Create Variables

Surveys provides raw data. Often you will need to combine information collected with different variables into a new variable. So you will need to add new columns that are functions of existing columns.

mutate() adds new columns at the end of the table.

data %>% 
  select(row_no, gdpPercap, pop) %>%
  mutate(GDP = gdpPercap * pop)

## # A tibble: 1,704 x 4
##    row_no gdpPercap      pop          GDP
##     <int>     <dbl>    <int>        <dbl>
##  1      1      779.  8425333  6567086330.
##  2      2      821.  9240934  7585448670.
##  3      3      853. 10267083  8758855797.
##  4      4      836. 11537966  9648014150.
##  5      5      740. 13079460  9678553274.
##  6      6      786. 14880372 11697659231.
##  7      7      978. 12881816 12598563401.
##  8      8      852. 13867957 11820990309.
##  9      9      649. 16317921 10595901589.
## 10     10      635. 22227415 14121995875.
## # ... with 1,694 more rows

Arithmetic operators can also be very useful. For example, x / sum(x) calculates the proportion of a total, and y - mean(y) computes the difference from the mean.

data %>% 
  select(row_no, gdpPercap) %>%
  mutate(diffMeansGdp = gdpPercap - mean(gdpPercap) )

## # A tibble: 1,704 x 3
##    row_no gdpPercap diffMeansGdp
##     <int>     <dbl>        <dbl>
##  1      1      779.       -6436.
##  2      2      821.       -6394.
##  3      3      853.       -6362.
##  4      4      836.       -6379.
##  5      5      740.       -6475.
##  6      6      786.       -6429.
##  7      7      978.       -6237.
##  8      8      852.       -6363.
##  9      9      649.       -6566.
## 10     10      635.       -6580.
## # ... with 1,694 more rows

A special case: create dummy variables

In regression analysis, you will often use dummy variables. These are necessary when your variable of interest contains categories and characteristics do not necessarily have an inherent ranking. Obvious examples are variables such as gender, or different counties were the research was conducted.

As a reminder, a dummy variable is a variable that indicates whether an observation has a particular characteristic and assumes the values 0 and 1, where 0 indicates the absence of the property, and 1 indicates the presence of the same. For example, if you want to dummy code a variable region that has three unique values, you could create the following dummies:

To create a dummy variable in R you can use the function ifelse()

With our example with regions, you could create three new variables in your dataset df using the command:

df$dR1 <- ifelse(df$Region == 'Reg1', 1, 0)
df$dR2 <- ifelse(df$Region == 'Reg2', 1, 0)
df$dR3 <- ifelse(df$Region == 'Reg3', 1, 0) 

This code will create three new columns where, in the column “dR1” you will get the number “1” when the the interview was in conducted in Region 1 and “0” when conducted in another region. The same would apply for the following two columns.

Indeed, you can use the ifelse() function within a tidyverse syntax.

df  %>%
  mutate( dR1 = ifelse(Region == 'Reg1', 1, 0), 
          dR2 = ifelse(Region == 'Reg2', 1, 0),
          dR3 = ifelse(Region == 'Reg3', 1, 0)) 

Exercise 1:

Show a table that displays, the annual GDP of Zimbabwe after 1972 expressed in million USD and sort the results by year.

data %>% filter(country=="Zimbabwe" & year >1972) %>%
  mutate(GDP = gdpPercap * pop / 1000000) %>%
  select(country, year, GDP) %>%
  arrange(year)

## # A tibble: 7 x 3
##   country   year   GDP
##   <fct>    <int> <dbl>
## 1 Zimbabwe  1977 4554.
## 2 Zimbabwe  1982 6024.
## 3 Zimbabwe  1987 6508.
## 4 Zimbabwe  1992 7423.
## 5 Zimbabwe  1997 9038.
## 6 Zimbabwe  2002 8015.
## 7 Zimbabwe  2007 5783.

Exercise 2:

Show a table that displays, the annual 2007 GDP of African countries whose name starts with an A or a B expressed in million USD and sort the results by increasing GDP.

data %>% filter(continent=="Africa" & substr(country,1,1) %in% c("A", "B") & year==2007) %>%
  mutate(GDP = gdpPercap * pop / 1000000) %>%
  select(country,  GDP) %>%
  arrange(GDP)

## # A tibble: 6 x 2
##   country          GDP
##   <fct>          <dbl>
## 1 Burundi        3609.
## 2 Benin         11643.
## 3 Burkina Faso  17435.
## 4 Botswana      20604.
## 5 Angola        59584.
## 6 Algeria      207445.