Some times it is necessary to download actually large csv files to deliver some analysis. When yous hitting file sizes inward Gigabytes it is useful to operate R instead of spreadsheets. This practise teaches us to manipulate this sort of files.
Answers to the exercises are available here.
Exercise 1
Create a directory canada immigration/Work/Income as well as position all files related to income as well as thence charge dplyr.
Download information laid upwards from here.
Create a directory canada immigration/Work/Income as well as position all files related to income as well as thence charge dplyr.
Download information laid upwards from here.
Exercise 2
Create a string vector alongside file names: 00540002-eng, 00540005-eng, 00540007-eng, 00540009-eng, 00540011-eng, 00540013-eng, 00540015-eng, as well as 00540017-eng.
Create a string vector alongside file names: 00540002-eng, 00540005-eng, 00540007-eng, 00540009-eng, 00540011-eng, 00540013-eng, 00540015-eng, as well as 00540017-eng.
Exercise 3
Create a listing of information frames as well as position the information of each file inward listing position. For example,
Create a listing of information frames as well as position the information of each file inward listing position. For example,
data[[1]] will incorporate the start file. To bring down this information size, for each information laid upwards guide exclusively information from 2014. Exercise 4
Clean upwards the start information sets inward the listing (data[[1]]) as well as exclude registers that summarizes other like: “Both sexes” to avoid double operations spell summarizing.
Clean upwards the start information sets inward the listing (data[[1]]) as well as exclude registers that summarizes other like: “Both sexes” to avoid double operations spell summarizing.
Exercise 5
Clean upwards all other information sets inward the listing as well as exclude registers the same agency discribed at practise 4. Then, pile upwards all information inward a sigle information set.
Clean upwards all other information sets inward the listing as well as exclude registers the same agency discribed at practise 4. Then, pile upwards all information inward a sigle information set.
Learn more about Data Pre-Processing inward the online course R Data Pre-Processing & Data Management – Shape your Data!. In this course of instruction yous volition larn how to:
- import information into R inward several ways spell also beeing able to seat a suitable import tool
- use SQL code inside R
- And much more
Exercise 6
Write a csv file alongside the recent do information set.
Write a csv file alongside the recent do information set.
Exercise 7
Create a directory canada immigration/Work/Income as well as position all files related to income as well as thence charge dplyr.
Download information laid upwards from here.
Create a string vector alongside file names: 00540018-eng, 00540019-eng, 00540020-eng, 00540021-eng, 00540022-eng, 00540023-eng, 00540024-eng, as well as 00540025-eng.
Create a listing of information frames as well as position the information of each file inward listing position. For example,
Create a directory canada immigration/Work/Income as well as position all files related to income as well as thence charge dplyr.
Download information laid upwards from here.
Create a string vector alongside file names: 00540018-eng, 00540019-eng, 00540020-eng, 00540021-eng, 00540022-eng, 00540023-eng, 00540024-eng, as well as 00540025-eng.
Create a listing of information frames as well as position the information of each file inward listing position. For example,
data[[1]] will incorporate the start file. To bring down this information size, for each information laid upwards guide exclusively information from 2014. Exercise 8
Clean upwards the start information sets inward the listing (data[[1]]) as well as exclude registers that summarizes other like: “Both sexes” to avoid double operations spell summarizing.
Clean upwards the start information sets inward the listing (data[[1]]) as well as exclude registers that summarizes other like: “Both sexes” to avoid double operations spell summarizing.
Exercise 9
Clean upwards all other information sets inward the listing as well as exclude registers the same agency discribed at practise 8. Then, pile upwards all information inward a sigle information set.
Clean upwards all other information sets inward the listing as well as exclude registers the same agency discribed at practise 8. Then, pile upwards all information inward a sigle information set.
Exercise 10
Write a csv file alongside the recent do information set.
__________________________________________
Write a csv file alongside the recent do information set.
__________________________________________
Below are the solutions to these exercises on Data Manipulation.
#################### # # # Exercise 1 # # # #################### setwd(" /canada immigration/Work/Income") library(dplyr) #################### # # # Exercise two # # # #################### file <- c("00540002-eng", "00540005-eng", "00540007-eng", "00540009-eng", "00540011-eng", "00540013-eng", "00540015-eng", "00540017-eng") #################### # # # Exercise three # # # #################### data <- list() for (i in 1:length(file)){ data[[i]] <- read.csv(paste(file[i],".csv", sep = "")) data[[i]] <- as.tbl(data[[i]]) %>% filter(Ref_Date >=2014) } #################### # # # Exercise iv # # # #################### INCOME <- data[[1]] INCOME <- filter(INCOME, SEX != "Both sexes", IMMIG != "Total, immigrant admission category", LANG != "Total, language", EDUCA != "Total, education", WORLD == "Total, basis area", INCOME != "All income") %>% select(-Coordinate) #################### # # # Exercise five # # # #################### for(i in 2:length(file)){ income <- data[[i]] income <- filter(income, SEX != "Both sexes", IMMIG != "Total, immigrant admission category", LANG != "Total, language", EDUCA != "Total, education", WORLD == "Total, basis area", INCOME != "All income") %>% select(-Coordinate) INCOME <- rbind(INCOME, income) } #################### # # # Exercise half dozen # # # #################### write.csv(INCOME, "Income Canada.csv") #################### # # # Exercise seven # # # #################### file <- c("00540018-eng", "00540019-eng", "00540020-eng", "00540021-eng", "00540022-eng", "00540023-eng", "00540024-eng", "00540025-eng") data <- list() for (i in 1:length(file)){ data[[i]] <- read.csv(paste(file[i],".csv", sep = "")) data[[i]] <- as.tbl(data[[i]]) %>% filter(Ref_Date >=2014) } #################### # # # Exercise 8 # # # #################### INCOME2 <- data[[1]] INCOME2 <- filter(INCOME2, SEX != "Both sexes", IMMIGRA != "Total, immigrant admission category", PERIOD == "2009 to 2014, menstruum of immigration", STATUS != "Total, household unit of measurement status", INCOME != "All income") %>% select(-Coordinate) #################### # # # Exercise nine # # # #################### for(i in 2:length(file)){ income <- data[[i]] income <- filter(income, SEX != "Both sexes", IMMIGRA != "Total, immigrant admission category", PERIOD == "2009 to 2014, menstruum of immigration", STATUS != "Total, household unit of measurement status", INCOME != "All income") %>% select(-Coordinate) INCOME2 <- rbind(INCOME2, income) } #################### # # # Exercise 10 # # # #################### write.csv(INCOME2, "Income Canada2.csv")