In this first part of the assignment we will brush up your programming skills, and make you familiar with the data sets you will be analysing for the next parts of the assignment.
In this warm-up assignment you will: 1) Create a Github (or gitlab) account, link it to your RStudio, and create a new repository/project 2) Use small nifty lines of code to transform several data sets into just one. The final data set will contain only the variables that are needed for the analysis in the next parts of the assignment 3) Warm up your tidyverse skills (especially the sub-packages stringr and dplyr), which you will find handy for later assignments.
N.B: Usually you'll also have to doc/pdf with a text. Not for Assignment 1.
- Become comfortable with tidyverse (and R in general)
- Test out the git integration with RStudio
- Build expertise in data wrangling (which will be used in future assignments)
- First an introduction on the data
Reference to the study: https://www.ncbi.nlm.nih.gov/pubmed/30396129
Background: Autism Spectrum Disorder (ASD) is often related to language impairment, and language impairment strongly affects the patients ability to function socially (maintaining a social network, thriving at work, etc.). It is therefore crucial to understand how language abilities develop in children with ASD, and which factors affect them (to figure out e.g. how a child will develop in the future and whether there is a need for language therapy). However, language impairment is always quantified by relying on the parent, teacher or clinician subjective judgment of the child, and measured very sparcely (e.g. at 3 years of age and again at 6).
In this study we videotaped circa 30 kids with ASD and circa 30 comparison kids (matched by linguistic performance at visit 1) for ca. 30 minutes of naturalistic interactions with a parent. We repeated the data collection 6 times per kid, with 4 months between each visit. We transcribed the data and counted: i) the amount of words that each kid uses in each video. Same for the parent. ii) the amount of unique words that each kid uses in each video. Same for the parent. iii) the amount of morphemes per utterance (Mean Length of Utterance) displayed by each child in each video. Same for the parent.
Different researchers involved in the project provide you with different datasets: 1) demographic and clinical data about the children (recorded by a clinical psychologist) 2) length of utterance data (calculated by a linguist) 3) amount of unique and total words used (calculated by a fumbling jack-of-all-trade, let's call him RF)
Your job in this assignment is to double check the data and make sure that it is ready for the analysis proper (Assignment 2), in which we will try to understand how the children's language develops as they grow as a function of cognitive and social factors and which are the "cues" suggesting a likely future language impairment.
- Let's get started on GitHub
In the assignments you will be asked to upload your code on Github and the GitHub repositories will be part of the portfolio, therefore all students must make an account and link it to their RStudio (you'll thank us later for this!).
Follow the link to one of the tutorials indicated in the syllabus: * Recommended: https://happygitwithr.com/ * Alternative (if the previous doesn't work): https://support.rstudio.com/hc/en-us/articles/200532077-Version-Control-with-Git-and-SVN * Alternative (if the previous doesn't work): https://docs.google.com/document/d/1WvApy4ayQcZaLRpD6bvAqhWncUaPmmRimT016-PrLBk/mobilebasic
N.B. Create a GitHub repository for the Assignment 1 and link it to a project on your RStudio.
- Now let's take dirty dirty data sets and make them into a tidy one
If you're not in a project in Rstudio, make sure to set your working directory here. If you created an RStudio project, then your working directory (the directory with your data and code for these assignments) is the project directory.
pacman::p_load(tidyverse,janitor, dplyr)Load the three data sets, after downloading them from dropbox and saving them in your working directory: * Demographic data for the participants: https://www.dropbox.com/s/w15pou9wstgc8fe/demo_train.csv?dl=0 * Length of utterance data: https://www.dropbox.com/s/usyauqm37a76of6/LU_train.csv?dl=0 * Word data: https://www.dropbox.com/s/8ng1civpl2aux58/token_train.csv?dl=0
# Read the data into R
utterance <- read.csv("LU_train.csv")
token<- read.csv("token_train.csv")
demographics <- read.csv("demo_train.csv")Explore the 3 datasets (e.g. visualize them, summarize them, etc.). You will see that the data is messy, since the psychologist collected the demographic data, the linguist analyzed the length of utterance in May 2014 and the fumbling jack-of-all-trades analyzed the words several months later. In particular: - the same variables might have different names (e.g. participant and visit identifiers) - the same variables might report the values in different ways (e.g. participant and visit IDs) Welcome to real world of messy data :-)
Before being able to combine the data sets we need to make sure the relevant variables have the same names and the same kind of values.
So:
2a. Identify which variable names do not match (that is are spelled differently) and find a way to transform variable names. Pay particular attention to the variables indicating participant and visit.
Tip: look through the chapter on data transformation in R for data science (http://r4ds.had.co.nz). Alternatively you can look into the package dplyr (part of tidyverse), or google "how to rename variables in R". Or check the janitor R package. There are always multiple ways of solving any problem and no absolute best method.
# Changing names of mismatching variable names
demographics <- dplyr::rename(demographics, SUBJ=Child.ID)
demographics <- dplyr::rename(demographics, VISIT=Visit)2b. Find a way to homogeneize the way "visit" is reported (visit1 vs. 1).
Tip: The stringr package is what you need. str_extract () will allow you to extract only the digit (number) from a string, by using the regular expression \d.
# Homogenizing the 'VISIT' collumn in utterance and token data with the other data frames
utterance$VISIT <- str_extract(utterance$VISIT, "\\d")
token$VISIT <- str_extract(token$VISIT, "\\d")2c. We also need to make a small adjustment to the content of the Child.ID coloumn in the demographic data. Within this column, names that are not abbreviations do not end with "." (i.e. Adam), which is the case in the other two data sets (i.e. Adam.). If The content of the two variables isn't identical the rows will not be merged. A neat way to solve the problem is simply to remove all "." in all datasets.
Tip: stringr is helpful again. Look up str_replace_all Tip: You can either have one line of code for each child name that is to be changed (easier, more typing) or specify the pattern that you want to match (more complicated: look up "regular expressions", but less typing)
# Removing punctuation from names
demographics$SUBJ <- str_replace_all(demographics$SUBJ, "[[:punct:]]", "")
utterance$SUBJ <- str_replace_all(utterance$SUBJ, "[[:punct:]]", "")
token$SUBJ <- str_replace_all(token$SUBJ, "[[:punct:]]", "")2d. Now that the nitty gritty details of the different data sets are fixed, we want to make a subset of each data set only containig the variables that we wish to use in the final data set. For this we use the tidyverse package dplyr, which contains the function select().
The variables we need are: * Child.ID, * Visit, * Diagnosis, * Ethnicity, * Gender, * Age, * ADOS, * MullenRaw, * ExpressiveLangRaw, * Socialization * MOT_MLU, * CHI_MLU, * types_MOT, * types_CHI, * tokens_MOT, * tokens_CHI.
Most variables should make sense, here the less intuitive ones. * ADOS (Autism Diagnostic Observation Schedule) indicates the severity of the autistic symptoms (the higher the score, the worse the symptoms). Ref: https://link.springer.com/article/10.1023/A:1005592401947 * MLU stands for mean length of utterance (usually a proxy for syntactic complexity) * types stands for unique words (e.g. even if "doggie" is used 100 times it only counts for 1) * tokens stands for overall amount of words (if "doggie" is used 100 times it counts for 100) * MullenRaw indicates non verbal IQ, as measured by Mullen Scales of Early Learning (MSEL https://link.springer.com/referenceworkentry/10.1007%2F978-1-4419-1698-3_596) * ExpressiveLangRaw indicates verbal IQ, as measured by MSEL * Socialization indicates social interaction skills and social responsiveness, as measured by Vineland (https://cloudfront.ualberta.ca/-/media/ualberta/faculties-and-programs/centres-institutes/community-university-partnership/resources/tools---assessment/vinelandjune-2012.pdf)
Feel free to rename the variables into something you can remember (i.e. nonVerbalIQ, verbalIQ)
# Selecting only the relevant collumns
demographics <- select(demographics, SUBJ, VISIT, Diagnosis, Ethnicity, Age, Gender, ADOS, Socialization, ExpressiveLangRaw,MullenRaw)
utterance <- select(utterance, SUBJ, VISIT, MOT_MLU, CHI_MLU)
token <- select(token, SUBJ, VISIT, types_MOT, types_CHI, tokens_MOT, tokens_CHI)2e. Finally we are ready to merge all the data sets into just one.
Some things to pay attention to: * make sure to check that the merge has included all relevant data (e.g. by comparing the number of rows) * make sure to understand whether (and if so why) there are NAs in the dataset (e.g. some measures were not taken at all visits, some recordings were lost or permission to use was withdrawn)
# Merging data into one dataframe
merged_initial <- merge(demographics, token, by=c("SUBJ", "VISIT"))
merged_final <- merge(merged_initial, utterance, by=c("SUBJ", "VISIT"))2f. Only using clinical measures from Visit 1 In order for our models to be useful, we want to miimize the need to actually test children as they develop. In other words, we would like to be able to understand and predict the children's linguistic development after only having tested them once. Therefore we need to make sure that our ADOS, MullenRaw, ExpressiveLangRaw and Socialization variables are reporting (for all visits) only the scores from visit 1.
A possible way to do so: * create a new dataset with only visit 1, child id and the 4 relevant clinical variables to be merged with the old dataset * rename the clinical variables (e.g. ADOS to ADOS1) and remove the visit (so that the new clinical variables are reported for all 6 visits) * merge the new dataset with the old
# Creating 4 new collumns only containing data from visit1
visit1 <- filter(demographics, VISIT==1) %>% select(SUBJ, ADOS, MullenRaw, ExpressiveLangRaw, Socialization) %>% dplyr::rename(ADOS1=ADOS, MullenRaw1=MullenRaw, ExpressiveLangRaw1=ExpressiveLangRaw, Socialization1=Socialization)
# Merging new collumns with final dataframe
merged_final <- merge(merged_final, visit1 , by=c("SUBJ"))2g. Final touches Now we want to * anonymize our participants (they are real children!). * make sure the variables have sensible values. E.g. right now gender is marked 1 and 2, but in two weeks you will not be able to remember, which gender were connected to which number, so change the values from 1 and 2 to F and M in the gender variable. For the same reason, you should also change the values of Diagnosis from A and B to ASD (autism spectrum disorder) and TD (typically developing). Tip: Try taking a look at ifelse(), or google "how to rename levels in R". * Save the data set using into a csv file. Hint: look into write.csv()
# Changing names of collumns to something more meaningful
merged_final$Gender <- as.character(merged_final$Gender)
levels(merged_final$Gender) <- c("F","M")
levels(merged_final$Diagnosis) <- c("ASD","TD")
# Anonymzing the data
merged_final$SUBJ <- as.numeric(as.factor(merged_final$SUBJ))
# Save as csv
write.csv(merged_final, "Merged_data.csv")-
BONUS QUESTIONS The aim of this last section is to make sure you are fully fluent in the tidyverse. Here's the link to a very helpful book, which explains each function: http://r4ds.had.co.nz/index.html
-
USING FILTER List all kids who:
- have a mean length of utterance (across all visits) of more than 2.7 morphemes.
- have a mean length of utterance of less than 1.5 morphemes at the first visit
- have not completed all trials. Tip: Use pipes to solve this
# Kids with mean MLU > 2.7
merged_final %>% group_by(SUBJ) %>% filter(mean(CHI_MLU)>2.7) %>% select(SUBJ) %>% unique()## # A tibble: 11 x 1
## # Groups: SUBJ [11]
## SUBJ
## <dbl>
## 1 2
## 2 3
## 3 4
## 4 12
## 5 18
## 6 25
## 7 26
## 8 42
## 9 49
## 10 53
## 11 60
# Kids with MLU on visit 1 < 1.5
merged_final %>% filter(VISIT=="1") %>% filter(CHI_MLU<1.5) %>% select(SUBJ) %>% unique()## SUBJ
## 1 1
## 2 5
## 3 6
## 4 7
## 5 9
## 6 10
## 7 11
## 8 12
## 9 14
## 10 15
## 11 17
## 12 19
## 13 20
## 14 21
## 15 22
## 16 23
## 17 24
## 18 27
## 19 28
## 20 29
## 21 30
## 22 33
## 23 34
## 24 35
## 25 36
## 26 37
## 27 38
## 28 39
## 29 40
## 30 41
## 31 42
## 32 44
## 33 45
## 34 46
## 35 47
## 36 48
## 37 49
## 38 50
## 39 51
## 40 52
## 41 54
## 42 55
## 43 56
## 44 57
## 45 58
## 46 59
## 47 61
# Kids who have not completed all 6 trials sby CHI_MLU)
merged_final %>% group_by(SUBJ) %>% filter(is.na(CHI_MLU) | n()<6) %>% select(SUBJ) %>% unique() ## # A tibble: 13 x 1
## # Groups: SUBJ [13]
## SUBJ
## <dbl>
## 1 2
## 2 7
## 3 8
## 4 9
## 5 17
## 6 26
## 7 38
## 8 40
## 9 44
## 10 45
## 11 48
## 12 55
## 13 56
USING ARRANGE
- Sort kids to find the kid who produced the most words on the 6th visit
- Sort kids to find the kid who produced the least amount of words on the 1st visit.
# Aranging after most words on visit 6
merged_final %>% filter(VISIT=="6") %>% arrange(desc(tokens_CHI))## SUBJ VISIT Diagnosis Ethnicity Age Gender ADOS Socialization
## 1 55 6 TD White 41.93 1 NA 95
## 2 26 6 ASD White 51.00 1 NA 90
## 3 18 6 TD White 44.07 1 NA 116
## 4 25 6 TD White 42.47 1 NA 103
## 5 43 6 ASD White 57.37 1 NA 77
## 6 37 6 ASD White 58.77 1 NA 116
## 7 13 6 TD White 39.40 1 NA 92
## 8 22 6 TD White 39.23 2 NA 110
## 9 14 6 TD White 39.43 1 NA 83
## 10 4 6 ASD White/Latino 51.37 1 NA 74
## 11 42 6 TD White 40.13 1 NA 101
## 12 60 6 ASD White 54.73 1 NA 97
## 13 16 6 TD White 42.93 1 NA 97
## 14 41 6 TD White 39.93 1 NA 101
## 15 2 6 ASD White 49.70 1 NA 81
## 16 1 6 TD White 40.13 1 NA 100
## 17 11 6 TD White 40.27 1 NA 101
## 18 59 6 TD White 41.00 1 NA NA
## 19 49 6 TD White 43.40 1 NA 101
## 20 12 6 TD White 41.50 1 NA 97
## 21 58 6 ASD White 46.07 1 NA 79
## 22 35 6 TD White 39.07 1 NA 97
## 23 34 6 ASD White 53.77 1 NA 92
## 24 20 6 ASD White 37.30 1 NA 103
## 25 40 6 ASD Lebanese 46.40 1 NA 94
## 26 36 6 TD White 38.53 1 NA 97
## 27 5 6 ASD White 54.13 1 NA 75
## 28 27 6 TD White 41.17 1 NA 106
## 29 28 6 TD White 39.43 1 NA 95
## 30 54 6 TD White 39.30 1 NA 101
## 31 3 6 TD White 45.07 2 NA 86
## 32 52 6 TD White 43.03 1 NA 97
## 33 31 6 TD White 43.80 1 NA 108
## 34 15 6 TD White 40.30 1 NA 94
## 35 51 6 TD Asian 42.10 2 NA 108
## 36 53 6 TD White 44.43 1 NA 101
## 37 48 6 ASD White NA 1 NA 63
## 38 6 6 ASD Bangladeshi 46.53 2 NA 74
## 39 19 6 ASD White 56.73 1 NA 72
## 40 23 6 ASD White/Latino 47.50 1 NA 65
## 41 61 6 ASD White 62.33 1 NA 103
## 42 29 6 ASD White 55.17 1 NA 72
## 43 8 6 TD White 40.17 1 NA 108
## 44 30 6 ASD White 56.43 1 NA 85
## 45 10 6 TD White 40.43 1 NA 103
## 46 24 6 ASD White 62.40 1 NA 68
## 47 46 6 ASD White 57.43 1 NA 59
## 48 39 6 ASD White/Asian 53.63 1 NA 63
## 49 32 6 ASD White 54.63 1 NA 66
## 50 33 6 ASD African American 46.17 2 NA 83
## 51 57 6 ASD White 61.70 2 NA 68
## 52 47 6 TD White 40.37 2 NA 101
## 53 17 6 ASD White 54.43 1 NA 65
## 54 56 6 ASD White NA 1 NA NA
## 55 50 6 ASD White 62.40 1 NA 66
## 56 21 6 ASD African American 48.97 1 NA 65
## ExpressiveLangRaw MullenRaw types_MOT types_CHI tokens_MOT tokens_CHI
## 1 38 45 367 260 1731 1294
## 2 46 46 452 273 3076 1249
## 3 50 50 516 235 2576 1079
## 4 39 48 555 237 2895 1010
## 5 45 49 374 219 2227 921
## 6 48 50 429 217 2510 897
## 7 41 47 388 210 1959 864
## 8 40 43 478 221 2044 847
## 9 37 44 359 178 1802 793
## 10 44 44 410 166 2171 738
## 11 46 46 357 219 1764 719
## 12 50 50 505 226 3072 713
## 13 46 49 491 250 2264 710
## 14 36 45 397 213 1741 702
## 15 48 48 304 245 999 698
## 16 44 42 595 210 2586 686
## 17 27 42 260 168 1138 666
## 18 NA NA 303 158 1460 659
## 19 48 45 383 217 1701 646
## 20 47 44 339 201 1498 640
## 21 30 45 335 197 2221 618
## 22 34 45 411 156 2438 611
## 23 32 44 342 157 1540 605
## 24 48 43 433 155 2389 590
## 25 37 41 417 170 2508 571
## 26 31 46 324 165 1978 568
## 27 37 40 462 179 3182 538
## 28 35 43 385 154 1788 530
## 29 32 39 391 164 1889 521
## 30 41 41 437 183 2347 517
## 31 45 45 486 173 2564 460
## 32 44 47 548 163 2887 436
## 33 45 45 475 185 2363 418
## 34 40 40 327 156 1395 410
## 35 40 46 383 140 1866 395
## 36 36 45 249 102 1024 358
## 37 21 32 319 98 1565 306
## 38 26 39 158 66 536 300
## 39 32 30 256 55 995 274
## 40 18 30 372 47 1748 236
## 41 31 41 311 58 1481 210
## 42 21 39 454 52 2391 204
## 43 39 43 322 64 1352 197
## 44 22 49 425 101 2219 195
## 45 33 33 400 73 2271 189
## 46 12 30 309 62 1349 143
## 47 10 32 351 10 1918 137
## 48 29 30 330 55 1987 110
## 49 10 28 585 12 2202 100
## 50 18 33 326 14 1852 79
## 51 9 32 444 2 2450 64
## 52 46 43 322 34 1371 61
## 53 11 27 281 3 1454 37
## 54 17 34 284 6 1390 36
## 55 11 24 370 4 1396 8
## 56 16 26 388 2 2077 2
## MOT_MLU CHI_MLU ADOS1 MullenRaw1 ExpressiveLangRaw1 Socialization1
## 1 3.957230 2.9092742 0 26 17 96
## 2 4.111413 3.3643411 11 32 33 100
## 3 4.013353 2.9095355 0 29 33 106
## 4 4.472993 3.0614525 0 29 22 90
## 5 4.250853 2.6794872 14 42 27 65
## 6 4.240798 3.0774194 7 33 26 70
## 7 4.847418 3.7011952 0 25 17 102
## 8 4.211321 3.0911950 0 21 19 106
## 9 4.664286 3.8114035 0 23 17 92
## 10 3.532374 3.2781955 8 31 27 82
## 11 3.391525 3.0727969 0 24 19 96
## 12 4.080446 3.4415584 13 30 30 87
## 13 4.445872 2.9482072 0 29 26 102
## 14 4.061475 2.8526316 0 25 17 102
## 15 4.588477 3.4135021 13 34 27 85
## 16 4.664013 2.8651685 0 28 14 108
## 17 4.287582 2.7571429 0 21 15 106
## 18 4.113158 2.8480000 4 29 22 108
## 19 5.247093 3.5950000 0 27 27 108
## 20 4.468493 3.5049505 0 30 16 104
## 21 3.320000 2.1553398 15 30 24 88
## 22 4.239198 2.3815789 1 23 21 102
## 23 3.370937 2.2745098 10 27 22 82
## 24 5.379798 2.9027778 9 26 14 86
## 25 3.926928 2.1586207 13 27 13 86
## 26 4.388235 2.5614754 3 19 13 98
## 27 4.587179 2.7665198 9 34 27 82
## 28 4.254505 2.4800000 0 26 18 96
## 29 4.239437 2.9607843 0 20 16 102
## 30 4.186161 2.8921569 1 24 22 115
## 31 5.229885 3.7103448 1 29 18 88
## 32 5.587332 2.4292929 0 29 22 94
## 33 3.603473 2.0717489 0 27 22 100
## 34 4.347709 2.8690476 0 24 15 86
## 35 5.153639 2.7612903 1 22 14 102
## 36 3.706790 3.2439024 0 30 30 98
## 37 3.370093 1.4734513 17 28 10 82
## 38 2.483146 1.4967320 17 20 17 68
## 39 3.156695 2.1450382 11 28 20 88
## 40 3.534173 1.3052632 14 25 19 65
## 41 3.514403 1.4012346 15 27 16 79
## 42 4.349353 1.2883436 17 26 14 72
## 43 4.366972 2.2258065 5 32 31 102
## 44 4.341549 1.0843373 12 31 13 70
## 45 4.235585 2.7051282 3 27 18 104
## 46 3.860795 1.1680000 20 13 11 67
## 47 3.460548 1.1610169 20 21 9 75
## 48 4.100707 1.6447368 11 26 19 88
## 49 4.003257 2.6315789 21 21 9 69
## 50 3.965392 0.7536232 14 25 11 76
## 51 3.241422 0.0156250 15 28 10 66
## 52 4.676101 2.7600000 0 27 20 102
## 53 3.650235 1.0000000 14 25 11 74
## 54 3.474725 1.0588235 14 27 11 77
## 55 3.886842 1.3333333 19 17 10 64
## 56 3.943636 0.5000000 21 22 8 72
#Aranging after least words on visit 1
merged_final %>% filter(VISIT=="6") %>% arrange(tokens_CHI)## SUBJ VISIT Diagnosis Ethnicity Age Gender ADOS Socialization
## 1 21 6 ASD African American 48.97 1 NA 65
## 2 50 6 ASD White 62.40 1 NA 66
## 3 56 6 ASD White NA 1 NA NA
## 4 17 6 ASD White 54.43 1 NA 65
## 5 47 6 TD White 40.37 2 NA 101
## 6 57 6 ASD White 61.70 2 NA 68
## 7 33 6 ASD African American 46.17 2 NA 83
## 8 32 6 ASD White 54.63 1 NA 66
## 9 39 6 ASD White/Asian 53.63 1 NA 63
## 10 46 6 ASD White 57.43 1 NA 59
## 11 24 6 ASD White 62.40 1 NA 68
## 12 10 6 TD White 40.43 1 NA 103
## 13 30 6 ASD White 56.43 1 NA 85
## 14 8 6 TD White 40.17 1 NA 108
## 15 29 6 ASD White 55.17 1 NA 72
## 16 61 6 ASD White 62.33 1 NA 103
## 17 23 6 ASD White/Latino 47.50 1 NA 65
## 18 19 6 ASD White 56.73 1 NA 72
## 19 6 6 ASD Bangladeshi 46.53 2 NA 74
## 20 48 6 ASD White NA 1 NA 63
## 21 53 6 TD White 44.43 1 NA 101
## 22 51 6 TD Asian 42.10 2 NA 108
## 23 15 6 TD White 40.30 1 NA 94
## 24 31 6 TD White 43.80 1 NA 108
## 25 52 6 TD White 43.03 1 NA 97
## 26 3 6 TD White 45.07 2 NA 86
## 27 54 6 TD White 39.30 1 NA 101
## 28 28 6 TD White 39.43 1 NA 95
## 29 27 6 TD White 41.17 1 NA 106
## 30 5 6 ASD White 54.13 1 NA 75
## 31 36 6 TD White 38.53 1 NA 97
## 32 40 6 ASD Lebanese 46.40 1 NA 94
## 33 20 6 ASD White 37.30 1 NA 103
## 34 34 6 ASD White 53.77 1 NA 92
## 35 35 6 TD White 39.07 1 NA 97
## 36 58 6 ASD White 46.07 1 NA 79
## 37 12 6 TD White 41.50 1 NA 97
## 38 49 6 TD White 43.40 1 NA 101
## 39 59 6 TD White 41.00 1 NA NA
## 40 11 6 TD White 40.27 1 NA 101
## 41 1 6 TD White 40.13 1 NA 100
## 42 2 6 ASD White 49.70 1 NA 81
## 43 41 6 TD White 39.93 1 NA 101
## 44 16 6 TD White 42.93 1 NA 97
## 45 60 6 ASD White 54.73 1 NA 97
## 46 42 6 TD White 40.13 1 NA 101
## 47 4 6 ASD White/Latino 51.37 1 NA 74
## 48 14 6 TD White 39.43 1 NA 83
## 49 22 6 TD White 39.23 2 NA 110
## 50 13 6 TD White 39.40 1 NA 92
## 51 37 6 ASD White 58.77 1 NA 116
## 52 43 6 ASD White 57.37 1 NA 77
## 53 25 6 TD White 42.47 1 NA 103
## 54 18 6 TD White 44.07 1 NA 116
## 55 26 6 ASD White 51.00 1 NA 90
## 56 55 6 TD White 41.93 1 NA 95
## ExpressiveLangRaw MullenRaw types_MOT types_CHI tokens_MOT tokens_CHI
## 1 16 26 388 2 2077 2
## 2 11 24 370 4 1396 8
## 3 17 34 284 6 1390 36
## 4 11 27 281 3 1454 37
## 5 46 43 322 34 1371 61
## 6 9 32 444 2 2450 64
## 7 18 33 326 14 1852 79
## 8 10 28 585 12 2202 100
## 9 29 30 330 55 1987 110
## 10 10 32 351 10 1918 137
## 11 12 30 309 62 1349 143
## 12 33 33 400 73 2271 189
## 13 22 49 425 101 2219 195
## 14 39 43 322 64 1352 197
## 15 21 39 454 52 2391 204
## 16 31 41 311 58 1481 210
## 17 18 30 372 47 1748 236
## 18 32 30 256 55 995 274
## 19 26 39 158 66 536 300
## 20 21 32 319 98 1565 306
## 21 36 45 249 102 1024 358
## 22 40 46 383 140 1866 395
## 23 40 40 327 156 1395 410
## 24 45 45 475 185 2363 418
## 25 44 47 548 163 2887 436
## 26 45 45 486 173 2564 460
## 27 41 41 437 183 2347 517
## 28 32 39 391 164 1889 521
## 29 35 43 385 154 1788 530
## 30 37 40 462 179 3182 538
## 31 31 46 324 165 1978 568
## 32 37 41 417 170 2508 571
## 33 48 43 433 155 2389 590
## 34 32 44 342 157 1540 605
## 35 34 45 411 156 2438 611
## 36 30 45 335 197 2221 618
## 37 47 44 339 201 1498 640
## 38 48 45 383 217 1701 646
## 39 NA NA 303 158 1460 659
## 40 27 42 260 168 1138 666
## 41 44 42 595 210 2586 686
## 42 48 48 304 245 999 698
## 43 36 45 397 213 1741 702
## 44 46 49 491 250 2264 710
## 45 50 50 505 226 3072 713
## 46 46 46 357 219 1764 719
## 47 44 44 410 166 2171 738
## 48 37 44 359 178 1802 793
## 49 40 43 478 221 2044 847
## 50 41 47 388 210 1959 864
## 51 48 50 429 217 2510 897
## 52 45 49 374 219 2227 921
## 53 39 48 555 237 2895 1010
## 54 50 50 516 235 2576 1079
## 55 46 46 452 273 3076 1249
## 56 38 45 367 260 1731 1294
## MOT_MLU CHI_MLU ADOS1 MullenRaw1 ExpressiveLangRaw1 Socialization1
## 1 3.943636 0.5000000 21 22 8 72
## 2 3.886842 1.3333333 19 17 10 64
## 3 3.474725 1.0588235 14 27 11 77
## 4 3.650235 1.0000000 14 25 11 74
## 5 4.676101 2.7600000 0 27 20 102
## 6 3.241422 0.0156250 15 28 10 66
## 7 3.965392 0.7536232 14 25 11 76
## 8 4.003257 2.6315789 21 21 9 69
## 9 4.100707 1.6447368 11 26 19 88
## 10 3.460548 1.1610169 20 21 9 75
## 11 3.860795 1.1680000 20 13 11 67
## 12 4.235585 2.7051282 3 27 18 104
## 13 4.341549 1.0843373 12 31 13 70
## 14 4.366972 2.2258065 5 32 31 102
## 15 4.349353 1.2883436 17 26 14 72
## 16 3.514403 1.4012346 15 27 16 79
## 17 3.534173 1.3052632 14 25 19 65
## 18 3.156695 2.1450382 11 28 20 88
## 19 2.483146 1.4967320 17 20 17 68
## 20 3.370093 1.4734513 17 28 10 82
## 21 3.706790 3.2439024 0 30 30 98
## 22 5.153639 2.7612903 1 22 14 102
## 23 4.347709 2.8690476 0 24 15 86
## 24 3.603473 2.0717489 0 27 22 100
## 25 5.587332 2.4292929 0 29 22 94
## 26 5.229885 3.7103448 1 29 18 88
## 27 4.186161 2.8921569 1 24 22 115
## 28 4.239437 2.9607843 0 20 16 102
## 29 4.254505 2.4800000 0 26 18 96
## 30 4.587179 2.7665198 9 34 27 82
## 31 4.388235 2.5614754 3 19 13 98
## 32 3.926928 2.1586207 13 27 13 86
## 33 5.379798 2.9027778 9 26 14 86
## 34 3.370937 2.2745098 10 27 22 82
## 35 4.239198 2.3815789 1 23 21 102
## 36 3.320000 2.1553398 15 30 24 88
## 37 4.468493 3.5049505 0 30 16 104
## 38 5.247093 3.5950000 0 27 27 108
## 39 4.113158 2.8480000 4 29 22 108
## 40 4.287582 2.7571429 0 21 15 106
## 41 4.664013 2.8651685 0 28 14 108
## 42 4.588477 3.4135021 13 34 27 85
## 43 4.061475 2.8526316 0 25 17 102
## 44 4.445872 2.9482072 0 29 26 102
## 45 4.080446 3.4415584 13 30 30 87
## 46 3.391525 3.0727969 0 24 19 96
## 47 3.532374 3.2781955 8 31 27 82
## 48 4.664286 3.8114035 0 23 17 92
## 49 4.211321 3.0911950 0 21 19 106
## 50 4.847418 3.7011952 0 25 17 102
## 51 4.240798 3.0774194 7 33 26 70
## 52 4.250853 2.6794872 14 42 27 65
## 53 4.472993 3.0614525 0 29 22 90
## 54 4.013353 2.9095355 0 29 33 106
## 55 4.111413 3.3643411 11 32 33 100
## 56 3.957230 2.9092742 0 26 17 96
USING SELECT
- Make a subset of the data including only kids with ASD, mlu and word tokens
- What happens if you include the name of a variable multiple times in a select() call?
ads_mlu_tokens <- merged_final %>% filter(Diagnosis=="ASD") %>% select(SUBJ, VISIT, Diagnosis, CHI_MLU, tokens_CHI)USING MUTATE, SUMMARISE and PIPES 1. Add a column to the data set that represents the mean number of words spoken during all visits. 2. Use the summarise function and pipes to add an column in the data set containing the mean amount of words produced by each trial across all visits. HINT: group by Child.ID 3. The solution to task above enables us to assess the average amount of words produced by each child. Why don't we just use these average values to describe the language production of the children? What is the advantage of keeping all the data?
# Adding collumn with mean MLU (with NAs marked as O) per visit
mean_tokens_CHI <- aggregate(merged_final[, 14], list(merged_final$SUBJ), mean, na.rm=TRUE)
mean_tokens_CHI <- dplyr::rename(mean_tokens_CHI, SUBJ=Group.1, mean_tokens_CHI=x)
merged_final_2 <- merge(merged_final, mean_tokens_CHI , by=c("SUBJ"), all = T)
# We are throwing away valuable data about the variance in the data