mean(x): takes the mean of xsd(x): takes the standard deviation of xmedian(x): takes the median of xquantile(x): displays sample quantiles of x. Default is min, IQR, maxrange(x): displays the range. Same as c(min(x), max(x))sum(x): sum of xmax(x): maximum value in xmin(x): minimum value in xna.rm = argument for missing dataThese functions work on vectors:
x <- c(1, 5, 7, 4, 2, 8) mean(x)
[1] 4.5
mean(x, na.rm = TRUE) # Remove NAs if needed
[1] 4.5
Summarization on a data.frame/tibble:
mtcars %>% pull(hp) %>% mean() # alt: pull(mtcars, hp) %>% mean()
[1] 146.6875
mean(mtcars$hp)
[1] 146.6875
Let’s use the Youth Tobacco Survey data again:
yts <-
read_csv("https://sisbid.github.io/Data-Wrangling/data/Youth_Tobacco_Survey_YTS_Data.csv")
head(yts)
# A tibble: 6 × 31 YEAR LocationAbbr LocationDesc TopicType TopicDesc MeasureDesc DataSource Response Data_Value_Unit <dbl> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> 1 2015 AZ Arizona Tobacco Use – Sur… Cessatio… Percent of… YTS <NA> % 2 2015 AZ Arizona Tobacco Use – Sur… Cessatio… Percent of… YTS <NA> % 3 2015 AZ Arizona Tobacco Use – Sur… Cessatio… Percent of… YTS <NA> % 4 2015 AZ Arizona Tobacco Use – Sur… Cessatio… Quit Attem… YTS <NA> % 5 2015 AZ Arizona Tobacco Use – Sur… Cessatio… Quit Attem… YTS <NA> % 6 2015 AZ Arizona Tobacco Use – Sur… Cessatio… Quit Attem… YTS <NA> % # ℹ 22 more variables: Data_Value_Type <chr>, Data_Value <dbl>, Data_Value_Footnote_Symbol <chr>, # Data_Value_Footnote <chr>, Data_Value_Std_Err <dbl>, Low_Confidence_Limit <dbl>, # High_Confidence_Limit <dbl>, Sample_Size <dbl>, Gender <chr>, Race <chr>, Age <chr>, Education <chr>, # GeoLocation <chr>, TopicTypeId <chr>, TopicId <chr>, MeasureId <chr>, StratificationID1 <chr>, # StratificationID2 <chr>, StratificationID3 <chr>, StratificationID4 <chr>, SubMeasureID <chr>, # DisplayOrder <dbl>
Let’s work with one column as a vector using pull().
locations <- yts %>% pull(LocationDesc) locations
[1] "Arizona" "Arizona" "Arizona"
[4] "Arizona" "Arizona" "Arizona"
[7] "Arizona" "Arizona" "Arizona"
[10] "Arizona" "Arizona" "Arizona"
[13] "Arizona" "Arizona" "Arizona"
[16] "Arizona" "Arizona" "Arizona"
[19] "Arizona" "Arizona" "Arizona"
[22] "Arizona" "Arizona" "Arizona"
[25] "Connecticut" "Connecticut" "Connecticut"
[28] "Connecticut" "Connecticut" "Connecticut"
[31] "Connecticut" "Connecticut" "Connecticut"
[34] "Connecticut" "Connecticut" "Connecticut"
[37] "Connecticut" "Connecticut" "Connecticut"
[40] "Connecticut" "Connecticut" "Connecticut"
[43] "Connecticut" "Connecticut" "Connecticut"
[46] "Connecticut" "Connecticut" "Connecticut"
[49] "Connecticut" "Connecticut" "Connecticut"
[52] "Connecticut" "Connecticut" "Connecticut"
[55] "Connecticut" "Connecticut" "Connecticut"
[58] "Connecticut" "Connecticut" "Connecticut"
[61] "Connecticut" "Connecticut" "Connecticut"
[64] "Connecticut" "Connecticut" "Connecticut"
[67] "Connecticut" "Connecticut" "Connecticut"
[70] "Connecticut" "Connecticut" "Connecticut"
[73] "Georgia" "Georgia" "Georgia"
[76] "Georgia" "Georgia" "Georgia"
[79] "Georgia" "Georgia" "Georgia"
[82] "Georgia" "Georgia" "Georgia"
[85] "Georgia" "Georgia" "Georgia"
[88] "Georgia" "Georgia" "Georgia"
[91] "Georgia" "Georgia" "Georgia"
[94] "Georgia" "Georgia" "Georgia"
[97] "Hawaii" "Hawaii" "Hawaii"
[100] "Hawaii" "Hawaii" "Hawaii"
[103] "Hawaii" "Hawaii" "Hawaii"
[106] "Hawaii" "Hawaii" "Hawaii"
[109] "Hawaii" "Hawaii" "Hawaii"
[112] "Hawaii" "Hawaii" "Hawaii"
[115] "Hawaii" "Hawaii" "Hawaii"
[118] "Hawaii" "Hawaii" "Hawaii"
[121] "Hawaii" "Hawaii" "Hawaii"
[124] "Hawaii" "Hawaii" "Hawaii"
[127] "Hawaii" "Hawaii" "Hawaii"
[130] "Hawaii" "Hawaii" "Hawaii"
[133] "Hawaii" "Hawaii" "Hawaii"
[136] "Hawaii" "Hawaii" "Hawaii"
[139] "Hawaii" "Hawaii" "Hawaii"
[142] "Hawaii" "Hawaii" "Hawaii"
[145] "Illinois" "Illinois" "Illinois"
[148] "Illinois" "Illinois" "Illinois"
[151] "Illinois" "Illinois" "Illinois"
[154] "Illinois" "Illinois" "Illinois"
[157] "Illinois" "Illinois" "Illinois"
[160] "Illinois" "Illinois" "Illinois"
[163] "Illinois" "Illinois" "Illinois"
[166] "Illinois" "Illinois" "Illinois"
[169] "Illinois" "Illinois" "Illinois"
[172] "Illinois" "Illinois" "Illinois"
[175] "Illinois" "Illinois" "Illinois"
[178] "Illinois" "Illinois" "Illinois"
[181] "Illinois" "Illinois" "Illinois"
[184] "Illinois" "Illinois" "Illinois"
[187] "Illinois" "Illinois" "Illinois"
[190] "Illinois" "Illinois" "Illinois"
[193] "Louisiana" "Louisiana" "Louisiana"
[196] "Louisiana" "Louisiana" "Louisiana"
[199] "Louisiana" "Louisiana" "Louisiana"
[202] "Louisiana" "Louisiana" "Louisiana"
[205] "Louisiana" "Louisiana" "Louisiana"
[208] "Louisiana" "Louisiana" "Louisiana"
[211] "Louisiana" "Louisiana" "Louisiana"
[214] "Louisiana" "Louisiana" "Louisiana"
[217] "Mississippi" "Mississippi" "Mississippi"
[220] "Mississippi" "Mississippi" "Mississippi"
[223] "Mississippi" "Mississippi" "Mississippi"
[226] "Mississippi" "Mississippi" "Mississippi"
[229] "Mississippi" "Mississippi" "Mississippi"
[232] "Mississippi" "Mississippi" "Mississippi"
[235] "Mississippi" "Mississippi" "Mississippi"
[238] "Mississippi" "Mississippi" "Mississippi"
[241] "Mississippi" "Mississippi" "Mississippi"
[244] "Mississippi" "Mississippi" "Mississippi"
[247] "Mississippi" "Mississippi" "Mississippi"
[250] "Mississippi" "Mississippi" "Mississippi"
[253] "Utah" "Mississippi" "Mississippi"
[256] "Mississippi" "Mississippi" "Mississippi"
[259] "Mississippi" "Mississippi" "Mississippi"
[262] "Mississippi" "Mississippi" "Mississippi"
[265] "Mississippi" "Missouri" "Missouri"
[268] "Missouri" "Missouri" "Missouri"
[271] "Missouri" "Missouri" "Missouri"
[274] "Missouri" "Missouri" "Missouri"
[277] "Missouri" "Missouri" "Missouri"
[280] "Missouri" "Missouri" "Missouri"
[283] "Missouri" "Missouri" "Missouri"
[286] "Missouri" "National (States and DC)" "National (States and DC)"
[289] "National (States and DC)" "National (States and DC)" "Nebraska"
[292] "Nebraska" "Nebraska" "Nebraska"
[295] "Nebraska" "Nebraska" "Nebraska"
[298] "Nebraska" "Nebraska" "Nebraska"
[301] "Nebraska" "Nebraska" "Nebraska"
[304] "Nebraska" "Nebraska" "Nebraska"
[307] "Nebraska" "Nebraska" "Nebraska"
[310] "Nebraska" "Nebraska" "Nebraska"
[313] "Nebraska" "Nebraska" "New Jersey"
[316] "New Jersey" "New Jersey" "New Jersey"
[319] "New Jersey" "New Jersey" "New Jersey"
[322] "New Jersey" "New Jersey" "New Jersey"
[325] "New Jersey" "New Jersey" "New Jersey"
[328] "New Jersey" "New Jersey" "New Jersey"
[331] "New Jersey" "New Jersey" "New Jersey"
[334] "New Jersey" "New Jersey" "New Jersey"
[337] "New Jersey" "New Jersey" "New Jersey"
[340] "New Jersey" "New Jersey" "New Jersey"
[343] "New Jersey" "New Jersey" "New Jersey"
[346] "New Jersey" "New Jersey" "New Jersey"
[349] "New Jersey" "New Jersey" "New Jersey"
[352] "New Jersey" "New Jersey" "New Jersey"
[355] "New Jersey" "New Jersey" "New Jersey"
[358] "New Jersey" "New Jersey" "New Jersey"
[361] "New Jersey" "New Jersey" "North Carolina"
[364] "North Carolina" "North Carolina" "North Carolina"
[367] "North Carolina" "North Carolina" "North Carolina"
[370] "North Carolina" "North Carolina" "North Carolina"
[373] "North Carolina" "North Carolina" "North Carolina"
[376] "North Carolina" "North Carolina" "North Carolina"
[379] "North Carolina" "North Carolina" "North Carolina"
[382] "North Carolina" "North Carolina" "North Carolina"
[385] "North Carolina" "North Carolina" "North Carolina"
[388] "North Carolina" "North Carolina" "North Carolina"
[391] "North Carolina" "North Carolina" "North Carolina"
[394] "North Carolina" "North Carolina" "North Carolina"
[397] "North Carolina" "North Carolina" "North Carolina"
[400] "North Carolina" "North Carolina" "North Carolina"
[403] "North Carolina" "North Carolina" "North Carolina"
[406] "North Carolina" "North Carolina" "North Carolina"
[409] "North Carolina" "North Carolina" "North Dakota"
[412] "North Dakota" "North Dakota" "North Dakota"
[415] "North Dakota" "North Dakota" "North Dakota"
[418] "North Dakota" "North Dakota" "North Dakota"
[421] "North Dakota" "North Dakota" "North Dakota"
[424] "North Dakota" "North Dakota" "North Dakota"
[427] "North Dakota" "North Dakota" "North Dakota"
[430] "North Dakota" "North Dakota" "North Dakota"
[433] "North Dakota" "North Dakota" "North Dakota"
[436] "North Dakota" "North Dakota" "North Dakota"
[439] "North Dakota" "North Dakota" "North Dakota"
[442] "North Dakota" "North Dakota" "North Dakota"
[445] "North Dakota" "North Dakota" "North Dakota"
[448] "North Dakota" "North Dakota" "North Dakota"
[451] "North Dakota" "North Dakota" "North Dakota"
[454] "North Dakota" "North Dakota" "North Dakota"
[457] "North Dakota" "North Dakota" "Pennsylvania"
[460] "Pennsylvania" "Pennsylvania" "Pennsylvania"
[463] "Pennsylvania" "Pennsylvania" "Pennsylvania"
[466] "Pennsylvania" "Pennsylvania" "Pennsylvania"
[469] "Pennsylvania" "Pennsylvania" "Pennsylvania"
[472] "Pennsylvania" "Pennsylvania" "Pennsylvania"
[475] "Pennsylvania" "Pennsylvania" "Pennsylvania"
[478] "Pennsylvania" "Pennsylvania" "Pennsylvania"
[481] "Pennsylvania" "Pennsylvania" "Pennsylvania"
[484] "Pennsylvania" "Pennsylvania" "Pennsylvania"
[487] "Pennsylvania" "Pennsylvania" "Pennsylvania"
[490] "Pennsylvania" "Pennsylvania" "Pennsylvania"
[493] "Pennsylvania" "Pennsylvania" "Pennsylvania"
[496] "Pennsylvania" "Pennsylvania" "Pennsylvania"
[499] "Pennsylvania" "Pennsylvania" "Pennsylvania"
[502] "Pennsylvania" "Pennsylvania" "Pennsylvania"
[505] "Pennsylvania" "Pennsylvania" "South Carolina"
[508] "South Carolina" "South Carolina" "South Carolina"
[511] "South Carolina" "South Carolina" "South Carolina"
[514] "South Carolina" "South Carolina" "South Carolina"
[517] "South Carolina" "South Carolina" "South Carolina"
[520] "South Carolina" "South Carolina" "South Carolina"
[523] "South Carolina" "South Carolina" "South Carolina"
[526] "South Carolina" "South Carolina" "South Carolina"
[529] "South Carolina" "South Carolina" "West Virginia"
[532] "West Virginia" "West Virginia" "West Virginia"
[535] "West Virginia" "West Virginia" "West Virginia"
[538] "West Virginia" "West Virginia" "West Virginia"
[541] "West Virginia" "West Virginia" "West Virginia"
[544] "West Virginia" "West Virginia" "West Virginia"
[547] "West Virginia" "West Virginia" "West Virginia"
[550] "West Virginia" "West Virginia" "West Virginia"
[553] "West Virginia" "West Virginia" "West Virginia"
[556] "West Virginia" "West Virginia" "West Virginia"
[559] "West Virginia" "West Virginia" "West Virginia"
[562] "West Virginia" "West Virginia" "West Virginia"
[565] "West Virginia" "West Virginia" "West Virginia"
[568] "West Virginia" "West Virginia" "West Virginia"
[571] "West Virginia" "West Virginia" "West Virginia"
[574] "West Virginia" "West Virginia" "West Virginia"
[577] "West Virginia" "West Virginia" "Alabama"
[580] "Alabama" "Alabama" "Alabama"
[583] "Alabama" "Alabama" "Alabama"
[586] "Alabama" "Alabama" "Alabama"
[589] "Alabama" "Alabama" "Alabama"
[592] "Alabama" "Alabama" "Alabama"
[595] "Alabama" "Alabama" "Alabama"
[598] "Alabama" "Alabama" "Alabama"
[601] "Alabama" "Alabama" "Alabama"
[604] "Alabama" "Alabama" "Alabama"
[607] "Alabama" "Alabama" "Alabama"
[610] "Alabama" "Alabama" "Alabama"
[613] "Alabama" "Alabama" "Alabama"
[616] "Alabama" "Alabama" "Alabama"
[619] "Alabama" "Alabama" "Alabama"
[622] "Alabama" "Alabama" "Alabama"
[625] "Alabama" "Alabama" "Delaware"
[628] "Delaware" "Delaware" "Delaware"
[631] "Delaware" "Delaware" "Delaware"
[634] "Delaware" "Delaware" "Delaware"
[637] "Delaware" "Delaware" "Delaware"
[640] "Delaware" "Delaware" "Delaware"
[643] "Delaware" "Delaware" "Delaware"
[646] "Delaware" "Delaware" "Delaware"
[649] "Delaware" "Delaware" "Delaware"
[652] "Delaware" "Delaware" "Delaware"
[655] "Delaware" "Delaware" "Delaware"
[658] "Delaware" "Delaware" "Delaware"
[661] "Delaware" "Delaware" "Delaware"
[664] "Delaware" "Delaware" "Delaware"
[667] "Delaware" "Delaware" "Minnesota"
[670] "Minnesota" "Minnesota" "Minnesota"
[673] "Minnesota" "Minnesota" "Minnesota"
[676] "Minnesota" "Minnesota" "Minnesota"
[679] "Minnesota" "Minnesota" "Minnesota"
[682] "Minnesota" "Minnesota" "Minnesota"
[685] "Minnesota" "Minnesota" "Minnesota"
[688] "Minnesota" "Minnesota" "Minnesota"
[691] "Minnesota" "Minnesota" "Minnesota"
[694] "Minnesota" "Minnesota" "Minnesota"
[697] "Minnesota" "Minnesota" "Minnesota"
[700] "Minnesota" "Minnesota" "Minnesota"
[703] "Minnesota" "Minnesota" "Minnesota"
[706] "Minnesota" "Minnesota" "Minnesota"
[709] "Minnesota" "Minnesota" "Minnesota"
[712] "Minnesota" "Minnesota" "Minnesota"
[715] "Minnesota" "Minnesota" "Mississippi"
[718] "Mississippi" "Mississippi" "Mississippi"
[721] "Mississippi" "Mississippi" "Mississippi"
[724] "Mississippi" "Mississippi" "Mississippi"
[727] "Mississippi" "Mississippi" "Mississippi"
[730] "Mississippi" "Mississippi" "Mississippi"
[733] "Mississippi" "Mississippi" "Mississippi"
[736] "Mississippi" "Mississippi" "Mississippi"
[739] "Mississippi" "Mississippi" "Mississippi"
[742] "Mississippi" "Mississippi" "Mississippi"
[745] "Mississippi" "Mississippi" "Mississippi"
[748] "Mississippi" "Mississippi" "Mississippi"
[751] "Mississippi" "Mississippi" "Mississippi"
[754] "Guam" "Mississippi" "Mississippi"
[757] "Mississippi" "Mississippi" "Mississippi"
[760] "Mississippi" "Mississippi" "Mississippi"
[763] "Mississippi" "Mississippi" "Mississippi"
[766] "National (States and DC)" "National (States and DC)" "National (States and DC)"
[769] "National (States and DC)" "Nebraska" "Nebraska"
[772] "Nebraska" "Nebraska" "Nebraska"
[775] "Nebraska" "Nebraska" "Nebraska"
[778] "Nebraska" "Nebraska" "Nebraska"
[781] "Nebraska" "Nebraska" "Nebraska"
[784] "Nebraska" "Nebraska" "Nebraska"
[787] "Nebraska" "Nebraska" "Nebraska"
[790] "Nebraska" "Nebraska" "Nebraska"
[793] "Nebraska" "New Jersey" "New Jersey"
[796] "New Jersey" "New Jersey" "New Jersey"
[799] "New Jersey" "New Jersey" "New Jersey"
[802] "New Jersey" "New Jersey" "New Jersey"
[805] "New Jersey" "New Jersey" "New Jersey"
[808] "New Jersey" "New Jersey" "New Jersey"
[811] "New Jersey" "New Jersey" "New Jersey"
[814] "New Jersey" "New Jersey" "New Jersey"
[817] "New Jersey" "New Jersey" "New Jersey"
[820] "New Jersey" "New Jersey" "New Jersey"
[823] "New Jersey" "New Jersey" "New Jersey"
[826] "New Jersey" "New Jersey" "New Jersey"
[829] "New Jersey" "New Jersey" "New Jersey"
[832] "New Jersey" "New Jersey" "New Jersey"
[835] "New Jersey" "New Jersey" "New Jersey"
[838] "New Jersey" "New Jersey" "New Jersey"
[841] "New Jersey" "Ohio" "Ohio"
[844] "Ohio" "Ohio" "Ohio"
[847] "Ohio" "Ohio" "Ohio"
[850] "Ohio" "Ohio" "Ohio"
[853] "Ohio" "Ohio" "Ohio"
[856] "Ohio" "Ohio" "Ohio"
[859] "Ohio" "Ohio" "Ohio"
[862] "Ohio" "Ohio" "Ohio"
[865] "Ohio" "Pennsylvania" "Pennsylvania"
[868] "Pennsylvania" "Pennsylvania" "Pennsylvania"
[871] "Pennsylvania" "Pennsylvania" "Pennsylvania"
[874] "Pennsylvania" "Pennsylvania" "Pennsylvania"
[877] "Pennsylvania" "Pennsylvania" "Pennsylvania"
[880] "Pennsylvania" "Pennsylvania" "Pennsylvania"
[883] "Pennsylvania" "Pennsylvania" "Pennsylvania"
[886] "Pennsylvania" "Pennsylvania" "Pennsylvania"
[889] "Pennsylvania" "Pennsylvania" "Pennsylvania"
[892] "Pennsylvania" "Pennsylvania" "Pennsylvania"
[895] "Pennsylvania" "Pennsylvania" "Pennsylvania"
[898] "Pennsylvania" "Pennsylvania" "Pennsylvania"
[901] "Guam" "Pennsylvania" "Pennsylvania"
[904] "Pennsylvania" "Pennsylvania" "Pennsylvania"
[907] "Pennsylvania" "Pennsylvania" "Pennsylvania"
[910] "Pennsylvania" "Pennsylvania" "Pennsylvania"
[913] "Pennsylvania" "Pennsylvania" "Arizona"
[916] "Arizona" "Arizona" "Arizona"
[919] "Arizona" "Arizona" "Arizona"
[922] "Arizona" "Arizona" "Arizona"
[925] "Arizona" "Arizona" "Arizona"
[928] "Arizona" "Arizona" "Arizona"
[931] "Arizona" "Arizona" "Arizona"
[934] "Arizona" "Arizona" "Arizona"
[937] "Arizona" "Arizona" "Connecticut"
[940] "Connecticut" "Connecticut" "Connecticut"
[943] "Connecticut" "Connecticut" "Connecticut"
[946] "Connecticut" "Connecticut" "Connecticut"
[949] "Connecticut" "Connecticut" "Connecticut"
[952] "Connecticut" "Connecticut" "Connecticut"
[955] "Connecticut" "Connecticut" "Connecticut"
[958] "Connecticut" "Connecticut" "Connecticut"
[961] "Connecticut" "Connecticut" "Guam"
[964] "Connecticut" "Connecticut" "Connecticut"
[967] "Connecticut" "Connecticut" "Connecticut"
[970] "Connecticut" "Connecticut" "Connecticut"
[973] "Connecticut" "Connecticut" "Connecticut"
[976] "Connecticut" "Connecticut" "Connecticut"
[979] "Connecticut" "Connecticut" "Connecticut"
[982] "Connecticut" "Connecticut" "Connecticut"
[985] "Connecticut" "Connecticut" "Connecticut"
[988] "Georgia" "Georgia" "Georgia"
[991] "Georgia" "Georgia" "Georgia"
[994] "Georgia" "Georgia" "Georgia"
[997] "Georgia" "Georgia" "Georgia"
[1000] "Georgia"
[ reached getOption("max.print") -- omitted 8794 entries ]
unique(x) will return the unique elements of x
unique(locations)
[1] "Arizona" "Connecticut" "Georgia" [4] "Hawaii" "Illinois" "Louisiana" [7] "Mississippi" "Utah" "Missouri" [10] "National (States and DC)" "Nebraska" "New Jersey" [13] "North Carolina" "North Dakota" "Pennsylvania" [16] "South Carolina" "West Virginia" "Alabama" [19] "Delaware" "Minnesota" "Guam" [22] "Ohio" "Indiana" "Kansas" [25] "Oklahoma" "Wisconsin" "Michigan" [28] "New Hampshire" "Arkansas" "Kentucky" [31] "Iowa" "South Dakota" "Virginia" [34] "Puerto Rico" "Rhode Island" "New Mexico" [37] "Tennessee" "Vermont" "Virgin Islands" [40] "California" "Idaho" "Florida" [43] "Maryland" "Massachusetts" "New York" [46] "Maine" "Colorado" "District of Columbia" [49] "Texas" "Wyoming"
length will tell you the length of a vector. Combined with unique, tells you the number of unique elements:
length(unique(locations))
[1] 50
use sum(is.na()):
sum(is.na(locations))
[1] 0
dplyr: countUse count directly on a data.frame and column: count the number of rows in each group.
yts %>% count(LocationDesc)
# A tibble: 50 × 2 LocationDesc n <chr> <int> 1 Alabama 378 2 Arizona 240 3 Arkansas 210 4 California 96 5 Colorado 48 6 Connecticut 384 7 Delaware 312 8 District of Columbia 48 9 Florida 96 10 Georgia 282 # ℹ 40 more rows
dplyr: countMultiple columns listed further subdivides the count.
yts %>% count(LocationDesc, TopicDesc)
# A tibble: 146 × 3 LocationDesc TopicDesc n <chr> <chr> <int> 1 Alabama Cessation (Youth) 90 2 Alabama Cigarette Use (Youth) 144 3 Alabama Smokeless Tobacco Use (Youth) 144 4 Arizona Cessation (Youth) 60 5 Arizona Cigarette Use (Youth) 99 6 Arizona Smokeless Tobacco Use (Youth) 81 7 Arkansas Cessation (Youth) 42 8 Arkansas Cigarette Use (Youth) 78 9 Arkansas Smokeless Tobacco Use (Youth) 90 10 California Cessation (Youth) 24 # ℹ 136 more rows
dplyr: countOption to sort the results with sort = TRUE
yts %>% count(LocationDesc, sort = TRUE)
# A tibble: 50 × 2 LocationDesc n <chr> <int> 1 Mississippi 567 2 New Jersey 387 3 Connecticut 384 4 Alabama 378 5 North Carolina 366 6 Wisconsin 360 7 West Virginia 336 8 North Dakota 330 9 Pennsylvania 330 10 Oklahoma 318 # ℹ 40 more rows
dplyr: countInstead of counting the number of rows in each group, wt computes sum(wt) for each group.
# Add up "Data_Value" for each LocationDesc category yts %>% count(LocationDesc, wt = Data_Value)
# A tibble: 50 × 2 LocationDesc n <chr> <dbl> 1 Alabama 9220. 2 Arizona 3937. 3 Arkansas 5443. 4 California 2059. 5 Colorado 1136. 6 Connecticut 5838. 7 Delaware 5886 8 District of Columbia 853. 9 Florida 2786. 10 Georgia 5625. # ℹ 40 more rows
group_by allows you group the data set by variables/columns you specify:
# Regular data yts
# A tibble: 9,794 × 31
YEAR LocationAbbr LocationDesc TopicType TopicDesc MeasureDesc DataSource Response Data_Value_Unit
<dbl> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
1 2015 AZ Arizona Tobacco Use – Su… Cessatio… Percent of… YTS <NA> %
2 2015 AZ Arizona Tobacco Use – Su… Cessatio… Percent of… YTS <NA> %
3 2015 AZ Arizona Tobacco Use – Su… Cessatio… Percent of… YTS <NA> %
4 2015 AZ Arizona Tobacco Use – Su… Cessatio… Quit Attem… YTS <NA> %
5 2015 AZ Arizona Tobacco Use – Su… Cessatio… Quit Attem… YTS <NA> %
6 2015 AZ Arizona Tobacco Use – Su… Cessatio… Quit Attem… YTS <NA> %
7 2015 AZ Arizona Tobacco Use – Su… Cigarett… Smoking St… YTS Current %
8 2015 AZ Arizona Tobacco Use – Su… Cigarett… Smoking St… YTS Current %
9 2015 AZ Arizona Tobacco Use – Su… Cigarett… Smoking St… YTS Current %
10 2015 AZ Arizona Tobacco Use – Su… Cigarett… Smoking St… YTS Ever %
# ℹ 9,784 more rows
# ℹ 22 more variables: Data_Value_Type <chr>, Data_Value <dbl>, Data_Value_Footnote_Symbol <chr>,
# Data_Value_Footnote <chr>, Data_Value_Std_Err <dbl>, Low_Confidence_Limit <dbl>,
# High_Confidence_Limit <dbl>, Sample_Size <dbl>, Gender <chr>, Race <chr>, Age <chr>, Education <chr>,
# GeoLocation <chr>, TopicTypeId <chr>, TopicId <chr>, MeasureId <chr>, StratificationID1 <chr>,
# StratificationID2 <chr>, StratificationID3 <chr>, StratificationID4 <chr>, SubMeasureID <chr>,
# DisplayOrder <dbl>
group_by allows you group the data set by variables/columns you specify:
yts_grouped <- yts %>% group_by(Response) yts_grouped
# A tibble: 9,794 × 31
# Groups: Response [4]
YEAR LocationAbbr LocationDesc TopicType TopicDesc MeasureDesc DataSource Response Data_Value_Unit
<dbl> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
1 2015 AZ Arizona Tobacco Use – Su… Cessatio… Percent of… YTS <NA> %
2 2015 AZ Arizona Tobacco Use – Su… Cessatio… Percent of… YTS <NA> %
3 2015 AZ Arizona Tobacco Use – Su… Cessatio… Percent of… YTS <NA> %
4 2015 AZ Arizona Tobacco Use – Su… Cessatio… Quit Attem… YTS <NA> %
5 2015 AZ Arizona Tobacco Use – Su… Cessatio… Quit Attem… YTS <NA> %
6 2015 AZ Arizona Tobacco Use – Su… Cessatio… Quit Attem… YTS <NA> %
7 2015 AZ Arizona Tobacco Use – Su… Cigarett… Smoking St… YTS Current %
8 2015 AZ Arizona Tobacco Use – Su… Cigarett… Smoking St… YTS Current %
9 2015 AZ Arizona Tobacco Use – Su… Cigarett… Smoking St… YTS Current %
10 2015 AZ Arizona Tobacco Use – Su… Cigarett… Smoking St… YTS Ever %
# ℹ 9,784 more rows
# ℹ 22 more variables: Data_Value_Type <chr>, Data_Value <dbl>, Data_Value_Footnote_Symbol <chr>,
# Data_Value_Footnote <chr>, Data_Value_Std_Err <dbl>, Low_Confidence_Limit <dbl>,
# High_Confidence_Limit <dbl>, Sample_Size <dbl>, Gender <chr>, Race <chr>, Age <chr>, Education <chr>,
# GeoLocation <chr>, TopicTypeId <chr>, TopicId <chr>, MeasureId <chr>, StratificationID1 <chr>,
# StratificationID2 <chr>, StratificationID3 <chr>, StratificationID4 <chr>, SubMeasureID <chr>,
# DisplayOrder <dbl>
dplyr summarize() functionsummarize is a helpful function to use after group_by(). It creates a summary table of a column you’re interested in.
yts %>% summarize(mean_value = mean(Data_Value, na.rm = TRUE))
# A tibble: 1 × 1
mean_value
<dbl>
1 21.0
It’s grouped! Grouping doesn’t change the data in any way, but how functions operate on it. Now we can summarize Data_Value (percent of respondents) by group:
yts_grouped %>% summarize(mean_value = mean(Data_Value, na.rm = TRUE))
# A tibble: 4 × 2 Response mean_value <chr> <dbl> 1 Current 9.68 2 Ever 26.1 3 Frequent 3.48 4 <NA> 53.5
pipe to string these together!Pipe yts into group_by, then pipe that into summarize:
yts %>%
group_by(Response) %>%
summarize(mean_value = mean(Data_Value, na.rm = TRUE),
max_value = max(Data_Value, na.rm = TRUE))
# A tibble: 4 × 3 Response mean_value max_value <chr> <dbl> <dbl> 1 Current 9.68 40.6 2 Ever 26.1 98 3 Frequent 3.48 23.9 4 <NA> 53.5 81.9
group_by with mutate - Useful for comparisonsUse group_by to calculate the mean value for each year. We can use mutate to add it as a column.
yts_year <- yts %>% group_by(YEAR) %>% mutate(year_avg = mean(Data_Value, na.rm = TRUE)) %>% select(LocationDesc, Data_Value, year_avg)
group_by with mutate - Useful for comparisonsCreate a “difference” variable:
yts_year %>% mutate(Diff = Data_Value - year_avg)
# A tibble: 9,794 × 5
# Groups: YEAR [17]
YEAR LocationDesc Data_Value year_avg Diff
<dbl> <chr> <dbl> <dbl> <dbl>
1 2015 Arizona NA 15.2 NA
2 2015 Arizona NA 15.2 NA
3 2015 Arizona NA 15.2 NA
4 2015 Arizona NA 15.2 NA
5 2015 Arizona NA 15.2 NA
6 2015 Arizona NA 15.2 NA
7 2015 Arizona 3.2 15.2 -12.0
8 2015 Arizona 3.2 15.2 -12.0
9 2015 Arizona 3.1 15.2 -12.1
10 2015 Arizona 12.5 15.2 -2.66
# ℹ 9,784 more rows
n() for sample size by groupThere are other functions, such as n() count the number of observations.
yts %>%
group_by(YEAR) %>%
summarize(n = n(),
mean = mean(Data_Value, na.rm = TRUE))
# A tibble: 17 × 3
YEAR n mean
<dbl> <int> <dbl>
1 1999 372 26.1
2 2000 1224 26.7
3 2001 426 23.4
4 2002 1016 25.2
5 2003 498 21.3
6 2004 611 20.7
7 2005 636 21.8
8 2006 518 21.8
9 2007 516 20.0
10 2008 483 18.2
11 2009 686 18.3
12 2010 447 17.8
13 2011 521 17.8
14 2012 244 15.5
15 2013 685 16.7
16 2014 334 15.7
17 2015 577 15.2
dplyr summarize() and across() functionsUse the across function with summarize() to summarize across multiple columns of your data.
# General format - Not the code!
across({ columns to go across }, ~ { summarization_function(.x, na.rm = ..) })
dplyr summarize() and across() functionsUse the across function with summarize() to summarize across multiple columns of your data.
# General format - Not the code!
across({ columns to go across }, ~ { summarization_function(.x, na.rm = ..) })
yts %>%
group_by(YEAR) %>%
summarize(across( c(Data_Value, Data_Value_Std_Err, Sample_Size),
~ mean(.x, na.rm = TRUE)))
# A tibble: 17 × 4
YEAR Data_Value Data_Value_Std_Err Sample_Size
<dbl> <dbl> <dbl> <dbl>
1 1999 26.1 1.98 1591.
2 2000 26.7 2.03 1743.
3 2001 23.4 1.79 2060.
4 2002 25.2 1.81 2653.
5 2003 21.3 1.92 2325.
6 2004 20.7 1.84 1246.
7 2005 21.8 2.17 1017.
8 2006 21.8 2.15 1191.
9 2007 20.0 1.96 1093.
10 2008 18.2 1.73 1203.
11 2009 18.3 1.90 1033.
12 2010 17.8 1.71 1202.
13 2011 17.8 1.84 1274.
14 2012 15.5 1.58 1053.
15 2013 16.7 1.75 1158.
16 2014 15.7 1.61 1139.
17 2015 15.2 1.61 1187.
dplyr summarize() and across() functionsAnother example using select helpers (??tidyr_tidy_select):
yts %>% summarize(across( where(is.numeric), ~ mean(.x, na.rm = TRUE)))
# A tibble: 1 × 7 YEAR Data_Value Data_Value_Std_Err Low_Confidence_Limit High_Confidence_Limit Sample_Size DisplayOrder <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> 1 2006. 21.0 1.87 17.3 24.6 1505. 8.09
rowMeans(x): takes the means of each row of xcolMeans(x): takes the means of each column of xrowSums(x): takes the sum of each row of xcolSums(x): takes the sum of each column of xsummary(x): for data frames, displays the quantile informationsummary() FunctionUsing summary() can give you rough snapshots of each numeric column (character columns are skipped):
summary(yts)
YEAR LocationAbbr LocationDesc TopicType TopicDesc
Min. :1999 Length:9794 Length:9794 Length:9794 Length:9794
1st Qu.:2002 Class :character Class :character Class :character Class :character
Median :2006 Mode :character Mode :character Mode :character Mode :character
Mean :2006
3rd Qu.:2010
Max. :2015
MeasureDesc DataSource Response Data_Value_Unit Data_Value_Type
Length:9794 Length:9794 Length:9794 Length:9794 Length:9794
Class :character Class :character Class :character Class :character Class :character
Mode :character Mode :character Mode :character Mode :character Mode :character
Data_Value Data_Value_Footnote_Symbol Data_Value_Footnote Data_Value_Std_Err Low_Confidence_Limit
Min. : 0.00 Length:9794 Length:9794 Min. : 0.000 Min. : 0.00
1st Qu.: 3.20 Class :character Class :character 1st Qu.: 0.600 1st Qu.: 1.90
Median :11.30 Mode :character Mode :character Median : 1.300 Median : 8.50
Mean :20.97 Mean : 1.874 Mean :17.31
3rd Qu.:39.10 3rd Qu.: 2.500 3rd Qu.:31.60
Max. :98.00 Max. :16.100 Max. :97.60
NA's :425 NA's :425 NA's :425
High_Confidence_Limit Sample_Size Gender Race Age
Min. : 0.00 Min. : 50 Length:9794 Length:9794 Length:9794
1st Qu.: 4.60 1st Qu.: 668 Class :character Class :character Class :character
Median :14.10 Median : 994 Mode :character Mode :character Mode :character
Mean :24.64 Mean : 1505
3rd Qu.:45.60 3rd Qu.: 1648
Max. :98.40 Max. :36910
NA's :425 NA's :425
Education GeoLocation TopicTypeId TopicId MeasureId
Length:9794 Length:9794 Length:9794 Length:9794 Length:9794
Class :character Class :character Class :character Class :character Class :character
Mode :character Mode :character Mode :character Mode :character Mode :character
StratificationID1 StratificationID2 StratificationID3 StratificationID4 SubMeasureID
Length:9794 Length:9794 Length:9794 Length:9794 Length:9794
Class :character Class :character Class :character Class :character Class :character
Mode :character Mode :character Mode :character Mode :character Mode :character
DisplayOrder
Min. : 1.000
1st Qu.: 7.000
Median : 9.000
Mean : 8.094
3rd Qu.:11.000
Max. :12.000
mean()) work with pull()count(x): what unique values do you have?
pull() to get vectorsunique() combined with length()group_by(): changes all subsequent functions
summarize() to get statistics per groupacross() to programmatically select columnssummary(x): quantile information