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The data is about the health and income outcomes for 184 countries from 1960 to 2016.

# read csv file and assign it to mydata
mydata <- read.csv("gapminder.csv")
# By default the head() function shows 6 rows but we can specify the desired number of rows with n argument
head(mydata, n = 10, )

  1. use summary function to gain an overview of the data set. Then display the mean and median for at least two attributes.
# pull summary from the data set and assign it to mydata_summary
mydata_summary <- summary(mydata)
print(mydata_summary)
##        X           country               year      infant_mortality
##  Min.   :    1   Length:10545       Min.   :1960   Min.   :  1.50  
##  1st Qu.: 2637   Class :character   1st Qu.:1974   1st Qu.: 16.00  
##  Median : 5273   Mode  :character   Median :1988   Median : 41.50  
##  Mean   : 5273                      Mean   :1988   Mean   : 55.31  
##  3rd Qu.: 7909                      3rd Qu.:2002   3rd Qu.: 85.10  
##  Max.   :10545                      Max.   :2016   Max.   :276.90  
##                                                    NA's   :1453    
##  life_expectancy   fertility       population             gdp           
##  Min.   :13.20   Min.   :0.840   Min.   :3.124e+04   Min.   :4.040e+07  
##  1st Qu.:57.50   1st Qu.:2.200   1st Qu.:1.333e+06   1st Qu.:1.846e+09  
##  Median :67.54   Median :3.750   Median :5.009e+06   Median :7.794e+09  
##  Mean   :64.81   Mean   :4.084   Mean   :2.701e+07   Mean   :1.480e+11  
##  3rd Qu.:73.00   3rd Qu.:6.000   3rd Qu.:1.523e+07   3rd Qu.:5.540e+10  
##  Max.   :83.90   Max.   :9.220   Max.   :1.376e+09   Max.   :1.174e+13  
##                  NA's   :187     NA's   :185         NA's   :2972       
##   continent            region         
##  Length:10545       Length:10545      
##  Class :character   Class :character  
##  Mode  :character   Mode  :character  
##                                       
##                                       
##                                       
## 
# calculate the mean and median of life_expectancy
mean_life_expect <- mean(mydata[["life_expectancy"]])
mean_life_expect
## [1] 64.81162
median_life_expect <- median(mydata[["life_expectancy"]])
median_life_expect
## [1] 67.54
  1. Create a new data frame with a subset of the columns and rows. Make sure to rename it.
new_data <- subset(mydata, infant_mortality != "NA", select = c("year", "infant_mortality", "gdp", "life_expectancy"))
  new_data
  1. Create new column names for the new data
new_data$cleaned_infant_mortality <- new_data$infant_mortality
new_data <- subset(new_data, select = -infant_mortality)
new_data
  1. Use the summary function to create an overview of your new data frame. Print the mean and median for the same two attributes. Please compare.
new_summary <- summary(new_data)
new_summary
##       year           gdp            life_expectancy cleaned_infant_mortality
##  Min.   :1960   Min.   :4.040e+07   Min.   :13.20   Min.   :  1.50          
##  1st Qu.:1977   1st Qu.:1.974e+09   1st Qu.:57.24   1st Qu.: 16.00          
##  Median :1990   Median :8.249e+09   Median :67.58   Median : 41.50          
##  Mean   :1989   Mean   :1.556e+11   Mean   :64.86   Mean   : 55.31          
##  3rd Qu.:2003   3rd Qu.:5.843e+10   3rd Qu.:73.18   3rd Qu.: 85.10          
##  Max.   :2015   Max.   :1.174e+13   Max.   :83.30   Max.   :276.90          
##                 NA's   :1953

5. Rename at least 3 values in a column

# lets start with checking NA values in fertility
naInFertility <- sum(is.na(mydata$fertility))
# find mean value of fertility rate excluding the NA
mean_fertility <- mean(mydata$fertility, na.rm = TRUE)
# replace NA with mean value
mydata$fertility <- replace(mydata$fertility, is.na(mydata$fertility), mean_fertility)
mydata
# replace Australia with country of Kangaroo
mydata$country[mydata$country == 'Australia'] <- 'Country_kangaroo' 
# Replace fertility > than 7 with "healthAtRisk"
mydata$fertility[mydata$fertility > 7] <- 'healthAtRisk'
mydata

6. display enough row

head(mydata, n = 25)