Homework 1 assignment
lz2377
Learn to create a branch, commit, pull, push in github, and use Rmarkdown.to create html document.
This is an R Markdown document. Markdown is a simple formatting syntax for authoring HTML, PDF, and MS Word documents. For more details on using R Markdown see http://rmarkdown.rstudio.com.
When you click the Knit button a document will be generated that includes both content as well as the output of any embedded R code chunks within the document. You can embed an R code chunk like this:
summary(cars)## speed dist
## Min. : 4.0 Min. : 2
## 1st Qu.:12.0 1st Qu.: 26
## Median :15.0 Median : 36
## Mean :15.4 Mean : 43
## 3rd Qu.:19.0 3rd Qu.: 56
## Max. :25.0 Max. :120You can also embed plots, for example:
Now let’s exploe the history of the process.
Now it’s time to make the second commit.
Then we can change the axes of speed and dist.
And make the third commit.
Note that the echo = FALSE parameter was added to the code chunk to prevent printing of the R code that generated the plot.
Now let’s finish today’s hw(2014-09-18). First load sample data.
setwd("C:/Liangquan Zhou/Study/2014 fall/data visualization/hw1")
sample_data= read.csv("sample_health_facilities.csv") # read the .csv file- Select all facilities located in the southern zones of Nigeria.
new_data= subset(sample_data, zone %in% c("Southwest","Southeast","South-South"))- Incorporate the pop2006 column from the
lgas.csvfile into the new data.frame containing only those facilities located in Southern Nigeria. (Hint: your id column islga_id)
lgas= read.csv("lgas.csv", stringsAsFactors=T)
new_data= merge(new_data, lgas[c("lga_id","pop_2006")],by= "lga_id")And we can use str to see the new dataset:
str(new_data)## 'data.frame': 26 obs. of 11 variables:
## $ lga_id : int 49 67 76 101 183 191 218 304 312 316 ...
## $ lga : Factor w/ 50 levels "Aliero","Anaocha",..: 2 3 4 8 12 13 14 19 20 21 ...
## $ state : Factor w/ 24 levels "Abia","Adamawa",..: 3 20 3 6 10 3 10 20 3 19 ...
## $ zone : Factor w/ 6 levels "North-Central",..: 5 6 5 4 5 5 5 6 5 6 ...
## $ c_section_yn : logi FALSE FALSE FALSE FALSE FALSE TRUE ...
## $ num_nurses_fulltime : int 2 0 0 3 1 0 7 6 2 0 ...
## $ gps : Factor w/ 50 levels "10.50716994 7.39845258 633.4000244140625 5.0",..: 27 41 30 34 22 26 18 44 25 37 ...
## $ num_lab_techs_fulltime: int NA 0 1 2 0 0 1 1 0 0 ...
## $ management : Factor w/ 1 level "public": 1 1 1 1 NA NA NA NA NA 1 ...
## $ num_doctors_fulltime : int NA 0 1 0 0 1 0 1 1 0 ...
## $ pop_2006 : int 285002 68643 158410 105822 130931 158231 165593 96748 302158 284336 ...- Calculate the total number of full time nurses and doctors for all health facilities in each state.
tapply(new_data$num_doctors_fulltime, new_data$state,sum)## Abia Adamawa Anambra Bauchi Benue Cross River
## 308 NA NA NA NA 0
## Delta Edo Ekiti Imo Jigawa Kaduna
## 2 0 1 0 NA NA
## Kano Katsina Kebbi Kogi Lagos Niger
## NA NA NA NA 4 NA
## Ogun Osun Plateau Rivers Taraba Zamfara
## 2 1 NA 2 NA NAtapply(new_data$num_nurses_fulltime, new_data$state,sum)## Abia Adamawa Anambra Bauchi Benue Cross River
## NA NA 4 NA NA 3
## Delta Edo Ekiti Imo Jigawa Kaduna
## 10 0 2 8 NA NA
## Kano Katsina Kebbi Kogi Lagos Niger
## NA NA NA NA 4 NA
## Ogun Osun Plateau Rivers Taraba Zamfara
## 0 6 NA 2 NA NA- Sort the resulting dataset by state population, in descending order.
data1=subset(new_data,select=c(num_doctors_fulltime,num_nurses_fulltime,pop_2006,state))
data1$state=as.factor(as.character(data1$state))
result=data.frame(tapply(data1$num_doctors_fulltime, data1$state,sum),
tapply(data1$num_nurses_fulltime, data1$state,sum),
tapply(data1$pop_2006, data1$state,sum))
names(result)=c("num_doctors_fulltime","num_nurses_fulltime","pop_2006")
result=result[order(result$pop_2006),]The result is:
result## num_doctors_fulltime num_nurses_fulltime pop_2006
## Ekiti 1 2 113754
## Edo 0 0 120813
## Osun 1 6 165391
## Abia 308 NA 220660
## Rivers 2 2 284010
## Imo 0 8 439241
## Cross River 0 3 470167
## Ogun 2 0 597659
## Delta 2 10 828912
## Anambra NA 4 903801
## Lagos 4 4 1802377