Introduction to R Lecture 3- Data Manipulation.ppt

1、Introduction to R Lecture 3: Data Manipulation,Andrew Jaffe 9/27/10,Overview,Practice Solutions Indexing Data Management Data Summaries,Practice,Make a 2 x 2 table of sex and dog, table(dat$sex, dat$dog)no yesF 264 229M 254 253,Practice,Create a BMI variable using height and weight, dat$bmi = dat$we

2、ight*703/dat$height2 head(dat$bmi) 1 23.44931 31.29991 25.69422 23.89881 23.11172 28.13324,Practice,Create an overweight variable, which gives the value 1 for people with BMI 30 and 0 otherwise, dat$overweight = ifelse(dat$bmi 30, 1, 0) head(dat$overweight) 1 0 1 0 0 0 0,Practice,Add those two varia

3、bles to the datasets and save it as a text file somewhere,write.table(dat, “lec2_practice.txt“, quote = F, row.names = F, sep=“t“),Overview,Practice Solutions Indexing Data Management Data Summaries,Indexing,Vectors: vectorindex takes index elements from vector and returns them, x = c(1,3,7,34,435)

4、x1 1 1 xc(1,4) 1 1 34 x2:4 1 3 7 34, 2:4 1 2 3 4,Indexing,Replace elements in a vector combining indexing, is.na(), and rep(), x = c(1,3,NA,6,NA,8) which(is.na(x) 1 3 5 xis.na(x) = 0 # or rep(0) x 1 1 3 0 6 0 8,Indexing,Data.frames/matrices: datrow,col Can subset/extract a row: datrow, Can subset/ex

5、tract a column: dat,col, x = matrix(c(1,2,3,4,5,6), ncol = 3) x,1 ,2 ,3 1, 1 3 5 2, 2 4 6,Indexing, x1, 1 1 3 5 x,1 1 1 2 x1,1 1 1 x1:2,1:2,1 ,2 1, 1 3 2, 2 4, x,1 ,2 ,3 1, 1 3 5 2, 2 4 6,Indexing, x1, = rep(1) x,1 ,2 ,3 1, 1 1 1 2, 2 4 6 x,1 = rep(2) x,1 ,2 ,3 1, 2 1 1 2, 2 4 6, x,1 ,2 ,3 1, 1 3 5

6、2, 2 4 6,Overview,Practice Solutions Indexing Data Management Data Summaries,Data Management,An aside: save() and load() save(obj_1,obj_n, file = “filename.rda”) Saves R objects (vectors, matrices, or data.frames) as an .rda file (similar to .dta) load(“filename.rda”) Loads whatever files were saved

7、 in the .rda Easier than reading/writing tables,Data Management,Your workspace can be saved an .rda file You get asked this every time you close R save.image(“filename.Rdata”) saves all objects in your workspace (what ls() returns) Each folder might have its own .Rdata file Doing this is personal pr

8、eference - if you have a script and its a quick analysis, probably dont need a saved image,Data Management,“lec3_data.rda” can be downloaded from the website Similar method to read in the data: load(“lec3_data.rda”) Put in the same directory as your script Set your working directory Use the full fil

9、ename,Data Management,What are the dimensions of the dataset?,Data Management,What are the dimensions of the dataset?, dim(dog_dat) 1 482 6,Data Management,How many dogs are in this dataset? Is this dataset unique?,Data Management,How many dogs are in this dataset? Is this dataset unique?, length(un

10、ique(dog_dat$dog_id) 1 482 length(dog_dat$dog_id) 1 482,Data Management,What are the column/variable names?,Data Management,What are the column/variable names?, head(dog_dat)dog_id owner_id dog_type dog_wt_mo1 dog_len_mo1 dog_food_mo1 1 1 394 lab 51.5 13.8 25.8 2 2 571 lab 48.3 24.6 33.1 3 3 986 poo

11、dle 59.3 22.7 29.2 4 4 750 lab 46.4 22.3 27.6 5 5 882 husky 48.0 20.9 28.0 6 6 762 poodle 47.0 19.1 31.0, names(dog_dat) 1 “dog_id“ “owner_id“ “dog_type” “dog_wt_mo1“ 5 “dog_len_mo1“ “dog_food_mo1“,Data Management,Some explanation of the variables dog_id: id of dog owner_id: id of owner dog_type: ty

12、pe of dog dog_wt_mo1: dog weight at month 1 (baseline) dog_len_mo1: dog length at month 1 dog_food_mo1: baseline dog food consumption,Data Management,Subsetting data: separate data into two data.frames based on a variable:, lab = dog_datdog_dat$dog_type = “lab“, head(lab)dog_id owner_id dog_type dog

13、wt_mo1 dog_len_mo1 dog_food_mo1 1 1 394 lab 51.5 13.8 25.8 2 2 571 lab 48.3 24.6 33.1 4 4 750 lab 46.4 22.3 27.6 7 7 664 lab 53.0 18.2 25.7 13 13 713 lab 48.3 23.4 31.8 15 15 480 lab 46.6 20.8 31.3,Data Management, lab = dog_datdog_dat$dog_type = “lab“, head(which(dog_dat$dog_type = “lab“) 1 1 2 4

14、7 13 15,Taking those specific rows, and all of the columns of the original data,Data Management, lab2 = dog_datdog_dat$dog_type = ”lab“,1:3 head(lab2,3)dog_id owner_id dog_type 1 1 394 lab 2 2 571 lab 4 4 750 lab,Taking those specific rows, and the first 3 columns of the original data,Data Managemen

15、t,Note (stata users) that we have two data.frames in our workspace! ls(),Data Management,Remember we used ifelse() for binary conversions?, heavy = ifelse(dog_dat,4 mean(dog_dat,4), 1, 0) head(heavy) 1 1 0 1 0 0 0,Note that you can use column indexing instead of $name for data.frames,This is just th

16、e mean of that column: mean(dog_dat,4) 1 49.69606,Data Management,The cut() function can split data into more groups quintiles, tertiles, etc cut(dat, breaks) dat is a vector of numerical or integer values breaks is where to make the cuts,Data Management,If breaks is one number (n), it splits the da

17、ta into n equal sized groups, x = 1:5 # 1 2 3 4 5 or seq(1,5) cut(x, 2) 1 (0.996,3 (0.996,3 (0.996,3 (3,5 (3,5 Levels: (0.996,3 (3,5 cut(x, 3) 1 (0.996,2.33 (0.996,2.33 (2.33,3.67 (3.67,5 (3.67,5 Levels: (0.996,2.33 (2.33,3.67 (3.67,5 cut(x,3, labels=F) # returns integers of groups, not factors 1 1

18、1 2 3 3,FACTORS!,Data Management,What is a factor? Similar to terms like category and enumerated type Has levels associated with it could be ordinal if factor(,ordered = T) Must only have an as.character() method and be sortable to be converted to a factor using factor(),Data Management,If breaks ar

19、e more than one number, splits the vector by those numbers, x = 1:10 cut(x, c(0,3,6,10)1 (0,3 (0,3 (0,3 (3,6 (3,6 (3,6 (6,10 (6,10 (6,10 (6,10 Levels: (0,3 (3,6 (6,10 cut(x, c(0,3,6,10), FALSE)1 1 1 1 2 2 2 3 3 3 3,Data Management,Something more applicable for cut: the quantile(x,probs) function - d

20、efault probs is seq(0,1,0.25), ie quintiles seq(start, end, by) creates a sequence from the starting value, to the ending value by the specified amount seq(0,10) 0:10 # 0, 1, 2, , 9, 10 seq(0,10,0.5) # 0, 0.5, 1.0, , 9.5, 10.0,Data Management,Now for stuff with our data:, quantile(dog_dat$dog_wt_mo1

21、)0% 25% 50% 75% 100% 10.600 44.600 49.200 55.275 72.500 quantile(dog_dat$dog_wt_mo1, seq(0,1,0.5)0% 50% 100% 10.6 49.2 72.5 quantile(dog_dat$dog_wt_mo1, 0.6)60% 51.5 quantile(dog_dat$dog_wt_mo1, c(0.4,0.6)40% 60% 47.24 51.50,Data Management, sp = quantile(dog_dat$dog_wt_mo1, 0.75) big = ifelse(dog_d

22、at$dog_wt_mo1 sp, 1, 0) head(big) 1 0 0 1 0 0 0, quant = cut(dog_dat$dog_wt_mo1, quantile(dog_dat$dog_wt_mo1) head(quant) 1 (49.2,55.3 (44.6,49.2 (55.3,72.5 (44.6,49.2 (44.6,49.2 (44.6,49.2 Levels: (10.6,44.6 (44.6,49.2 (49.2,55.3 (55.3,72.5,Overview,Practice Solutions Indexing Data Management Data

23、Summaries,Data Summaries,This is some of the only “statistics” in the course R functions can perform statistics well, here are some basics for summaries,Data Summaries,mean(dat, na.rm = F) median(dat, na.rm=F), x = c(1,2,4,6,NA) mean(x) 1 NA mean(x, na.rm=T) 1 3.25 median(x,na.rm=T) 1 3,Data Summari

24、es, x = c(1,2,4,7,9,11) mean(x) 1 5.666667 median(x) 1 5.5 var(x) 1 15.86667 sd(x) 1 3.983298,Data Summaries,Lets combine some concepts! Take the mean food consumption of all of the labs,Data Summaries,First, figure out which entries correspond to dogs that are labs, Index = which(dog_dat$dog_type =

25、 “lab“) head(Index) 1 1 2 4 7 13 15,Data Summaries,Then, take the mean of the data you want, mean(dog_dat$dog_food_mo1Index) 1 30.04,Note that we first created a vector of dog food, then indexed it - there are no commas needed for the indexing (because its a vector),Data Summaries,Combined into 1 li

26、ne/command:, mean(dog_dat$dog_food_mo1dog_dat$dog_type = “lab“) 1 30.04, mean(dog_datdog_dat$dog_type = “lab“,6) 1 30.04 mean(dog_datdog_dat$dog_type = “lab“,“dog_food_mo1“) 1 30.04,Pick your favorite theyre all the same! Note that the first option might make the most sense,Practice,Compute the average dog weight, dog length, and dog food consumption for each dog type at baseline Reminder: the dog types are lab, poodle, husky, and retriever,