The sample data is a .csv file that has some of the offensive statistics for the Jets and Giants. Here is what the sample data looked like.

library(tidyr)
library(dplyr)
library(zoo)
library(stringr)
library(scales)

#import .csv file
theFile = "nfl_stats.csv"
stats <- read.csv(theFile, header = FALSE, stringsAsFactors = FALSE)
unlink(theFile)

head(stats)
    V1                  V2   V3   V4   V5   V6   V7   V8   V9  V10
1 Team               Stats Year   NA   NA   NA   NA   NA   NA   NA
2                          2007 2008 2009 2010 2011 2012 2013 2014
3 Jets   Total Passing Yds 3014 3303 2380 3242 3297 2891 2932 2946
4      Total Rushing Yards 1701 2004 2756 2374 1692 1896 2158 2280
5        Total First Downs  286  308  280  307  301  299  280  289
6               Touchdowns   26   48   37   39   45   31   27   27
#place header in same row
stats$V1[2] <- stats$V1[1]
stats$V2[2] <- stats$V2[1]

#add header row, remove superflorous rows
header <- c("Team","Stats",2007:2014)
colnames(stats) <- header
stats <- stats[-c(1,2,7), ]

#change all blank cells in team column to NA
stats$Team <- ifelse(stats$Team == "", NA, stats$Team)
#so I can repeat the team names in the columns
stats$Team <- na.locf(stats$Team, na.rm=TRUE)

#change Yds to Yards
stats$Stats <- str_replace_all(stats$Stats, "Yds", "Yards")

#gather the data to create an observation for each team by year per stat
new_stats <- stats %>%
  gather(Year, Amount, 3:10)

#change all data to numeric
new_stats$Amount <- as.numeric(new_stats$Amount)

The data has been transformed from wide to long.

head(new_stats)
    Team               Stats Year Amount
1   Jets Total Passing Yards 2007   3014
2   Jets Total Rushing Yards 2007   1701
3   Jets   Total First Downs 2007    286
4   Jets          Touchdowns 2007     26
5 Giants Total Passing Yards 2007   3154
6 Giants Total Rushing Yards 2007   2148

Now, we can perform some analysis.

#add column for total yards and % of yards for passing/rushing
yards <- as.data.frame(new_stats %>%
  filter(grepl('Yards', Stats)) %>%
  group_by(Team, Year) %>%
  mutate(total_yards=sum(Amount)) %>%
  mutate(rate=(Amount/total_yards)) %>%
  filter(rate > .66)) %>%
  arrange(-rate, Year)

The following is every instance where a team passed or rushed more than 66% of the time:

yards
    Team               Stats Year Amount total_yards      rate
1 Giants Total Passing Yards 2011   4734        6161 0.7683818
2 Giants Total Passing Yards 2013   3588        4920 0.7292683
3 Giants Total Passing Yards 2014   4272        5875 0.7271489
4 Giants Total Passing Yards 2009   4019        5856 0.6863046
5 Giants Total Passing Yards 2012   3825        5687 0.6725866
6   Jets Total Passing Yards 2011   3297        4989 0.6608539