r - Extracting the slope for individual observation -

i'm newbie in r. have data set 3 set of lung function measurements 3 corresponding dates given below each observation. extract slope each observation(decline in lung function) using r software , insert in new column each observation.
1. how should approach problem?
2. data set arranged in right format?

id      fev1_date11 fev1_date12 fev1_date13  date11     date12     date13 18105   1.35        1.25        1.04         6/9/1990   8/16/1991  8/27/1993 18200   0.87        0.85                     9/12/1991  3/11/1993    18303   0.79                                 4/23/1992       24204   4.05        3.95        3.99         6/8/1992   3/22/1993  11/5/1994 28102   1.19        1.04        0.96         10/31/1990 7/24/1991  6/27/1992 34104   1.03        1.16        1.15         7/25/1992  12/8/1993  12/7/1994 43108   0.92        0.83        0.79         6/23/1993  1/12/1994  1/11/1995 103114  2.43        2.28        2.16         6/5/1994   6/21/1995  4/7/1996 114101  0.73        0.59        0.6          6/25/1989  8/5/1990   8/24/1991 

example 1st observation, slope=0.0003 thanks..

if understood question, think want slope between each set of visits:

library(dplyr)  group_by(df, id) %>%    mutate_at(vars(starts_with("date")), funs(as.date(., "%m/%d/%y"))) %>%    do(data_frame(slope=diff(unlist(.[,2:4]))/diff(unlist(.[,5:7])),                 after_visit=1+(1:length(slope))))  ## source: local data frame [18 x 3] ## groups: id [9] ##  ##        id         slope after_visit ##     <int>         <dbl>       <dbl> ## 1   18105 -2.309469e-04           2 ## 2   18105 -2.830189e-04           3 ## 3   18200 -3.663004e-05           2 ## 4   18200            na           3 ## 5   18303            na           2 ## 6   18303            na           3 ## 7   24204 -3.484321e-04           2 ## 8   24204  6.745363e-05           3 ## 9   28102 -5.639098e-04           2 ## 10  28102 -2.359882e-04           3 ## 11  34104  2.594810e-04           2 ## 12  34104 -2.747253e-05           3 ## 13  43108 -4.433498e-04           2 ## 14  43108 -1.098901e-04           3 ## 15 103114 -3.937008e-04           2 ## 16 103114 -4.123711e-04           3 ## 17 114101 -3.448276e-04           2 ## 18 114101  2.604167e-05           3 

alternate munging:

group_by(df, id) %>%    mutate_at(vars(starts_with("date")), funs(as.date(., "%m/%d/%y"))) %>%    do(data_frame(date=as.date(unlist(.[,5:7]), origin="1970-01-01"), # in event wanted keep data less awful , have 1 observation per row, preserves date class                 reading=unlist(.[,2:4]))) %>%    do(data_frame(slope=diff(.$reading)/unclass(diff(.$date))))