string input to dplyr group_by

Question

I need to understand how to input string values (NSE) in dplyr's group_by function. My data set and code below works fine with "group_by" but does not work with "group_by_" version. I am unable to find my mistake in this regard.

ID,Region,Dimension,BlogsInd.,BlogsNews,BlogsTech,Columns
1,PK,Dim1,-4.75,NA,NA,NA
2,PK,Dim1,-5.69,NA,NA,NA
3,PK,Dim1,-0.27,NA,NA,NA
4,PK,Dim1,-2.76,NA,NA,NA
5,PK,Dim1,-8.24,NA,NA,NA
6,PK,Dim1,-12.51,NA,NA,NA
7,PK,Dim1,-1.28,NA,NA,NA
8,PK,Dim1,0.95,NA,NA,NA
9,PK,Dim1,-5.96,NA,NA,NA
10,PK,Dim1,-8.81,NA,NA,NA
11,PK,Dim1,-8.46,NA,NA,NA
12,PK,Dim1,-6.15,NA,NA,NA
13,PK,Dim1,-13.98,NA,NA,NA
14,PK,Dim1,-16.43,NA,NA,NA
15,PK,Dim1,-4.09,NA,NA,NA
16,PK,Dim1,-11.06,NA,NA,NA
17,PK,Dim1,-9.04,NA,NA,NA
18,PK,Dim1,-8.56,NA,NA,NA
19,PK,Dim1,-8.13,NA,NA,NA
20,PK,Dim2,-14.46,NA,NA,NA
21,PK,Dim2,-4.21,NA,NA,NA
22,PK,Dim2,-4.96,NA,NA,NA
23,PK,Dim2,-5.48,NA,NA,NA
24,PK,Dim2,-4.53,NA,NA,NA
25,PK,Dim2,6.31,NA,NA,NA
26,PK,Dim2,-11.16,NA,NA,NA
27,PK,Dim2,-1.27,NA,NA,NA
28,PK,Dim2,-11.49,NA,NA,NA
29,PK,Dim2,-0.9,NA,NA,NA
30,PK,Dim2,-12.27,NA,NA,NA
31,PK,Dim2,6.85,NA,NA,NA
32,PK,Dim2,-5.21,NA,NA,NA
33,PK,Dim2,-1.06,NA,NA,NA
34,PK,Dim2,-2.6,NA,NA,NA
35,PK,Dim2,-0.95,NA,NA,NA
36,PK,Dim3,-0.82,NA,NA,NA
37,PK,Dim3,-7.65,NA,NA,NA
38,PK,Dim3,0.64,NA,NA,NA
39,PK,Dim3,-2.25,NA,NA,NA
40,PK,Dim3,-1.58,NA,NA,NA
41,PK,Dim3,-5.73,NA,NA,NA
42,PK,Dim3,0.37,NA,NA,NA
43,PK,Dim3,-5.46,NA,NA,NA
44,PK,Dim3,-3.48,NA,NA,NA
45,PK,Dim3,0.88,NA,NA,NA
46,PK,Dim3,-2.11,NA,NA,NA
47,PK,Dim3,-10.13,NA,NA,NA
48,PK,Dim3,-2.08,NA,NA,NA
49,PK,Dim3,-4.33,NA,NA,NA
50,PK,Dim3,1.09,NA,NA,NA
51,PK,Dim3,-4.23,NA,NA,NA
52,PK,Dim3,-1.46,NA,NA,NA
53,PK,Dim3,9.37,NA,NA,NA
54,PK,Dim3,5.84,NA,NA,NA
55,PK,Dim3,8.21,NA,NA,NA
56,PK,Dim3,7.34,NA,NA,NA
57,PK,Dim4,1.83,NA,NA,NA
58,PK,Dim4,14.39,NA,NA,NA
59,PK,Dim4,22.02,NA,NA,NA
60,PK,Dim4,4.83,NA,NA,NA
61,PK,Dim4,-3.24,NA,NA,NA
62,PK,Dim4,-5.69,NA,NA,NA
63,PK,Dim4,-22.92,NA,NA,NA
64,PK,Dim4,0.41,NA,NA,NA
65,PK,Dim4,-4.42,NA,NA,NA
66,PK,Dim4,-10.72,NA,NA,NA
67,PK,Dim4,-11.29,NA,NA,NA
68,PK,Dim4,-2.89,NA,NA,NA
69,PK,Dim4,-7.59,NA,NA,NA
70,PK,Dim4,-7.45,NA,NA,NA
71,US,Dim1,-12.49,NA,NA,NA
72,US,Dim1,-11.59,NA,NA,NA
73,US,Dim1,-4.6,NA,NA,NA
74,US,Dim1,-22.83,NA,NA,NA
75,US,Dim1,-4.83,NA,NA,NA
76,US,Dim1,-14.76,NA,NA,NA
77,US,Dim1,-15.93,NA,NA,NA
78,US,Dim1,-2.78,NA,NA,NA
79,US,Dim1,-16.39,NA,NA,NA
80,US,Dim1,-15.22,NA,NA,NA
81,US,Dim1,3.25,NA,NA,NA
82,US,Dim1,-2.73,NA,NA,NA
83,US,Dim1,0.96,NA,NA,NA
84,US,Dim1,-1.12,NA,NA,NA
85,US,Dim1,-0.33,NA,NA,NA
86,US,Dim1,-6.45,NA,NA,NA
87,US,Dim1,2.52,NA,NA,NA
88,US,Dim1,3.18,NA,NA,NA
89,US,Dim1,4.65,NA,NA,NA
90,US,Dim2,-1.75,NA,NA,NA
91,US,Dim2,-0.22,NA,NA,NA
92,US,Dim2,8.16,NA,NA,NA
93,US,Dim2,1.89,NA,NA,NA
94,US,Dim2,4.31,NA,NA,NA
95,US,Dim2,-0.41,NA,NA,NA
96,US,Dim2,-23.02,NA,NA,NA
97,US,Dim2,3.87,NA,NA,NA
98,US,Dim2,-4.76,NA,NA,NA
99,US,Dim2,4.95,NA,NA,NA
100,US,Dim2,4.78,NA,NA,NA
101,US,Dim2,-15.11,NA,NA,NA
102,US,Dim2,-3.74,NA,NA,NA
103,US,Dim2,-6.15,NA,NA,NA
104,US,Dim2,-8.33,NA,NA,NA
105,US,Dim2,-5.55,NA,NA,NA
106,US,Dim3,-5.1,NA,NA,NA
107,US,Dim3,-0.41,NA,NA,NA
108,US,Dim3,-8,NA,NA,NA
109,US,Dim3,-11.8,NA,NA,NA
110,US,Dim3,-10.39,NA,NA,NA
111,US,Dim3,-14.98,NA,NA,NA
112,US,Dim3,-13.14,NA,NA,NA
113,US,Dim3,-16.06,NA,NA,NA
114,US,Dim3,-16.75,NA,NA,NA
115,US,Dim3,-17.58,NA,NA,NA
116,US,Dim3,-13.12,NA,NA,NA
117,US,Dim3,-15.69,NA,NA,NA
118,US,Dim3,-9.29,NA,NA,NA
119,US,Dim3,-14.93,NA,NA,NA
120,US,Dim3,-18.75,NA,NA,NA
121,US,Dim3,-16.15,NA,NA,NA
122,US,Dim3,-14.38,NA,NA,NA
123,US,Dim3,-11.33,NA,NA,NA
124,US,Dim3,2.06,NA,NA,NA
125,US,Dim3,1.55,NA,NA,NA
126,US,Dim3,3.17,NA,NA,NA
127,US,Dim4,3.33,NA,NA,NA
128,US,Dim4,-3.31,NA,NA,NA
129,US,Dim4,5.67,NA,NA,NA
130,US,Dim4,-1.94,NA,NA,NA
131,US,Dim4,-4.2,NA,NA,NA
132,US,Dim4,-13.53,NA,NA,NA
133,US,Dim4,-10.84,NA,NA,NA
134,US,Dim4,-1.04,NA,NA,NA
135,US,Dim4,-8.02,NA,NA,NA
136,US,Dim4,-14.65,NA,NA,NA
137,US,Dim4,-6.39,NA,NA,NA
138,US,Dim4,-3.69,NA,NA,NA
139,US,Dim4,-11.62,NA,NA,NA
140,US,Dim4,-3.02,NA,NA,NA
141,US,Dim4,-28.84,NA,NA,NA

.

attach(dims_Blog)
d1 <- dims_Blog %>% group_by(Dimension, Region) %>% summarise(mean=mean(BlogsInd., na.rm=TRUE))
d1
Dimension Region       mean
     <fctr> <fctr>      <dbl>
1      Dim1     PK -3.7385551
2      Dim1     US -4.2264179
3      Dim2     PK  1.9985551
4      Dim2     US  1.3509577
5      Dim3     PK  0.8965019
6      Dim3     US  1.5335199
7      Dim4     PK  1.4830672
8      Dim4     US  0.3913806

But the same code with the other version does not work. Where am I wrong?

d1 <- dims_Blog %>% group_by_("Dimension", "Region") %>% summarise_(mean="mean(BlogsInd.)", na.rm=TRUE)
> d1
Source: local data frame [8 x 4]
Groups: Dimension [?]

  Dimension Region  mean na.rm
     <fctr> <fctr> <dbl> <lgl>
1      Dim1     PK    NA  TRUE
2      Dim1     US    NA  TRUE
3      Dim2     PK    NA  TRUE
4      Dim2     US    NA  TRUE
5      Dim3     PK    NA  TRUE
6      Dim3     US    NA  TRUE
7      Dim4     PK    NA  TRUE
8      Dim4     US    NA  TRUE

Answer 1

The problem is not the nse, but in the na.rm argument. In the first example you send this argument to mean, in the second example it's split off and summarise interprets it as a new variable to be added. By moving the na.rm back into the mean call I get the same results from both methods:

d1 <- dims_Blog %>% group_by(Dimension, Region) %>% summarise(mean=mean(BlogsInd., na.rm=TRUE))
d2 <- dims_Blog %>% group_by_("Dimension", "Region") %>% summarise_(mean="mean(BlogsInd., na.rm=TRUE)")
identical(d1,d2) #Returns TRUE