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C:\Users\MEGHANA REDDY\AppData\Local\Temp\RtmpkPKDOz\downloaded_packagesinstall.packages ("dplyr")Error in install.packages : Updating loaded packagesinstall.packages ("tidyr")Error in install.packages : Updating loaded packagesinstall.packages ("deductive")WARNING: Rtools is required to build R packages but is not currently installed. Please download and install the appropriate version of Rtools before proceeding:
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Warning in install.packages :
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C:\Users\MEGHANA REDDY\AppData\Local\Temp\RtmpkPKDOz\downloaded_packagesinstall.packages("tidyr")Error in install.packages : Updating loaded packagesinstall.packages ("knitr")Error in install.packages : Updating loaded packagesPreprocessing is the process where the raw data is made in an understable form.The steps take to preprocessthe data were:firstly the data was imported, then checked out the missing values, the variable type. The untidy data was converted into tidy data, the values were scanned for further process.
The data types was taken from https://data.worldbank.org/indicator/SH.MED.BEDS.ZSA and discover.data.vic.gov.au.The first data is data the about hospital beds in hospitlas for patients in Australia (per 1000 patients) , this data is untidy data,the variable in this data are years 1960 to 2019 and hospital beds.The other data was about the response rate of ambulance and the data is tidy, the variable in this data are UCL Name,FinalDispatch Code, %<=15mins, AVG RT - Seconds,Total Number.
library(readxl)
View(HSP_BEDS)
View(RESPONSE_RATE)
class(HSP_BEDS)[1] "tbl_df" "tbl" "data.frame"class(RESPONSE_RATE)[1] "tbl_df" "tbl" "data.frame"head(HSP_BEDS)head(RESPONSE_RATE)factor(HSP_BEDS) YEARS
c(1960, 1961, 1962, 1963, 1964, 1965, 1966, 1967, 1968, 1969, 1970, 1971, 1972, 1973, 1974, 1975, 1976, 1977, 1978, 1979, 1980, 1981, 1982, 1983, 1984, 1985, 1986, 1987, 1988, 1989, 1990, 1991, 1992, 1993, 1994, 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2008, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019)
BEDS
c(11.19999981, 11.3, 11.4, 11.7, 11.80000019, 11.89999962, 11.80000019, 11.80000019, 11.69999981, 11.69999981, 11.60000038, 11.8, 11.8, 12, 11.9, 11.9, 12.1, 12.1, 12.2, 12.3, 12.2, 12.1, 11.9, 11.7, 10.9, NA, 10.5, NA, 9.8, NA, 9.2, 8.9, 8.9, 8.7, 8.7, 8.5, 8.3, 8.2, 7.9, 7.8, 7.6, 4, NA, 3.9, 4, 3.97, NA, NA, 3.8, 3.9, 3.8, NA, NA, 3.8, NA, NA, 4.6, NA, 3, NA)
2 Levels: c(1960, 1961, 1962, 1963, 1964, 1965, 1966, 1967, 1968, 1969, 1970, 1971, 1972, 1973, 1974, 1975, 1976, 1977, 1978, 1979, 1980, 1981, 1982, 1983, 1984, 1985, 1986, 1987, 1988, 1989, 1990, 1991, 1992, 1993, 1994, 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2008, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019) ...Here we get to know thw variable type and class of the variables in the data set.Here years,beds, AVG RT Seconds,less than 15mins abd Total Number varicbles are numberic class and UCL NAME and Finak Dispatch Code are character class.
# This is the R chunk for the Understand Section
colnames(HSP_BEDS) <-c("YEARS","BEDS")
class(HSP_BEDS $YEARS)[1] "numeric"typeof(HSP_BEDS $YEARS)[1] "double"class(HSP_BEDS $BEDS)[1] "numeric"typeof(HSP_BEDS $BEDS)[1] "double"class (RESPONSE_RATE $ `UCL Name`)[1] "character"class(RESPONSE_RATE $`Final Dispatch Code`)[1] "character"class(RESPONSE_RATE $`less than15mins`)[1] "numeric"class(RESPONSE_RATE $`AVG RT - Seconds`)[1] "numeric"class(RESPONSE_RATE $`Total Number`)[1] "numeric"typeof (RESPONSE_RATE $ `UCL Name`)[1] "character"typeof(RESPONSE_RATE $`Final Dispatch Code`)[1] "character"typeof(RESPONSE_RATE $`less than15mins`)[1] "double"typeof(RESPONSE_RATE $`AVG RT - Seconds`)[1] "double"typeof(RESPONSE_RATE $`Total Number`)[1] "double"The tidy principle doesnt coform because the data is tidy and only has missing valves and special values only.
gather(RESPONSE_RATE)gather(HSP_BEDS)Here one variable eas mutated wih the other.
bind_rows(HSP_BEDS, RESPONSE_RATE)
bind_cols(HSP_BEDS, RESPONSE_RATE)
RESPONSE_RATE %>%leftjoin(years)'Here I have scanned the data to find the missing values and special values in the data.After the data has been scanned I got few missing values in hsp bed data and no misisng values in the response rate data.The missing values are omitted from the data.
is.na(HSP_BEDS) YEARS BEDS
[1,] FALSE FALSE
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[60,] FALSE TRUEis.na(RESPONSE_RATE) UCL Name Final Dispatch Code less than15mins
[1,] FALSE FALSE FALSE
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[40,]Here I have scanned the data to find the outliners.The variable beds has maximum number of possible outliners and years have less outliners.
boxplot(HSP_BEDS)
boxplot(RESPONSE_RATE)Here the variable was tranformed to histogram.
ln_HSP_BEDS <- log(YEARS $YEARS)
hist(years)