HW Exercise 0323-1
Here is a copy of the student roster in csv format from NCKU for a course I taught. Dispaly the number of students from each major.
Loading and display the number of students from each major
dta <-read.csv("C:/Users/USER/Desktop/R_data management/0323/ncku_roster.csv",sep=",", header = T, stringsAsFactors = FALSE)
dta$major<-substr(dta$"系.年.班", 1,3)
dta_n<-dta[-1, ]
table(dta_n$major)##
## 心理系 心理所 教育所
## 4 7 4HW Exercise 0323-2
Display the relationship between Income and Taxes
#Loading data and check data structure
fl<-"http://www1.aucegypt.edu/faculty/hadi/RABE5/Data5/P005.txt"
dta<-read.table(fl, sep="\t",header=TRUE)
str(dta)## 'data.frame': 38 obs. of 8 variables:
## $ City : Factor w/ 38 levels "Atlanta","Austin",..: 1 2 3 4 5 6 7 9 8 10 ...
## $ COL : int 169 143 339 173 99 363 253 117 294 291 ...
## $ PD : int 414 239 43 951 255 1257 834 162 229 1886 ...
## $ URate : num 13.6 11 23.7 21 16 24.4 39.2 31.5 18.2 31.5 ...
## $ Pop : int 1790128 396891 349874 2147850 411725 3914071 1326848 162304 164145 7015251 ...
## $ Taxes : int 5128 4303 4166 5001 3965 4928 4471 4813 4839 5408 ...
## $ Income: int 2961 1711 2122 4654 1620 5634 7213 5535 7224 6113 ...
## $ RTWL : int 1 1 0 0 1 0 0 0 1 0 ...#examing the realtionship between Income and Taxes
##
## Pearson's product-moment correlation
##
## data: dta$Income and dta$Taxes
## t = 0.33696, df = 36, p-value = 0.7381
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.2684231 0.3691383
## sample estimates:
## cor
## 0.0560718plot(Taxes ~ Income, data = dta, pch = 20, col = 'red',
xlab = "Income", ylab = "Taxes")
abline(lm(Taxes ~ Income, data = dta))
grid()
p-value of correlation test is >0.05. Hence, Income is not siginificantly correlated with taxes.
HW Exercise 0323-3
Download the data file in junior school project and read it into your currect R session. Assign the data set to a data frame object called jsp. (1) Re-name the variable ‘sex’ as ‘Gender’. (2) Re-label the values of the social class variable using the (long character strings) descriptive terms to produce the following plot. (3) Save the edited jsp data object out as a comma-separated-value file and in a R data format to a data folder and read them back into your R session, separately.
#Loading data and as a data frame
dta<-read.table("C:/Users/USER/Desktop/R_data management/0323/juniorSchools.txt",sep="\t", header = T, stringsAsFactors = FALSE)
jsp<-as.data.frame(dta)
str(jsp)## 'data.frame': 3236 obs. of 9 variables:
## $ school : chr "S1" "S1" "S1" "S1" ...
## $ class : chr "C1" "C1" "C1" "C1" ...
## $ sex : chr "G" "G" "G" "B" ...
## $ soc : int 9 9 9 2 2 2 2 2 9 9 ...
## $ ravens : int 23 23 23 15 15 22 22 22 14 14 ...
## $ pupil : chr "P1" "P1" "P1" "P2" ...
## $ english: int 72 80 39 7 17 88 89 83 12 25 ...
## $ math : int 23 24 23 14 11 36 32 39 24 26 ...
## $ year : int 0 1 2 0 1 0 1 2 0 1 ...#(1) Re-name the variable ‘sex’ as ‘Gender’.
## 'data.frame': 3236 obs. of 9 variables:
## $ school : chr "S1" "S1" "S1" "S1" ...
## $ class : chr "C1" "C1" "C1" "C1" ...
## $ Gender : chr "G" "G" "G" "B" ...
## $ soc : int 9 9 9 2 2 2 2 2 9 9 ...
## $ ravens : int 23 23 23 15 15 22 22 22 14 14 ...
## $ pupil : chr "P1" "P1" "P1" "P2" ...
## $ english: int 72 80 39 7 17 88 89 83 12 25 ...
## $ math : int 23 24 23 14 11 36 32 39 24 26 ...
## $ year : int 0 1 2 0 1 0 1 2 0 1 ...#(2) Re-label the values of the social class variable using the (long character strings) descriptive terms to produce the following plot.
jsp$soc<-as.factor(jsp$soc)
soc<-c("I", "II", "III_0man", "III_man", "IV", "V", "VI_Unemp_L", "VII_emp_NC", "VIII_Miss_Dad")
levels(jsp$soc)<-soc
levels(jsp$soc)## [1] "I" "II" "III_0man" "III_man"
## [5] "IV" "V" "VI_Unemp_L" "VII_emp_NC"
## [9] "VIII_Miss_Dad"## 'data.frame': 3236 obs. of 9 variables:
## $ school : chr "S1" "S1" "S1" "S1" ...
## $ class : chr "C1" "C1" "C1" "C1" ...
## $ Gender : chr "G" "G" "G" "B" ...
## $ soc : Factor w/ 9 levels "I","II","III_0man",..: 9 9 9 2 2 2 2 2 9 9 ...
## $ ravens : int 23 23 23 15 15 22 22 22 14 14 ...
## $ pupil : chr "P1" "P1" "P1" "P2" ...
## $ english: int 72 80 39 7 17 88 89 83 12 25 ...
## $ math : int 23 24 23 14 11 36 32 39 24 26 ...
## $ year : int 0 1 2 0 1 0 1 2 0 1 ...plot(jsp$soc, jsp$math, xaxt='n', xlab="soc", ylab="math")
axis(1, at=seq(1:9),labels=c("I", "II", "III_0man", "III_man", "IV", "V", "VI_Unemp_L", "VII_emp_NC", "VIII_Miss_Dad"), cex.axis=0.5, las = 1, font = 2)
- Save the edited jsp data object out as a comma-separated-value file and in a R data format to a data folder and read them back into your R session, separately.
read the data back into R session and show the first 6 rows
## school class Gender soc ravens pupil english math year
## 1 S1 C1 G VIII_Miss_Dad 23 P1 72 23 0
## 2 S1 C1 G VIII_Miss_Dad 23 P1 80 24 1
## 3 S1 C1 G VIII_Miss_Dad 23 P1 39 23 2
## 4 S1 C1 B II 15 P2 7 14 0
## 5 S1 C1 B II 15 P2 17 11 1
## 6 S1 C1 B II 22 P3 88 36 0##HW Exercise 0323-4 The following zip file contains one subject’s laser-event potentials (LEP) data for 4 separate conditions (different level of stimulus intensity), each in a plain text file (1w.dat, 2w.dat, 3w.dat and 4w.dat). The rows are time points from -100 to 800 ms sampled at 2 ms per record. The columns are channel IDs. Input all the files into R for graphical exploration.
Import zipped stata file
The zip files was not found on the server.