUE) gene_values = data.frame(gene = rownames(gene_values), gene_values, stringsAsFactors = FALSE) gene_values=gene_values[,-c(1)] View(gene_values)
gene_values_transpose <- as.data.frame(as.matrix(t(gene_values))) snp_values_transpose <- as.data.frame(as.matrix(t(snp_values)))
cis_eqtl_res = me$cis$eqtls cis_eqtl_res = cis_eqtl_res[cis_eqtl_res$FDR < 0.05,] top_eqtls = cis_eqtl_res[order(cis_eqtl_res$pvalue),] top_eqtls = top_eqtls[!duplicated(top_eqtls$gene),] mafs = apply(as.matrix(snp_values[-1]),1,mean)/2 mafs = pmin(mafs, 1 - mafs) mafs = data.frame(snps=names(mafs), maf = mafs) top_eqtls = merge(top_eqtls, mafs, by="snps") top_eqtls = top_eqtls[order(top_eqtls$FDR),] head(top_eqtls) write.table(top_eqtls, "Desktop/Abhilash/study/Post-Doc/Lab_notebook/eQTL/Input_3/Differential_regulation_files/eQTL_input/MATRIX_eQTL/whole_genome/output/top_cis_eQTL_FP_20kb.txt", sep = "\t")
# Get gene name of gene with lowest association p-value gene_id = top_eqtls$gene[1] gene_id = as.character(gene_id) gene_id # Get corresponding SNP snp_id = top_eqtls[top_eqtls$gene == gene_id,"snps"][1] snp_id = as.character(snp_id) snp_id data = data.frame(t(snp_values[snp_id,]), t(gene_values[gene_id,]))
# # Get reference and alternative allele of the SNP # ref_alt = unlist(snpPos[snpPos$snpid== snp_id, c("ref", "alt")]) # # Prepare the genotype labels # gt_states= c(paste(ref_alt[1], ref_alt[1], sep="/"), paste(ref_alt[1], # ref_alt[2], sep="/"), paste(ref_alt[2], ref_alt[2], sep="/")) # gt_states = factor(gt_states, levels=gt_states) # # Assign the labels # data$gt = gt_states[data[,snp_id]+1] # # Subset to only genotype labels and expression # data = data[,c("gt", gene_id)] # colnames(data) = c("genotype", "expression") # # Plot # p = ggplot(data, aes(genotype, expression)) + # ggtitle(paste("eQTL of gene",gene_id, "with",snp_id))+ # geom_jitter(colour="darkgrey", position=position_jitter(width=0.25)) + # geom_boxplot(outlier.size=0, alpha=0.6, fill="grey") + theme_bw() # print(p) # # # # # # snpPos = data.frame(snps = rownames(snpspos), snpspos, stringsAsFactors = FALSE) # snpPos = snpPos[,-c(1)] # top_snp = top_eqtls$snps[1] # top_gene = as.character(top_eqtls$gene[1]) # # top_snp_data = filter(snp_values, snp_values$snps == top_snp) # top_gene_data = filter(gene_values, gene == top_gene)
plot_data = as.data.frame(data) colnames(plot_data) = c("snp", "gene_expr")
plot_data$snp = as.factor(plot_data$snp) head(plot_data)
lm_top = lm(plot_data[,"gene_expr"] ~ as.numeric(plot_data[,"snp"])) summary(lm_top)
plot(plot_data, col="steelblue", main = paste0(gene_id, "vs", snp_id)) abline(lm_top, col="darkorange", lwd = 2, lty = 2) y_range = range(plot_data[,"gene_expr"]) text(x=2, y=y_range[1] + 0.5*diff(y_range), paste0("p=", format(summary(lm_top)$coefficients[2,4], scentific=TRUE, digits=2)), col = "black") gene_id = "MDR1"
gene_values = read.table(expression_file_name, row.names=1, header=TRUE) single_gene_exp = SlicedData$new() single_gene_exp$CreateFromMatrix(as.matrix(gene_values[gene_id, , drop=FALSE])) single_gene_exp
snpspos = read.table(snps_location_file_name, header = TRUE, stringsAsFactors = FALSE)
genepos = read.table(gene_location_file_name, header = TRUE, stringsAsFactors = FALSE)
single_cis_eqtl_res = Matrix_eQTL_main(snps, single_gene_exp, verbose = FALSE, output_file_name.cis = NULL, output_file_name = NULL, pvOutputThreshold.cis=1, snpspos = snpspos, genepos = genepos)
manh_data = merge(single_cis_eqtl_res$cis$eqtls, snpspos, by.x =c("snps") , by.y = c("snpid")) manh_data = manh_data [,c("pos", "chr", "pvalue", "snps")] head(manh_data)
# Plot the Manhattanplot with(manh_data ,plot(pos, -log10(pvalue), xlab = "genomic position (bp)", main=paste(gene_id, "associated SNPs"))) # Highlight the lead SNP with(manh_data[which.min(manh_data$pvalue),,drop=FALSE] , points(pos, -log10(pvalue), pch=20, col="red")) # Add a label to the lead SNP with(manh_data[which.min(manh_data$pvalue),,drop=FALSE], text(pos + diff(range(manh_data$pos))*0.2, -log10(pvalue), labels = snps))
for (gene_id in top_eqtls$gene[c(1:50,(nrow(top_eqtls)-3):nrow(top_eqtls))]){ print(gene_id) single_gene_exp = SlicedData$new() single_gene_exp$CreateFromMatrix(as.matrix(gene_values[gene_id,, drop=FALSE]))
single_cis_eqtl_res = Matrix_eQTL_main(snps, single_gene_exp, output_file_name.cis = NULL, output_file_name = NULL, pvOutputThreshold.cis=1, verbose = FALSE, snpspos=snpspos, genepos=genepos)
manh_data = merge(single_cis_eqtl_res$cis$eqtl, snpspos, by.x =c("snps") , by.y = c("snpid")) manh_data =manh_data[,c("pos", "chr", "pvalue", "snps")] par(mfrow=c(1,1)) # Plot the Manhattanplot with(manh_data ,plot(pos, -log10(pvalue), xlab = "genomic position (bp)", main=paste(gene_id, "associated SNPs"))) # Highlight the lead SNP with(manh_data[which.min(manh_data$pvalue),,drop=FALSE] , points(pos, -log10(pvalue), pch=20, col="red")) # Add a label to the lead SNP with(manh_data[which.min(manh_data$pvalue),,drop=FALSE], text(pos + diff(range(manh_data$pos))*0.2, -log10(pvalue), labels = snps)) scan(stdin()) }
par(mfrow=c(3,3))
# Get gene name of gene with lowest association p-value gene_id = top_eqtls$gene[63] gene_id = as.character(gene_id) gene_id # Get corresponding SNP snp_id = top_eqtls[top_eqtls$gene == gene_id,"snps"][1] snp_id = as.character(snp_id) snp_id data = data.frame(t(snp_values[snp_id,]), t(gene_values[gene_id,]))
plot_data = as.data.frame(data) colnames(plot_data) = c("snp", "gene_expr")
plot_data$snp = as.factor(plot_data$snp) head(plot_data)
lm_top = lm(plot_data[,"gene_expr"] ~ as.numeric(plot_data[,"snp"])) summary(lm_top)
plot(plot_data, col="steelblue", main = paste0(gene_id,"vs" , snp_id)) abline(lm_top, col="darkorange", lwd = 2, lty = 2) y_range = range(plot_data[,"gene_expr"]) text(x=2, y=y_range[1] + 0.5*diff(y_range), paste0("p=", format(summary(lm_top)$coefficients[2,4], scentific=TRUE, digits=2)), col = "black")
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Testing Data-Intensive Code With Go, Part 1
Overview
Many non-trivial systems are also data-intensive or data-driven. Testing the parts of the systems that are data-intensive is very different than testing code-intensive systems. First, there may be a lot of sophistication in the data layer itself, such as hybrid data stores, caching, backup, and redundancy.
All this machinery has nothing to do with the application itself, but has to be tested. Second, the code may be very generic, and in order to test it, you need to generate data that is structured in a certain way. In this series of five tutorials, I will address all these aspects, explore several strategies for designing testable data-intensive systems with Go, and dive into specific examples.
In part one, I'll go over the design of an abstract data layer that enables proper testing, how to do error handling in the data layer, how to mock data access code, and how to test against an abstract data layer.
Testing Against a Data Layer
Dealing with real data stores and their intricacies is complicated and unrelated to the business logic. The concept of a data layer allows you to expose a neat interface to your data and hide the gory details of exactly how the data is stored and how to access it. I'll use a sample application called "Songify" for personal music management to illustrate the concepts with real code.
Designing an Abstract Data Layer
Let's review the personal music management domain—users can add songs and label them—and consider what data we need to store and how to access it. The objects in our domain are users, songs, and labels. There are two categories of operations that you want to perform on any data: queries (read-only) and state changes (create, update, delete). Here is a basic interface for the data layer:
package abstract_data_layer import "time" type Song struct { Url string Name string Description string } type Label struct { Name string } type User struct { Name string Email string RegisteredAt time.Time LastLogin time.Time } type DataLayer interface { // Queries (read-only) GetUsers() ([]User, error) GetUserByEmail(email string) (User, error) GetLabels() ([]Label, error) GetSongs() ([]Song, error) GetSongsByUser(user User) ([]Song, error) GetSongsByLabel(label string) ([]Song, error) // State changing operations CreateUser(user User) error ChangeUserName(user User, name string) error AddLabel(label string) error AddSong(user User, song Song, labels []Label) error }
Note that the purpose of this domain model is to present a simple yet not completely trivial data layer to demonstrate the testing aspects. Obviously, in a real application there will be more objects like albums, genres, artists, and much more information about each song. If push comes to shove, you can always store arbitrary information about a song in its description, as well as attaching as many labels as you want.
In practice, you may want to divide your data layer into multiple interfaces. Some of the structs may have more attributes, and the methods may require more arguments (e.g. all the GetXXX() methods will probably require some paging arguments). You may need other data access interfaces and methods for maintenance operations like bulk loading, backups, and migrations. It sometimes makes sense to expose an asynchronous data access interface instead or in addition to the synchronous interface.
What did we gain from this abstract data layer?
One-stop shop for data access operations.
Clear view of the data management requirements of our applications in domain terms.
Ability to change the concrete data layer implementation at will.
Ability to develop the domain/business logic layer early against the interface before the concrete data layer is complete or stable.
Last but not least, the ability to mock the data layer for fast and flexible testing of the domain/business logic.
Errors and Error Handling in the Data Layer
The data may be stored in multiple distributed data stores, on multiple clusters across different geographical locations in a combination of on-premise data centers and the cloud.
There will be failures, and those failures need to be handled. Ideally, the error handling logic (retries, timeouts, notification of catastrophic failures) can be handled by the concrete data layer. The domain logic code should just get back the data or a generic error when the data is unreachable.
In some cases, the domain logic may want more granular access to the data and select a fallback strategy in certain situations (e.g. only partial data is available because part of the cluster is inaccessible, or the data is stale because the cache wasn't refreshed). Those aspects have implications for the design of your data layer and for its testing.
As far as testing goes, you should return your own errors defined in the abstract data layer and map all concrete error messages to your own error types or rely on very generic error messages.
Mocking Data Access Code
Let's mock our data layer. The purpose of the mock is to replace the real data layer during tests. That requires the mock data layer to expose the same interface and to be able to respond to each sequence of methods with a canned (or calculated) response.
In addition, it's useful to keep track of how many times each method was called. I will not demonstrate it here, but it is even possible to keep track of the order of calls to different methods and which arguments were passed to each method to ensure a certain chain of calls.
Here is the mock data layer struct.
package concrete_data_layer import ( . "abstract_data_layer" ) const ( GET_USERS = iota GET_USER_BY_EMAIL GET_LABELS GET_SONGS GET_SONGS_BY_USER GET_SONG_BY_LABEL ERRORS ) type MockDataLayer struct { Errors []error GetUsersResponses [][]User GetUserByEmailResponses []User GetLabelsResponses [][]Label GetSongsResponses [][]Song GetSongsByUserResponses [][]Song GetSongsByLabelResponses[][]Song Indices []int } func NewMockDataLayer() MockDataLayer { return MockDataLayer{Indices: []int{0, 0, 0, 0, 0, 0, 0, 0}} }
The const statement lists all the supported operations and the errors. Each operation has its own index in the Indices slice. The index for each operation represents how many times the corresponding method was called as well as what the next response and error should be.
For each method that has a return value in addition to an error, there is a slice of responses. When the mock method is called, the corresponding response and error (based on the index for this method) are returned. For methods that don't have a return value except an error, there is no need to define a XXXResponses slice.
Note that the Errors are shared by all methods. That means that if you want to test a sequence of calls, you'll need to inject the correct number of errors in the correct order. An alternative design would use for each response a pair consisting of the return value and error. The NewMockDataLayer() function returns a new mock data layer struct with all indices initialized to zero.
Here is the implementation of the GetUsers() method, which illustrates these concepts.
func(m *MockDataLayer) GetUsers() (users []User, err error) { i := m.Indices[GET_USERS] users = m.GetUsersResponses[i] if len(m.Errors) > 0 { err = m.Errors[m.Indices[ERRORS]] m.Indices[ERRORS]++ } m.Indices[GET_USERS]++ return }
The first line gets the current index of the GET_USERS operation (will be 0 initially).
The second line gets the response for the current index.
The third through fifth lines assign the error of the current index if the Errors field was populated and increment the errors index. When testing the happy path, the error will be nil. To make it easier to use, you can just avoid initializing the Errors field and then every method will return nil for the error.
The next line increments the index, so the next call will get the proper response.
The last line just returns. The named return values for users and err are already populated (or nil by default for err).
Here is another method, GetLabels(), which follows the same pattern. The only difference is which index is used and what collection of canned responses is used.
func(m *MockDataLayer) GetLabels() (labels []Label, err error) { i := m.Indices[GET_LABELS] labels = m.GetLabelsResponses[i] if len(m.Errors) > 0 { err = m.Errors[m.Indices[ERRORS]] m.Indices[ERRORS]++ } m.Indices[GET_LABELS]++ return }
This is a prime example of a use case where generics could save a lot of boilerplate code. It's possible to take advantage of reflection to the same effect, but it's outside the scope of this tutorial. The main take-away here is that the mock data layer can follow a general-purpose pattern and support any testing scenario, as you'll see soon.
How about some methods that just return an error? Check out the CreateUser() method. It is even simpler because it only deals with errors and doesn't need to manage the canned responses.
func(m *MockDataLayer) CreateUser(user User) (err error) { if len(m.Errors) > 0 { i := m.Indices[CREATE_USER] err = m.Errors[m.Indices[ERRORS]] m.Indices[ERRORS]++ } return }
This mock data layer is just an example of what it takes to mock an interface and provide some useful services to test. You can come up with your own mock implementation or use available mock libraries. There is even a standard GoMock framework.
I personally find mock frameworks easy to implement and prefer to roll my own (often generating them automatically) because I spend most of my development time writing tests and mocking dependencies. YMMV.
Testing Against an Abstract Data Layer
Now that we have a mock data layer, let's write some tests against it. It's important to realize that here we don't test the data layer itself. We will test the data layer itself with other methods later in this series. The purpose here is to test the logic of the code that depends on the abstract data layer.
For example, suppose a user wants to add a song, but we have a quota of 100 songs per user. The expected behavior is that if the user has fewer than 100 songs and the added song is new, it will be added. If the song already exists then it returns a "Duplicate song" error. If the user already has 100 songs then it returns a "Song quota exceeded" error.
Let's write a test for these test cases using our mock data layer. This is a white-box test, meaning you need to know which methods of the data layer the code under test is going to call and in which order so you can populate the mock responses and errors properly. So the test-first approach is not ideal here. Let's write the code first.
Here is the SongManager struct. It depends only on the abstract data layer. That will enable you to pass it an implementation of a real data layer in production, but a mock data layer during testing.
The SongManager itself is completely agnostic to the concrete implementation of the DataLayer interface. The SongManager struct also accepts a user, which it stores. Presumably, each active user has its own SongManager instance, and users can only add songs for themselves. The NewSongManager() function ensures the input DataLayer interface is not nil.
package song_manager import ( "errors" . "abstract_data_layer" ) const ( MAX_SONGS_PER_USER = 100 ) type SongManager struct { user User dal DataLayer } func NewSongManager(user User, dal DataLayer) (*SongManager, error) { if dal == nil { return nil, errors.New("DataLayer can't be nil") } return &SongManager{user, dal}, nil }
Let's implement an AddSong() method. The method calls the data layer's GetSongsByUser() first, and then it goes through several checks. If everything is OK, it calls the data layer's AddSong() method and returns the result.
func(lm *SongManager) AddSong(newSong Song, labels []Label) error { songs, err := lm.dal.GetSongsByUser(lm.user) if err != nil { return nil } // Check if song is a duplicate for _, song := range songs { if song.Url == newSong.Url { return errors.New("Duplicate song") } } // Check if user has max number of songs if len(songs) == MAX_SONGS_PER_USER { return errors.New("Song quota exceeded") } return lm.dal.AddSong(user, newSong, labels) }
Looking at this code, you can see that there are two other test cases we neglected: the calls to the data layer's methods GetSongByUser() and AddSong() might fail for other reasons. Now, with the implementation of SongManager.AddSong() in front of us, we can write a comprehensive test that covers all the use cases. Let's start with the happy path. The TestAddSong_Success() method creates a user named Gigi and a mock data layer.
It populates the GetSongsByUserResponses field with a slice that contains an empty slice, which will result in an empty slice when the SongManager calls GetSongsByUser() on the mock data layer with no error. There is no need to do anything for the call to the mock data layer's AddSong() method, which will return nil error by default. The test just verifies that indeed no error was returned from the parent call to the SongManager's AddSong() method.
package song_manager import ( "testing" . "abstract_data_layer" . "concrete_data_layer" ) func TestAddSong_Success(t *testing.T) { u := User{Name:"Gigi", Email: "[email protected]"} mock := NewMockDataLayer() // Prepare mock responses mock.GetSongsByUserResponses = [][]Song lm, err := NewSongManager(u, &mock) if err != nil { t.Error("NewSongManager() returned 'nil'") } url := https://www.youtube.com/watch?v=MlW7T0SUH0E" err = lm.AddSong(Song{Url: url", Name: "Chacarron"}, nil) if err != nil { t.Error("AddSong() failed") } } $ go test PASS ok song_manager 0.006s
Testing error conditions is super easy too. You have full control on what the data layer returns from the calls to GetSongsByUser() and AddSong(). Here is a test to verify that when adding a duplicate song you get the proper error message back.
func TestAddSong_Duplicate(t *testing.T) { u := User{Name:"Gigi", Email: "[email protected]"} mock := NewMockDataLayer() // Prepare mock responses mock.GetSongsByUserResponses = [][]Song lm, err := NewSongManager(u, &mock) if err != nil { t.Error("NewSongManager() returned 'nil'") } err = lm.AddSong(testSong, nil) if err == nil { t.Error("AddSong() should have failed") } if err.Error() != "Duplicate song" { t.Error("AddSong() wrong error: " + err.Error()) } }
The following two test cases test that the correct error message is returned when the data layer itself fails. In the first case the data layer's GetSongsByUser() returns an error.
func TestAddSong_DataLayerFailure_1(t *testing.T) { u := User{Name:"Gigi", Email: "[email protected]"} mock := NewMockDataLayer() // Prepare mock responses mock.GetSongsByUserResponses = [][]Song e := errors.New("GetSongsByUser() failure") mock.Errors = []error{e} lm, err := NewSongManager(u, &mock) if err != nil { t.Error("NewSongManager() returned 'nil'") } err = lm.AddSong(testSong, nil) if err == nil { t.Error("AddSong() should have failed") } if err.Error() != "GetSongsByUser() failure" { t.Error("AddSong() wrong error: " + err.Error()) } }
In the second case, the data layer's AddSong() method returns an error. Since the first call to GetSongsByUser() should succeed, the mock.Errors slice contains two items: nil for the first call and the error for the second call.
func TestAddSong_DataLayerFailure_2(t *testing.T) { u := User{Name:"Gigi", Email: "[email protected]"} mock := NewMockDataLayer() // Prepare mock responses mock.GetSongsByUserResponses = [][]Song e := errors.New("AddSong() failure") mock.Errors = []error{nil, e} lm, err := NewSongManager(u, &mock) if err != nil { t.Error("NewSongManager() returned 'nil'") } err = lm.AddSong(testSong, nil) if err == nil { t.Error("AddSong() should have failed") } if err.Error() != "AddSong() failure" { t.Error("AddSong() wrong error: " + err.Error()) } }
Conclusion
In this tutorial, we introduced the concept of an abstract data layer. Then, using the personal music management domain, we demonstrated how to design a data layer, build a mock data layer, and use the mock data layer to test the application.
In part two, we will focus on testing using a real in-memory data layer. Stay tuned.
by Gigi Sayfan via Envato Tuts+ Code http://ift.tt/2AdwbD7
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