Merging two dataframes in R with date

Question

I have the following 2 dataframes:

> bvg1
                         Parameters X18.Oct.14 X19.Oct.14 X20.Oct.14 X21.Oct.14 X22.Oct.14 X23.Oct.14 X24.Oct.14
1               24K Equivalent Plan      29.00      29.60      33.80      36.60      35.30      31.90      29.00
2                24K Equivalent Act      28.80      31.00      35.40      35.90      34.70      33.40      31.90
3                       Plan Rep WS    2463.00    2513.00    2869.00    3115.00    2999.00    2714.00    2468.00
4                        Act Rep WS    2447.00    2633.00    3013.00    3054.00    2953.00    2842.00    2714.00
5                        Rep WS Var     -16.00     120.00     144.00     -61.00     -46.00     128.00     246.00
6                  Plan Rep Intakes     568.00     461.00    1159.00    1146.00    1126.00    1124.00    1106.00
7                   Act Rep Intakes     707.00     494.00    1106.00    1096.00    1274.00    1087.00    1101.00
8                   Rep Intakes Var     139.00      33.00     -53.00     -50.00     148.00     -37.00      -5.00
9                 Plan Rep Comps_DL     468.00      54.00     836.00    1190.00    1327.00    1286.00    1108.00
10                 Act Rep Comps_DL     471.00      70.00     995.00    1137.00    1323.00    1150.00    1073.00
11                 Rep Comps Var_DL       3.00      16.00     159.00     -53.00      -4.00    -136.00     -35.00
12              Plan Rep Mandays_DL     148.00      19.00     260.00     368.00     412.00     398.00     345.00
13               Act Rep Mandays_DL     147.00      19.00     303.00     359.00     423.00     374.00     348.00
14               Rep Mandays Var_DL      -1.00       1.00      43.00      -9.00      12.00     -24.00       3.00
15              Plan FVR Mandays_DL       0.00       0.00       4.00      18.00      18.00      18.00      18.00
16               Act FVR Mandays_DL       0.00       0.00       4.00       7.00       8.00       8.00       7.00
17               FVR Mandays Var_DL       0.00       0.00       0.00     -11.00     -10.00     -10.00     -11.00
18                 Plan Rep Prod_DL       3.16       2.88       3.21       3.23       3.22       3.23       3.21
19                  Act Rep Prod_DL       3.21       3.62       3.28       3.16       3.12       3.07       3.08
20                  Rep Prod Var_DL       0.05       0.74       0.07      -0.07      -0.10      -0.16      -0.13


> bvg2
                         Parameters  X18.Oct  X19.Oct  X20.Oct  X21.Oct  X22.Oct  X23.Oct  X24.Oct
1               24K Equivalent Plan    30.50    31.30    35.10    36.10    33.60    28.80    25.50
2                24K Equivalent Act    31.40    33.40    36.60    38.10    36.80    34.40    32.10
3                       Plan Rep WS  3419.00  3509.00  3933.00  4041.00  3764.00  3220.00  2859.00
4                        Act Rep WS  3514.00  3734.00  4098.00  4271.00  4122.00  3852.00  3591.00
5                        Rep WS Var    95.00   225.00   165.00   230.00   358.00   632.00   732.00
6                  Plan Rep Intakes   813.00   613.00  1559.00  1560.00  1506.00  1454.00  1410.00
7                   Act Rep Intakes   964.00   602.00  1629.00  1532.00  1657.00  1507.00  1439.00
8                   Rep Intakes Var   151.00   -11.00    70.00   -28.00   151.00    53.00    29.00
9                 Plan Rep Comps_DL   675.00   175.00  1331.00  1732.00  1938.00  1706.00  1493.00
10                 Act Rep Comps_DL   718.00   224.00  1389.00  1609.00  1848.00  1698.00  1537.00
11                 Rep Comps Var_DL    43.00    49.00    58.00  -123.00   -90.00    -8.00    44.00
12              Plan Rep Mandays_DL   203.00    58.00   428.00   541.00   605.00   536.00   475.00
13               Act Rep Mandays_DL   215.00    63.00   472.00   542.00   608.00   556.00   523.00
14               Rep Mandays Var_DL    12.00     5.00    44.00     2.00     3.00    20.00    48.00
15              Plan FVR Mandays_DL     0.00     0.00     1.00    12.00     2.00    32.00    57.00
16               Act FVR Mandays_DL     0.00     0.00     2.00     2.00     5.00     5.00     5.00
17               FVR Mandays Var_DL     0.00     0.00     1.00   -10.00     3.00   -27.00   -52.00
18                 Plan Rep Prod_DL     3.33     3.03     3.11     3.20     3.20     3.18     3.14
19                  Act Rep Prod_DL     3.34     3.56     2.94     2.97     3.04     3.05     2.94
20                  Rep Prod Var_DL     0.01     0.53    -0.17    -0.23    -0.16    -0.13    -0.20

It is a time series data ie 24K Equivalent Plan was 29 on 18th Oct, 29.60 on 19th Oct and 33.80 on 20th Oct. First dataframe have data for one business unit and second dataframe have the data for a different business unit.

I want to merge dataframes into 1 and want to analyse the variance ie where they differ in values. Draw ggplots like 2 histograms showing the difference, timeseries plots etc.

I have tried the following: I can merge the two dataframes by:

joined = rbind(bvg1, bvg2)

however, i can't identify the record whether it belongs to bvg1 or bvg2 df.

if i add an additional column ie

bvg1$id = "bvg1"
bvg2$id = "bvg2"

then merge command doesn't work and gives the following error:

Error in match.names(clabs, names(xi)) : 
  names do not match previous names

Any sample code would be highly appreciated.

Answer 1

You can match the column names of the two datasets by stripping the . followed by the digits in the bvg1 . This can be done using regex . In the below code, a lookbehind regex is used. It matches the lookbehind (?<=[A-Za-]) ie an alphabet followed by . followed by one or more elements .* to the end of string $ and remove those "" .

colnames(bvg1) <-gsub("(?<=[A-Za-z])\\..*$", "", colnames(bvg1), perl=TRUE)
res <- rbind(bvg1, bvg2)
dim(res)
#[1] 40  9

 head(res,3)
 #           Parameters X18.Oct X19.Oct X20.Oct X21.Oct X22.Oct X23.Oct X24.Oct
 #1 24K Equivalent Plan    29.0    29.6    33.8    36.6    35.3    31.9    29.0
 #2  24K Equivalent Act    28.8    31.0    35.4    35.9    34.7    33.4    31.9
 #3         Plan Rep WS  2463.0  2513.0  2869.0  3115.0  2999.0  2714.0  2468.0
 #   id
 #1 bvg1
 #2 bvg1
 #3 bvg1