R for reproducible scientific analysis
Data frames and reading in data
Learning objectives
- Become familiar with data frames
- To be able to read in regular data into R
Data frames
Data frames are similar to matrices, except each column can be a different atomic type. Underneath the hood, data frames are really lists, where each element is an atomic vector, with the added restriction that they’re all the same length. As you will see, if we pull out one column of a data frame, we will have a vector.
Data frames can be created manually with the data.frame function:
df <- data.frame(id = c('a', 'b', 'c', 'd', 'e', 'f'), x = 1:6, y = c(214:219))
df id x y
1 a 1 214
2 b 2 215
3 c 3 216
4 d 4 217
5 e 5 218
6 f 6 219
Challenge: Data frames
Try using the length function to query your data frame df. Does it give the result you expect?
Each column in the data frame is simply a list element, which is why when you ask for the length of the data frame, it tells you the number of columns. If you actually want the number of rows, you can use the nrow function.
We can add rows or columns to a data.frame using rbind or cbind (these are the two-dimensional equivalents of the c function):
df <- rbind(df, list("g", 11, 42))Warning in `[<-.factor`(`*tmp*`, ri, value = "g"): invalid factor level, NA
generated
This doesn’t work as expected! What does this error message tell us?
It sounds like it was trying to generate a factor level. Why? Perhaps our first column (containing characters) is to blame… We can access a column in a data.frame by using the $ operator.
class(df$id)[1] "factor"
Indeed, R automatically made this first column a factor, not a character vector. We can change this in place by converting the type of this column.
df$id <- as.character(df$id)
class(df$id)[1] "character"
Okay, now let’s try adding that row again.
df <- rbind(df, list("g", 11, 42))
tail(df, n=3) id x y
6 f 6 219
7 <NA> 11 42
8 g 11 42
Note that to add a row, we need to use a list, because each column is a different type! If you want to add multiple rows to a data.frame, you will need to separate the new columns in the list:
df <- rbind(df, list(c("l", "m"), c(12, 13), c(534, -20)))
tail(df, n=3) id x y
8 g 11 42
9 l 12 534
10 m 13 -20
You can also row-bind data.frames together:
rbind(df, df) id x y
1 a 1 214
2 b 2 215
3 c 3 216
4 d 4 217
5 e 5 218
6 f 6 219
7 <NA> 11 42
8 g 11 42
9 l 12 534
10 m 13 -20
11 a 1 214
12 b 2 215
13 c 3 216
14 d 4 217
15 e 5 218
16 f 6 219
17 <NA> 11 42
18 g 11 42
19 l 12 534
20 m 13 -20
To add a column we can use cbind:
df <- cbind(df, 10:1)
df id x y 10:1
1 a 1 214 10
2 b 2 215 9
3 c 3 216 8
4 d 4 217 7
5 e 5 218 6
6 f 6 219 5
7 <NA> 11 42 4
8 g 11 42 3
9 l 12 534 2
10 m 13 -20 1
Challenge 1
Create a data frame that holds the following information for yourself:
- First name
- Last name
- Age
Then use rbind to add the same information for the people sitting near you.
Now use cbind to add a column of logicals answering the question, “Is there anything in this workshop you’re finding confusing?”
Reading in data
Remember earlier we obtained the gapminder dataset. If you’re curious about where this data comes from you might like to look at the Gapminder website
Now we want to load the gapminder data into R. Before reading in data, it’s a good idea to have a look at its structure. Let’s take a look using our newly-learned shell skills:
cd data/ # navigate to the data directory of the project folder
head gapminder-FiveYearData.csvcountry,year,pop,continent,lifeExp,gdpPercap
Afghanistan,1952,8425333,Asia,28.801,779.4453145
Afghanistan,1957,9240934,Asia,30.332,820.8530296
Afghanistan,1962,10267083,Asia,31.997,853.10071
Afghanistan,1967,11537966,Asia,34.02,836.1971382
Afghanistan,1972,13079460,Asia,36.088,739.9811058
Afghanistan,1977,14880372,Asia,38.438,786.11336
Afghanistan,1982,12881816,Asia,39.854,978.0114388
Afghanistan,1987,13867957,Asia,40.822,852.3959448
Afghanistan,1992,16317921,Asia,41.674,649.3413952
As its file extension would suggest, the file contains comma-separated values, and seems to contain a header row.
We can use read.table to read this into R
gapminder <- read.table(
file="data/gapminder-FiveYearData.csv",
header=TRUE, sep=","
)
head(gapminder) country year pop continent lifeExp gdpPercap
1 Afghanistan 1952 8425333 Asia 28.801 779.4453
2 Afghanistan 1957 9240934 Asia 30.332 820.8530
3 Afghanistan 1962 10267083 Asia 31.997 853.1007
4 Afghanistan 1967 11537966 Asia 34.020 836.1971
5 Afghanistan 1972 13079460 Asia 36.088 739.9811
6 Afghanistan 1977 14880372 Asia 38.438 786.1134
Because we know the structure of the data, we’re able to specify the appropriate arguments to read.table. Without these arguments, read.table will do its best to do something sensible, but it is always more reliable to explicitly tell read.table the structure of the data. read.csv function provides a convenient shortcut for loading in CSV files.
# Here is the read.csv version of the above command
gapminder <- read.csv(
file="data/gapminder-FiveYearData.csv"
)
head(gapminder) country year pop continent lifeExp gdpPercap
1 Afghanistan 1952 8425333 Asia 28.801 779.4453
2 Afghanistan 1957 9240934 Asia 30.332 820.8530
3 Afghanistan 1962 10267083 Asia 31.997 853.1007
4 Afghanistan 1967 11537966 Asia 34.020 836.1971
5 Afghanistan 1972 13079460 Asia 36.088 739.9811
6 Afghanistan 1977 14880372 Asia 38.438 786.1134
To make sure our analysis is reproducible, we should put the code into a script file so we can come back to it later.
Challenge 2
Go to file -> new file -> R script, and write an R script to load in the gapminder dataset. Put it in the scripts/ directory and add it to version control.
Run the script using the source function, using the file path as its argument (or by pressing the “source” button in RStudio).
Using data frames: the gapminder dataset
To recap what we’ve just learnt, let’s have a look at our example data (life expectancy in various countries for various years).
Remember, there are a few functions we can use to interrogate data structures in R:
class() # what is the data structure?
typeof() # what is its atomic type?
length() # how long is it? What about two dimensional objects?
attributes() # does it have any metadata?
str() # A full summary of the entire object
dim() # Dimensions of the object - also try nrow(), ncol()Let’s use them to explore the gapminder dataset.
typeof(gapminder)[1] "list"
Remember, data frames are lists ‘under the hood’.
class(gapminder)[1] "data.frame"
The gapminder data is stored in a “data.frame”. This is the default data structure when you read in data, and (as we’ve heard) is useful for storing data with mixed types of columns.
Let’s look at some of the columns.
Challenge 3: Data types in a real dataset
Look at the first 6 rows of the gapminder data frame we loaded before:
head(gapminder) country year pop continent lifeExp gdpPercap
1 Afghanistan 1952 8425333 Asia 28.801 779.4453
2 Afghanistan 1957 9240934 Asia 30.332 820.8530
3 Afghanistan 1962 10267083 Asia 31.997 853.1007
4 Afghanistan 1967 11537966 Asia 34.020 836.1971
5 Afghanistan 1972 13079460 Asia 36.088 739.9811
6 Afghanistan 1977 14880372 Asia 38.438 786.1134
Write down what data type you think is in each column
typeof(gapminder$year)[1] "integer"
typeof(gapminder$lifeExp)[1] "double"
Can anyone guess what we should expect the type of the continent column to be?
typeof(gapminder$continent)[1] "integer"
If you were expecting a the answer to be “character”, you would rightly be surprised by the answer. Let’s take a look at the class of this column:
class(gapminder$continent)[1] "factor"
One of the default behaviours of R is to treat any text columns as “factors” when reading in data. The reason for this is that text columns often represent categorical data, which need to be factors to be handled appropriately by the statistical modeling functions in R.
However it’s not obvious behaviour, and something that trips many people up. We can disable this behaviour and read in the data again.
options(stringsAsFactors=FALSE)
gapminder <- read.table(
file="data/gapminder-FiveYearData.csv", header=TRUE, sep=","
)Remember, if you do turn it off automatic conversion to factors you will need to explicitly convert the variables into factors when running statistical models. This can be useful, because it forces you to think about the question you’re asking, and makes it easier to specify the ordering of the categories.
However there are many in the R community who find it more sensible to leave this as the default behaviour.
The first thing you should do when reading data in, is check that it matches what you expect, even if the command ran without warnings or errors. The str function, short for “structure”, is really useful for this:
str(gapminder)'data.frame': 1704 obs. of 6 variables:
$ country : chr "Afghanistan" "Afghanistan" "Afghanistan" "Afghanistan" ...
$ year : int 1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 ...
$ pop : num 8425333 9240934 10267083 11537966 13079460 ...
$ continent: chr "Asia" "Asia" "Asia" "Asia" ...
$ lifeExp : num 28.8 30.3 32 34 36.1 ...
$ gdpPercap: num 779 821 853 836 740 ...
We can see that the object is a data.frame with 1,704 observations (rows), and 6 variables (columns). Below that, we see the name of each column, followed by a “:”, followed by the type of variable in that column, along with the first few entries.
We can also retrieve or modify the column or rownames of the data.frame:
colnames(gapminder)[1] "country" "year" "pop" "continent" "lifeExp" "gdpPercap"
rownames(gapminder) [1] "1" "2" "3" "4" "5" "6" "7" "8" "9"
[10] "10" "11" "12" "13" "14" "15" "16" "17" "18"
[19] "19" "20" "21" "22" "23" "24" "25" "26" "27"
[28] "28" "29" "30" "31" "32" "33" "34" "35" "36"
[37] "37" "38" "39" "40" "41" "42" "43" "44" "45"
[46] "46" "47" "48" "49" "50" "51" "52" "53" "54"
[55] "55" "56" "57" "58" "59" "60" "61" "62" "63"
[64] "64" "65" "66" "67" "68" "69" "70" "71" "72"
[73] "73" "74" "75" "76" "77" "78" "79" "80" "81"
[82] "82" "83" "84" "85" "86" "87" "88" "89" "90"
[91] "91" "92" "93" "94" "95" "96" "97" "98" "99"
[100] "100" "101" "102" "103" "104" "105" "106" "107" "108"
[109] "109" "110" "111" "112" "113" "114" "115" "116" "117"
[118] "118" "119" "120" "121" "122" "123" "124" "125" "126"
[127] "127" "128" "129" "130" "131" "132" "133" "134" "135"
[136] "136" "137" "138" "139" "140" "141" "142" "143" "144"
[145] "145" "146" "147" "148" "149" "150" "151" "152" "153"
[154] "154" "155" "156" "157" "158" "159" "160" "161" "162"
[163] "163" "164" "165" "166" "167" "168" "169" "170" "171"
[172] "172" "173" "174" "175" "176" "177" "178" "179" "180"
[181] "181" "182" "183" "184" "185" "186" "187" "188" "189"
[190] "190" "191" "192" "193" "194" "195" "196" "197" "198"
[199] "199" "200" "201" "202" "203" "204" "205" "206" "207"
[208] "208" "209" "210" "211" "212" "213" "214" "215" "216"
[217] "217" "218" "219" "220" "221" "222" "223" "224" "225"
[226] "226" "227" "228" "229" "230" "231" "232" "233" "234"
[235] "235" "236" "237" "238" "239" "240" "241" "242" "243"
[244] "244" "245" "246" "247" "248" "249" "250" "251" "252"
[253] "253" "254" "255" "256" "257" "258" "259" "260" "261"
[262] "262" "263" "264" "265" "266" "267" "268" "269" "270"
[271] "271" "272" "273" "274" "275" "276" "277" "278" "279"
[280] "280" "281" "282" "283" "284" "285" "286" "287" "288"
[289] "289" "290" "291" "292" "293" "294" "295" "296" "297"
[298] "298" "299" "300" "301" "302" "303" "304" "305" "306"
[307] "307" "308" "309" "310" "311" "312" "313" "314" "315"
[316] "316" "317" "318" "319" "320" "321" "322" "323" "324"
[325] "325" "326" "327" "328" "329" "330" "331" "332" "333"
[334] "334" "335" "336" "337" "338" "339" "340" "341" "342"
[343] "343" "344" "345" "346" "347" "348" "349" "350" "351"
[352] "352" "353" "354" "355" "356" "357" "358" "359" "360"
[361] "361" "362" "363" "364" "365" "366" "367" "368" "369"
[370] "370" "371" "372" "373" "374" "375" "376" "377" "378"
[379] "379" "380" "381" "382" "383" "384" "385" "386" "387"
[388] "388" "389" "390" "391" "392" "393" "394" "395" "396"
[397] "397" "398" "399" "400" "401" "402" "403" "404" "405"
[406] "406" "407" "408" "409" "410" "411" "412" "413" "414"
[415] "415" "416" "417" "418" "419" "420" "421" "422" "423"
[424] "424" "425" "426" "427" "428" "429" "430" "431" "432"
[433] "433" "434" "435" "436" "437" "438" "439" "440" "441"
[442] "442" "443" "444" "445" "446" "447" "448" "449" "450"
[451] "451" "452" "453" "454" "455" "456" "457" "458" "459"
[460] "460" "461" "462" "463" "464" "465" "466" "467" "468"
[469] "469" "470" "471" "472" "473" "474" "475" "476" "477"
[478] "478" "479" "480" "481" "482" "483" "484" "485" "486"
[487] "487" "488" "489" "490" "491" "492" "493" "494" "495"
[496] "496" "497" "498" "499" "500" "501" "502" "503" "504"
[505] "505" "506" "507" "508" "509" "510" "511" "512" "513"
[514] "514" "515" "516" "517" "518" "519" "520" "521" "522"
[523] "523" "524" "525" "526" "527" "528" "529" "530" "531"
[532] "532" "533" "534" "535" "536" "537" "538" "539" "540"
[541] "541" "542" "543" "544" "545" "546" "547" "548" "549"
[550] "550" "551" "552" "553" "554" "555" "556" "557" "558"
[559] "559" "560" "561" "562" "563" "564" "565" "566" "567"
[568] "568" "569" "570" "571" "572" "573" "574" "575" "576"
[577] "577" "578" "579" "580" "581" "582" "583" "584" "585"
[586] "586" "587" "588" "589" "590" "591" "592" "593" "594"
[595] "595" "596" "597" "598" "599" "600" "601" "602" "603"
[604] "604" "605" "606" "607" "608" "609" "610" "611" "612"
[613] "613" "614" "615" "616" "617" "618" "619" "620" "621"
[622] "622" "623" "624" "625" "626" "627" "628" "629" "630"
[631] "631" "632" "633" "634" "635" "636" "637" "638" "639"
[640] "640" "641" "642" "643" "644" "645" "646" "647" "648"
[649] "649" "650" "651" "652" "653" "654" "655" "656" "657"
[658] "658" "659" "660" "661" "662" "663" "664" "665" "666"
[667] "667" "668" "669" "670" "671" "672" "673" "674" "675"
[676] "676" "677" "678" "679" "680" "681" "682" "683" "684"
[685] "685" "686" "687" "688" "689" "690" "691" "692" "693"
[694] "694" "695" "696" "697" "698" "699" "700" "701" "702"
[703] "703" "704" "705" "706" "707" "708" "709" "710" "711"
[712] "712" "713" "714" "715" "716" "717" "718" "719" "720"
[721] "721" "722" "723" "724" "725" "726" "727" "728" "729"
[730] "730" "731" "732" "733" "734" "735" "736" "737" "738"
[739] "739" "740" "741" "742" "743" "744" "745" "746" "747"
[748] "748" "749" "750" "751" "752" "753" "754" "755" "756"
[757] "757" "758" "759" "760" "761" "762" "763" "764" "765"
[766] "766" "767" "768" "769" "770" "771" "772" "773" "774"
[775] "775" "776" "777" "778" "779" "780" "781" "782" "783"
[784] "784" "785" "786" "787" "788" "789" "790" "791" "792"
[793] "793" "794" "795" "796" "797" "798" "799" "800" "801"
[802] "802" "803" "804" "805" "806" "807" "808" "809" "810"
[811] "811" "812" "813" "814" "815" "816" "817" "818" "819"
[820] "820" "821" "822" "823" "824" "825" "826" "827" "828"
[829] "829" "830" "831" "832" "833" "834" "835" "836" "837"
[838] "838" "839" "840" "841" "842" "843" "844" "845" "846"
[847] "847" "848" "849" "850" "851" "852" "853" "854" "855"
[856] "856" "857" "858" "859" "860" "861" "862" "863" "864"
[865] "865" "866" "867" "868" "869" "870" "871" "872" "873"
[874] "874" "875" "876" "877" "878" "879" "880" "881" "882"
[883] "883" "884" "885" "886" "887" "888" "889" "890" "891"
[892] "892" "893" "894" "895" "896" "897" "898" "899" "900"
[901] "901" "902" "903" "904" "905" "906" "907" "908" "909"
[910] "910" "911" "912" "913" "914" "915" "916" "917" "918"
[919] "919" "920" "921" "922" "923" "924" "925" "926" "927"
[928] "928" "929" "930" "931" "932" "933" "934" "935" "936"
[937] "937" "938" "939" "940" "941" "942" "943" "944" "945"
[946] "946" "947" "948" "949" "950" "951" "952" "953" "954"
[955] "955" "956" "957" "958" "959" "960" "961" "962" "963"
[964] "964" "965" "966" "967" "968" "969" "970" "971" "972"
[973] "973" "974" "975" "976" "977" "978" "979" "980" "981"
[982] "982" "983" "984" "985" "986" "987" "988" "989" "990"
[991] "991" "992" "993" "994" "995" "996" "997" "998" "999"
[1000] "1000" "1001" "1002" "1003" "1004" "1005" "1006" "1007" "1008"
[1009] "1009" "1010" "1011" "1012" "1013" "1014" "1015" "1016" "1017"
[1018] "1018" "1019" "1020" "1021" "1022" "1023" "1024" "1025" "1026"
[1027] "1027" "1028" "1029" "1030" "1031" "1032" "1033" "1034" "1035"
[1036] "1036" "1037" "1038" "1039" "1040" "1041" "1042" "1043" "1044"
[1045] "1045" "1046" "1047" "1048" "1049" "1050" "1051" "1052" "1053"
[1054] "1054" "1055" "1056" "1057" "1058" "1059" "1060" "1061" "1062"
[1063] "1063" "1064" "1065" "1066" "1067" "1068" "1069" "1070" "1071"
[1072] "1072" "1073" "1074" "1075" "1076" "1077" "1078" "1079" "1080"
[1081] "1081" "1082" "1083" "1084" "1085" "1086" "1087" "1088" "1089"
[1090] "1090" "1091" "1092" "1093" "1094" "1095" "1096" "1097" "1098"
[1099] "1099" "1100" "1101" "1102" "1103" "1104" "1105" "1106" "1107"
[1108] "1108" "1109" "1110" "1111" "1112" "1113" "1114" "1115" "1116"
[1117] "1117" "1118" "1119" "1120" "1121" "1122" "1123" "1124" "1125"
[1126] "1126" "1127" "1128" "1129" "1130" "1131" "1132" "1133" "1134"
[1135] "1135" "1136" "1137" "1138" "1139" "1140" "1141" "1142" "1143"
[1144] "1144" "1145" "1146" "1147" "1148" "1149" "1150" "1151" "1152"
[1153] "1153" "1154" "1155" "1156" "1157" "1158" "1159" "1160" "1161"
[1162] "1162" "1163" "1164" "1165" "1166" "1167" "1168" "1169" "1170"
[1171] "1171" "1172" "1173" "1174" "1175" "1176" "1177" "1178" "1179"
[1180] "1180" "1181" "1182" "1183" "1184" "1185" "1186" "1187" "1188"
[1189] "1189" "1190" "1191" "1192" "1193" "1194" "1195" "1196" "1197"
[1198] "1198" "1199" "1200" "1201" "1202" "1203" "1204" "1205" "1206"
[1207] "1207" "1208" "1209" "1210" "1211" "1212" "1213" "1214" "1215"
[1216] "1216" "1217" "1218" "1219" "1220" "1221" "1222" "1223" "1224"
[1225] "1225" "1226" "1227" "1228" "1229" "1230" "1231" "1232" "1233"
[1234] "1234" "1235" "1236" "1237" "1238" "1239" "1240" "1241" "1242"
[1243] "1243" "1244" "1245" "1246" "1247" "1248" "1249" "1250" "1251"
[1252] "1252" "1253" "1254" "1255" "1256" "1257" "1258" "1259" "1260"
[1261] "1261" "1262" "1263" "1264" "1265" "1266" "1267" "1268" "1269"
[1270] "1270" "1271" "1272" "1273" "1274" "1275" "1276" "1277" "1278"
[1279] "1279" "1280" "1281" "1282" "1283" "1284" "1285" "1286" "1287"
[1288] "1288" "1289" "1290" "1291" "1292" "1293" "1294" "1295" "1296"
[1297] "1297" "1298" "1299" "1300" "1301" "1302" "1303" "1304" "1305"
[1306] "1306" "1307" "1308" "1309" "1310" "1311" "1312" "1313" "1314"
[1315] "1315" "1316" "1317" "1318" "1319" "1320" "1321" "1322" "1323"
[1324] "1324" "1325" "1326" "1327" "1328" "1329" "1330" "1331" "1332"
[1333] "1333" "1334" "1335" "1336" "1337" "1338" "1339" "1340" "1341"
[1342] "1342" "1343" "1344" "1345" "1346" "1347" "1348" "1349" "1350"
[1351] "1351" "1352" "1353" "1354" "1355" "1356" "1357" "1358" "1359"
[1360] "1360" "1361" "1362" "1363" "1364" "1365" "1366" "1367" "1368"
[1369] "1369" "1370" "1371" "1372" "1373" "1374" "1375" "1376" "1377"
[1378] "1378" "1379" "1380" "1381" "1382" "1383" "1384" "1385" "1386"
[1387] "1387" "1388" "1389" "1390" "1391" "1392" "1393" "1394" "1395"
[1396] "1396" "1397" "1398" "1399" "1400" "1401" "1402" "1403" "1404"
[1405] "1405" "1406" "1407" "1408" "1409" "1410" "1411" "1412" "1413"
[1414] "1414" "1415" "1416" "1417" "1418" "1419" "1420" "1421" "1422"
[1423] "1423" "1424" "1425" "1426" "1427" "1428" "1429" "1430" "1431"
[1432] "1432" "1433" "1434" "1435" "1436" "1437" "1438" "1439" "1440"
[1441] "1441" "1442" "1443" "1444" "1445" "1446" "1447" "1448" "1449"
[1450] "1450" "1451" "1452" "1453" "1454" "1455" "1456" "1457" "1458"
[1459] "1459" "1460" "1461" "1462" "1463" "1464" "1465" "1466" "1467"
[1468] "1468" "1469" "1470" "1471" "1472" "1473" "1474" "1475" "1476"
[1477] "1477" "1478" "1479" "1480" "1481" "1482" "1483" "1484" "1485"
[1486] "1486" "1487" "1488" "1489" "1490" "1491" "1492" "1493" "1494"
[1495] "1495" "1496" "1497" "1498" "1499" "1500" "1501" "1502" "1503"
[1504] "1504" "1505" "1506" "1507" "1508" "1509" "1510" "1511" "1512"
[1513] "1513" "1514" "1515" "1516" "1517" "1518" "1519" "1520" "1521"
[1522] "1522" "1523" "1524" "1525" "1526" "1527" "1528" "1529" "1530"
[1531] "1531" "1532" "1533" "1534" "1535" "1536" "1537" "1538" "1539"
[1540] "1540" "1541" "1542" "1543" "1544" "1545" "1546" "1547" "1548"
[1549] "1549" "1550" "1551" "1552" "1553" "1554" "1555" "1556" "1557"
[1558] "1558" "1559" "1560" "1561" "1562" "1563" "1564" "1565" "1566"
[1567] "1567" "1568" "1569" "1570" "1571" "1572" "1573" "1574" "1575"
[1576] "1576" "1577" "1578" "1579" "1580" "1581" "1582" "1583" "1584"
[1585] "1585" "1586" "1587" "1588" "1589" "1590" "1591" "1592" "1593"
[1594] "1594" "1595" "1596" "1597" "1598" "1599" "1600" "1601" "1602"
[1603] "1603" "1604" "1605" "1606" "1607" "1608" "1609" "1610" "1611"
[1612] "1612" "1613" "1614" "1615" "1616" "1617" "1618" "1619" "1620"
[1621] "1621" "1622" "1623" "1624" "1625" "1626" "1627" "1628" "1629"
[1630] "1630" "1631" "1632" "1633" "1634" "1635" "1636" "1637" "1638"
[1639] "1639" "1640" "1641" "1642" "1643" "1644" "1645" "1646" "1647"
[1648] "1648" "1649" "1650" "1651" "1652" "1653" "1654" "1655" "1656"
[1657] "1657" "1658" "1659" "1660" "1661" "1662" "1663" "1664" "1665"
[1666] "1666" "1667" "1668" "1669" "1670" "1671" "1672" "1673" "1674"
[1675] "1675" "1676" "1677" "1678" "1679" "1680" "1681" "1682" "1683"
[1684] "1684" "1685" "1686" "1687" "1688" "1689" "1690" "1691" "1692"
[1693] "1693" "1694" "1695" "1696" "1697" "1698" "1699" "1700" "1701"
[1702] "1702" "1703" "1704"
See those numbers in the square brackets on the left? That tells you the number of the first entry in that row of output. So in the last line, we see that the “[1701]” element has “1701” stored in it. The rownames in this case are simply the row numbers.
We can also modify this information:
copy <- gapminder # lets create a copy so we don't mess up the original
colnames(copy) <- c("a", "b", "c", "d", "e", "f")
head(copy) a b c d e f
1 Afghanistan 1952 8425333 Asia 28.801 779.4453
2 Afghanistan 1957 9240934 Asia 30.332 820.8530
3 Afghanistan 1962 10267083 Asia 31.997 853.1007
4 Afghanistan 1967 11537966 Asia 34.020 836.1971
5 Afghanistan 1972 13079460 Asia 36.088 739.9811
6 Afghanistan 1977 14880372 Asia 38.438 786.1134
There are a few related ways of retrieving and modifying this information. attributes will give you both the row and column names, along with the class information, while dimnames will give you just the rownames and column names.
In both cases, the output object is stored in a list:
str(dimnames(gapminder))List of 2
$ : chr [1:1704] "1" "2" "3" "4" ...
$ : chr [1:6] "country" "year" "pop" "continent" ...
Understanding how lists are used in function output
Lets run a basic linear regression on the gapminder dataset:
# What is the relationship between life expectancy and year?
l1 <- lm(lifeExp ~ year, data=gapminder)We won’t go into too much detail of what I just wrote, but briefly; the ~ denotes a formula, which means treat the variable on the left of the ~ as the left hand side of the equation (or response in this case), and everything on the right as the right hand side. By telling the linear model function to use the gapminder data frame, it knows to look for those variable names as its columns.
Let’s look at the output:
l1
Call:
lm(formula = lifeExp ~ year, data = gapminder)
Coefficients:
(Intercept) year
-585.6522 0.3259
Not much there right? But if we look at the structure…
str(l1)List of 12
$ coefficients : Named num [1:2] -585.652 0.326
..- attr(*, "names")= chr [1:2] "(Intercept)" "year"
$ residuals : Named num [1:1704] -21.7 -21.8 -21.8 -21.4 -20.9 ...
..- attr(*, "names")= chr [1:1704] "1" "2" "3" "4" ...
$ effects : Named num [1:1704] -2455.1 232.2 -20.8 -20.5 -20.2 ...
..- attr(*, "names")= chr [1:1704] "(Intercept)" "year" "" "" ...
$ rank : int 2
$ fitted.values: Named num [1:1704] 50.5 52.1 53.8 55.4 57 ...
..- attr(*, "names")= chr [1:1704] "1" "2" "3" "4" ...
$ assign : int [1:2] 0 1
$ qr :List of 5
..$ qr : num [1:1704, 1:2] -41.2795 0.0242 0.0242 0.0242 0.0242 ...
.. ..- attr(*, "dimnames")=List of 2
.. .. ..$ : chr [1:1704] "1" "2" "3" "4" ...
.. .. ..$ : chr [1:2] "(Intercept)" "year"
.. ..- attr(*, "assign")= int [1:2] 0 1
..$ qraux: num [1:2] 1.02 1.03
..$ pivot: int [1:2] 1 2
..$ tol : num 1e-07
..$ rank : int 2
..- attr(*, "class")= chr "qr"
$ df.residual : int 1702
$ xlevels : Named list()
$ call : language lm(formula = lifeExp ~ year, data = gapminder)
$ terms :Classes 'terms', 'formula' length 3 lifeExp ~ year
.. ..- attr(*, "variables")= language list(lifeExp, year)
.. ..- attr(*, "factors")= int [1:2, 1] 0 1
.. .. ..- attr(*, "dimnames")=List of 2
.. .. .. ..$ : chr [1:2] "lifeExp" "year"
.. .. .. ..$ : chr "year"
.. ..- attr(*, "term.labels")= chr "year"
.. ..- attr(*, "order")= int 1
.. ..- attr(*, "intercept")= int 1
.. ..- attr(*, "response")= int 1
.. ..- attr(*, ".Environment")=<environment: R_GlobalEnv>
.. ..- attr(*, "predvars")= language list(lifeExp, year)
.. ..- attr(*, "dataClasses")= Named chr [1:2] "numeric" "numeric"
.. .. ..- attr(*, "names")= chr [1:2] "lifeExp" "year"
$ model :'data.frame': 1704 obs. of 2 variables:
..$ lifeExp: num [1:1704] 28.8 30.3 32 34 36.1 ...
..$ year : int [1:1704] 1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 ...
..- attr(*, "terms")=Classes 'terms', 'formula' length 3 lifeExp ~ year
.. .. ..- attr(*, "variables")= language list(lifeExp, year)
.. .. ..- attr(*, "factors")= int [1:2, 1] 0 1
.. .. .. ..- attr(*, "dimnames")=List of 2
.. .. .. .. ..$ : chr [1:2] "lifeExp" "year"
.. .. .. .. ..$ : chr "year"
.. .. ..- attr(*, "term.labels")= chr "year"
.. .. ..- attr(*, "order")= int 1
.. .. ..- attr(*, "intercept")= int 1
.. .. ..- attr(*, "response")= int 1
.. .. ..- attr(*, ".Environment")=<environment: R_GlobalEnv>
.. .. ..- attr(*, "predvars")= language list(lifeExp, year)
.. .. ..- attr(*, "dataClasses")= Named chr [1:2] "numeric" "numeric"
.. .. .. ..- attr(*, "names")= chr [1:2] "lifeExp" "year"
- attr(*, "class")= chr "lm"
There’s a lot of stuff, stored in nested lists! This is why the structure function is really useful, it allows you to see all the data available to you returned by a function.
For now, we can look at the summary:
summary(l1)
Call:
lm(formula = lifeExp ~ year, data = gapminder)
Residuals:
Min 1Q Median 3Q Max
-39.949 -9.651 1.697 10.335 22.158
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -585.65219 32.31396 -18.12 <2e-16 ***
year 0.32590 0.01632 19.96 <2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 11.63 on 1702 degrees of freedom
Multiple R-squared: 0.1898, Adjusted R-squared: 0.1893
F-statistic: 398.6 on 1 and 1702 DF, p-value: < 2.2e-16
As you might expect, life expectancy has slowly been increasing over time, so we see a significant positive association!
Challenge Solutions
Solution to challenge 1
Create a data frame that holds the following information for yourself:
- First name
- Last name
- Age
Then use rbind to add the same information for the people sitting near you.
Now use cbind to add a column of logicals answering the question, “Is there anything in this workshop you’re finding confusing?”
my_df <- data.frame(first_name = "Software", last_name = "Carpentry", age = 17)
my_df <- rbind(my_df, list("Jane", "Smith", 29))
my_df <- rbind(my_df, list(c("Jo", "John"), c("White", "Lee"), c(23, 41)))
my_df <- cbind(my_df, confused = c(FALSE, FALSE, TRUE, FALSE))