group_by: How to Transform my Data
ManufMktConc &lt;- read.csv("Downloads/Manufacturing.csv",
                         stringsAsFactors = FALSE)

ManufMktConc %&lt;&gt;% 
  tbl_df %&gt;% 
  slice(-1) %&gt;% 
  filter(CONCENFI.display.label!="All companies") %&gt;%
  select(-GEO.id,-GEO.id2,-GEO.display.label,-COMPANY,-RCPTOT,-YEAR.id)

##  use melt and cast from reshape2 
require(reshape2)

# melt makes the data set tall and thin using id variables and measure variables
# 
ManufMktConc_molten &lt;- as.data.frame(ManufMktConc) %&gt;%
  melt(id.vars=1:4, measure.vars=5:6) %&gt;%
  filter(value!="X")
ManufMktConc_molten[1:5,]

    NAICS.id       NAICS.display.label CONCENFI.id CONCENFI.display.label  variable value
1      311        Food manufacturing         856    4 largest companies CCORCPPCT  16.3
2      311        Food manufacturing         857    8 largest companies CCORCPPCT  24.2
3      311        Food manufacturing         858   20 largest companies CCORCPPCT  38.4
4      311        Food manufacturing         859   50 largest companies CCORCPPCT  50.9
5     3111 Animal food manufacturing         856    4 largest companies CCORCPPCT  30.2



# make a new column with the eventual column header. (Note this is different that your example)
ManufMktConc_molten$label &lt;- paste0(ManufMktConc_molten$variable,
                                    trimws(substr(ManufMktConc_molten$CONCENFI.display.label,1,2)))

# cast it into multiple columns (something like a pivot in Excel).
ManufMktConc_result  &lt;- ManufMktConc_molten %&gt;%
  cast(NAICS.id + NAICS.display.label ~ label) %&gt;%
  select(1,2,4,6,3,5,7)     ## reorder columns

ManufMktConc_result[1:5,]

      NAICS.id             NAICS.display.label CCORCPPCT4 CCORCPPCT8 CCORCPPCT20 CCORCPPCT50 VSHERFI50
    1      311              Food manufacturing       16.3       24.2        38.4        50.9     110.7
    2     3111       Animal food manufacturing       30.2       40.7        57.8        71.5     368.6
    3    31111       Animal food manufacturing       30.2       40.7        57.8        71.5     368.6
    4   311111  Dog and cat food manufacturing       67.8       80.6        89.6        96.5    2019.4
    5   311119 Other animal food manufacturing       24.3       36.2        51.5        68.2     228.3