How to categorize non-numerical variables

[Mangji Zo]

Hi,

I am having trouble coding these variables into two categories: edible foods and non-edible foods. I am not sure if the coding is the same way as continuous variables? I am relatively new to STATA, any advice is greatly appreciated!


Here are my codes:

tab Crop_name, mi nol
encode Crop_name, gen(Crop_namecodes)


tab Crop_namecodes, miss
gen Crop_namecodescat = Crop_namecodes
recode Crop_namecodescat bananafood beans maize =1 coffee cocao =2
label define Crop_namecodescatlab 1 "edible food" 2 "non-edible food"

* Example generated by -dataex-. To install: ssc install dataex
clear
input str20 Crop_name
""              
"Banana food"   
"Cassava"       
"Beans"         
"Dodo"          
"Dodo"          
"Maize"         
"Yam"           
"Banana food"   
"Beans"         
"Maize"         
"Banana food"   
"Beans"         
"Maize"         
"Coffee all"    
""              
"Banana food"   
"Banana food"   
"Banana food"   
"Cassava"       
"Banana food"   
"Coffee all"    
"Beans"         
"Maize"         
"Groundnuts"    
"Maize"         
"Cassava"       
"Maize"         
"Sweet potatoes"
""              
"Beans"         
""              
"Cocoa"         
"Coffee all"    
"Groundnuts"    
"Groundnuts"    
"Beans"         
"Cassava"       
""              
"Sweet potatoes"
"Banana food"   
"Cassava"       
"Maize"         
"Yam"           
"Sugarcane"     
""              
"Banana food"   
"Beans"         
"Beans"         
"Cassava"       
"Maize"         
"Banana food"   
""              
"Banana food"   
"Coffee all"    
"Sweet potatoes"
"Cassava"       
"Yam"           
"Groundnuts"    
"Maize"         
"Sweet potatoes"
"Maize"         
"Maize"         
"Beans"         
"Maize"         
""              
"Banana food"   
"Maize"         
"Beans"         
"Cassava"       
"Banana food"   
"Beans"         
"Coffee all"    
"Fallow"        
"Sweet potatoes"
"Beans"         
"Cassava"       
"Maize"         
"Banana food"   
""              
"Fallow"        
"Banana food"   
"Coffee all"    
"Banana beer"   
"Sweet potatoes"
"Maize"         
"Beans"         
"Maize"         
"Banana food"   
"Coffee all"    
"Banana food"   
"Beans"         
"Cassava"       
"Maize"         
""              
""              
""              
""              
"Banana beer"   
"Banana beer"   
end

------------------ copy up to and including the previous line ------------------

Listed 100 out of 15403 observations
Use the count() option to list more

[Carole J. Wilson]

You need to refer to the numeric values you created with encode in your recode command: You probably don't want to copy Crop_namecodes directly into your new variable Crop_namecodescat for fear of using the same codes in the recode command and confusing the categories.

encode Crop_name, gen(Crop_namecodes)
lab list Crop_namecodes
gen Crop_namecodescat=.
replace Crop_namecodescat=1 if inlist(Crop_namecodes, 2, 3, 10)
replace Crop_namecodescat=2 if inlist(Crop_namecodes, 6, 5)
label define Crop_namecodescatlab 1 "edible food" 2 "non-edible food" , modify
lab value Crop_namecodescat Crop_namecodescatlab

[Mangji Zo]

Hello,

Thank you for the code! It worked really well! I have another question with regard to calculating the total quantity of crops produced by each household if it is possible? HIDD is the variable for the identification of each household. For instance, I want to add up the all the quan_harvested for HHID -1021000113 and subtract the Quan_sold. I have tried collapsing the data but I am not sure if I am on the right track? Thank you for any advice!

input str14 HHID float Parcel_ID str20 Crop_name double(Quan_harvested Quan_sold)
"1013000204" 21 "" . .
"1021000108" 1 "Banana food" 48 0
"1021000108" 1 "Cassava" 3 0
"1021000113" 1 "Beans" 3 0
"1021000113" 1 "Dodo" 2 0
"1021000113" 1 "Dodo" 10 0
"1021000113" 1 "Maize" 1 0
"1021000113" 1 "Yam" 99999 .
"1021000408" 1 "Banana food" 99999 .
"1021000408" 1 "Beans" 2 0
"1021000408" 1 "Maize" 1 0
"1021000710" 1 "Banana food" 99999 .
"1021000710" 1 "Beans" 3.5 0
"1021000710" 1 "Maize" 60 0
"1021000807" 1 "Coffee all" 7 7
"1021000807" 1 "" . .
"1021000807" 2 "Banana food" 180 80
"1021000807" 2 "Banana food" 30 0
"1021000807" 2 "Banana food" 90 90
"1021000807" 2 "Cassava" 17 17
"102100080803" 1 "Banana food" 20 0
"102100080803" 1 "Coffee all" 0 .
"102100080803" 2 "Beans" 1 0
"102100080803" 2 "Maize" 1 1
"102100080803" 2 "Groundnuts" 2 0
"102100080803" 2 "Maize" 1 1
"102100080803" 21 "Cassava" 2 0
"102100080803" 21 "Maize" 2.5 1
"102100080803" 21 "Sweet potatoes" 1 0
"102100080803" 21 "" . .
"102100110201" 1 "Beans" 1 0
"102100110201" 1 "" . .
"102100110201" 2 "Cocoa" 4 4
"102100110201" 2 "Coffee all" 1 1
"102100110201" 2 "Groundnuts" 3 0
"102100110201" 3 "Groundnuts" 4 2
"102100110201" 3 "Beans" 1 0
"102100110201" 3 "Cassava" 99999 .
"102100110201" 4 "" . .
"1021001109" 1 "Sweet potatoes" 32 0
"1021001109" 1 "Banana food" 12 0
"1021001109" 1 "Cassava" 2 0
"1021001109" 1 "Maize" .5 0
"1021001109" 2 "Yam" 10 6
"1021001109" 2 "Sugarcane" 120 0
"1021001109" 3 "" . .
"1021001304" 1 "Banana food" 4 0
"1021001304" 1 "Beans" 4 0
"1021001304" 1 "Beans" 10 0
"1021001304" 1 "Cassava" 5 0
"1021001304" 1 "Maize" 1 0
"1021001910" 1 "Banana food" 99999 .
"1021002501" 1 "" . .
"1021002610" 1 "Banana food" 10 0
"1021002610" 1 "Coffee all" 99999 .
"1021002610" 1 "Sweet potatoes" 8 3
"1021002610" 1 "Cassava" 99999 .
"1021002610" 1 "Yam" 5 0
"1021002610" 1 "Groundnuts" 1 0
"1021002610" 1 "Maize" 1 0
"1021002610" 21 "Sweet potatoes" 2 0
"1021002611" 1 "Maize" 1 0
"1021002611" 1 "Maize" 2 2
"1021002810" 1 "Beans" .5 0
"1021002810" 1 "Maize" 2 0
"1021003309" 21 "" . .
"1033000301" 1 "Banana food" 60 0
"1033000301" 1 "Maize" 15 0
"1033000302" 1 "Beans" 3 1
"1033000302" 1 "Cassava" 0 .
"1033000302" 1 "Banana food" 30 20
"1033000302" 1 "Beans" 1 0
"1033000302" 1 "Coffee all" 0 .
"1033000302" 1 "Fallow" 99999 .
"1033000303" 1 "Sweet potatoes" 0 .
"1033000303" 1 "Beans" 0 .
"1033000303" 1 "Cassava" 0 .
"1033000303" 1 "Maize" 3 2
"1033000303" 1 "Banana food" 50 15
"1033000303" 2 "" . .
"1033000303" . "Fallow" 99999 .
"1033000304" 1 "Banana food" 40 0
"1033000304" 1 "Coffee all" 0 .
"1033000304" 1 "Banana beer" 30 0
"1033000304" 21 "Sweet potatoes" 0 .
"1033000304" 22 "Maize" 0 .
"1033000304" 22 "Beans" 1 0
"1033000304" 22 "Maize" 20 0
"103300030403" 1 "Banana food" 5 0
"103300030403" 1 "Coffee all" 2 2
"103300030403" 2 "Banana food" 6 0
"103300030403" 21 "Beans" 1 0
"103300030403" 21 "Cassava" 0 .
"103300030403" 21 "Maize" 7.5 7
"1033000305" 1 "" . .
"1033000305" 2 "" . .
"1033000305" 3 "" . .
"1033000305" 21 "" . .
"1033000307" 1 "Banana beer" 30 0
"1033000307" 2 "Banana beer" 10 0
end
[/CODE]
------------------ copy up to and including the previous line ------------------

Listed 100 out of 15403 observations
Use the count() option to list more

[Carlo Lazzaro]

Mangji:
you may want to try (I assume that -99999- codes missing values):

bysort HHID: egen tot_harv=total( Quan_harvested) if Quan_harvested!=99999

[Mangji Zo]

Hi Carlo,

Thank you! It worked! I used the same code to get the sum of the quan_sold for each household, but I want to subtract tot_sold from tot_harv. Is the following code correct? I am not too sure. Thanks for any advice!
Code: bysort HHID: egen tot_sold=total(Quan_sold) if Quan_sold!=99999
gen diff_harvest = cond(tot_harv) - cond(tot_sold)

[Carlo Lazzaro]

Mangji:
I would go:

. bysort HHID: egen tot_sold=total( Quan_sold ) if Quan_sold!=99999

. g tot_sold_2= tot_harv-tot_sold

[Mangji Zo]

Hi Carlo,

Thank you for your help! It worked! Can I collapse the data so that each household (HHID) number is matched to one tot_sold_2 value? Also, does collapsing affect the missing data in the dataset? Thanks again!

code:

collapse tot_sold_2, by (HHID)

* Example generated by -dataex-. To install: ssc install dataex
clear
input str14 HHID double tot_sold_2
"1013000204"       0
"1021000108"      51
"1021000108"      51
"1021000113"      16
"1021000113"      16
"1021000113"      16
"1021000113"      16
"1021000113"       .
"1021000408"       .
"1021000408"       3
"1021000408"       3
"1021000710"       .
"1021000710"    63.5
"1021000710"    63.5
"1021000807"     130
"1021000807"     130
"1021000807"     130
"1021000807"     130
"1021000807"     130
"1021000807"     130
"102100080803"  27.5
"102100080803"  27.5
"102100080803"  27.5
"102100080803"  27.5
"102100080803"  27.5
"102100080803"  27.5
"102100080803"  27.5
"102100080803"  27.5
"102100080803"  27.5
"102100080803"  27.5
"102100110201"     7
"102100110201"     7
"102100110201"     7
"102100110201"     7
"102100110201"     7
"102100110201"     7
"102100110201"     7
"102100110201"     .
"102100110201"     7
"1021001109"   170.5
"1021001109"   170.5
"1021001109"   170.5
"1021001109"   170.5
"1021001109"   170.5
"1021001109"   170.5
"1021001109"   170.5
"1021001304"      24
"1021001304"      24
"1021001304"      24
"1021001304"      24
"1021001304"      24
"1021001910"       .
"1021002501"       0
"1021002610"      24
"1021002610"       .
"1021002610"      24
"1021002610"       .
"1021002610"      24
"1021002610"      24
"1021002610"      24
"1021002610"      24
"1021002611"       1
"1021002611"       1
"1021002810"     2.5
"1021002810"     2.5
"1021003309"       0
"1033000301"      75
"1033000301"      75
"1033000302"      13
"1033000302"      13
"1033000302"      13
"1033000302"      13
"1033000302"      13
"1033000302"       .
"1033000303"      36
"1033000303"      36
"1033000303"      36
"1033000303"      36
"1033000303"      36
"1033000303"      36
"1033000303"       .
"1033000304"      91
"1033000304"      91
"1033000304"      91
"1033000304"      91
"1033000304"      91
"1033000304"      91
"1033000304"      91
"103300030403"  12.5
"103300030403"  12.5
"103300030403"  12.5
"103300030403"  12.5
"103300030403"  12.5
"103300030403"  12.5
"1033000305"       0
"1033000305"       0
"1033000305"       0
"1033000305"       0
"1033000307"     170
"1033000307"     170
end

------------------ copy up to and including the previous line ------------------

Listed 100 out of 15403 observations
Use the count() option to list more

.

[Carlo Lazzaro]

Mangji:
instead of -collapse- (which comes with the cautionary advice of saving your original dataset beforehand), you can operate on the original dataset:

 bysort HHID: gen flag=1 if _n==1
(77 missing values generated)

. tabstat tot_sold_2 if flag==1, stat(count mean sd p50 min max)

    variable |         N      mean        sd       p50       min       max
-------------+------------------------------------------------------------
  tot_sold_2 |        20     36.05  46.97533        20         0     170.5
--------------------------------------------------------------------------

[Mangji Zo]

Hi Carlo,

Thanks for the advice! I learned a lot! The reason I want to collapse the data according to HHID is to merge the dataset with another dataset using HHID. Do I have the correct code to do so? Many thanks!

Code:
collapse tot_sold_2, by (HHID)

[Carlo Lazzaro]

Mangji:
yes, your code is correct (I assume that you want the -mean- of -tot-sold-2- by HHID).
IHowever, it's better to make explicit what you want to get from -collapse-:

collapse (mean) tot_sold_2, by (HHID)

Missing values will disappear after -collapse-: be sure that it does not matter for your analysis.
At the risk of being pedantic, save your original dataset before -collapse-.

[Mangji Zo]

Hi Carlo,

Thank you! I will save my dataset before collapsing!

[Mangji Zo]

Hi Carlo,

I have collapse my data to match on HHID for each household and by the type of crops (food and cash crops). I am only interested in the food crop data and not sure how to delete all the cash crops for each households.

Here is my code:

collapse tot_sold_2, by (HHID Crop_namecodescat)

* Example generated by -dataex-. To install: ssc install dataex
clear
input str14 HHID double(Crop_namecodescat tot_sold_2)
"1013000204"   .                  0
"1021000108"   1                 51
"1021000113"   1                 16
"1021000408"   1                  3
"1021000710"   1               63.5
"1021000807"   1                130
"1021000807"   2                  0
"1021000807"   .                  0
"102100080803" 1               27.5
"102100080803" 2                  0
"102100080803" .                  0
"102100110201" 1                  7
"102100110201" 2                  0
"102100110201" .                  0
"1021001109"   1              170.5
"1021001109"   .                  0
"1021001304"   1                 24
"1021001910"   1                  .
"1021002501"   .                  0
"1021002610"   1                 24
"1021002610"   2                  .
"1021002611"   1                  1
"1021002810"   1                2.5
"1021003309"   .                  0
"1033000301"   1                 75
"1033000302"   1                 13
"1033000302"   2                  0
"1033000303"   1                 36
"1033000303"   .                  0
"1033000304"   1                 91
"1033000304"   2                  0
"103300030403" 1               12.5
"103300030403" 2                  0
"1033000305"   .                  0
"1033000307"   1                170
"1033000307"   .                  0
"103300030702" .                  0
"1033000308"   1                  6
"1033000308"   .                  0
"1033000309"   1                 37
"1033000310"   1                 20
"1033000310"   2                  0
"1033000310"   .                  0
"1033000502"   .                  0
"1033000504"   1                 51
"1033000504"   2                  0
"1033000504"   .                  0
"1033000505"   1                 31
"1033000505"   2                  0
"1033000506"   1                  9
"1033000507"   1                  8
"1033000509"   1                  3
"1033000510"   1                 17
"1033000510"   2                  0
"1033000511"   1                8.5
"103300051102" 1                 48
"103300051102" 2                  0
"103300051102" .                  0
"1041000207"   1                  0
"1041000210"   1                 15
"1043000402"   1                  8
"1043000402"   2                  0
"1043000403"   1               83.5
"1043000403"   2                  0
"1043000403"   .                  0
"1043000406"   1                1.5
"1043000407"   1                 54
"1043000408"   1                  5
"1043000408"   .                  0
"1043000409"   1 22.200000000000003
"104300040906" 1                503
"104300040906" 2                  0
"104300040908" 1                 72
"104300040908" 2                  0
"1043000410"   1                355
"1043000601"   1                  4
"1043000601"   .                  0
"1043000602"   1                 98
"1043000602"   2                  0
"1043000603"   1              152.5
"1043000603"   2                  0
"1043000603"   .                  0
"1043000604"   1               11.5
"1043000605"   1                  2
"1043000607"   1                 25
"1043000607"   2                  0
"1043000610"   1                  4
"1043000610"   2                  0
"1043000611"   1                 16
"1043000611"   .                  0
"1043000804"   1                 20
"1043000804"   .                  0
"1043000805"   1                  0
"1043000806"   1                 31
"1043000806"   2                  0
"1043000806"   .                  0
"104300080608" 1                 29
"1043000807"   1                 52
"1043000807"   2                  0
"1043000808"   1                 44
end
label values Crop_namecodescat Crop_namecodescatlab
label def Crop_namecodescatlab 1 "Food Crop", modify
label def Crop_namecodescatlab 2 "Cash Crop", modify

------------------ copy up to and including the previous line ------------------

Listed 100 out of 3935 observations
Use the count() option to list more

[Carole J. Wilson]

If you want them gone from the dataset:

drop if Crop_namecodescat==2

If you also want those with missing values gone,

drop if Crop_namecodescat==.

[Mangji Zo]

Hi Carole,

Thanks for your help! I want to count how many different types of crops each household produce (HHID). For instance, HHID 1021000108 produced 2 types of crops and 1021000113 with 4 types. Not sure what the code should be in this case. Thank you for any advice!

* Example generated by -dataex-. To install: ssc install dataex
clear
input str14 HHID str20 cropname
"1013000204"   ""                
"1021000108"   "Cassava"         
"1021000108"   "Banana food"     
"1021000113"   "Maize"           
"1021000113"   "Beans"           
"1021000113"   "Dodo"            
"1021000113"   "Yam"             
"1021000408"   "Banana food"     
"1021000408"   "Maize"           
"1021000408"   "Beans"           
"1021000710"   "Maize"           
"1021000710"   "Beans"           
"1021000710"   "Banana food"     
"1021000807"   "Coffee all"      
"1021000807"   "Natural pastures"
"1021000807"   "Banana food"     
"1021000807"   "Cassava"         
"102100080803" "Banana food"     
"102100080803" "Coffee all"      
"102100080803" "Maize"           
"102100080803" "Beans"           
"102100080803" "Maize"           
"102100080803" "Groundnuts"      
"102100080803" "Maize"           
"102100080803" "Cassava"         
"102100080803" "Sweet potatoes"  
"102100080803" ""                
"102100110201" ""                
"102100110201" ""                
"102100110201" "Groundnuts"      
"102100110201" "Coffee all"      
"102100110201" "Cocoa"           
"102100110201" "Groundnuts"      
"102100110201" "Beans"           
"102100110201" "Cassava"         
"102100110201" ""                
"1021001109"   "Sweet potatoes"  
"1021001109"   "Cassava"         
"1021001109"   "Banana food"     
"1021001109"   "Maize"           
"1021001109"   "Yam"             
"1021001109"   "Sugarcane"       
"1021001109"   ""                
"1021001304"   "Maize"           
"1021001304"   "Beans"           
"1021001304"   "Cassava"         
"1021001304"   "Banana food"     
"1021001910"   "Banana food"     
"1021002501"   ""                
"1021002610"   "Sweet potatoes"  
"1021002610"   "Banana food"     
"1021002610"   "Coffee all"      
"1021002610"   "Cassava"         
"1021002610"   "Yam"             
"1021002610"   "Maize"           
"1021002610"   "Groundnuts"      
"1021002610"   "Sweet potatoes"  
"1021002611"   "Maize"           
"1021002810"   "Maize"           
"1021002810"   "Beans"           
"1021003309"   ""                
"1033000301"   "Maize"           
"1033000301"   "Banana food"     
"1033000302"   "Beans"           
"1033000302"   "Cassava"         
"1033000302"   "Beans"           
"1033000302"   "Banana food"     
"1033000302"   "Coffee all"      
"1033000302"   ""                
"1033000303"   "Sweet potatoes"  
"1033000303"   "Beans"           
"1033000303"   "Cassava"         
"1033000303"   "Maize"           
"1033000303"   "Banana food"     
"1033000303"   ""                
"1033000303"   ""                
"1033000304"   "Banana food"     
"1033000304"   "Coffee all"      
"1033000304"   "Banana beer"     
"1033000304"   "Sweet potatoes"  
"1033000304"   "Maize"           
"1033000304"   "Maize"           
"1033000304"   "Beans"           
"103300030403" "Banana food"     
"103300030403" "Coffee all"      
"103300030403" "Banana food"     
"103300030403" "Maize"           
"103300030403" "Beans"           
"103300030403" "Cassava"         
"1033000305"   ""                
"1033000305"   ""                
"1033000305"   ""                
"1033000305"   ""                
"1033000307"   "Banana beer"     
"1033000307"   "Beans"           
"1033000307"   "Banana beer"     
"1033000307"   "Cassava"         
"1033000307"   "Banana food"     
"1033000307"   ""                
"1033000307"   ""                
end

------------------ copy up to and including the previous line ------------------

[Carole J. Wilson]

For this solution, you need to install the package -egenmore-. To install:

ssc install egenmore

Then you can use the command:

bysort HHID: egen num_crop=nvals( cropname)

When there are multiple cases of the same crop in the household (banana beer in household 1033000307 occurs twice) this calculation just counts it once (so 4 crops in that household).