There are several inbuilt functions as well as packages for checking the quality of data in R. The most commonly used among them is the summary function in base R:

## Packages Used: 
## psych, pastecs, dataMaid, daff 
 
# install.packages(c("psych","pastecs","dataMaid","daff")) 
 
state <- data.frame(state.x77) 
state$State <- row.names(state) 
state 
 
 
summary(state) 

The output of the preceding code is as follows:

library(psych) 
describe(state) 

The output of the preceding code is as follows:

You can also use describe.by to get summary information on a per group basis, as shown:

describe.by(state,state$State) 

The following is the output:

Or, for a comprehensive statistical description, you can use stat.desc from pastecs, as shown:

library(pastecs) 
stat.desc(state) 

The output of the preceding code is as follows:

Among other utilities, a more recent package, called dataMaid, makes is easy to capture a high-level comparison of all of the data contained in the dataset using a one-line command, as follows:

library(dataMaid) 
makeDataReport(state) 

The output of the preceding code is as follows:

We often need to find differences in datasets when some information changes. This can be done on an iterative basis by inspecting individual columns and so on, but a new package called daff can now be used to get very nice visual renderings of the changes, in a similar fashion to how you may have seen them on sites such as GitHub and elsewhere:

library(daff) 
state <- data.frame(state.x77) 
state2 <- state 
identical(state, state2) 
 
state2$Population <- state2$Population+1 
diff_data(state,state2) 

The output of the preceding code is as follows:

diff_info <- diff_data(state,state2) 
render_diff(diff_info) 

The output of the preceding code is as follows:

You can also patch the data using patch_data and merge datasets using merge_data. More information can be found on the developer's website.